librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
amitonHFace/q-Taxi-v3	amitonHFace	2023-09-21T11:25:11Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T11:25:09Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="amitonHFace/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ShivamMangale/XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0	ShivamMangale	2023-09-21T11:25:10Z	20	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-21T07:35:28Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
monsterapi/opt1.3B_codeinstruct	monsterapi	2023-09-21T11:23:26Z	0	0	peft	[ "peft", "facebook-opt-1.3b", "code", "instruct", "instruct-code", "code-alpaca", "alpaca-instruct", "alpaca", "opt-1.3b", "dataset:sahil2801/CodeAlpaca-20k", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]	null	2023-05-06T03:17:16Z	--- library_name: peft tags: - facebook-opt-1.3b - code - instruct - instruct-code - code-alpaca - alpaca-instruct - alpaca - opt-1.3b datasets: - sahil2801/CodeAlpaca-20k base_model: codellama/CodeLlama-7b-hf --- We finetuned Facebook/OPT-1.3B on Code-Alpaca-Instruct Dataset (sahil2801/CodeAlpaca-20k) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/CodeAlpaca_20K unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 1 hour and 30 minutes and costed us only `$6` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: facebook/opt-1.3b - Dataset: sahil2801/CodeAlpaca-20k - Learning rate: 0.0003 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---
monsterapi/llama7B_alpaca-lora	monsterapi	2023-09-21T11:23:24Z	1	1	peft	[ "peft", "llama1-7b", "code", "instruct", "alpaca-instruct", "alpaca", "llama7b", "dataset:tatsu-lab/alpaca", "region:us" ]	null	2023-05-10T05:39:31Z	--- library_name: peft tags: - llama1-7b - code - instruct - alpaca-instruct - alpaca - llama7b datasets: - tatsu-lab/alpaca base_model: decapoda-research/llama-7b-hf --- We finetuned huggyllama/llama-7b on tatsu-lab/alpaca Dataset for 5 epochs or ~ 25,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/tatsu-lab/alpaca unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 4 hours and costed us only `$16` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: huggyllama/llama-7b - Dataset: tatsu-lab/alpaca - Learning rate: 0.0003 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 license: apache-2.0 ---
monsterapi/opt125M_alpaca	monsterapi	2023-09-21T11:23:21Z	146	0	peft	[ "peft", "facebook/opt-125m", "code", "instruct", "alpaca-instruct", "alpaca", "dataset:tatsu-lab/alpaca", "base_model:facebook/opt-125m", "base_model:adapter:facebook/opt-125m", "region:us" ]	null	2023-05-13T05:38:51Z	--- library_name: peft tags: - facebook/opt-125m - code - instruct - alpaca-instruct - alpaca datasets: - tatsu-lab/alpaca base_model: facebook/opt-125m --- We finetuned facebook/opt-125m on tatsu-lab/alpaca Dataset for 10 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/tatsu-lab/alpaca unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 40 minutes and costed us only `$4` for the entire finetuning run! #### Hyperparameters & Run details: - Model: facebook/opt-125m - Dataset: tatsu-lab/alpaca - Learning rate: 0.0003 - Number of epochs: 10 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 - --- license: apache-2.0 ---
monsterapi/OpenPlatypus_LLAMA2_7b	monsterapi	2023-09-21T11:23:18Z	6	1	peft	[ "peft", "meta-llama/Llama-2-7b-hf", "code", "instruct", "instruct-code", "logical-reasoning", "Platypus2", "dataset:garage-bAInd/Open-Platypus", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2023-09-05T10:13:05Z	--- library_name: peft tags: - meta-llama/Llama-2-7b-hf - code - instruct - instruct-code - logical-reasoning - Platypus2 datasets: - garage-bAInd/Open-Platypus base_model: meta-llama/Llama-2-7b-hf --- We finetuned Meta-Llama/Llama-2-7b-hf on the Open-Platypus dataset (garage-bAInd/Open-Platypus) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). #### About OpenPlatypus Dataset OpenPlatypus is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. The dataset is comprised of various sub-datasets, including PRM800K, ScienceQA, SciBench, ReClor, TheoremQA, among others. These were filtered using keyword search and Sentence Transformers to remove questions with a similarity above 80%. The dataset includes contributions under various licenses like MIT, Creative Commons, and Apache 2.0. The finetuning session got completed in 1 hour and 30 minutes and costed us only `$15` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: meta-llama/Llama-2-7b-hf - Dataset: garage-bAInd/Open-Platypus - Learning rate: 0.0002 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---
alexalbala/llam2test	alexalbala	2023-09-21T11:23:16Z	0	0	peft	[ "peft", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2023-09-21T08:49:01Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- ## Training procedure ### Framework versions - PEFT 0.5.0
monsterapi/OpenPlatypus_Falcon_7b	monsterapi	2023-09-21T11:23:15Z	2	0	peft	[ "peft", "tiiuae/falcon-7b", "code", "instruct", "instruct-code", "logical-reasoning", "Platypus2", "dataset:garage-bAInd/Open-Platypus", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]	null	2023-09-05T11:28:00Z	--- library_name: peft tags: - tiiuae/falcon-7b - code - instruct - instruct-code - logical-reasoning - Platypus2 datasets: - garage-bAInd/Open-Platypus base_model: codellama/CodeLlama-7b-hf --- We finetuned TIIUAE/Falcon-7B on the Open-Platypus dataset (garage-bAInd/Open-Platypus) for 3 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). #### About OpenPlatypus Dataset OpenPlatypus is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. The dataset is comprised of various sub-datasets, including PRM800K, ScienceQA, SciBench, ReClor, TheoremQA, among others. These were filtered using keyword search and Sentence Transformers to remove questions with a similarity above 80%. The dataset includes contributions under various licenses like MIT, Creative Commons, and Apache 2.0. The finetuning session got completed in ~ 3 hrs and costed us only `$14` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: tiiuae/falcon-7b - Dataset: garage-bAInd/Open-Platypus - Learning rate: 0.0003 - Number of epochs: 3 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---
monsterapi/codellama7b_codealpaca20k	monsterapi	2023-09-21T11:23:11Z	3	2	peft	[ "peft", "codellama7b", "code", "instruct", "instruct-code", "code-alpaca", "alpaca-instruct", "alpaca", "gpt2", "dataset:sahil2801/CodeAlpaca-20k", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]	null	2023-08-30T15:27:52Z	--- library_name: peft tags: - codellama7b - code - instruct - instruct-code - code-alpaca - alpaca-instruct - alpaca - codellama7b - gpt2 datasets: - sahil2801/CodeAlpaca-20k base_model: codellama/CodeLlama-7b-hf --- We finetuned CodeLlama7B on Code-Alpaca-Instruct Dataset (sahil2801/CodeAlpaca-20k) for 5 epochs or ~ 25,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/CodeAlpaca_20K unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 4 hours and costed us only `$16` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: meta-llama/CodeLlama7B - Dataset: sahil2801/CodeAlpaca-20k - Learning rate: 0.0003 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---
monsterapi/llama2_SQL_Answers_finetuned	monsterapi	2023-09-21T11:23:00Z	7	1	peft	[ "peft", "meta-llama/Llama-2-7b", "code", "instruct", "instruct-code", "sql-create-context", "text-to-sql", "LLM", "dataset:b-mc2/sql-create-context", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2023-08-26T11:34:07Z	--- library_name: peft tags: - meta-llama/Llama-2-7b - code - instruct - instruct-code - sql-create-context - text-to-sql - LLM datasets: - b-mc2/sql-create-context base_model: meta-llama/Llama-2-7b-hf --- We finetuned Meta-Llama-2-7B on the SQL Create Context Dataset (b-mc2/sql-create-context) for 3 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is an enhanced version of WikiSQL and Spider, focused on providing natural language queries and corresponding SQL CREATE TABLE statements. The dataset contains 78,577 examples and aims to improve the model's grounding in text-to-SQL tasks. The CREATE TABLE statements are particularly useful for limiting token usage and avoiding exposure to sensitive data. The finetuning session took 7hrs and 21 mins and costed us a total of `$15.33`. #### Hyperparameters & Run details: - Model Path: meta-llama/Llama-2-7b - Dataset: b-mc2/sql-create-context - Learning rate: 0.0003 - Number of epochs: 3 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 Loss metrics: ![training loss](train-loss.png "Training loss") --- license: apache-2.0 ---
monsterapi/falcon-7b-python-code-instructions-18k-alpaca	monsterapi	2023-09-21T11:22:57Z	7	0	peft	[ "peft", "falcon", "falcon-7b", "code", "code instruct", "instruct code", "code alpaca", "python code", "code copilot", "copilot", "python coding assistant", "coding assistant", "dataset:iamtarun/python_code_instructions_18k_alpaca", "base_model:tiiuae/falcon-7b", "base_model:adapter:tiiuae/falcon-7b", "license:apache-2.0", "region:us" ]	null	2023-08-24T03:52:24Z	--- license: apache-2.0 library_name: peft tags: - falcon - falcon-7b - code - code instruct - instruct code - code alpaca - python code - code copilot - copilot - python coding assistant - coding assistant datasets: - iamtarun/python_code_instructions_18k_alpaca base_model: tiiuae/falcon-7b --- ## Training procedure We finetuned Falcon-7B LLM on Python-Code-Instructions Dataset ([iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)) for 10 epochs or ~ 23,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). The dataset contains problem descriptions and code in python language. This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style. The finetuning session got completed in 7.3 hours and costed us only `$17.5` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: tiiuae/falcon-7b - Dataset: iamtarun/python_code_instructions_18k_alpaca - Learning rate: 0.0002 - Number of epochs: 10 - Data split: Training: 95% / Validation: 5% - Gradient accumulation steps: 1 ### Framework versions - PEFT 0.4.0 ### Loss metrics: ![training loss](train-loss.png "Training loss")
mesa44/ppo-LunarLander-v2	mesa44	2023-09-21T11:17:54Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-19T09:43:00Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 250.37 +/- 33.48 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
amitonHFace/q-FrozenLake-v1-4x4-noSlippery	amitonHFace	2023-09-21T11:17:28Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T11:17:25Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="amitonHFace/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
iamshnoo/alpaca-2-70b-bengali	iamshnoo	2023-09-21T11:03:15Z	4	0	peft	[ "peft", "bn", "en", "dataset:iamshnoo/alpaca-cleaned-bengali", "base_model:meta-llama/Llama-2-70b-hf", "base_model:adapter:meta-llama/Llama-2-70b-hf", "license:cc-by-4.0", "region:us" ]	null	2023-09-10T20:28:27Z	--- language: - bn - en license: cc-by-4.0 library_name: peft datasets: - iamshnoo/alpaca-cleaned-bengali base_model: meta-llama/Llama-2-70b-hf --- This represents the PEFT weights only. The base model is LLaMA 2. Instruction finetuning was done using 4 bit QLoRA on a single A100 GPU with the PEFT config as given below. The dataset used for this instruction finetuning process is a translated version of the cleaned alpaca dataset (translated using NLLB-1.3B). Do note that this model might have inferior performance on some language specific tasks compared to full finetuning or a different base model trained with more language specific data. ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0
Ammok/Taxi-v3	Ammok	2023-09-21T10:59:02Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T10:58:59Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.40 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Ammok/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
hyeongjin99/vit_base_aihub_model_py	hyeongjin99	2023-09-21T10:58:45Z	216	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-21T07:27:29Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: vit_base_aihub_model_py results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9977631269131152 - name: Precision type: precision value: 0.998134723737648 - name: Recall type: recall value: 0.9974298183920257 - name: F1 type: f1 value: 0.9977816548360952 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit_base_aihub_model_py This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0228 - Accuracy: 0.9978 - Precision: 0.9981 - Recall: 0.9974 - F1: 0.9978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.1415 \| 1.0 \| 149 \| 0.1286 \| 0.9712 \| 0.9788 \| 0.9623 \| 0.9700 \| \| 0.0671 \| 2.0 \| 299 \| 0.0463 \| 0.9948 \| 0.9917 \| 0.9946 \| 0.9932 \| \| 0.0423 \| 3.0 \| 448 \| 0.0356 \| 0.9952 \| 0.9970 \| 0.9908 \| 0.9939 \| \| 0.0383 \| 4.0 \| 598 \| 0.0242 \| 0.9976 \| 0.9980 \| 0.9972 \| 0.9976 \| \| 0.033 \| 4.98 \| 745 \| 0.0228 \| 0.9978 \| 0.9981 \| 0.9974 \| 0.9978 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
zac/zac	zac	2023-09-21T10:58:10Z	1	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-21T10:58:06Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: z4c tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - zac These are LoRA adaption weights for [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). The weights were trained on the instance prompt "z4c" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: z4c4 ![image_0](test_images/image_0.png) ![image_1](test_images/image_1.png) ![image_2](test_images/image_2.png) ![image_3](test_images/image_3.png)
Ammok/q-FrozenLake-v1-4x4-noSlippery	Ammok	2023-09-21T10:56:08Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T10:56:04Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Ammok/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
linoyts/huggy-lora-sdxl-v7	linoyts	2023-09-21T10:54:14Z	227	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-21T10:53:59Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 pivotal_tuning: true textual_embeddings: embeddings.pti instance_prompt: <s0><s1> inference: false --- # huggy-lora-sdxl-v7 LoRA by [linoytsaban](https://replicate.com/linoytsaban) ### caption prefix: a TOK emoji, steps: 1500, lr: 2e-4 ![lora_image](https://pbxt.replicate.delivery/wJf4lByhD10xCyqaAp2sgsJYW8Xw99sbgue5Fyvj176pD2kRA/out-0.png) > ## Inference with Replicate API Grab your replicate token [here](https://replicate.com/account) ```bash pip install replicate export REPLICATE_API_TOKEN=r8_************************************* ``` ```py import replicate output = replicate.run( "linoy_lora@sha256:6e68d04d64a29ce25df2002570d535b6582310304dd4360f15517c95f89033a7", input={"prompt": "a hugging face emoji in the style of TOK, dressed as yoda"} ) print(output) ``` You may also do inference via the API with Node.js or curl, and locally with COG and Docker, [check out the Replicate API page for this model](https://replicate.com/linoytsaban/linoy_lora/api) ## Inference with 🧨 diffusers Replicate SDXL LoRAs are trained with Pivotal Tuning, which combines training a concept via Dreambooth LoRA with training a new token with Textual Inversion. As `diffusers` doesn't yet support textual inversion for SDXL, we will use cog-sdxl `TokenEmbeddingsHandler` class. The trigger tokens for your prompt will be `<s0><s1>` ```shell pip install diffusers transformers accelerate safetensors huggingface_hub git clone https://github.com/replicate/cog-sdxl cog_sdxl ``` ```py import torch from huggingface_hub import hf_hub_download from diffusers import DiffusionPipeline from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler from diffusers.models import AutoencoderKL pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", ).to("cuda") pipe.load_lora_weights("LinoyTsaban/huggy-lora-sdxl-v7", weight_name="lora.safetensors") text_encoders = [pipe.text_encoder, pipe.text_encoder_2] tokenizers = [pipe.tokenizer, pipe.tokenizer_2] embedding_path = hf_hub_download(repo_id="LinoyTsaban/huggy-lora-sdxl-v7", filename="embeddings.pti", repo_type="model") embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers) embhandler.load_embeddings(embedding_path) prompt="a hugging face emoji in the style of <s0><s1>, dressed as yoda" images = pipe( prompt, cross_attention_kwargs={"scale": 0.8}, ).images #your output image images[0] ```
zamankh/my_awesome_mind_model	zamankh	2023-09-21T10:49:14Z	160	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-09-21T10:44:31Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind_model results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.05309734513274336 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6510 - Accuracy: 0.0531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 2.6492 \| 0.0442 \| \| No log \| 1.87 \| 7 \| 2.6548 \| 0.0531 \| \| 2.6331 \| 2.93 \| 11 \| 2.6597 \| 0.0708 \| \| 2.6331 \| 4.0 \| 15 \| 2.6611 \| 0.0531 \| \| 2.6331 \| 4.8 \| 18 \| 2.6578 \| 0.0531 \| \| 2.6244 \| 5.87 \| 22 \| 2.6493 \| 0.0619 \| \| 2.6244 \| 6.93 \| 26 \| 2.6509 \| 0.0619 \| \| 2.6149 \| 8.0 \| 30 \| 2.6510 \| 0.0531 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/matsumoto_sarina_idolmastercinderellagirls	CyberHarem	2023-09-21T10:44:21Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/matsumoto_sarina_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-21T10:33:22Z	--- license: mit datasets: - CyberHarem/matsumoto_sarina_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of matsumoto_sarina_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5100, you need to download `5100/matsumoto_sarina_idolmastercinderellagirls.pt` as the embedding and `5100/matsumoto_sarina_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 5100, with the score of 0.976. The trigger words are: 1. `matsumoto_sarina_idolmastercinderellagirls` 2. `long_hair, blue_eyes, smile, brown_hair, breasts, large_breasts, cleavage, blush` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:--------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.976 \| [Download](5100/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| [<NSFW, click to see>](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.968 \| [Download](4760/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| [<NSFW, click to see>](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.961 \| [Download](4420/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| [<NSFW, click to see>](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.973 \| [Download](4080/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| [<NSFW, click to see>](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.943 \| [Download](3740/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| [<NSFW, click to see>](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.974 \| [Download](3400/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| [<NSFW, click to see>](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.917 \| [Download](3060/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| [<NSFW, click to see>](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.974 \| [Download](2720/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| [<NSFW, click to see>](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.934 \| [Download](2380/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| [<NSFW, click to see>](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.892 \| [Download](2040/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| [<NSFW, click to see>](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.958 \| [Download](1700/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| [<NSFW, click to see>](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.922 \| [Download](1360/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| [<NSFW, click to see>](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.897 \| [Download](1020/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| [<NSFW, click to see>](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.917 \| [Download](680/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| [<NSFW, click to see>](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.882 \| [Download](340/matsumoto_sarina_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| [<NSFW, click to see>](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
philolathai/philolathai	philolathai	2023-09-21T10:44:16Z	0	0	null	[ "region:us" ]	null	2023-09-21T10:43:50Z	Philola ได้รับการผสมสูตรโดยใช้ส่วนผสมจากธรรมชาติที่แสดงให้เห็นว่ามีประโยชน์ในการปรับปรุงสุขภาพโดยรวมของดวงตา Philola ซื้อเลย!! คลิกลิงก์ด้านล่างเพื่อดูข้อมูลเพิ่มเติมและรับส่วนลด 50% ทันที !! รีบหน่อย!! อ่านเพิ่มเติม: https://www.boxdrug.com/PhiloThail https://sites.google.com/view/philola-thailand/home ➤ ชื่อผลิตภัณฑ์ — Philola ➤ ใช้สำหรับ: สุขภาพดวงตา ➤ ประโยชน์หลัก — ปรับปรุงสายตา ➤ ส่วนประกอบ — สารประกอบอินทรีย์ธรรมชาติ ➤ ผลข้างเคียง—NA เรตติ้งสุดท้าย: — 4.7 ➤ มีจำหน่าย — ออนไลน์ ➤ข้อเสนอและส่วนลด; ประหยัดวันนี้! ช้อปตอนนี้เพื่อซื้อข้อเสนอพิเศษ!!! Philola คืออะไร? สำหรับผู้ที่ไม่คุ้นเคย Philola เป็นผลิตภัณฑ์เสริมอาหารที่ช่วยปรับปรุงการมองเห็นที่ลอยอยู่บนอินเทอร์เน็ตโดยอ้างว่าช่วยเพิ่มการมองเห็นของบุคคลโดยจัดการกับ 3 สาเหตุหลักของความบกพร่องทางการมองเห็น สาเหตุหลักคือการได้รับสารพิษบางชนิดที่อาจทำลายดวงตาอย่างรุนแรงได้
chendelong/ChatGLM-PSP	chendelong	2023-09-21T10:37:11Z	104	0	transformers	[ "transformers", "pytorch", "chatglm", "feature-extraction", "custom_code", "arxiv:2309.11000", "region:us" ]	feature-extraction	2023-09-19T04:14:39Z	<div align="center"> 🎙 [Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model](https://huggingface.co/papers/2309.11000) [Xinyu Zhou (周欣宇)](https://www.linkedin.com/in/xinyu-zhou2000/), [Delong Chen (陈德龙)](https://chendelong.world/), [Yudong Chen (陈玉东)](https://rwxy.cuc.edu.cn/2019/0730/c5134a133504/pagem.htm) [ArXiv](https://arxiv.org/abs/2309.11000) \| [Poster](doc/YFRSW_Poster.pdf) \| [Notebook](prosody_prediction.ipynb) \| [Github](https://github.com/XinyuZhou2000/Spoken-Dialogue) </div> This project explores the potential of constructing an AI spoken dialogue system that "thinks how to respond" and "thinks how to speak" simultaneously, which more closely aligns with the human speech production process compared to the current cascade pipeline of independent chatbot and Text-to-Speech (TTS) modules. We hypothesize that Large Language Models (LLMs) with billions of parameters possess significant speech understanding capabilities and can jointly model dialogue responses and linguistic features. We investigate the task of Prosodic structure prediction (PSP), a typical front-end task in TTS, demonstrating the speech understanding ability of LLMs.
reeen115/lora_output	reeen115	2023-09-21T10:36:45Z	0	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-2-1", "base_model:adapter:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-20T08:39:18Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: cardboards, grayscale tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - reeen115/lora_output These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on cardboards, grayscale using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False.
OpenDILabCommunity/Hopper-v3-SAC	OpenDILabCommunity	2023-09-21T10:31:12Z	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "Hopper-v3", "en", "license:apache-2.0", "region:us" ]	reinforcement-learning	2023-04-14T08:16:42Z	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - Hopper-v3 benchmark_name: OpenAI/Gym/MuJoCo task_name: Hopper-v3 pipeline_tag: reinforcement-learning model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/MuJoCo-Hopper-v3 type: OpenAI/Gym/MuJoCo-Hopper-v3 metrics: - type: mean_reward value: 3899.4 +/- 362.09 name: mean_reward --- # Play Hopper-v3 with SAC Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This is a simple SAC implementation to OpenAI/Gym/MuJoCo Hopper-v3 using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). DI-engine is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed sudo apt update -y && sudo apt install -y build-essential libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev libglfw3 libglfw3-dev libsdl2-dev libsdl2-image-dev libglm-dev libfreetype6-dev patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import SACAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = SACAgent(env_id="Hopper-v3", exp_name="Hopper-v3-SAC", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import SACAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/Hopper-v3-SAC") # Instantiate the agent agent = SACAgent(env_id="Hopper-v3", exp_name="Hopper-v3-SAC", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from ding.bonus import SACAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = SACAgent(env_id="Hopper-v3", exp_name="Hopper-v3-SAC") # Train the agent return_ = agent.train(step=int(10000000), collector_env_num=4, evaluator_env_num=4, debug=False) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/MuJoCo", task_name="Hopper-v3", algo_name="SAC", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/sac.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html", installation_guide=''' sudo apt update -y \ && sudo apt install -y \ build-essential \ libgl1-mesa-dev \ libgl1-mesa-glx \ libglew-dev \ libosmesa6-dev \ libglfw3 \ libglfw3-dev \ libsdl2-dev \ libsdl2-image-dev \ libglm-dev \ libfreetype6-dev \ patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ''', usage_file_by_git_clone="./sac/hopper_sac_deploy.py", usage_file_by_huggingface_ding="./sac/hopper_sac_download.py", train_file="./sac/hopper_sac.py", repo_id="OpenDILabCommunity/Hopper-v3-SAC", create_repo=False ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 6000, 'n_evaluator_episode': 8, 'env_id': 'Hopper-v3', 'collector_env_num': 8, 'evaluator_env_num': 8, 'env_wrapper': 'mujoco_default' }, 'policy': { 'model': { 'twin_critic': True, 'action_space': 'reparameterization', 'obs_shape': 11, 'action_shape': 3, 'actor_head_hidden_size': 256, 'critic_head_hidden_size': 256 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 1, 'batch_size': 256, 'learning_rate_q': 0.001, 'learning_rate_policy': 0.001, 'learning_rate_alpha': 0.0003, 'target_theta': 0.005, 'discount_factor': 0.99, 'alpha': 0.2, 'auto_alpha': False, 'log_space': True, 'target_entropy': None, 'ignore_done': False, 'init_w': 0.003, 'reparameterization': True }, 'collect': { 'collector': {}, 'n_sample': 1, 'unroll_len': 1, 'collector_logit': False }, 'eval': { 'evaluator': { 'eval_freq': 1000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 6000, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 1000000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'sac', 'priority': False, 'priority_IS_weight': False, 'random_collect_size': 10000, 'transition_with_policy_data': True, 'multi_agent': False, 'cfg_type': 'SACPolicyDict' }, 'exp_name': 'Hopper-v3-SAC', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](https://wandb.ai/zjowowen/Hopper-v3-SAC) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/DI-engine) - Doc: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/sac.html) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/Hopper-v3-SAC/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/Hopper-v3-SAC/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 1642.06 KB - Last Update Date: 2023-09-21 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/MuJoCo - Task: Hopper-v3 - Gym version: 0.25.1 - DI-engine version: v0.4.9 - PyTorch version: 2.0.1+cu117 - Doc: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html)
dss107/mini_lm_base	dss107	2023-09-21T10:28:16Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-09-21T10:27:59Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # dss107/mini_lm_base This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("dss107/mini_lm_base") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
sophiaaez/distilhubert-finetuned-gtzan	sophiaaez	2023-09-21T10:25:51Z	160	0	transformers	[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-09-15T09:07:00Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.79 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7146 - Accuracy: 0.79 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0711 \| 1.0 \| 75 \| 1.9438 \| 0.49 \| \| 1.4944 \| 2.0 \| 150 \| 1.4307 \| 0.53 \| \| 1.2562 \| 3.0 \| 225 \| 1.2180 \| 0.65 \| \| 0.9436 \| 4.0 \| 300 \| 1.0209 \| 0.71 \| \| 0.7543 \| 5.0 \| 375 \| 0.9073 \| 0.73 \| \| 0.5742 \| 6.0 \| 450 \| 0.8047 \| 0.75 \| \| 0.4728 \| 7.0 \| 525 \| 0.7736 \| 0.78 \| \| 0.3622 \| 8.0 \| 600 \| 0.7412 \| 0.78 \| \| 0.2447 \| 9.0 \| 675 \| 0.7117 \| 0.79 \| \| 0.2692 \| 10.0 \| 750 \| 0.7146 \| 0.79 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0
Wariano/bsc-bio-ehr-es-vih-10k	Wariano	2023-09-21T10:24:33Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-21T10:09:24Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: bsc-bio-ehr-es-vih-10k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bsc-bio-ehr-es-vih-10k This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9958 - Positives Preds: 598 - Negative Preds: 402 - Positives Refs: 500 - Negative Refs: 500 - Tp: 411 - Fn: 89 - Fp: 187 - Tn: 313 - Accuracy: 0.724 - Precision: 0.6873 - Recall: 0.822 - F1: 0.7486 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Positives Preds \| Negative Preds \| Positives Refs \| Negative Refs \| Tp \| Fn \| Fp \| Tn \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------------:\|:--------------:\|:--------------:\|:-------------:\|:---:\|:---:\|:---:\|:---:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.4309 \| 1.0 \| 250 \| 0.4999 \| 384 \| 616 \| 500 \| 500 \| 316 \| 184 \| 68 \| 432 \| 0.748 \| 0.8229 \| 0.632 \| 0.7149 \| \| 0.2849 \| 2.0 \| 500 \| 0.6391 \| 546 \| 454 \| 500 \| 500 \| 396 \| 104 \| 150 \| 350 \| 0.746 \| 0.7253 \| 0.792 \| 0.7572 \| \| 0.1931 \| 3.0 \| 750 \| 0.7333 \| 610 \| 390 \| 500 \| 500 \| 414 \| 86 \| 196 \| 304 \| 0.718 \| 0.6787 \| 0.828 \| 0.7459 \| \| 0.1255 \| 4.0 \| 1000 \| 0.8917 \| 604 \| 396 \| 500 \| 500 \| 417 \| 83 \| 187 \| 313 \| 0.73 \| 0.6904 \| 0.834 \| 0.7554 \| \| 0.0918 \| 5.0 \| 1250 \| 0.9958 \| 598 \| 402 \| 500 \| 500 \| 411 \| 89 \| 187 \| 313 \| 0.724 \| 0.6873 \| 0.822 \| 0.7486 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
ghost9023/DEEPNOID-llama2-7b-PoC-Only	ghost9023	2023-09-21T10:16:48Z	6	0	peft	[ "peft", "region:us" ]	null	2023-09-21T02:26:54Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0
yejeekang/qlora-koalpaca-polyglot-12.8b-50step	yejeekang	2023-09-21T10:15:07Z	1	0	peft	[ "peft", "region:us" ]	null	2023-09-20T05:03:58Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
aident-ai/bge-base-en-onnx	aident-ai	2023-09-21T10:10:14Z	13	2	sentence-transformers	[ "sentence-transformers", "pytorch", "onnx", "bert", "feature-extraction", "sentence-similarity", "transformers", "mteb", "en", "arxiv:2309.07597", "license:mit", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-09-06T12:25:41Z	--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb license: mit language: - en --- This is a fork from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) and exported to onnx for inference. ======= <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> \| <a href=#frequently-asked-questions>FAQ</a> \| <a href=#usage>Usage</a> \| <a href="#evaluation">Evaluation</a> \| <a href="#train">Train</a> \| <a href="#contact">Contact</a> \| <a href="#citation">Citation</a> \| <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). [English](README.md) \| [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. *********** 🌟Updates🌟 ********* - 09/15/2023: Release [paper](https://arxiv.org/pdf/2309.07597.pdf) and [dataset](https://data.baai.ac.cn/details/BAAI-MTP). - 09/12/2023: New Release: - New reranker model: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - update embedding model*: release `bge--v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into Langchain, you can use it like [this](#using-langchain); C-MTEB leaderboard is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, best performance among the models of the same size 🤗 - 08/02/2023: Release `bge-large-`(short for BAAI General Embedding) Models, rank 1st on MTEB and C-MTEB benchmark!* :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (C-MTEB), consisting of 31 test dataset. ## Model List `bge` is short for `BAAI general embedding`. \| Model \| Language \| \| Description \| query instruction for retrieval\* \| \|:-------------------------------\|:--------:\| :--------:\| :--------:\|:--------:\| \| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) \| Chinese and English \| [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) \| a cross-encoder model which is more accurate but less efficient \ \| \| \| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) \| Chinese and English \| [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) \| a cross-encoder model which is more accurate but less efficient \ \| \| \| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| :trophy: rank 1st in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a base-scale model but with similar ability to `bge-large-en` \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \|a small-scale model but with competitive performance \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| :trophy: rank 1st in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a base-scale model but with similar ability to `bge-large-zh` \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a small-scale model but with competitive performance \| `为这个句子生成表示以用于检索相关文章：` \| \: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, no instruction* needs to be added to passages. \: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> Suggest to use bge v1.5, which alleviates the issue of the similarity distribution. Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, what matters is the relative order of the scores, not the absolute value. If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task. In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve state-of-the-art performance on both MTEB and C-MTEB leaderboard! For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - MTEB: \| Model Name \| Dimension \| Sequence Length \| Average (56) \| Retrieval (15) \|Clustering (11) \| Pair Classification (3) \| Reranking (4) \| STS (10) \| Summarization (1) \| Classification (12) \| \|:----:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\| \| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) \| 1024 \| 512 \| 64.23 \| 54.29 \| 46.08 \| 87.12 \| 60.03 \| 83.11 \| 31.61 \| 75.97 \| \| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) \| 768 \| 512 \| 63.55 \| 53.25 \| 45.77 \| 86.55 \| 58.86 \| 82.4 \| 31.07 \| 75.53 \| \| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) \| 384 \| 512 \| 62.17 \|51.68 \| 43.82 \| 84.92 \| 58.36 \| 81.59 \| 30.12 \| 74.14 \| \| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) \| 1024 \| 512 \| 63.98 \| 53.9 \| 46.98 \| 85.8 \| 59.48 \| 81.56 \| 32.06 \| 76.21 \| \| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) \| 768 \| 512 \| 63.36 \| 53.0 \| 46.32 \| 85.86 \| 58.7 \| 81.84 \| 29.27 \| 75.27 \| \| [gte-large](https://huggingface.co/thenlper/gte-large) \| 1024 \| 512 \| 63.13 \| 52.22 \| 46.84 \| 85.00 \| 59.13 \| 83.35 \| 31.66 \| 73.33 \| \| [gte-base](https://huggingface.co/thenlper/gte-base) \| 768 \| 512 \| 62.39 \| 51.14 \| 46.2 \| 84.57 \| 58.61 \| 82.3 \| 31.17 \| 73.01 \| \| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) \| 1024\| 512 \| 62.25 \| 50.56 \| 44.49 \| 86.03 \| 56.61 \| 82.05 \| 30.19 \| 75.24 \| \| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) \| 384 \| 512 \| 62.11 \| 51.82 \| 44.31 \| 83.78 \| 57.97 \| 80.72 \| 30.53 \| 74.37 \| \| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) \| 768 \| 512 \| 61.79 \| 49.26 \| 44.74 \| 86.62 \| 57.29 \| 83.06 \| 32.32 \| 61.79 \| \| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) \| 768 \| 512 \| 61.5 \| 50.29 \| 43.80 \| 85.73 \| 55.91 \| 81.05 \| 30.28 \| 73.84 \| \| [gte-small](https://huggingface.co/thenlper/gte-small) \| 384 \| 512 \| 61.36 \| 49.46 \| 44.89 \| 83.54 \| 57.7 \| 82.07 \| 30.42 \| 72.31 \| \| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) \| 1536 \| 8192 \| 60.99 \| 49.25 \| 45.9 \| 84.89 \| 56.32 \| 80.97 \| 30.8 \| 70.93 \| \| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) \| 384 \| 512 \| 59.93 \| 49.04 \| 39.92 \| 84.67 \| 54.32 \| 80.39 \| 31.16 \| 72.94 \| \| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) \| 768 \| 512 \| 59.51 \| 42.24 \| 43.72 \| 85.06 \| 56.42 \| 82.63 \| 30.08 \| 73.42 \| \| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) \| 768 \| 514 \| 57.78 \| 43.81 \| 43.69 \| 83.04 \| 59.36 \| 80.28 \| 27.49 \| 65.07 \| \| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) \| 4096 \| 2048 \| 57.59 \| 48.22 \| 38.93 \| 81.9 \| 55.65 \| 77.74 \| 33.6 \| 66.19 \| - C-MTEB: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. \| Model \| Embedding dimension \| Avg \| Retrieval \| STS \| PairClassification \| Classification \| Reranking \| Clustering \| \|:-------------------------------\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\| \| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) \| 1024 \| 64.53 \| 70.46 \| 56.25 \| 81.6 \| 69.13 \| 65.84 \| 48.99 \| \| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) \| 768 \| 63.13 \| 69.49 \| 53.72 \| 79.75 \| 68.07 \| 65.39 \| 47.53 \| \| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) \| 512 \| 57.82 \| 61.77 \| 49.11 \| 70.41 \| 63.96 \| 60.92 \| 44.18 \| \| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) \| 1024 \| 64.20 \| 71.53 \| 54.98 \| 78.94 \| 68.32 \| 65.11 \| 48.39 \| \| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) \| 1024 \| 63.53 \| 70.55 \| 53 \| 76.77 \| 68.58 \| 64.91 \| 50.01 \| \| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) \| 768 \| 62.96 \| 69.53 \| 54.12 \| 77.5 \| 67.07 \| 64.91 \| 47.63 \| \| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) \| 1024 \| 58.79 \| 63.66 \| 48.44 \| 69.89 \| 67.34 \| 56.00 \| 48.23 \| \| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) \| 512 \| 58.27 \| 63.07 \| 49.45 \| 70.35 \| 63.64 \| 61.48 \| 45.09 \| \| [m3e-base](https://huggingface.co/moka-ai/m3e-base) \| 768 \| 57.10 \| 56.91 \| 50.47 \| 63.99 \| 67.52 \| 59.34 \| 47.68 \| \| [m3e-large](https://huggingface.co/moka-ai/m3e-large) \| 1024 \| 57.05 \| 54.75 \| 50.42 \| 64.3 \| 68.2 \| 59.66 \| 48.88 \| \| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) \| 768 \| 55.48 \| 61.63 \| 46.49 \| 67.07 \| 65.35 \| 54.35 \| 40.68 \| \| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) \| 384 \| 55.38 \| 59.95 \| 45.27 \| 66.45 \| 65.85 \| 53.86 \| 45.26 \| \| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) \| 1536 \| 53.02 \| 52.0 \| 43.35 \| 69.56 \| 64.31 \| 54.28 \| 45.68 \| \| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) \| 1024 \| 49.37 \| 44.4 \| 42.78 \| 66.62 \| 61 \| 49.25 \| 44.39 \| \| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) \| 768 \| 47.63 \| 38.79 \| 43.41 \| 67.41 \| 62.19 \| 49.45 \| 37.66 \| \| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) \| 1024 \| 47.36 \| 41.94 \| 44.97 \| 70.86 \| 60.66 \| 49.16 \| 30.02 \| - Reranking*: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. \| Model \| T2Reranking \| T2RerankingZh2En\ \| T2RerankingEn2Zh\* \| MMarcoReranking \| CMedQAv1 \| CMedQAv2 \| Avg \| \|:-------------------------------\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\| \| text2vec-base-multilingual \| 64.66 \| 62.94 \| 62.51 \| 14.37 \| 48.46 \| 48.6 \| 50.26 \| \| multilingual-e5-small \| 65.62 \| 60.94 \| 56.41 \| 29.91 \| 67.26 \| 66.54 \| 57.78 \| \| multilingual-e5-large \| 64.55 \| 61.61 \| 54.28 \| 28.6 \| 67.42 \| 67.92 \| 57.4 \| \| multilingual-e5-base \| 64.21 \| 62.13 \| 54.68 \| 29.5 \| 66.23 \| 66.98 \| 57.29 \| \| m3e-base \| 66.03 \| 62.74 \| 56.07 \| 17.51 \| 77.05 \| 76.76 \| 59.36 \| \| m3e-large \| 66.13 \| 62.72 \| 56.1 \| 16.46 \| 77.76 \| 78.27 \| 59.57 \| \| bge-base-zh-v1.5 \| 66.49 \| 63.25 \| 57.02 \| 29.74 \| 80.47 \| 84.88 \| 63.64 \| \| bge-large-zh-v1.5 \| 65.74 \| 63.39 \| 57.03 \| 28.74 \| 83.45 \| 85.44 \| 63.97 \| \| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) \| 67.28 \| 63.95 \| 60.45 \| 35.46 \| 81.26 \| 84.1 \| 65.42 \| \| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) \| 67.6 \| 64.03 \| 61.44 \| 37.16 \| 82.15 \| 84.18 \| 66.09 \| \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune). We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details pelease refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). ## Citation If you find our work helpful, please cite us: ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
0xk1h0/pythia-6.9b-deduped-py150k-r20-LoRA	0xk1h0	2023-09-21T10:05:51Z	3	1	peft	[ "peft", "region:us" ]	null	2023-09-21T10:02:32Z	--- library_name: peft --- ## Model Usage ```python import torch import transformers from finetune_peft import get_peft_config, PEFTArguments from peft import get_peft_model model_path = 'EleutherAI/pythia-6.9b-deduped' # peft_path = 'models/codegen25_7b/checkpoint' peft_path = '0xk1h0/pythia-6.9b-deduped-py150k-r20-LoRA' # peft_path = 'models/alpaca-llama-7b-peft/params.p' torch.set_default_tensor_type(torch.cuda.HalfTensor) model = transformers.AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, cache_dir='models') peft_config = get_peft_config(peft_args=PEFTArguments(peft_mode="lora")) model = get_peft_model(model, peft_config) # model.load_state_dict(torch.load(peft_path), strict=False) torch.set_default_tensor_type(torch.cuda.FloatTensor) tokenizer = transformers.AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) batch = tokenizer(""" ### Generate AES MODE encrypt function. """, return_tensors="pt") with torch.no_grad(): out = model.generate( input_ids=batch["input_ids"], attention_mask=torch.ones_like(batch["input_ids"]), max_length=256, do_sample=True, temperature = 0.4, top_p=0.95 ) print(tokenizer.decode(out[0])) ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0
nikinetrahutama/afx-grouping-model	nikinetrahutama	2023-09-21T10:04:23Z	105	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-21T09:01:00Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: afx-grouping-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # afx-grouping-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0154 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 6 \| 0.9060 \| 0.7674 \| \| No log \| 2.0 \| 12 \| 0.7408 \| 0.7674 \| \| No log \| 3.0 \| 18 \| 0.6209 \| 0.8023 \| \| No log \| 4.0 \| 24 \| 0.5090 \| 0.8023 \| \| No log \| 5.0 \| 30 \| 0.4159 \| 0.8488 \| \| No log \| 6.0 \| 36 \| 0.3407 \| 0.8837 \| \| No log \| 7.0 \| 42 \| 0.2719 \| 0.9535 \| \| No log \| 8.0 \| 48 \| 0.2218 \| 0.9535 \| \| No log \| 9.0 \| 54 \| 0.1801 \| 0.9535 \| \| No log \| 10.0 \| 60 \| 0.1476 \| 0.9535 \| \| No log \| 11.0 \| 66 \| 0.1164 \| 0.9767 \| \| No log \| 12.0 \| 72 \| 0.0937 \| 0.9884 \| \| No log \| 13.0 \| 78 \| 0.0723 \| 1.0 \| \| No log \| 14.0 \| 84 \| 0.0604 \| 1.0 \| \| No log \| 15.0 \| 90 \| 0.0485 \| 1.0 \| \| No log \| 16.0 \| 96 \| 0.0395 \| 1.0 \| \| No log \| 17.0 \| 102 \| 0.0339 \| 1.0 \| \| No log \| 18.0 \| 108 \| 0.0307 \| 1.0 \| \| No log \| 19.0 \| 114 \| 0.0262 \| 1.0 \| \| No log \| 20.0 \| 120 \| 0.0240 \| 1.0 \| \| No log \| 21.0 \| 126 \| 0.0215 \| 1.0 \| \| No log \| 22.0 \| 132 \| 0.0200 \| 1.0 \| \| No log \| 23.0 \| 138 \| 0.0189 \| 1.0 \| \| No log \| 24.0 \| 144 \| 0.0178 \| 1.0 \| \| No log \| 25.0 \| 150 \| 0.0170 \| 1.0 \| \| No log \| 26.0 \| 156 \| 0.0164 \| 1.0 \| \| No log \| 27.0 \| 162 \| 0.0160 \| 1.0 \| \| No log \| 28.0 \| 168 \| 0.0157 \| 1.0 \| \| No log \| 29.0 \| 174 \| 0.0155 \| 1.0 \| \| No log \| 30.0 \| 180 \| 0.0154 \| 1.0 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
ldos/text_shortening_model_v46	ldos	2023-09-21T10:02:51Z	7	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-xsum", "base_model:finetune:facebook/bart-large-xsum", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-21T07:26:22Z	--- license: mit base_model: facebook/bart-large-xsum tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v46 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # text_shortening_model_v46 This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8536 - Rouge1: 0.485 - Rouge2: 0.271 - Rougel: 0.4374 - Rougelsum: 0.4371 - Bert precision: 0.8676 - Bert recall: 0.8761 - Average word count: 9.1032 - Max word count: 17 - Min word count: 4 - Average token count: 15.8254 - % shortened texts with length > 12: 9.5238 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bert precision \| Bert recall \| Average word count \| Max word count \| Min word count \| Average token count \| % shortened texts with length > 12 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:--------------:\|:-----------:\|:------------------:\|:--------------:\|:--------------:\|:-------------------:\|:----------------------------------:\| \| 1.8213 \| 1.0 \| 42 \| 2.1030 \| 0.4561 \| 0.2433 \| 0.4131 \| 0.4131 \| 0.8606 \| 0.8724 \| 9.0529 \| 15 \| 5 \| 14.7249 \| 7.4074 \| \| 0.8874 \| 2.0 \| 84 \| 1.8034 \| 0.4778 \| 0.2609 \| 0.4316 \| 0.4316 \| 0.8569 \| 0.8787 \| 10.6323 \| 21 \| 5 \| 16.2963 \| 19.3122 \| \| 0.603 \| 3.0 \| 126 \| 1.6613 \| 0.4749 \| 0.2594 \| 0.425 \| 0.4253 \| 0.8576 \| 0.8796 \| 10.5106 \| 21 \| 5 \| 16.2751 \| 23.0159 \| \| 0.5413 \| 4.0 \| 168 \| 1.5975 \| 0.4729 \| 0.249 \| 0.4258 \| 0.4254 \| 0.8635 \| 0.8696 \| 8.6481 \| 16 \| 4 \| 14.3677 \| 4.2328 \| \| 0.3393 \| 5.0 \| 210 \| 1.6755 \| 0.4959 \| 0.28 \| 0.4476 \| 0.4473 \| 0.8687 \| 0.8772 \| 8.8942 \| 20 \| 5 \| 15.8915 \| 8.4656 \| \| 0.2573 \| 6.0 \| 252 \| 1.6908 \| 0.4775 \| 0.2589 \| 0.4309 \| 0.4307 \| 0.866 \| 0.873 \| 8.9868 \| 22 \| 4 \| 15.4339 \| 10.3175 \| \| 0.173 \| 7.0 \| 294 \| 1.8536 \| 0.485 \| 0.271 \| 0.4374 \| 0.4371 \| 0.8676 \| 0.8761 \| 9.1032 \| 17 \| 4 \| 15.8254 \| 9.5238 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
irexyc/Qwen-VL-Chat	irexyc	2023-09-21T09:57:47Z	12	0	transformers	[ "transformers", "pytorch", "qwen", "text-generation", "custom_code", "zh", "en", "arxiv:2308.12966", "autotrain_compatible", "region:us" ]	text-generation	2023-09-21T09:57:47Z	--- language: - zh - en tags: - qwen pipeline_tag: text-generation inference: false --- # Qwen-VL-Chat <br> <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/logo_vl.jpg" width="400"/> <p> <br> <p align="center"> Qwen-VL <a href="https://modelscope.cn/models/qwen/Qwen-VL/summary">🤖 <a> \| <a href="https://huggingface.co/Qwen/Qwen-VL">🤗</a>&nbsp ｜ Qwen-VL-Chat <a href="https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary">🤖 <a>\| <a href="https://huggingface.co/Qwen/Qwen-VL-Chat">🤗</a>&nbsp ｜ Qwen-VL-Chat-Int4 <a href="https://huggingface.co/Qwen/Qwen-VL-Chat-Int4">🤗</a> <br> <a href="assets/wechat.png">WeChat</a>&nbsp&nbsp \| &nbsp&nbsp<a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>&nbsp&nbsp \| &nbsp&nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/summary">Demo</a>&nbsp ｜ &nbsp<a href="https://arxiv.org/abs/2308.12966">Report</a> </p> <br> Qwen-VL 是阿里云研发的大规模视觉语言模型（Large Vision Language Model, LVLM）。Qwen-VL 可以以图像、文本、检测框作为输入，并以文本和检测框作为输出。Qwen-VL 系列模型性能强大，具备多语言对话、多图交错对话等能力，并支持中文开放域定位和细粒度图像识别与理解。 Qwen-VL (Qwen Large Vision Language Model) is the visual multimodal version of the large model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-VL accepts image, text, and bounding box as inputs, outputs text and bounding box. The features of Qwen-VL include: 目前，我们提供了Qwen-VL和Qwen-VL-Chat两个模型，分别为预训练模型和Chat模型。如果想了解更多关于模型的信息，请点击[链接](https://github.com/QwenLM/Qwen-VL/blob/master/visual_memo.md)查看我们的技术备忘录。本仓库为Qwen-VL-Chat仓库。 We release Qwen-VL and Qwen-VL-Chat, which are pretrained model and Chat model respectively. For more details about Qwen-VL, please refer to our [technical memo](https://github.com/QwenLM/Qwen-VL/blob/master/visual_memo.md). This repo is the one for Qwen-VL-Chat. <br> ## 安装要求 (Requirements) * python 3.8及以上版本 * pytorch 1.12及以上版本，推荐2.0及以上版本 * 建议使用CUDA 11.4及以上（GPU用户需考虑此选项） * python 3.8 and above * pytorch 1.12 and above, 2.0 and above are recommended * CUDA 11.4 and above are recommended (this is for GPU users) <br> ## 快速开始 (Quickstart) 我们提供简单的示例来说明如何利用 🤗 Transformers 快速使用Qwen-VL-Chat。在开始前，请确保你已经配置好环境并安装好相关的代码包。最重要的是，确保你满足上述要求，然后安装相关的依赖库。 Below, we provide simple examples to show how to use Qwen-VL-Chat with 🤗 Transformers. Before running the code, make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries. ```bash pip install -r requirements.txt ``` 接下来你可以开始使用Transformers来使用我们的模型。关于视觉模块的更多用法，请参考[教程](TUTORIAL.md)。 Now you can start with Transformers. More usage aboue vision encoder, please refer to [tutorial](TUTORIAL_zh.md). #### 🤗 Transformers To use Qwen-VL-Chat for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, please make sure that you are using the latest code. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation import GenerationConfig import torch torch.manual_seed(1234) # Note: The default behavior now has injection attack prevention off. tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True) # use bf16 # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval() # use fp16 # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval() # use cpu only # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="cpu", trust_remote_code=True).eval() # use cuda device model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="cuda", trust_remote_code=True).eval() # Specify hyperparameters for generation (No need to do this if you are using transformers>=4.32.0) # model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True) # 1st dialogue turn query = tokenizer.from_list_format([ {'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'}, {'text': '这是什么'}, ]) response, history = model.chat(tokenizer, query=query, history=None) print(response) # 图中是一名年轻女子在沙滩上和她的狗玩耍，狗的品种可能是拉布拉多。她们坐在沙滩上，狗的前腿抬起来，似乎在和人类击掌。两人之间充满了信任和爱。 # 2nd dialogue turn response, history = model.chat(tokenizer, '输出"击掌"的检测框', history=history) print(response) # <ref>击掌</ref><box>(517,508),(589,611)</box> image = tokenizer.draw_bbox_on_latest_picture(response, history) if image: image.save('1.jpg') else: print("no box") ``` <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo_highfive.jpg" width="500"/> <p> <br> ## 量化 (Quantization) ### 用法 (Usage) 当前我们提供了基于[AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ)的量化方案，并提供了Qwen-VL-Chat的Int4量化版本Qwen-VL-Chat-Int4 [点击此处](https://huggingface.co/Qwen/Qwen-VL-Chat-Int4)。该模型在效果评测上几乎无损，并在显存占用和推理速度上具有明显优势。下文说明如何使用该量化模型。开始之前，请确保你满足要求（如torch2.0及以上、transformers 4.32.0及以上，等）并安装所需的代码库： We provide a new solution based on [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), and release an Int4 quantized model for Qwen-VL-Chat, Qwen-VL-Chat-Int4 [Click here](https://huggingface.co/Qwen/Qwen-VL-Chat-Int4), which achieves nearly lossless model effects but improved performance on both memory costs and inference speed. Here we demonstrate how to use our provided quantized models for inference. Before you start, make sure you meet the requirements (e.g., torch 2.0 and above, transformers 4.32.0 and above, etc.) and install the required packages: ```bash pip install optimum git clone https://github.com/JustinLin610/AutoGPTQ.git & cd AutoGPTQ pip install -v . ``` 如遇到安装 `auto-gptq` 的问题，建议您前往官方[repo](https://github.com/PanQiWei/AutoGPTQ) 寻找合适的wheel。随后你便可以按照上述用法，轻松调用量化模型： If you meet problems installing `auto-gptq`, we advise you to check out the official [repo](https://github.com/PanQiWei/AutoGPTQ) to find a wheel. Then you can load the quantized model easily and run inference as same as usual: ```python model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen-VL-Chat-Int4", device_map="auto", trust_remote_code=True ).eval() # Either a local path or an u[](https://)rl between <img></img> tags. image_path = 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg' response, history = model.chat(tokenizer, query=f'<img>{image_path}</img>这是什么', history=None) print(response) ``` ### 效果评测 (Performance) 我们列出不同精度下模型在评测基准 [TouchStone](https://github.com/OFA-Sys/TouchStone) 上的表现，并发现量化模型并没有显著性能损失。结果如下所示： We illustrate the model performance of both BF16 and Int4 models on the benchmark [TouchStone](https://github.com/OFA-Sys/TouchStone), and we find that the quantized model does not suffer from significant performance degradation. Results are shown below: \| Quantization \| ZH. \| EN \| \| ------------ \| :--------: \| :-----------: \| \| BF16 \| 401.2 \| 645.2 \| \| Int4 \| 386.6 \| 651.4 \| ### 推理速度 (Inference Speed) 我们测算了在输入一张图片（即258个token）的条件下BF16和Int4的模型生成1792 (2048-258) 和 7934 (8192-258) 个token的平均速度。 We measured the average inference speed (tokens/s) of generating 1792 (2048-258) and 7934 (8192-258) tokens with the context of an image (which takes 258 tokens) under BF16 precision and Int4 quantization, respectively. \| Quantization \| Speed (2048 tokens) \| Speed (8192 tokens) \| \| ------------ \| :-----------------: \| :-----------------: \| \| BF16 \| 28.87 \| 24.32 \| \| Int4 \| 37.79 \| 34.34 \| 推理速度测算是在单卡 A100-SXM4-80G GPU上运行，使用PyTorch 2.0.1及CUDA 11.4。 The profiling runs on a single A100-SXM4-80G GPU with PyTorch 2.0.1 and CUDA 11.4. ### GPU显存占用 (GPU Memory Usage) 我们还测算了在一张图片输入的条件下BF16和Int4模型生成1792 (2048-258) 和 7934 (8192-258) 个token所需显存。结果如下所示： We also profile the peak GPU memory usage for encoding 1792 (2048-258) tokens (including an image) as context (and generating single token) and generating 7934 (8192-258) tokens (with an image as context) under BF16 or Int4 quantization level, respectively. The results are shown below. \| Quantization \| Peak Usage for Encoding 2048 Tokens \| Peak Usage for Generating 8192 Tokens \| \| ------------ \| :---------------------------------: \| :-----------------------------------: \| \| BF16 \| 22.60GB \| 28.01GB \| \| Int4 \| 11.82GB \| 17.23GB \| 上述速度和显存测算使用[此脚本](https://qianwen-res.oss-cn-beijing.aliyuncs.com/profile_mm.py)完成。 The above speed and memory profiling are conducted using [this script](https://qianwen-res.oss-cn-beijing.aliyuncs.com/profile_mm.py). <br> ## 评测我们从两个角度评测了两个模型的能力： 1. 在英文标准 Benchmark 上评测模型的基础任务能力。目前评测了四大类多模态任务： - Zero-shot Caption: 评测模型在未见过数据集上的零样本图片描述能力； - General VQA: 评测模型的通用问答能力，例如判断题、颜色、个数、类目等问答能力； - Text-based VQA：评测模型对于图片中文字相关的识别/问答能力，例如文档问答、图表问答、文字问答等； - Referring Expression Compression：评测模型给定物体描述画检测框的能力； 2. 试金石 (TouchStone)：为了评测模型整体的图文对话能力和人类对齐水平。我们为此构建了一个基于 GPT4 打分来评测 LVLM 模型的 Benchmark：TouchStone。在 TouchStone-v0.1 中： - 评测基准总计涵盖 300+张图片、800+道题目、27个类别。包括基础属性问答、人物地标问答、影视作品问答、视觉推理、反事实推理、诗歌创作、故事写作，商品比较、图片解题等尽可能广泛的类别。 - 为了弥补目前 GPT4 无法直接读取图片的缺陷，我们给所有的带评测图片提供了人工标注的充分详细描述，并且将图片的详细描述、问题和模型的输出结果一起交给 GPT4 打分。 - 评测同时包含英文版本和中文版本。评测结果如下： We evaluated the model's ability from two perspectives: 1. Standard Benchmarks: We evaluate the model's basic task capabilities on four major categories of multimodal tasks: - Zero-shot Caption: Evaluate model's zero-shot image captioning ability on unseen datasets; - General VQA: Evaluate the general question-answering ability of pictures, such as the judgment, color, number, category, etc; - Text-based VQA: Evaluate the model's ability to recognize text in pictures, such as document QA, chart QA, etc; - Referring Expression Comprehension: Evaluate the ability to localize a target object in an image described by a referring expression. 2. TouchStone: To evaluate the overall text-image dialogue capability and alignment level with humans, we have constructed a benchmark called TouchStone, which is based on scoring with GPT4 to evaluate the LVLM model. - The TouchStone benchmark covers a total of 300+ images, 800+ questions, and 27 categories. Such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc; - In order to break the current limitation of GPT4 in terms of direct image input, TouchStone provides fine-grained image annotations by human labeling. These detailed annotations, along with the questions and the model's output, are then presented to GPT4 for scoring. - The benchmark includes both English and Chinese versions. The results of the evaluation are as follows: Qwen-VL outperforms current SOTA generalist models on multiple VL tasks and has a more comprehensive coverage in terms of capability range. <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/radar.png" width="600"/> <p> ### 零样本图像描述 & 通用视觉问答 (Zero-shot Captioning & General VQA) <table> <thead> <tr> <th rowspan="2">Model type</th> <th rowspan="2">Model</th> <th colspan="2">Zero-shot Captioning</th> <th colspan="5">General VQA</th> </tr> <tr> <th>NoCaps</th> <th>Flickr30K</th> <th>VQAv2<sup>dev</sup></th> <th>OK-VQA</th> <th>GQA</th> <th>SciQA-Img<br>(0-shot)</th> <th>VizWiz<br>(0-shot)</th> </tr> </thead> <tbody align="center"> <tr> <td rowspan="10">Generalist<br>Models</td> <td>Flamingo-9B</td> <td>-</td> <td>61.5</td> <td>51.8</td> <td>44.7</td> <td>-</td> <td>-</td> <td>28.8</td> </tr> <tr> <td>Flamingo-80B</td> <td>-</td> <td>67.2</td> <td>56.3</td> <td>50.6</td> <td>-</td> <td>-</td> <td>31.6</td> </tr> <tr> <td>Unified-IO-XL</td> <td>100.0</td> <td>-</td> <td>77.9</td> <td>54.0</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>Kosmos-1</td> <td>-</td> <td>67.1</td> <td>51.0</td> <td>-</td> <td>-</td> <td>-</td> <td>29.2</td> </tr> <tr> <td>Kosmos-2</td> <td>-</td> <td>66.7</td> <td>45.6</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>BLIP-2 (Vicuna-13B)</td> <td>103.9</td> <td>71.6</td> <td>65.0</td> <td>45.9</td> <td>32.3</td> <td>61.0</td> <td>19.6</td> </tr> <tr> <td>InstructBLIP (Vicuna-13B)</td> <td><strong>121.9</strong></td> <td>82.8</td> <td>-</td> <td>-</td> <td>49.5</td> <td>63.1</td> <td>33.4</td> </tr> <tr> <td>Shikra (Vicuna-13B)</td> <td>-</td> <td>73.9</td> <td>77.36</td> <td>47.16</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td><strong>Qwen-VL (Qwen-7B)</strong></td> <td>121.4</td> <td><b>85.8</b></td> <td><b>78.8</b></td> <td><b>58.6</b></td> <td><b>59.3</b></td> <td>67.1</td> <td>35.2</td> </tr> <!-- <tr> <td>Qwen-VL (4-shot)</td> <td>-</td> <td>-</td> <td>-</td> <td>63.6</td> <td>-</td> <td>-</td> <td>39.1</td> </tr> --> <tr> <td>Qwen-VL-Chat</td> <td>120.2</td> <td>81.0</td> <td>78.2</td> <td>56.6</td> <td>57.5</td> <td><b>68.2</b></td> <td><b>38.9</b></td> </tr> <!-- <tr> <td>Qwen-VL-Chat (4-shot)</td> <td>-</td> <td>-</td> <td>-</td> <td>60.6</td> <td>-</td> <td>-</td> <td>44.45</td> </tr> --> <tr> <td>Previous SOTA<br>(Per Task Fine-tuning)</td> <td>-</td> <td>127.0<br>(PALI-17B)</td> <td>84.5<br>(InstructBLIP<br>-FlanT5-XL)</td> <td>86.1<br>(PALI-X<br>-55B)</td> <td>66.1<br>(PALI-X<br>-55B)</td> <td>72.1<br>(CFR)</td> <td>92.53<br>(LLaVa+<br>GPT-4)</td> <td>70.9<br>(PALI-X<br>-55B)</td> </tr> </tbody> </table> - 在 Zero-shot Caption 中，Qwen-VL 在 Flickr30K 数据集上取得了 SOTA 的结果，并在 Nocaps 数据集上取得了和 InstructBlip 可竞争的结果。 - 在 General VQA 中，Qwen-VL 取得了 LVLM 模型同等量级和设定下 SOTA 的结果。 - For zero-shot image captioning, Qwen-VL achieves the SOTA on Flickr30K and competitive results on Nocaps with InstructBlip. - For general VQA, Qwen-VL achieves the SOTA under the same generalist LVLM scale settings. ### 文本导向的视觉问答 (Text-oriented VQA) <table> <thead> <tr> <th>Model type</th> <th>Model</th> <th>TextVQA</th> <th>DocVQA</th> <th>ChartQA</th> <th>AI2D</th> <th>OCR-VQA</th> </tr> </thead> <tbody align="center"> <tr> <td rowspan="5">Generalist Models</td> <td>BLIP-2 (Vicuna-13B)</td> <td>42.4</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>InstructBLIP (Vicuna-13B)</td> <td>50.7</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>mPLUG-DocOwl (LLaMA-7B)</td> <td>52.6</td> <td>62.2</td> <td>57.4</td> <td>-</td> <td>-</td> </tr> <tr> <td>Pic2Struct-Large (1.3B)</td> <td>-</td> <td><b>76.6</b></td> <td>58.6</td> <td>42.1</td> <td>71.3</td> </tr> <tr> <td>Qwen-VL (Qwen-7B)</td> <td><b>63.8</b></td> <td>65.1</td> <td><b>65.7</b></td> <td><b>62.3</b></td> <td><b>75.7</b></td> </tr> <tr> <td>Specialist SOTAs<br>(Specialist/Finetuned)</td> <td>PALI-X-55B (Single-task FT)<br>(Without OCR Pipeline)</td> <td>71.44</td> <td>80.0</td> <td>70.0</td> <td>81.2</td> <td>75.0</td> </tr> </tbody> </table> - 在文字相关的识别/问答评测上，取得了当前规模下通用 LVLM 达到的最好结果。 - 分辨率对上述某几个评测非常重要，大部分 224 分辨率的开源 LVLM 模型无法完成以上评测，或只能通过切图的方式解决。Qwen-VL 将分辨率提升到 448，可以直接以端到端的方式进行以上评测。Qwen-VL 在很多任务上甚至超过了 1024 分辨率的 Pic2Struct-Large 模型。 - In text-related recognition/QA evaluation, Qwen-VL achieves the SOTA under the generalist LVLM scale settings. - Resolution is important for several above evaluations. While most open-source LVLM models with 224 resolution are incapable of these evaluations or can only solve these by cutting images, Qwen-VL scales the resolution to 448 so that it can be evaluated end-to-end. Qwen-VL even outperforms Pic2Struct-Large models of 1024 resolution on some tasks. ### 细粒度视觉定位 (Referring Expression Comprehension) <table> <thead> <tr> <th rowspan="2">Model type</th> <th rowspan="2">Model</th> <th colspan="3">RefCOCO</th> <th colspan="3">RefCOCO+</th> <th colspan="2">RefCOCOg</th> <th>GRIT</th> </tr> <tr> <th>val</th> <th>test-A</th> <th>test-B</th> <th>val</th> <th>test-A</th> <th>test-B</th> <th>val-u</th> <th>test-u</th> <th>refexp</th> </tr> </thead> <tbody align="center"> <tr> <td rowspan="8">Generalist Models</td> <td>GPV-2</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>51.50</td> </tr> <tr> <td>OFA-L</td> <td>79.96</td> <td>83.67</td> <td>76.39</td> <td>68.29</td> <td>76.00</td> <td>61.75</td> <td>67.57</td> <td>67.58</td> <td>61.70</td> </tr> <tr> <td>Unified-IO</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td><b>78.61</b></td> </tr> <tr> <td>VisionLLM-H</td> <td></td> <td>86.70</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>Shikra-7B</td> <td>87.01</td> <td>90.61</td> <td>80.24 </td> <td>81.60</td> <td>87.36</td> <td>72.12</td> <td>82.27</td> <td>82.19</td> <td>69.34</td> </tr> <tr> <td>Shikra-13B</td> <td>87.83 </td> <td>91.11</td> <td>81.81</td> <td>82.89</td> <td>87.79</td> <td>74.41</td> <td>82.64</td> <td>83.16</td> <td>69.03</td> </tr> <tr> <td>Qwen-VL-7B</td> <td><b>89.36</b></td> <td>92.26</td> <td><b>85.34</b></td> <td><b>83.12</b></td> <td>88.25</td> <td><b>77.21</b></td> <td>85.58</td> <td>85.48</td> <td>78.22</td> </tr> <tr> <td>Qwen-VL-7B-Chat</td> <td>88.55</td> <td><b>92.27</b></td> <td>84.51</td> <td>82.82</td> <td><b>88.59</b></td> <td>76.79</td> <td><b>85.96</b></td> <td><b>86.32</b></td> <td>-</td> <tr> <td rowspan="3">Specialist SOTAs<br>(Specialist/Finetuned)</td> <td>G-DINO-L</td> <td>90.56  </td> <td>93.19</td> <td>88.24</td> <td>82.75</td> <td>88.95</td> <td>75.92</td> <td>86.13</td> <td>87.02</td> <td>-</td> </tr> <tr> <td>UNINEXT-H</td> <td>92.64 </td> <td>94.33</td> <td>91.46</td> <td>85.24</td> <td>89.63</td> <td>79.79</td> <td>88.73</td> <td>89.37</td> <td>-</td> </tr> <tr> <td>ONE-PEACE</td> <td>92.58 </td> <td>94.18</td> <td>89.26</td> <td>88.77</td> <td>92.21</td> <td>83.23</td> <td>89.22</td> <td>89.27</td> <td>-</td> </tr> </tbody> </table> - 在定位任务上，Qwen-VL 全面超过 Shikra-13B，取得了目前 Generalist LVLM 模型上在 Refcoco 上的 SOTA。 - Qwen-VL 并没有在任何中文定位数据上训练过，但通过中文 Caption 数据和英文 Grounding 数据的训练，可以 Zero-shot 泛化出中文 Grounding 能力。我们提供了以上所有评测脚本以供复现我们的实验结果。请阅读 [eval/EVALUATION.md](eval/EVALUATION.md) 了解更多信息。 - Qwen-VL achieves the SOTA* in all above referring expression comprehension benchmarks. - Qwen-VL has not been trained on any Chinese grounding data, but it can still generalize to the Chinese Grounding tasks in a zero-shot way by training Chinese Caption data and English Grounding data. We provide all of the above evaluation scripts for reproducing our experimental results. Please read [eval/EVALUATION.md](eval/EVALUATION.md) for more information. ### 闲聊能力测评 (Chat Evaluation) TouchStone 是一个基于 GPT4 打分来评测 LVLM 模型的图文对话能力和人类对齐水平的基准。它涵盖了 300+张图片、800+道题目、27个类别，包括基础属性、人物地标、视觉推理、诗歌创作、故事写作、商品比较、图片解题等尽可能广泛的类别。关于 TouchStone 的详细介绍，请参考[touchstone/README_CN.md](touchstone/README_CN.md)了解更多信息。 TouchStone is a benchmark based on scoring with GPT4 to evaluate the abilities of the LVLM model on text-image dialogue and alignment levels with humans. It covers a total of 300+ images, 800+ questions, and 27 categories, such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc. Please read [touchstone/README_CN.md](touchstone/README.md) for more information. #### 英语 (English) \| Model \| Score \| \|---------------\|-------\| \| PandaGPT \| 488.5 \| \| MiniGPT4 \| 531.7 \| \| InstructBLIP \| 552.4 \| \| LLaMA-AdapterV2 \| 590.1 \| \| mPLUG-Owl \| 605.4 \| \| LLaVA \| 602.7 \| \| Qwen-VL-Chat \| 645.2 \| #### 中文 (Chinese) \| Model \| Score \| \|---------------\|-------\| \| VisualGLM \| 247.1 \| \| Qwen-VL-Chat \| 401.2 \| Qwen-VL-Chat 模型在中英文的对齐评测中均取得当前 LVLM 模型下的最好结果。 Qwen-VL-Chat has achieved the best results in both Chinese and English alignment evaluation. <br> ## 常见问题 (FAQ) 如遇到问题，敬请查阅 [FAQ](https://github.com/QwenLM/Qwen-VL/blob/master/FAQ_zh.md)以及issue区，如仍无法解决再提交issue。 If you meet problems, please refer to [FAQ](https://github.com/QwenLM/Qwen-VL/blob/master/FAQ.md) and the issues first to search a solution before you launch a new issue. <br> ## 使用协议 (License Agreement) 研究人员与开发者可使用Qwen-VL和Qwen-VL-Chat或进行二次开发。我们同样允许商业使用，具体细节请查看[LICENSE](https://github.com/QwenLM/Qwen-VL/blob/master/LICENSE)。如需商用，请填写[问卷](https://dashscope.console.aliyun.com/openModelApply/qianwen)申请。 Researchers and developers are free to use the codes and model weights of both Qwen-VL and Qwen-VL-Chat. We also allow their commercial use. Check our license at [LICENSE](LICENSE) for more details. <br> ## 引用 (Citation) 如果你觉得我们的论文和代码对你的研究有帮助，请考虑:star: 和引用 :pencil: :) If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :) ```BibTeX @article{Qwen-VL, title={Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities}, author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren}, journal={arXiv preprint arXiv:2308.12966}, year={2023} } ``` <br> ## 联系我们 (Contact Us) 如果你想给我们的研发团队和产品团队留言，请通过邮件（qianwen_opensource@alibabacloud.com）联系我们。 If you are interested to leave a message to either our research team or product team, feel free to send an email to qianwen_opensource@alibabacloud.com. ``` ```
loupzeur/Pyramids	loupzeur	2023-09-21T09:56:07Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-09-21T09:54:54Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: loupzeur/Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Yntec/animeTEN	Yntec	2023-09-21T09:53:29Z	384	4	diffusers	[ "diffusers", "safetensors", "Anime", "General Purpose", "Ctuhulo", "realisticElves", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-21T08:18:50Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Anime - General Purpose - Ctuhulo - realisticElves - text-to-image - stable-diffusion - stable-diffusion-diffusers - diffusers --- # animeTEN This model with the zVAE baken in. Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/JyhNOB6-JV54HHztUelPU.png) chibi character, breathtaking, 8 k resolution, pop corn, visible brushstrokes, extremely detailed, Cartoon Pretty CUTE LITTLE Girl, beautiful, establishing shot, artistic, dangelico pino, Iconic, DETAILED CHIBI EYES, 1949, sharp focus, beautiful face, octane render, cinematic lighting, dramatic lighting, A magic garden with vegetables, performing, a beautiful detailed legs, fruitcake, gorgeous detailed hair, Magazine ad, ritual Original page: https://civitai.com/models/144023?modelVersionId=160609
EnzoZacharias/starcoder-fine-tuned-plc_V1	EnzoZacharias	2023-09-21T09:41:57Z	0	0	null	[ "generated_from_trainer", "base_model:bigcode/starcoder", "base_model:finetune:bigcode/starcoder", "license:bigcode-openrail-m", "region:us" ]	null	2023-09-21T09:20:41Z	--- license: bigcode-openrail-m base_model: bigcode/starcoder tags: - generated_from_trainer model-index: - name: starcoder-fine-tuned-plc_V1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # starcoder-fine-tuned-plc_V1 This model is a fine-tuned version of [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.1.0.dev20230823 - Datasets 2.14.4 - Tokenizers 0.13.3
hustvl/vitmatte-base-composition-1k	hustvl	2023-09-21T09:25:07Z	14,261	10	transformers	[ "transformers", "pytorch", "vitmatte", "vision", "arxiv:2305.15272", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2023-09-10T07:56:12Z	--- license: apache-2.0 tags: - vision --- # ViTMatte model ViTMatte model trained on Composition-1k. It was introduced in the paper [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Yao et al. and first released in [this repository](https://github.com/hustvl/ViTMatte). Disclaimer: The team releasing ViTMatte did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ViTMatte is a simple approach to image matting, the task of accurately estimating the foreground object in an image. The model consists of a Vision Transformer (ViT) with a lightweight head on top. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vitmatte_architecture.png" alt="drawing" width="600"/> <small> ViTMatte high-level overview. Taken from the <a href="https://arxiv.org/abs/2305.15272">original paper.</a> </small> ## Intended uses & limitations You can use the raw model for image matting. See the [model hub](https://huggingface.co/models?search=vitmatte) to look for other fine-tuned versions that may interest you. ### How to use We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/vitmatte#transformers.VitMatteForImageMatting.forward.example). ### BibTeX entry and citation info ```bibtex @misc{yao2023vitmatte, title={ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers}, author={Jingfeng Yao and Xinggang Wang and Shusheng Yang and Baoyuan Wang}, year={2023}, eprint={2305.15272}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
JoyboyXoXo/rl_course_vizdoom_health_gathering_supreme	JoyboyXoXo	2023-09-21T09:17:48Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T09:17:39Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.70 +/- 2.25 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r JoyboyXoXo/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
sebastiantrbl/test-DialoGPT-finetune	sebastiantrbl	2023-09-21T09:16:30Z	207	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "conversational", "dataset:daily_dialog", "base_model:microsoft/DialoGPT-medium", "base_model:finetune:microsoft/DialoGPT-medium", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-21T08:19:37Z	--- license: mit base_model: microsoft/DialoGPT-medium tags: - generated_from_trainer datasets: - daily_dialog model-index: - name: tmplo2wugb5 results: [] pipeline_tag: conversational --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tmplo2wugb5 This model is a fine-tuned version of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) on the daily_dialog dataset. It achieves the following results on the evaluation set: - Loss: 1.7233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Charishma010997/Falcon7b_finetuned	Charishma010997	2023-09-21T09:04:56Z	0	0	peft	[ "peft", "falcon", "custom_code", "base_model:vilsonrodrigues/falcon-7b-instruct-sharded", "base_model:adapter:vilsonrodrigues/falcon-7b-instruct-sharded", "4-bit", "bitsandbytes", "region:us" ]	null	2023-09-17T05:40:57Z	--- library_name: peft base_model: vilsonrodrigues/falcon-7b-instruct-sharded --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
JcKosmos74/my_awesome_billsum_model	JcKosmos74	2023-09-21T09:03:47Z	104	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-21T08:34:29Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1351 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4889 - Rouge1: 0.1351 - Rouge2: 0.0465 - Rougel: 0.1133 - Rougelsum: 0.1132 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.7805 \| 0.1295 \| 0.0394 \| 0.1095 \| 0.109 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.5686 \| 0.1312 \| 0.0443 \| 0.1118 \| 0.1115 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5062 \| 0.1351 \| 0.045 \| 0.1133 \| 0.1132 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.4889 \| 0.1351 \| 0.0465 \| 0.1133 \| 0.1132 \| 19.0 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
thiru1/distilgpt2-finetuned-wikitext2	thiru1	2023-09-21T09:02:53Z	187	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-21T08:22:01Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6421 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7602 \| 1.0 \| 2334 \| 3.6669 \| \| 3.653 \| 2.0 \| 4668 \| 3.6472 \| \| 3.6006 \| 3.0 \| 7002 \| 3.6421 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/ebihara_naho_idolmastercinderellagirls	CyberHarem	2023-09-21T09:00:22Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/ebihara_naho_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-21T08:45:07Z	--- license: mit datasets: - CyberHarem/ebihara_naho_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of ebihara_naho_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4080, you need to download `4080/ebihara_naho_idolmastercinderellagirls.pt` as the embedding and `4080/ebihara_naho_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4080, with the score of 0.956. The trigger words are: 1. `ebihara_naho_idolmastercinderellagirls` 2. `black_hair, green_eyes, breasts, blush, large_breasts, smile, ponytail, cleavage, hair_ornament` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:----------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:----------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.861 \| [Download](5100/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| [<NSFW, click to see>](5100/previews/pattern_4.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.946 \| [Download](4760/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| [<NSFW, click to see>](4760/previews/pattern_4.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.913 \| [Download](4420/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| [<NSFW, click to see>](4420/previews/pattern_4.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.956 \| [Download](4080/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| [<NSFW, click to see>](4080/previews/pattern_4.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.948 \| [Download](3740/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| [<NSFW, click to see>](3740/previews/pattern_4.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.914 \| [Download](3400/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| [<NSFW, click to see>](3400/previews/pattern_4.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.937 \| [Download](3060/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| [<NSFW, click to see>](3060/previews/pattern_4.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.845 \| [Download](2720/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| [<NSFW, click to see>](2720/previews/pattern_4.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.904 \| [Download](2380/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| [<NSFW, click to see>](2380/previews/pattern_4.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.904 \| [Download](2040/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| [<NSFW, click to see>](2040/previews/pattern_4.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.926 \| [Download](1700/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| [<NSFW, click to see>](1700/previews/pattern_4.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.940 \| [Download](1360/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| [<NSFW, click to see>](1360/previews/pattern_4.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.942 \| [Download](1020/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| [<NSFW, click to see>](1020/previews/pattern_4.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.924 \| [Download](680/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| [<NSFW, click to see>](680/previews/pattern_4.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.912 \| [Download](340/ebihara_naho_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| [<NSFW, click to see>](340/previews/pattern_4.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
boimbukanbaim/codeparrot-ds	boimbukanbaim	2023-09-21T09:00:12Z	132	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-21T04:25:46Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: codeparrot-ds results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.4899 \| 0.94 \| 5000 \| 1.6274 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
EnzoZacharias/replit-code-v1-3b-fine-tuned-plc_V1	EnzoZacharias	2023-09-21T08:45:48Z	0	0	null	[ "generated_from_trainer", "base_model:replit/replit-code-v1-3b", "base_model:finetune:replit/replit-code-v1-3b", "license:cc-by-sa-4.0", "region:us" ]	null	2023-09-21T08:41:16Z	--- license: cc-by-sa-4.0 base_model: replit/replit-code-v1-3b tags: - generated_from_trainer model-index: - name: replit-code-v1-3b-fine-tuned-plc_V1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # replit-code-v1-3b-fine-tuned-plc_V1 This model is a fine-tuned version of [replit/replit-code-v1-3b](https://huggingface.co/replit/replit-code-v1-3b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.1.0.dev20230823 - Datasets 2.14.4 - Tokenizers 0.13.3
Aharneish/qa-model	Aharneish	2023-09-21T08:42:04Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-04-05T15:31:45Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: distilbert-base-uncased model-index: - name: qa-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # qa-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
CyberHarem/rikka_4ninwasorezoreusootsuku	CyberHarem	2023-09-21T08:36:34Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/rikka_4ninwasorezoreusootsuku", "license:mit", "region:us" ]	text-to-image	2023-09-21T03:22:14Z	--- license: mit datasets: - CyberHarem/rikka_4ninwasorezoreusootsuku pipeline_tag: text-to-image tags: - art --- # Lora of rikka_4ninwasorezoreusootsuku This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5400, you need to download `5400/rikka_4ninwasorezoreusootsuku.pt` as the embedding and `5400/rikka_4ninwasorezoreusootsuku.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 5400, with the score of 0.944. The trigger words are: 1. `rikka_4ninwasorezoreusootsuku` 2. `twintails, blush, red_eyes, smile, ribbon, grey_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 9000 \| 0.936 \| [Download](9000/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-9000](9000/previews/pattern_1.png) \| ![pattern_2-9000](9000/previews/pattern_2.png) \| ![pattern_3-9000](9000/previews/pattern_3.png) \| ![pattern_4-9000](9000/previews/pattern_4.png) \| ![pattern_5-9000](9000/previews/pattern_5.png) \| ![pattern_6-9000](9000/previews/pattern_6.png) \| ![pattern_7-9000](9000/previews/pattern_7.png) \| ![pattern_8-9000](9000/previews/pattern_8.png) \| ![pattern_9-9000](9000/previews/pattern_9.png) \| ![pattern_10-9000](9000/previews/pattern_10.png) \| ![pattern_11-9000](9000/previews/pattern_11.png) \| [<NSFW, click to see>](9000/previews/bikini.png) \| [<NSFW, click to see>](9000/previews/bondage.png) \| ![free-9000](9000/previews/free.png) \| ![maid-9000](9000/previews/maid.png) \| ![miko-9000](9000/previews/miko.png) \| [<NSFW, click to see>](9000/previews/nude.png) \| [<NSFW, click to see>](9000/previews/nude2.png) \| ![suit-9000](9000/previews/suit.png) \| ![yukata-9000](9000/previews/yukata.png) \| \| 8400 \| 0.941 \| [Download](8400/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-8400](8400/previews/pattern_1.png) \| ![pattern_2-8400](8400/previews/pattern_2.png) \| ![pattern_3-8400](8400/previews/pattern_3.png) \| ![pattern_4-8400](8400/previews/pattern_4.png) \| ![pattern_5-8400](8400/previews/pattern_5.png) \| ![pattern_6-8400](8400/previews/pattern_6.png) \| ![pattern_7-8400](8400/previews/pattern_7.png) \| ![pattern_8-8400](8400/previews/pattern_8.png) \| ![pattern_9-8400](8400/previews/pattern_9.png) \| ![pattern_10-8400](8400/previews/pattern_10.png) \| ![pattern_11-8400](8400/previews/pattern_11.png) \| [<NSFW, click to see>](8400/previews/bikini.png) \| [<NSFW, click to see>](8400/previews/bondage.png) \| ![free-8400](8400/previews/free.png) \| ![maid-8400](8400/previews/maid.png) \| ![miko-8400](8400/previews/miko.png) \| [<NSFW, click to see>](8400/previews/nude.png) \| [<NSFW, click to see>](8400/previews/nude2.png) \| ![suit-8400](8400/previews/suit.png) \| ![yukata-8400](8400/previews/yukata.png) \| \| 7800 \| 0.932 \| [Download](7800/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-7800](7800/previews/pattern_1.png) \| ![pattern_2-7800](7800/previews/pattern_2.png) \| ![pattern_3-7800](7800/previews/pattern_3.png) \| ![pattern_4-7800](7800/previews/pattern_4.png) \| ![pattern_5-7800](7800/previews/pattern_5.png) \| ![pattern_6-7800](7800/previews/pattern_6.png) \| ![pattern_7-7800](7800/previews/pattern_7.png) \| ![pattern_8-7800](7800/previews/pattern_8.png) \| ![pattern_9-7800](7800/previews/pattern_9.png) \| ![pattern_10-7800](7800/previews/pattern_10.png) \| ![pattern_11-7800](7800/previews/pattern_11.png) \| [<NSFW, click to see>](7800/previews/bikini.png) \| [<NSFW, click to see>](7800/previews/bondage.png) \| ![free-7800](7800/previews/free.png) \| ![maid-7800](7800/previews/maid.png) \| ![miko-7800](7800/previews/miko.png) \| [<NSFW, click to see>](7800/previews/nude.png) \| [<NSFW, click to see>](7800/previews/nude2.png) \| ![suit-7800](7800/previews/suit.png) \| ![yukata-7800](7800/previews/yukata.png) \| \| 7200 \| 0.934 \| [Download](7200/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-7200](7200/previews/pattern_1.png) \| ![pattern_2-7200](7200/previews/pattern_2.png) \| ![pattern_3-7200](7200/previews/pattern_3.png) \| ![pattern_4-7200](7200/previews/pattern_4.png) \| ![pattern_5-7200](7200/previews/pattern_5.png) \| ![pattern_6-7200](7200/previews/pattern_6.png) \| ![pattern_7-7200](7200/previews/pattern_7.png) \| ![pattern_8-7200](7200/previews/pattern_8.png) \| ![pattern_9-7200](7200/previews/pattern_9.png) \| ![pattern_10-7200](7200/previews/pattern_10.png) \| ![pattern_11-7200](7200/previews/pattern_11.png) \| [<NSFW, click to see>](7200/previews/bikini.png) \| [<NSFW, click to see>](7200/previews/bondage.png) \| ![free-7200](7200/previews/free.png) \| ![maid-7200](7200/previews/maid.png) \| ![miko-7200](7200/previews/miko.png) \| [<NSFW, click to see>](7200/previews/nude.png) \| [<NSFW, click to see>](7200/previews/nude2.png) \| ![suit-7200](7200/previews/suit.png) \| ![yukata-7200](7200/previews/yukata.png) \| \| 6600 \| 0.939 \| [Download](6600/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-6600](6600/previews/pattern_1.png) \| ![pattern_2-6600](6600/previews/pattern_2.png) \| ![pattern_3-6600](6600/previews/pattern_3.png) \| ![pattern_4-6600](6600/previews/pattern_4.png) \| ![pattern_5-6600](6600/previews/pattern_5.png) \| ![pattern_6-6600](6600/previews/pattern_6.png) \| ![pattern_7-6600](6600/previews/pattern_7.png) \| ![pattern_8-6600](6600/previews/pattern_8.png) \| ![pattern_9-6600](6600/previews/pattern_9.png) \| ![pattern_10-6600](6600/previews/pattern_10.png) \| ![pattern_11-6600](6600/previews/pattern_11.png) \| [<NSFW, click to see>](6600/previews/bikini.png) \| [<NSFW, click to see>](6600/previews/bondage.png) \| ![free-6600](6600/previews/free.png) \| ![maid-6600](6600/previews/maid.png) \| ![miko-6600](6600/previews/miko.png) \| [<NSFW, click to see>](6600/previews/nude.png) \| [<NSFW, click to see>](6600/previews/nude2.png) \| ![suit-6600](6600/previews/suit.png) \| ![yukata-6600](6600/previews/yukata.png) \| \| 6000 \| 0.939 \| [Download](6000/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-6000](6000/previews/pattern_1.png) \| ![pattern_2-6000](6000/previews/pattern_2.png) \| ![pattern_3-6000](6000/previews/pattern_3.png) \| ![pattern_4-6000](6000/previews/pattern_4.png) \| ![pattern_5-6000](6000/previews/pattern_5.png) \| ![pattern_6-6000](6000/previews/pattern_6.png) \| ![pattern_7-6000](6000/previews/pattern_7.png) \| ![pattern_8-6000](6000/previews/pattern_8.png) \| ![pattern_9-6000](6000/previews/pattern_9.png) \| ![pattern_10-6000](6000/previews/pattern_10.png) \| ![pattern_11-6000](6000/previews/pattern_11.png) \| [<NSFW, click to see>](6000/previews/bikini.png) \| [<NSFW, click to see>](6000/previews/bondage.png) \| ![free-6000](6000/previews/free.png) \| ![maid-6000](6000/previews/maid.png) \| ![miko-6000](6000/previews/miko.png) \| [<NSFW, click to see>](6000/previews/nude.png) \| [<NSFW, click to see>](6000/previews/nude2.png) \| ![suit-6000](6000/previews/suit.png) \| ![yukata-6000](6000/previews/yukata.png) \| \| 5400 \| 0.944 \| [Download](5400/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-5400](5400/previews/pattern_1.png) \| ![pattern_2-5400](5400/previews/pattern_2.png) \| ![pattern_3-5400](5400/previews/pattern_3.png) \| ![pattern_4-5400](5400/previews/pattern_4.png) \| ![pattern_5-5400](5400/previews/pattern_5.png) \| ![pattern_6-5400](5400/previews/pattern_6.png) \| ![pattern_7-5400](5400/previews/pattern_7.png) \| ![pattern_8-5400](5400/previews/pattern_8.png) \| ![pattern_9-5400](5400/previews/pattern_9.png) \| ![pattern_10-5400](5400/previews/pattern_10.png) \| ![pattern_11-5400](5400/previews/pattern_11.png) \| [<NSFW, click to see>](5400/previews/bikini.png) \| [<NSFW, click to see>](5400/previews/bondage.png) \| ![free-5400](5400/previews/free.png) \| ![maid-5400](5400/previews/maid.png) \| ![miko-5400](5400/previews/miko.png) \| [<NSFW, click to see>](5400/previews/nude.png) \| [<NSFW, click to see>](5400/previews/nude2.png) \| ![suit-5400](5400/previews/suit.png) \| ![yukata-5400](5400/previews/yukata.png) \| \| 4800 \| 0.935 \| [Download](4800/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-4800](4800/previews/pattern_1.png) \| ![pattern_2-4800](4800/previews/pattern_2.png) \| ![pattern_3-4800](4800/previews/pattern_3.png) \| ![pattern_4-4800](4800/previews/pattern_4.png) \| ![pattern_5-4800](4800/previews/pattern_5.png) \| ![pattern_6-4800](4800/previews/pattern_6.png) \| ![pattern_7-4800](4800/previews/pattern_7.png) \| ![pattern_8-4800](4800/previews/pattern_8.png) \| ![pattern_9-4800](4800/previews/pattern_9.png) \| ![pattern_10-4800](4800/previews/pattern_10.png) \| ![pattern_11-4800](4800/previews/pattern_11.png) \| [<NSFW, click to see>](4800/previews/bikini.png) \| [<NSFW, click to see>](4800/previews/bondage.png) \| ![free-4800](4800/previews/free.png) \| ![maid-4800](4800/previews/maid.png) \| ![miko-4800](4800/previews/miko.png) \| [<NSFW, click to see>](4800/previews/nude.png) \| [<NSFW, click to see>](4800/previews/nude2.png) \| ![suit-4800](4800/previews/suit.png) \| ![yukata-4800](4800/previews/yukata.png) \| \| 4200 \| 0.939 \| [Download](4200/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-4200](4200/previews/pattern_1.png) \| ![pattern_2-4200](4200/previews/pattern_2.png) \| ![pattern_3-4200](4200/previews/pattern_3.png) \| ![pattern_4-4200](4200/previews/pattern_4.png) \| ![pattern_5-4200](4200/previews/pattern_5.png) \| ![pattern_6-4200](4200/previews/pattern_6.png) \| ![pattern_7-4200](4200/previews/pattern_7.png) \| ![pattern_8-4200](4200/previews/pattern_8.png) \| ![pattern_9-4200](4200/previews/pattern_9.png) \| ![pattern_10-4200](4200/previews/pattern_10.png) \| ![pattern_11-4200](4200/previews/pattern_11.png) \| [<NSFW, click to see>](4200/previews/bikini.png) \| [<NSFW, click to see>](4200/previews/bondage.png) \| ![free-4200](4200/previews/free.png) \| ![maid-4200](4200/previews/maid.png) \| ![miko-4200](4200/previews/miko.png) \| [<NSFW, click to see>](4200/previews/nude.png) \| [<NSFW, click to see>](4200/previews/nude2.png) \| ![suit-4200](4200/previews/suit.png) \| ![yukata-4200](4200/previews/yukata.png) \| \| 3600 \| 0.944 \| [Download](3600/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-3600](3600/previews/pattern_1.png) \| ![pattern_2-3600](3600/previews/pattern_2.png) \| ![pattern_3-3600](3600/previews/pattern_3.png) \| ![pattern_4-3600](3600/previews/pattern_4.png) \| ![pattern_5-3600](3600/previews/pattern_5.png) \| ![pattern_6-3600](3600/previews/pattern_6.png) \| ![pattern_7-3600](3600/previews/pattern_7.png) \| ![pattern_8-3600](3600/previews/pattern_8.png) \| ![pattern_9-3600](3600/previews/pattern_9.png) \| ![pattern_10-3600](3600/previews/pattern_10.png) \| ![pattern_11-3600](3600/previews/pattern_11.png) \| [<NSFW, click to see>](3600/previews/bikini.png) \| [<NSFW, click to see>](3600/previews/bondage.png) \| ![free-3600](3600/previews/free.png) \| ![maid-3600](3600/previews/maid.png) \| ![miko-3600](3600/previews/miko.png) \| [<NSFW, click to see>](3600/previews/nude.png) \| [<NSFW, click to see>](3600/previews/nude2.png) \| ![suit-3600](3600/previews/suit.png) \| ![yukata-3600](3600/previews/yukata.png) \| \| 3000 \| 0.929 \| [Download](3000/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-3000](3000/previews/pattern_1.png) \| ![pattern_2-3000](3000/previews/pattern_2.png) \| ![pattern_3-3000](3000/previews/pattern_3.png) \| ![pattern_4-3000](3000/previews/pattern_4.png) \| ![pattern_5-3000](3000/previews/pattern_5.png) \| ![pattern_6-3000](3000/previews/pattern_6.png) \| ![pattern_7-3000](3000/previews/pattern_7.png) \| ![pattern_8-3000](3000/previews/pattern_8.png) \| ![pattern_9-3000](3000/previews/pattern_9.png) \| ![pattern_10-3000](3000/previews/pattern_10.png) \| ![pattern_11-3000](3000/previews/pattern_11.png) \| [<NSFW, click to see>](3000/previews/bikini.png) \| [<NSFW, click to see>](3000/previews/bondage.png) \| ![free-3000](3000/previews/free.png) \| ![maid-3000](3000/previews/maid.png) \| ![miko-3000](3000/previews/miko.png) \| [<NSFW, click to see>](3000/previews/nude.png) \| [<NSFW, click to see>](3000/previews/nude2.png) \| ![suit-3000](3000/previews/suit.png) \| ![yukata-3000](3000/previews/yukata.png) \| \| 2400 \| 0.936 \| [Download](2400/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-2400](2400/previews/pattern_1.png) \| ![pattern_2-2400](2400/previews/pattern_2.png) \| ![pattern_3-2400](2400/previews/pattern_3.png) \| ![pattern_4-2400](2400/previews/pattern_4.png) \| ![pattern_5-2400](2400/previews/pattern_5.png) \| ![pattern_6-2400](2400/previews/pattern_6.png) \| ![pattern_7-2400](2400/previews/pattern_7.png) \| ![pattern_8-2400](2400/previews/pattern_8.png) \| ![pattern_9-2400](2400/previews/pattern_9.png) \| ![pattern_10-2400](2400/previews/pattern_10.png) \| ![pattern_11-2400](2400/previews/pattern_11.png) \| [<NSFW, click to see>](2400/previews/bikini.png) \| [<NSFW, click to see>](2400/previews/bondage.png) \| ![free-2400](2400/previews/free.png) \| ![maid-2400](2400/previews/maid.png) \| ![miko-2400](2400/previews/miko.png) \| [<NSFW, click to see>](2400/previews/nude.png) \| [<NSFW, click to see>](2400/previews/nude2.png) \| ![suit-2400](2400/previews/suit.png) \| ![yukata-2400](2400/previews/yukata.png) \| \| 1800 \| 0.882 \| [Download](1800/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-1800](1800/previews/pattern_1.png) \| ![pattern_2-1800](1800/previews/pattern_2.png) \| ![pattern_3-1800](1800/previews/pattern_3.png) \| ![pattern_4-1800](1800/previews/pattern_4.png) \| ![pattern_5-1800](1800/previews/pattern_5.png) \| ![pattern_6-1800](1800/previews/pattern_6.png) \| ![pattern_7-1800](1800/previews/pattern_7.png) \| ![pattern_8-1800](1800/previews/pattern_8.png) \| ![pattern_9-1800](1800/previews/pattern_9.png) \| ![pattern_10-1800](1800/previews/pattern_10.png) \| ![pattern_11-1800](1800/previews/pattern_11.png) \| [<NSFW, click to see>](1800/previews/bikini.png) \| [<NSFW, click to see>](1800/previews/bondage.png) \| ![free-1800](1800/previews/free.png) \| ![maid-1800](1800/previews/maid.png) \| ![miko-1800](1800/previews/miko.png) \| [<NSFW, click to see>](1800/previews/nude.png) \| [<NSFW, click to see>](1800/previews/nude2.png) \| ![suit-1800](1800/previews/suit.png) \| ![yukata-1800](1800/previews/yukata.png) \| \| 1200 \| 0.909 \| [Download](1200/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-1200](1200/previews/pattern_1.png) \| ![pattern_2-1200](1200/previews/pattern_2.png) \| ![pattern_3-1200](1200/previews/pattern_3.png) \| ![pattern_4-1200](1200/previews/pattern_4.png) \| ![pattern_5-1200](1200/previews/pattern_5.png) \| ![pattern_6-1200](1200/previews/pattern_6.png) \| ![pattern_7-1200](1200/previews/pattern_7.png) \| ![pattern_8-1200](1200/previews/pattern_8.png) \| ![pattern_9-1200](1200/previews/pattern_9.png) \| ![pattern_10-1200](1200/previews/pattern_10.png) \| ![pattern_11-1200](1200/previews/pattern_11.png) \| [<NSFW, click to see>](1200/previews/bikini.png) \| [<NSFW, click to see>](1200/previews/bondage.png) \| ![free-1200](1200/previews/free.png) \| ![maid-1200](1200/previews/maid.png) \| ![miko-1200](1200/previews/miko.png) \| [<NSFW, click to see>](1200/previews/nude.png) \| [<NSFW, click to see>](1200/previews/nude2.png) \| ![suit-1200](1200/previews/suit.png) \| ![yukata-1200](1200/previews/yukata.png) \| \| 600 \| 0.741 \| [Download](600/rikka_4ninwasorezoreusootsuku.zip) \| ![pattern_1-600](600/previews/pattern_1.png) \| ![pattern_2-600](600/previews/pattern_2.png) \| ![pattern_3-600](600/previews/pattern_3.png) \| ![pattern_4-600](600/previews/pattern_4.png) \| ![pattern_5-600](600/previews/pattern_5.png) \| ![pattern_6-600](600/previews/pattern_6.png) \| ![pattern_7-600](600/previews/pattern_7.png) \| ![pattern_8-600](600/previews/pattern_8.png) \| ![pattern_9-600](600/previews/pattern_9.png) \| ![pattern_10-600](600/previews/pattern_10.png) \| ![pattern_11-600](600/previews/pattern_11.png) \| [<NSFW, click to see>](600/previews/bikini.png) \| [<NSFW, click to see>](600/previews/bondage.png) \| ![free-600](600/previews/free.png) \| ![maid-600](600/previews/maid.png) \| ![miko-600](600/previews/miko.png) \| [<NSFW, click to see>](600/previews/nude.png) \| [<NSFW, click to see>](600/previews/nude2.png) \| ![suit-600](600/previews/suit.png) \| ![yukata-600](600/previews/yukata.png) \|
TemporalGames/opt-1.3b-lambada_rmt_ms7_bptt7_sl2028_mt10_lTrue_LORA_cur2	TemporalGames	2023-09-21T08:28:42Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-21T08:28:39Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
martinnnnn/ppo-Huggy	martinnnnn	2023-09-21T08:21:39Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-09-21T08:21:26Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: martinnnnn/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
McMilly/TNF-Milly	McMilly	2023-09-21T08:17:33Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-09-21T08:17:33Z	--- license: bigscience-openrail-m ---
hihisu1231/mbti_230921_4	hihisu1231	2023-09-21T08:10:51Z	140	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-21T08:06:27Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: polyglot-1.3b-koalpaca-v1.1a-rtx3090__230921_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # polyglot-1.3b-koalpaca-v1.1a-rtx3090__230921_4 This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
EnzoZacharias/LLama2-70b-fine-tuned-plc_V2	EnzoZacharias	2023-09-21T08:09:34Z	0	0	null	[ "generated_from_trainer", "base_model:meta-llama/Llama-2-70b-chat-hf", "base_model:finetune:meta-llama/Llama-2-70b-chat-hf", "region:us" ]	null	2023-09-21T06:41:09Z	--- base_model: meta-llama/Llama-2-70b-chat-hf tags: - generated_from_trainer model-index: - name: LLama2-70b-fine-tuned-plc_V2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # LLama2-70b-fine-tuned-plc_V2 This model is a fine-tuned version of [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.1.0.dev20230823 - Datasets 2.14.4 - Tokenizers 0.13.3
hbbz/cyberhbbz	hbbz	2023-09-21T08:02:30Z	29	0	diffusers	[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-21T07:59:13Z	--- license: creativeml-openrail-m ---
QWW/dreambooth_beacon	QWW	2023-09-21T07:55:28Z	29	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-21T07:42:22Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - QWW/dreambooth_beacon This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
loupzeur/ppo-SnowballTarget	loupzeur	2023-09-21T07:53:51Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-09-21T07:52:53Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: loupzeur/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Apptware/D_tell_market_falcon7b_sharded	Apptware	2023-09-21T07:47:05Z	4	0	peft	[ "peft", "region:us" ]	null	2023-09-21T07:47:02Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
yezituan/test	yezituan	2023-09-21T07:46:42Z	0	0	null	[ "en", "dataset:allenai/dolma", "license:openrail", "region:us" ]	null	2023-09-21T07:44:03Z	--- license: openrail datasets: - allenai/dolma language: - en metrics: - accuracy ---
andriydovgal/bert-base-banking77-pt2	andriydovgal	2023-09-21T07:36:07Z	106	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:banking77", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-13T12:11:42Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - banking77 metrics: - f1 model-index: - name: bert-base-banking77-pt2 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 config: default split: test args: default metrics: - name: F1 type: f1 value: 0.9292385279025629 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-banking77-pt2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.2993 - F1: 0.9292 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.0521 \| 1.0 \| 626 \| 0.7762 \| 0.8277 \| \| 0.3536 \| 2.0 \| 1252 \| 0.3612 \| 0.9208 \| \| 0.1678 \| 3.0 \| 1878 \| 0.2993 \| 0.9292 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
ArturoGL/practica2	ArturoGL	2023-09-21T07:34:32Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-20T20:41:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: practica2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8480392156862745 - name: F1 type: f1 value: 0.8888888888888888 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # practica2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5060 - Accuracy: 0.8480 - F1: 0.8889 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5475 \| 1.09 \| 500 \| 0.6785 \| 0.7598 \| 0.8281 \| \| 0.3811 \| 2.18 \| 1000 \| 0.5060 \| 0.8480 \| 0.8889 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
OpenDILabCommunity/PongNoFrameskip-v4-C51	OpenDILabCommunity	2023-09-21T07:24:11Z	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "PongNoFrameskip-v4", "en", "license:apache-2.0", "region:us" ]	reinforcement-learning	2023-05-18T09:52:30Z	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - PongNoFrameskip-v4 benchmark_name: OpenAI/Gym/Atari task_name: PongNoFrameskip-v4 pipeline_tag: reinforcement-learning model-index: - name: C51 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Atari-PongNoFrameskip-v4 type: OpenAI/Gym/Atari-PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.3 +/- 0.64 name: mean_reward --- # Play PongNoFrameskip-v4 with C51 Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This is a simple C51 implementation to OpenAI/Gym/Atari PongNoFrameskip-v4 using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). DI-engine is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import C51Agent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = C51Agent( env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import C51Agent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/PongNoFrameskip-v4-C51") # Instantiate the agent agent = C51Agent( env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from ding.bonus import C51Agent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = C51Agent(env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-C51") # Train the agent return_ = agent.train(step=int(20000000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Atari", task_name="PongNoFrameskip-v4", algo_name="C51", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./c51/pong_c51_deploy.py", usage_file_by_huggingface_ding="./c51/pong_c51_download.py", train_file="./c51/pong_c51.py", repo_id="OpenDILabCommunity/PongNoFrameskip-v4-C51", create_repo=False ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 30, 'n_evaluator_episode': 8, 'collector_env_num': 8, 'evaluator_env_num': 8, 'env_id': 'PongNoFrameskip-v4', 'frame_stack': 4, 'env_wrapper': 'atari_default' }, 'policy': { 'model': { 'encoder_hidden_size_list': [128, 128, 512], 'v_min': -10, 'v_max': 10, 'n_atom': 51, 'obs_shape': [4, 84, 84], 'action_shape': 6 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 10, 'batch_size': 32, 'learning_rate': 0.0001, 'target_update_freq': 500, 'target_theta': 0.005, 'ignore_done': False }, 'collect': { 'collector': {}, 'n_sample': 100, 'unroll_len': 1 }, 'eval': { 'evaluator': { 'eval_freq': 4000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 30, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 100000 }, 'eps': { 'type': 'exp', 'start': 1.0, 'end': 0.05, 'decay': 250000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'c51', 'priority': False, 'priority_IS_weight': False, 'discount_factor': 0.99, 'nstep': 3, 'cfg_type': 'C51PolicyDict' }, 'exp_name': 'PongNoFrameskip-v4-C51', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](https://wandb.ai/zjowowen/PongNoFrameskip-v4-C51) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/DI-engine) - Doc: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-C51/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-C51/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 55276.2 KB - Last Update Date: 2023-09-21 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/Atari - Task: PongNoFrameskip-v4 - Gym version: 0.25.1 - DI-engine version: v0.4.9 - PyTorch version: 2.0.1+cu117 - Doc: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html)
CJ-gyuwonpark/filtering-platypus-13b	CJ-gyuwonpark	2023-09-21T07:10:45Z	1	0	peft	[ "peft", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2023-09-21T01:36:15Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
CyberHarem/wakui_rumi_idolmastercinderellagirls	CyberHarem	2023-09-21T07:09:06Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/wakui_rumi_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-21T06:58:24Z	--- license: mit datasets: - CyberHarem/wakui_rumi_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of wakui_rumi_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/wakui_rumi_idolmastercinderellagirls.pt` as the embedding and `4760/wakui_rumi_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4760, with the score of 0.939. The trigger words are: 1. `wakui_rumi_idolmastercinderellagirls` 2. `short_hair, blue_hair, jewelry, black_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:--------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.923 \| [Download](5100/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![pattern_6-5100](5100/previews/pattern_6.png) \| ![pattern_7-5100](5100/previews/pattern_7.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.939 \| [Download](4760/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![pattern_6-4760](4760/previews/pattern_6.png) \| ![pattern_7-4760](4760/previews/pattern_7.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.854 \| [Download](4420/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![pattern_6-4420](4420/previews/pattern_6.png) \| ![pattern_7-4420](4420/previews/pattern_7.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.820 \| [Download](4080/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![pattern_6-4080](4080/previews/pattern_6.png) \| ![pattern_7-4080](4080/previews/pattern_7.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.873 \| [Download](3740/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![pattern_6-3740](3740/previews/pattern_6.png) \| ![pattern_7-3740](3740/previews/pattern_7.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.766 \| [Download](3400/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![pattern_6-3400](3400/previews/pattern_6.png) \| ![pattern_7-3400](3400/previews/pattern_7.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.829 \| [Download](3060/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![pattern_6-3060](3060/previews/pattern_6.png) \| ![pattern_7-3060](3060/previews/pattern_7.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.758 \| [Download](2720/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![pattern_6-2720](2720/previews/pattern_6.png) \| ![pattern_7-2720](2720/previews/pattern_7.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.755 \| [Download](2380/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![pattern_6-2380](2380/previews/pattern_6.png) \| ![pattern_7-2380](2380/previews/pattern_7.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.911 \| [Download](2040/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![pattern_6-2040](2040/previews/pattern_6.png) \| ![pattern_7-2040](2040/previews/pattern_7.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.842 \| [Download](1700/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![pattern_6-1700](1700/previews/pattern_6.png) \| ![pattern_7-1700](1700/previews/pattern_7.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.824 \| [Download](1360/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![pattern_6-1360](1360/previews/pattern_6.png) \| ![pattern_7-1360](1360/previews/pattern_7.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.908 \| [Download](1020/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![pattern_5-1020](1020/previews/pattern_5.png) \| ![pattern_6-1020](1020/previews/pattern_6.png) \| ![pattern_7-1020](1020/previews/pattern_7.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.903 \| [Download](680/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![pattern_6-680](680/previews/pattern_6.png) \| ![pattern_7-680](680/previews/pattern_7.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.898 \| [Download](340/wakui_rumi_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![pattern_6-340](340/previews/pattern_6.png) \| ![pattern_7-340](340/previews/pattern_7.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
mirfan899/urdu-roberta-ner	mirfan899	2023-09-21T06:52:08Z	101	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-21T06:50:23Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: urdu-roberta-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # urdu-roberta-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1387 - Precision: 0.7735 - Recall: 0.8129 - F1: 0.7927 - Accuracy: 0.9541 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.165 \| 1.0 \| 2272 \| 0.1521 \| 0.7204 \| 0.7960 \| 0.7564 \| 0.9454 \| \| 0.1208 \| 2.0 \| 4544 \| 0.1413 \| 0.7577 \| 0.8101 \| 0.7830 \| 0.9510 \| \| 0.0977 \| 3.0 \| 6816 \| 0.1387 \| 0.7735 \| 0.8129 \| 0.7927 \| 0.9541 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3
martinnnnn/ppo-LunarLander-v2	martinnnnn	2023-09-21T06:47:04Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-21T06:46:44Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.64 +/- 19.86 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
turboderp/Llama2-13B-exl2	turboderp	2023-09-21T06:44:13Z	19	2	null	[ "region:us" ]	null	2023-09-21T06:42:13Z	EXL2 quants of Llama2-13B [2.50 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/2.5bpw) [3.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/3.0bpw) [3.50 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/3.5bpw) [4.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/4.0bpw) [4.65 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/4.65bpw) [5.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/5.0bpw) [6.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/6.0bpw) [8.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/8.0bpw) [measurement.json](https://huggingface.co/turboderp/Llama2-13B-exl2/blob/main/measurement.json)
chenxiang204/sd-pokemon-model-lora-sdxl	chenxiang204	2023-09-21T06:37:45Z	1	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-20T09:28:47Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: lambdalabs/pokemon-blip-captions tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - chenxiang204/sd-pokemon-model-lora-sdxl These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Sandra26/Sandy	Sandra26	2023-09-21T06:29:44Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-20T21:04:51Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: Sandy results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8406862745098039 - name: F1 type: f1 value: 0.8820326678765881 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Sandy This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6410 - Accuracy: 0.8407 - F1: 0.8820 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4994 \| 1.09 \| 500 \| 0.7821 \| 0.8211 \| 0.8793 \| \| 0.3466 \| 2.18 \| 1000 \| 0.6410 \| 0.8407 \| 0.8820 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/tsuchiya_ako_idolmastercinderellagirls	CyberHarem	2023-09-21T06:19:56Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/tsuchiya_ako_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-21T06:09:58Z	--- license: mit datasets: - CyberHarem/tsuchiya_ako_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of tsuchiya_ako_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 3060, you need to download `3060/tsuchiya_ako_idolmastercinderellagirls.pt` as the embedding and `3060/tsuchiya_ako_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 3060, with the score of 0.968. The trigger words are: 1. `tsuchiya_ako_idolmastercinderellagirls` 2. `brown_hair, short_hair, glasses, hair_ornament, mole, hairclip, ahoge, green_eyes, smile, mole_under_mouth, open_mouth` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:----------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-----------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.960 \| [Download](5100/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| [<NSFW, click to see>](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.953 \| [Download](4760/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| [<NSFW, click to see>](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.950 \| [Download](4420/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| [<NSFW, click to see>](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.950 \| [Download](4080/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| [<NSFW, click to see>](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.953 \| [Download](3740/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| [<NSFW, click to see>](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.961 \| [Download](3400/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| [<NSFW, click to see>](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.968 \| [Download](3060/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| [<NSFW, click to see>](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.957 \| [Download](2720/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| [<NSFW, click to see>](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.925 \| [Download](2380/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| [<NSFW, click to see>](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.917 \| [Download](2040/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| [<NSFW, click to see>](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.880 \| [Download](1700/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| [<NSFW, click to see>](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.942 \| [Download](1360/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| [<NSFW, click to see>](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.908 \| [Download](1020/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| [<NSFW, click to see>](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.915 \| [Download](680/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| [<NSFW, click to see>](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.835 \| [Download](340/tsuchiya_ako_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| [<NSFW, click to see>](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
li-ping/songgodv2	li-ping	2023-09-21T06:11:47Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-21T05:54:04Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
Spacetimetravel/autotrain-financial-conversation_financial-summary-bart-90558144325	Spacetimetravel	2023-09-21T06:00:57Z	113	1	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autotrain", "summarization", "unk", "dataset:Spacetimetravel/autotrain-data-financial-conversation_financial-summary-bart", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-09-21T05:59:10Z	--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain" datasets: - Spacetimetravel/autotrain-data-financial-conversation_financial-summary-bart co2_eq_emissions: emissions: 0.05543082382688346 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 90558144325 - CO2 Emissions (in grams): 0.0554 ## Validation Metrics - Loss: 1.555 - Rouge1: 61.365 - Rouge2: 33.249 - RougeL: 48.538 - RougeLsum: 51.545 - Gen Len: 72.500 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Spacetimetravel/autotrain-financial-conversation_financial-summary-bart-90558144325 ```
mirfan899/urdu-distilbert-ner	mirfan899	2023-09-21T05:56:53Z	115	0	transformers	[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-21T05:56:31Z	--- license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: urdu-distilbert-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # urdu-distilbert-ner This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1387 - Precision: 0.7575 - Recall: 0.8057 - F1: 0.7809 - Accuracy: 0.9535 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1637 \| 1.0 \| 2272 \| 0.1505 \| 0.7131 \| 0.7800 \| 0.7451 \| 0.9457 \| \| 0.1159 \| 2.0 \| 4544 \| 0.1390 \| 0.7377 \| 0.8037 \| 0.7693 \| 0.9507 \| \| 0.0882 \| 3.0 \| 6816 \| 0.1387 \| 0.7575 \| 0.8057 \| 0.7809 \| 0.9535 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3
actualbrain/Reinforce-CartPolev1	actualbrain	2023-09-21T05:55:54Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-09-03T11:07:02Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPolev1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
OpenDILabCommunity/Hopper-v3-DDPG	OpenDILabCommunity	2023-09-21T05:49:02Z	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "Hopper-v3", "en", "license:apache-2.0", "region:us" ]	reinforcement-learning	2023-04-19T01:05:47Z	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - Hopper-v3 benchmark_name: OpenAI/Gym/MuJoCo task_name: Hopper-v3 pipeline_tag: reinforcement-learning model-index: - name: DDPG results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/MuJoCo-Hopper-v3 type: OpenAI/Gym/MuJoCo-Hopper-v3 metrics: - type: mean_reward value: 3784.92 +/- 29.08 name: mean_reward --- # Play Hopper-v3 with DDPG Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This is a simple DDPG implementation to OpenAI/Gym/MuJoCo Hopper-v3 using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). DI-engine is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed sudo apt update -y && sudo apt install -y build-essential libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev libglfw3 libglfw3-dev libsdl2-dev libsdl2-image-dev libglm-dev libfreetype6-dev patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import DDPGAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = DDPGAgent(env_id="Hopper-v3", exp_name="Hopper-v3-DDPG", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import DDPGAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/Hopper-v3-DDPG") # Instantiate the agent agent = DDPGAgent(env_id="Hopper-v3", exp_name="Hopper-v3-DDPG", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from ding.bonus import DDPGAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = DDPGAgent(env_id="Hopper-v3", exp_name="Hopper-v3-DDPG") # Train the agent return_ = agent.train(step=int(10000000), collector_env_num=4, evaluator_env_num=4, debug=False) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/MuJoCo", task_name="Hopper-v3", algo_name="DDPG", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/ddpg.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html", installation_guide=''' sudo apt update -y \ && sudo apt install -y \ build-essential \ libgl1-mesa-dev \ libgl1-mesa-glx \ libglew-dev \ libosmesa6-dev \ libglfw3 \ libglfw3-dev \ libsdl2-dev \ libsdl2-image-dev \ libglm-dev \ libfreetype6-dev \ patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ''', usage_file_by_git_clone="./ddpg/hopper_ddpg_deploy.py", usage_file_by_huggingface_ding="./ddpg/hopper_ddpg_download.py", train_file="./ddpg/hopper_ddpg.py", repo_id="OpenDILabCommunity/Hopper-v3-DDPG", create_repo=False ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 6000, 'n_evaluator_episode': 8, 'env_id': 'Hopper-v3', 'norm_obs': { 'use_norm': False }, 'norm_reward': { 'use_norm': False }, 'collector_env_num': 1, 'evaluator_env_num': 8, 'env_wrapper': 'mujoco_default' }, 'policy': { 'model': { 'obs_shape': 11, 'action_shape': 3, 'twin_critic': False, 'actor_head_hidden_size': 256, 'critic_head_hidden_size': 256, 'action_space': 'regression' }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 1, 'batch_size': 256, 'learning_rate_actor': 0.001, 'learning_rate_critic': 0.001, 'ignore_done': False, 'target_theta': 0.005, 'discount_factor': 0.99, 'actor_update_freq': 1, 'noise': False }, 'collect': { 'collector': {}, 'unroll_len': 1, 'noise_sigma': 0.1, 'n_sample': 1 }, 'eval': { 'evaluator': { 'eval_freq': 5000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 6000, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 1000000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'ddpg', 'priority': False, 'priority_IS_weight': False, 'random_collect_size': 25000, 'transition_with_policy_data': False, 'action_space': 'continuous', 'reward_batch_norm': False, 'multi_agent': False, 'cfg_type': 'DDPGPolicyDict' }, 'exp_name': 'Hopper-v3-DDPG', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](https://wandb.ai/zjowowen/Hopper-v3-DDPG) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/DI-engine) - Doc: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/ddpg.html) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/Hopper-v3-DDPG/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/Hopper-v3-DDPG/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 1090.03 KB - Last Update Date: 2023-09-21 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/MuJoCo - Task: Hopper-v3 - Gym version: 0.25.1 - DI-engine version: v0.4.9 - PyTorch version: 2.0.1+cu117 - Doc: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html)
Spacetimetravel/autotrain-financial-conversation-backstory-bart-90555144323	Spacetimetravel	2023-09-21T05:46:48Z	123	0	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autotrain", "summarization", "unk", "dataset:Spacetimetravel/autotrain-data-financial-conversation-backstory-bart", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-09-21T05:45:14Z	--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain" datasets: - Spacetimetravel/autotrain-data-financial-conversation-backstory-bart co2_eq_emissions: emissions: 0.05137309412154303 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 90555144323 - CO2 Emissions (in grams): 0.0514 ## Validation Metrics - Loss: 2.399 - Rouge1: 32.368 - Rouge2: 4.298 - RougeL: 20.788 - RougeLsum: 28.288 - Gen Len: 71.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Spacetimetravel/autotrain-financial-conversation-backstory-bart-90555144323 ```
xizhn/output_model_dir	xizhn	2023-09-21T05:38:41Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-20T04:59:23Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dress tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - xizhn/output_model_dir This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dress using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
li-ping/songgod	li-ping	2023-09-21T05:36:00Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-21T05:29:53Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
Prakstar/Bloom_3b_1_fine_tuned	Prakstar	2023-09-21T05:25:30Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-09-21T05:25:30Z	--- license: bigscience-openrail-m ---
masta-g3/phi-1_5-psychology	masta-g3	2023-09-21T05:08:14Z	59	0	transformers	[ "transformers", "pytorch", "mixformer-sequential", "text-generation", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-09-21T03:53:42Z	--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer model-index: - name: phi-1_5-psychology results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-1_5-psychology This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7574 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.8667 \| 0.04 \| 100 \| 0.8554 \| \| 0.8401 \| 0.09 \| 200 \| 0.8524 \| \| 0.8492 \| 0.13 \| 300 \| 0.8437 \| \| 0.8563 \| 0.18 \| 400 \| 0.8393 \| \| 0.8353 \| 0.22 \| 500 \| 0.8367 \| \| 0.8232 \| 0.26 \| 600 \| 0.8305 \| \| 0.8299 \| 0.31 \| 700 \| 0.8226 \| \| 0.8307 \| 0.35 \| 800 \| 0.8233 \| \| 0.8087 \| 0.39 \| 900 \| 0.8170 \| \| 0.8124 \| 0.44 \| 1000 \| 0.8160 \| \| 0.7943 \| 0.48 \| 1100 \| 0.8103 \| \| 0.7924 \| 0.53 \| 1200 \| 0.8076 \| \| 0.7918 \| 0.57 \| 1300 \| 0.8026 \| \| 0.807 \| 0.61 \| 1400 \| 0.8012 \| \| 0.788 \| 0.66 \| 1500 \| 0.8034 \| \| 0.7946 \| 0.7 \| 1600 \| 0.7946 \| \| 0.7959 \| 0.75 \| 1700 \| 0.7926 \| \| 0.7878 \| 0.79 \| 1800 \| 0.7921 \| \| 0.754 \| 0.83 \| 1900 \| 0.7890 \| \| 0.7762 \| 0.88 \| 2000 \| 0.7850 \| \| 0.7651 \| 0.92 \| 2100 \| 0.7849 \| \| 0.7868 \| 0.97 \| 2200 \| 0.7855 \| \| 0.7651 \| 1.01 \| 2300 \| 0.7820 \| \| 0.7323 \| 1.05 \| 2400 \| 0.7818 \| \| 0.7316 \| 1.1 \| 2500 \| 0.7804 \| \| 0.7311 \| 1.14 \| 2600 \| 0.7808 \| \| 0.7221 \| 1.18 \| 2700 \| 0.7782 \| \| 0.722 \| 1.23 \| 2800 \| 0.7736 \| \| 0.7217 \| 1.27 \| 2900 \| 0.7780 \| \| 0.7226 \| 1.32 \| 3000 \| 0.7730 \| \| 0.7305 \| 1.36 \| 3100 \| 0.7731 \| \| 0.7237 \| 1.4 \| 3200 \| 0.7712 \| \| 0.7127 \| 1.45 \| 3300 \| 0.7710 \| \| 0.7252 \| 1.49 \| 3400 \| 0.7699 \| \| 0.7076 \| 1.54 \| 3500 \| 0.7687 \| \| 0.7185 \| 1.58 \| 3600 \| 0.7672 \| \| 0.6921 \| 1.62 \| 3700 \| 0.7639 \| \| 0.6882 \| 1.67 \| 3800 \| 0.7642 \| \| 0.7184 \| 1.71 \| 3900 \| 0.7633 \| \| 0.7048 \| 1.76 \| 4000 \| 0.7601 \| \| 0.7136 \| 1.8 \| 4100 \| 0.7598 \| \| 0.7063 \| 1.84 \| 4200 \| 0.7591 \| \| 0.7054 \| 1.89 \| 4300 \| 0.7589 \| \| 0.6945 \| 1.93 \| 4400 \| 0.7564 \| \| 0.6955 \| 1.97 \| 4500 \| 0.7544 \| \| 0.6869 \| 2.02 \| 4600 \| 0.7536 \| \| 0.6477 \| 2.06 \| 4700 \| 0.7566 \| \| 0.6593 \| 2.11 \| 4800 \| 0.7568 \| \| 0.6441 \| 2.15 \| 4900 \| 0.7562 \| \| 0.6527 \| 2.19 \| 5000 \| 0.7574 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
0xk1h0/codegen2.5-7b-py150k-r20-LoRA	0xk1h0	2023-09-21T04:58:49Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-21T04:48:19Z	--- library_name: peft --- ## Model Usage ```python import torch import transformers from finetune_peft import get_peft_config, PEFTArguments from peft import get_peft_model model_path = 'Salesforce/codegen25-7b-mono' # peft_path = 'models/codegen25_7b/checkpoint' peft_path = '0xk1h0/codegen25-7b-py150k-r20' # peft_path = 'models/alpaca-llama-7b-peft/params.p' torch.set_default_tensor_type(torch.cuda.HalfTensor) model = transformers.AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, cache_dir='models') peft_config = get_peft_config(peft_args=PEFTArguments(peft_mode="lora")) model = get_peft_model(model, peft_config) # model.load_state_dict(torch.load(peft_path), strict=False) torch.set_default_tensor_type(torch.cuda.FloatTensor) tokenizer = transformers.AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) batch = tokenizer(""" ### Generate AES MODE encrypt function. """, return_tensors="pt") with torch.no_grad(): out = model.generate( input_ids=batch["input_ids"], attention_mask=torch.ones_like(batch["input_ids"]), max_length=256, do_sample=True, temperature = 0.4, top_p=0.95 ) print(tokenizer.decode(out[0])) ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0
awrysfab/emotion_classification	awrysfab	2023-09-21T04:48:06Z	193	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-21T04:34:56Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: emotion_classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2383 - Accuracy: 0.6 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0769 \| 1.0 \| 10 \| 2.0617 \| 0.1812 \| \| 2.0383 \| 2.0 \| 20 \| 2.0104 \| 0.3 \| \| 1.9423 \| 3.0 \| 30 \| 1.8932 \| 0.425 \| \| 1.7923 \| 4.0 \| 40 \| 1.7442 \| 0.475 \| \| 1.6547 \| 5.0 \| 50 \| 1.6047 \| 0.4875 \| \| 1.5297 \| 6.0 \| 60 \| 1.5184 \| 0.5437 \| \| 1.4345 \| 7.0 \| 70 \| 1.4392 \| 0.5625 \| \| 1.337 \| 8.0 \| 80 \| 1.3847 \| 0.5875 \| \| 1.2722 \| 9.0 \| 90 \| 1.3442 \| 0.55 \| \| 1.217 \| 10.0 \| 100 \| 1.3058 \| 0.5625 \| \| 1.1497 \| 11.0 \| 110 \| 1.2914 \| 0.55 \| \| 1.0977 \| 12.0 \| 120 \| 1.2377 \| 0.6125 \| \| 1.0507 \| 13.0 \| 130 \| 1.2253 \| 0.5687 \| \| 1.0268 \| 14.0 \| 140 \| 1.2269 \| 0.5938 \| \| 0.967 \| 15.0 \| 150 \| 1.2260 \| 0.5938 \| \| 0.9269 \| 16.0 \| 160 \| 1.2421 \| 0.5687 \| \| 0.9102 \| 17.0 \| 170 \| 1.2218 \| 0.5687 \| \| 0.8883 \| 18.0 \| 180 \| 1.2207 \| 0.5687 \| \| 0.8633 \| 19.0 \| 190 \| 1.1933 \| 0.6062 \| \| 0.8557 \| 20.0 \| 200 \| 1.1830 \| 0.575 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/nagatomi_hasumi_idolmastercinderellagirls	CyberHarem	2023-09-21T04:38:53Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/nagatomi_hasumi_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-21T04:26:55Z	--- license: mit datasets: - CyberHarem/nagatomi_hasumi_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of nagatomi_hasumi_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2380, you need to download `2380/nagatomi_hasumi_idolmastercinderellagirls.pt` as the embedding and `2380/nagatomi_hasumi_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 2380, with the score of 0.979. The trigger words are: 1. `nagatomi_hasumi_idolmastercinderellagirls` 2. `brown_hair, brown_eyes, smile, short_hair, blush, hairband, open_mouth, bangs` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.958 \| [Download](5100/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.937 \| [Download](4760/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.869 \| [Download](4420/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.955 \| [Download](4080/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.920 \| [Download](3740/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.936 \| [Download](3400/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.945 \| [Download](3060/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.938 \| [Download](2720/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.979 \| [Download](2380/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.937 \| [Download](2040/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.929 \| [Download](1700/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.933 \| [Download](1360/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.909 \| [Download](1020/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.892 \| [Download](680/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.879 \| [Download](340/nagatomi_hasumi_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
rmuema/orca_mini_3B_test_guanaco	rmuema	2023-09-21T04:28:15Z	2	1	peft	[ "peft", "region:us" ]	null	2023-09-15T01:45:50Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 - base_model: psmathur/orca_mini_3b ### Framework versions - PEFT 0.6.0.dev0
Pradeep016/GAN	Pradeep016	2023-09-21T04:27:14Z	0	0	keras	[ "keras", "license:mit", "region:us" ]	null	2023-09-21T04:19:15Z	--- license: mit library_name: keras ---
antphb/pretrain-vit5-large	antphb	2023-09-21T04:03:03Z	3	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-19T17:20:50Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: pretrain-vit5-large results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pretrain-vit5-large This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2677 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.4276 \| 4.56 \| 200 \| 0.2848 \| \| 0.4608 \| 9.12 \| 400 \| 0.2677 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
JoseVallar01/practica2009	JoseVallar01	2023-09-21T03:51:33Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-21T03:46:16Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: practica2009 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8504901960784313 - name: F1 type: f1 value: 0.8908765652951698 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # practica2009 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5264 - Accuracy: 0.8505 - F1: 0.8909 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5107 \| 1.09 \| 500 \| 0.4968 \| 0.8333 \| 0.8832 \| \| 0.3606 \| 2.18 \| 1000 \| 0.5264 \| 0.8505 \| 0.8909 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Panchovix/airoboros-l2-70b-gpt4-1.4.1_2.5bpw-h6-exl2	Panchovix	2023-09-21T03:42:47Z	7	3	transformers	[ "transformers", "llama", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-09-15T18:37:34Z	--- license: other --- 2.5 bit quantization of airoboros 70b 1.4.1 (https://huggingface.co/jondurbin/airoboros-l2-70b-gpt4-1.4.1), using exllama2. Updated as 21 of September 2023, which should fix the bad ppl results. I suggest, if using Ubuntu, to use it with flash-attn. It reduces VRAM usage by a good margin, and is specially useful for this case (70B model on a single 24GB VRAM GPU)
nomsgadded/opt_RestaurantReview	nomsgadded	2023-09-21T03:38:05Z	0	0	adapter-transformers	[ "adapter-transformers", "safetensors", "opt", "code", "text-classification", "en", "region:us" ]	text-classification	2023-09-20T00:06:47Z	--- pipeline_tag: text-classification language: - en metrics: - accuracy library_name: adapter-transformers tags: - code --- This is the Finetune version of the facebook/opt-350m model Dataset is RestaurantReview from kaggle How to use? Input text must be in the form of ##Rating :{text} e.g. ##Rating :It was really nice to dine there, however the waiter is very mean. Then it will return the possible rating customer gave to the restaurant.
Arlethh/leth	Arlethh	2023-09-21T03:36:57Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-20T20:57:48Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: leth results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8504901960784313 - name: F1 type: f1 value: 0.8872458410351203 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # leth This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6024 - Accuracy: 0.8505 - F1: 0.8872 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5009 \| 1.09 \| 500 \| 0.5683 \| 0.8113 \| 0.8693 \| \| 0.3177 \| 2.18 \| 1000 \| 0.6024 \| 0.8505 \| 0.8872 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
zfox/finetuning-sentiment-model-3000-samples	zfox	2023-09-21T03:34:37Z	107	1	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "doi:10.57967/hf/1135", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-21T03:28:42Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8666666666666667 - name: F1 type: f1 value: 0.8692810457516339 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3195 - Accuracy: 0.8667 - F1: 0.8693 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Yntec/level4	Yntec	2023-09-21T03:14:00Z	1,673	1	diffusers	[ "diffusers", "safetensors", "Photorealistic", "Beautiful", "Fantasy", "AreThoseLevel4Plates", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-20T22:17:37Z	--- library_name: diffusers pipeline_tag: text-to-image license: creativeml-openrail-m tags: - Photorealistic - Beautiful - Fantasy - AreThoseLevel4Plates - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # level 4 v3 Original page: https://civitai.com/models/17449?modelVersionId=21896 Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/gaFiXeJAWFV2zkQaXnSVH.png) Pretty cute girl. Detailed coffee table in the vaporwave mid century modern livingroom. highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, artgerm, tomasz alen kopera, peter mohrbacher, little girl, donato giancola, joseph christian leyendecker, boris vallejo, wlop
yudiwbs/marian-finetuned-kde4-en-to-id	yudiwbs	2023-09-21T03:06:04Z	67	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-en-id", "base_model:finetune:Helsinki-NLP/opus-mt-en-id", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-30T04:58:11Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-id tags: - generated_from_keras_callback model-index: - name: yudiwbs/marian-finetuned-kde4-en-to-id results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # yudiwbs/marian-finetuned-kde4-en-to-id Penjelasan: https://yudiwbs.wordpress.com/2023/09/01/fine-tune-model-machine-translation-inggris-indonesia-en-id/ This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-id](https://huggingface.co/Helsinki-NLP/opus-mt-en-id) on KDE dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5779 - Validation Loss: 0.6892 - Epoch: 2 ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1245, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.0329 \| 0.7683 \| 0 \| \| 0.7086 \| 0.7042 \| 1 \| \| 0.5779 \| 0.6892 \| 2 \| ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
daliapv/new_model	daliapv	2023-09-21T03:02:30Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-20T21:04:29Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: new_model results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.821078431372549 - name: F1 type: f1 value: 0.8620037807183365 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # new_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6106 - Accuracy: 0.8211 - F1: 0.8620 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5319 \| 1.09 \| 500 \| 0.5276 \| 0.8284 \| 0.8822 \| \| 0.3708 \| 2.18 \| 1000 \| 0.6106 \| 0.8211 \| 0.8620 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
lilucheng/sourcedetection	lilucheng	2023-09-21T03:02:25Z	0	0	null	[ "region:us" ]	null	2023-09-21T02:50:44Z	Basalt Source Insight Basalt Source Insight is the models which can detect the source lithology, temperature and pressure based on the major elements content. We obtained accuracy of approximately 95% on the testing dataset. Furthermore, we also employ an XGBoost regression model to predict the pressure and temperature conditions of generation of basalts from diverse sources. Our predictions of temperature exhibit remarkable precision, with mean absolute errors of about 49°C. Similarly, our pressure estimations have also a high level of accuracy, with mean absolute errors hovering at approximately 0.37 GPa for various lithologies.
OpenDILabCommunity/PongNoFrameskip-v4-DQN	OpenDILabCommunity	2023-09-21T02:58:27Z	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "PongNoFrameskip-v4", "en", "license:apache-2.0", "region:us" ]	reinforcement-learning	2023-06-14T11:55:23Z	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - PongNoFrameskip-v4 benchmark_name: OpenAI/Gym/Atari task_name: PongNoFrameskip-v4 pipeline_tag: reinforcement-learning model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Atari-PongNoFrameskip-v4 type: OpenAI/Gym/Atari-PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.7 +/- 0.46 name: mean_reward --- # Play PongNoFrameskip-v4 with DQN Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This is a simple DQN implementation to OpenAI/Gym/Atari PongNoFrameskip-v4 using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). DI-engine is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import DQNAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = DQNAgent( env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-DQN", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import DQNAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/PongNoFrameskip-v4-DQN") # Instantiate the agent agent = DQNAgent( env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-DQN", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from ding.bonus import DQNAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = DQNAgent(env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-DQN") # Train the agent return_ = agent.train(step=int(20000000), collector_env_num=8, evaluator_env_num=8, debug=False) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Atari", task_name="PongNoFrameskip-v4", algo_name="DQN", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/dqn.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./dqn/pong_dqn_deploy.py", usage_file_by_huggingface_ding="./dqn/pong_dqn_download.py", train_file="./dqn/pong_dqn.py", repo_id="OpenDILabCommunity/PongNoFrameskip-v4-DQN", create_repo=False ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 30, 'n_evaluator_episode': 8, 'env_id': 'PongNoFrameskip-v4', 'collector_env_num': 8, 'evaluator_env_num': 8, 'fram_stack': 4, 'env_wrapper': 'atari_default' }, 'policy': { 'model': { 'encoder_hidden_size_list': [128, 128, 512], 'obs_shape': [4, 84, 84], 'action_shape': 6 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 10, 'batch_size': 32, 'learning_rate': 0.0001, 'target_update_freq': 500, 'target_theta': 0.005, 'ignore_done': False }, 'collect': { 'collector': {}, 'n_sample': 96, 'unroll_len': 1 }, 'eval': { 'evaluator': { 'eval_freq': 1000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 30, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 100000 }, 'eps': { 'type': 'exp', 'start': 1.0, 'end': 0.05, 'decay': 250000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'dqn', 'priority': False, 'priority_IS_weight': False, 'discount_factor': 0.99, 'nstep': 3, 'cfg_type': 'DQNPolicyDict' }, 'exp_name': 'PongNoFrameskip-v4-DQN', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](https://wandb.ai/zjowowen/PongNoFrameskip-v4-DQN) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/DI-engine) - Doc: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/dqn.html) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-DQN/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-DQN/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 55703.03 KB - Last Update Date: 2023-09-21 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/Atari - Task: PongNoFrameskip-v4 - Gym version: 0.25.1 - DI-engine version: v0.4.9 - PyTorch version: 2.0.1+cu117 - Doc: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html)
kcyu/Cifar100_LoRA_model_Vit-cifar_100	kcyu	2023-09-21T02:39:30Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-21T02:39:26Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
isashap/contexttrained-validationloss-waldomodel3	isashap	2023-09-21T01:41:15Z	31	0	peft	[ "peft", "text-generation", "region:us" ]	text-generation	2023-09-21T01:17:44Z	--- library_name: peft pipeline_tag: text-generation widget: - text: "Job: Skills: Resume Point" --- ## Training procedure ### Framework versions - PEFT 0.5.0
CyberHarem/kurokawa_chiaki_idolmastercinderellagirls	CyberHarem	2023-09-21T01:18:27Z	0	1	null	[ "art", "text-to-image", "dataset:CyberHarem/kurokawa_chiaki_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-21T01:05:33Z	--- license: mit datasets: - CyberHarem/kurokawa_chiaki_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of kurokawa_chiaki_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 3740, you need to download `3740/kurokawa_chiaki_idolmastercinderellagirls.pt` as the embedding and `3740/kurokawa_chiaki_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 3740, with the score of 0.675. The trigger words are: 1. `kurokawa_chiaki_idolmastercinderellagirls` 2. `long_hair, black_hair, brown_eyes, bangs, smile, breasts, blunt_bangs, blush, medium_breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:----------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.555 \| [Download](5100/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| [<NSFW, click to see>](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.614 \| [Download](4760/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| [<NSFW, click to see>](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.661 \| [Download](4420/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| [<NSFW, click to see>](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.543 \| [Download](4080/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| [<NSFW, click to see>](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.675 \| [Download](3740/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| [<NSFW, click to see>](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.489 \| [Download](3400/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| [<NSFW, click to see>](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.575 \| [Download](3060/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| [<NSFW, click to see>](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.576 \| [Download](2720/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| [<NSFW, click to see>](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.564 \| [Download](2380/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| [<NSFW, click to see>](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.541 \| [Download](2040/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| [<NSFW, click to see>](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.548 \| [Download](1700/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| [<NSFW, click to see>](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.493 \| [Download](1360/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| [<NSFW, click to see>](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.533 \| [Download](1020/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| [<NSFW, click to see>](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![pattern_5-1020](1020/previews/pattern_5.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.408 \| [Download](680/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| [<NSFW, click to see>](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.279 \| [Download](340/kurokawa_chiaki_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| [<NSFW, click to see>](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
Spacetimetravel/autotrain-financial-conversation-goals-90496144312	Spacetimetravel	2023-09-21T01:07:33Z	115	1	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain", "summarization", "en", "dataset:Spacetimetravel/autotrain-data-financial-conversation-goals", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2023-09-21T01:06:38Z	--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain" datasets: - Spacetimetravel/autotrain-data-financial-conversation-goals co2_eq_emissions: emissions: 0.005787519308560734 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 90496144312 - CO2 Emissions (in grams): 0.0058 ## Validation Metrics - Loss: 3.149 - Rouge1: 6.000 - Rouge2: 0.000 - RougeL: 4.000 - RougeLsum: 4.000 - Gen Len: 19.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Spacetimetravel/autotrain-financial-conversation-goals-90496144312 ```

amitonHFace/q-Taxi-v3

amitonHFace

2023-09-21T11:25:11Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-09-21T11:25:09Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="amitonHFace/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

ShivamMangale/XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0

ShivamMangale

2023-09-21T11:25:10Z

20

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-09-21T07:35:28Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0 results: [] ---  # XLM-Roberta-base-all_hi_weakdap_1st_iteration_d1_d0 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

monsterapi/opt1.3B_codeinstruct

monsterapi

2023-09-21T11:23:26Z

0

peft

[ "peft", "facebook-opt-1.3b", "code", "instruct", "instruct-code", "code-alpaca", "alpaca-instruct", "alpaca", "opt-1.3b", "dataset:sahil2801/CodeAlpaca-20k", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]

null

2023-05-06T03:17:16Z

--- library_name: peft tags: - facebook-opt-1.3b - code - instruct - instruct-code - code-alpaca - alpaca-instruct - alpaca - opt-1.3b datasets: - sahil2801/CodeAlpaca-20k base_model: codellama/CodeLlama-7b-hf --- We finetuned Facebook/OPT-1.3B on Code-Alpaca-Instruct Dataset (sahil2801/CodeAlpaca-20k) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/CodeAlpaca_20K unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 1 hour and 30 minutes and costed us only `$6` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: facebook/opt-1.3b - Dataset: sahil2801/CodeAlpaca-20k - Learning rate: 0.0003 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---

monsterapi/llama7B_alpaca-lora

monsterapi

2023-09-21T11:23:24Z

1

peft

[ "peft", "llama1-7b", "code", "instruct", "alpaca-instruct", "alpaca", "llama7b", "dataset:tatsu-lab/alpaca", "region:us" ]

null

2023-05-10T05:39:31Z

--- library_name: peft tags: - llama1-7b - code - instruct - alpaca-instruct - alpaca - llama7b datasets: - tatsu-lab/alpaca base_model: decapoda-research/llama-7b-hf --- We finetuned huggyllama/llama-7b on tatsu-lab/alpaca Dataset for 5 epochs or ~ 25,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/tatsu-lab/alpaca unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 4 hours and costed us only `$16` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: huggyllama/llama-7b - Dataset: tatsu-lab/alpaca - Learning rate: 0.0003 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 license: apache-2.0 ---

monsterapi/opt125M_alpaca

monsterapi

2023-09-21T11:23:21Z

146

0

peft

[ "peft", "facebook/opt-125m", "code", "instruct", "alpaca-instruct", "alpaca", "dataset:tatsu-lab/alpaca", "base_model:facebook/opt-125m", "base_model:adapter:facebook/opt-125m", "region:us" ]

null

2023-05-13T05:38:51Z

--- library_name: peft tags: - facebook/opt-125m - code - instruct - alpaca-instruct - alpaca datasets: - tatsu-lab/alpaca base_model: facebook/opt-125m --- We finetuned facebook/opt-125m on tatsu-lab/alpaca Dataset for 10 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/tatsu-lab/alpaca unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 40 minutes and costed us only `$4` for the entire finetuning run! #### Hyperparameters & Run details: - Model: facebook/opt-125m - Dataset: tatsu-lab/alpaca - Learning rate: 0.0003 - Number of epochs: 10 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 - --- license: apache-2.0 ---

monsterapi/OpenPlatypus_LLAMA2_7b

monsterapi

2023-09-21T11:23:18Z

6

1

peft

[ "peft", "meta-llama/Llama-2-7b-hf", "code", "instruct", "instruct-code", "logical-reasoning", "Platypus2", "dataset:garage-bAInd/Open-Platypus", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

2023-09-05T10:13:05Z

--- library_name: peft tags: - meta-llama/Llama-2-7b-hf - code - instruct - instruct-code - logical-reasoning - Platypus2 datasets: - garage-bAInd/Open-Platypus base_model: meta-llama/Llama-2-7b-hf --- We finetuned Meta-Llama/Llama-2-7b-hf on the Open-Platypus dataset (garage-bAInd/Open-Platypus) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). #### About OpenPlatypus Dataset OpenPlatypus is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. The dataset is comprised of various sub-datasets, including PRM800K, ScienceQA, SciBench, ReClor, TheoremQA, among others. These were filtered using keyword search and Sentence Transformers to remove questions with a similarity above 80%. The dataset includes contributions under various licenses like MIT, Creative Commons, and Apache 2.0. The finetuning session got completed in 1 hour and 30 minutes and costed us only `$15` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: meta-llama/Llama-2-7b-hf - Dataset: garage-bAInd/Open-Platypus - Learning rate: 0.0002 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---

alexalbala/llam2test

alexalbala

2023-09-21T11:23:16Z

0

peft

[ "peft", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

2023-09-21T08:49:01Z

--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- ## Training procedure ### Framework versions - PEFT 0.5.0

monsterapi/OpenPlatypus_Falcon_7b

monsterapi

2023-09-21T11:23:15Z

2

0

peft

[ "peft", "tiiuae/falcon-7b", "code", "instruct", "instruct-code", "logical-reasoning", "Platypus2", "dataset:garage-bAInd/Open-Platypus", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]

null

2023-09-05T11:28:00Z

--- library_name: peft tags: - tiiuae/falcon-7b - code - instruct - instruct-code - logical-reasoning - Platypus2 datasets: - garage-bAInd/Open-Platypus base_model: codellama/CodeLlama-7b-hf --- We finetuned TIIUAE/Falcon-7B on the Open-Platypus dataset (garage-bAInd/Open-Platypus) for 3 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). #### About OpenPlatypus Dataset OpenPlatypus is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. The dataset is comprised of various sub-datasets, including PRM800K, ScienceQA, SciBench, ReClor, TheoremQA, among others. These were filtered using keyword search and Sentence Transformers to remove questions with a similarity above 80%. The dataset includes contributions under various licenses like MIT, Creative Commons, and Apache 2.0. The finetuning session got completed in ~ 3 hrs and costed us only `$14` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: tiiuae/falcon-7b - Dataset: garage-bAInd/Open-Platypus - Learning rate: 0.0003 - Number of epochs: 3 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---

monsterapi/codellama7b_codealpaca20k

monsterapi

2023-09-21T11:23:11Z

3

2

peft

[ "peft", "codellama7b", "code", "instruct", "instruct-code", "code-alpaca", "alpaca-instruct", "alpaca", "gpt2", "dataset:sahil2801/CodeAlpaca-20k", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]

null

2023-08-30T15:27:52Z

--- library_name: peft tags: - codellama7b - code - instruct - instruct-code - code-alpaca - alpaca-instruct - alpaca - codellama7b - gpt2 datasets: - sahil2801/CodeAlpaca-20k base_model: codellama/CodeLlama-7b-hf --- We finetuned CodeLlama7B on Code-Alpaca-Instruct Dataset (sahil2801/CodeAlpaca-20k) for 5 epochs or ~ 25,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is HuggingFaceH4/CodeAlpaca_20K unfiltered, removing 36 instances of blatant alignment. The finetuning session got completed in 4 hours and costed us only `$16` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: meta-llama/CodeLlama7B - Dataset: sahil2801/CodeAlpaca-20k - Learning rate: 0.0003 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---

monsterapi/llama2_SQL_Answers_finetuned

monsterapi

2023-09-21T11:23:00Z

7

1

peft

[ "peft", "meta-llama/Llama-2-7b", "code", "instruct", "instruct-code", "sql-create-context", "text-to-sql", "LLM", "dataset:b-mc2/sql-create-context", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

2023-08-26T11:34:07Z

--- library_name: peft tags: - meta-llama/Llama-2-7b - code - instruct - instruct-code - sql-create-context - text-to-sql - LLM datasets: - b-mc2/sql-create-context base_model: meta-llama/Llama-2-7b-hf --- We finetuned Meta-Llama-2-7B on the SQL Create Context Dataset (b-mc2/sql-create-context) for 3 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is an enhanced version of WikiSQL and Spider, focused on providing natural language queries and corresponding SQL CREATE TABLE statements. The dataset contains 78,577 examples and aims to improve the model's grounding in text-to-SQL tasks. The CREATE TABLE statements are particularly useful for limiting token usage and avoiding exposure to sensitive data. The finetuning session took 7hrs and 21 mins and costed us a total of `$15.33`. #### Hyperparameters & Run details: - Model Path: meta-llama/Llama-2-7b - Dataset: b-mc2/sql-create-context - Learning rate: 0.0003 - Number of epochs: 3 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 Loss metrics: ![training loss](train-loss.png "Training loss") --- license: apache-2.0 ---

monsterapi/falcon-7b-python-code-instructions-18k-alpaca

monsterapi

2023-09-21T11:22:57Z

7

0

peft

[ "peft", "falcon", "falcon-7b", "code", "code instruct", "instruct code", "code alpaca", "python code", "code copilot", "copilot", "python coding assistant", "coding assistant", "dataset:iamtarun/python_code_instructions_18k_alpaca", "base_model:tiiuae/falcon-7b", "base_model:adapter:tiiuae/falcon-7b", "license:apache-2.0", "region:us" ]

null

2023-08-24T03:52:24Z

--- license: apache-2.0 library_name: peft tags: - falcon - falcon-7b - code - code instruct - instruct code - code alpaca - python code - code copilot - copilot - python coding assistant - coding assistant datasets: - iamtarun/python_code_instructions_18k_alpaca base_model: tiiuae/falcon-7b --- ## Training procedure We finetuned Falcon-7B LLM on Python-Code-Instructions Dataset ([iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)) for 10 epochs or ~ 23,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). The dataset contains problem descriptions and code in python language. This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style. The finetuning session got completed in 7.3 hours and costed us only `$17.5` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: tiiuae/falcon-7b - Dataset: iamtarun/python_code_instructions_18k_alpaca - Learning rate: 0.0002 - Number of epochs: 10 - Data split: Training: 95% / Validation: 5% - Gradient accumulation steps: 1 ### Framework versions - PEFT 0.4.0 ### Loss metrics: ![training loss](train-loss.png "Training loss")

mesa44/ppo-LunarLander-v2

mesa44

2023-09-21T11:17:54Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-19T09:43:00Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 250.37 +/- 33.48 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

amitonHFace/q-FrozenLake-v1-4x4-noSlippery

amitonHFace

2023-09-21T11:17:28Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-09-21T11:17:25Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="amitonHFace/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

iamshnoo/alpaca-2-70b-bengali

iamshnoo

2023-09-21T11:03:15Z

4

0

peft

[ "peft", "bn", "en", "dataset:iamshnoo/alpaca-cleaned-bengali", "base_model:meta-llama/Llama-2-70b-hf", "base_model:adapter:meta-llama/Llama-2-70b-hf", "license:cc-by-4.0", "region:us" ]

null

2023-09-10T20:28:27Z

--- language: - bn - en license: cc-by-4.0 library_name: peft datasets: - iamshnoo/alpaca-cleaned-bengali base_model: meta-llama/Llama-2-70b-hf --- This represents the PEFT weights only. The base model is LLaMA 2. Instruction finetuning was done using 4 bit QLoRA on a single A100 GPU with the PEFT config as given below. The dataset used for this instruction finetuning process is a translated version of the cleaned alpaca dataset (translated using NLLB-1.3B). Do note that this model might have inferior performance on some language specific tasks compared to full finetuning or a different base model trained with more language specific data. ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0

Ammok/Taxi-v3

Ammok

2023-09-21T10:59:02Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-09-21T10:58:59Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.40 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Ammok/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

hyeongjin99/vit_base_aihub_model_py

hyeongjin99

2023-09-21T10:58:45Z

216

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-21T07:27:29Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: vit_base_aihub_model_py results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9977631269131152 - name: Precision type: precision value: 0.998134723737648 - name: Recall type: recall value: 0.9974298183920257 - name: F1 type: f1 value: 0.9977816548360952 ---  # vit_base_aihub_model_py This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0228 - Accuracy: 0.9978 - Precision: 0.9981 - Recall: 0.9974 - F1: 0.9978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.1415 | 1.0 | 149 | 0.1286 | 0.9712 | 0.9788 | 0.9623 | 0.9700 | | 0.0671 | 2.0 | 299 | 0.0463 | 0.9948 | 0.9917 | 0.9946 | 0.9932 | | 0.0423 | 3.0 | 448 | 0.0356 | 0.9952 | 0.9970 | 0.9908 | 0.9939 | | 0.0383 | 4.0 | 598 | 0.0242 | 0.9976 | 0.9980 | 0.9972 | 0.9976 | | 0.033 | 4.98 | 745 | 0.0228 | 0.9978 | 0.9981 | 0.9974 | 0.9978 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

zac/zac

zac

2023-09-21T10:58:10Z

1

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-09-21T10:58:06Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: z4c tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - zac These are LoRA adaption weights for [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). The weights were trained on the instance prompt "z4c" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: z4c4 ![image_0](test_images/image_0.png) ![image_1](test_images/image_1.png) ![image_2](test_images/image_2.png) ![image_3](test_images/image_3.png)

Ammok/q-FrozenLake-v1-4x4-noSlippery

Ammok

2023-09-21T10:56:08Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-09-21T10:56:04Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Ammok/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

linoyts/huggy-lora-sdxl-v7

linoyts

2023-09-21T10:54:14Z

227

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-09-21T10:53:59Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 pivotal_tuning: true textual_embeddings: embeddings.pti instance_prompt: <s0><s1> inference: false --- # huggy-lora-sdxl-v7 LoRA by [linoytsaban](https://replicate.com/linoytsaban) ### caption prefix: a TOK emoji, steps: 1500, lr: 2e-4 ![lora_image](https://pbxt.replicate.delivery/wJf4lByhD10xCyqaAp2sgsJYW8Xw99sbgue5Fyvj176pD2kRA/out-0.png) > ## Inference with Replicate API Grab your replicate token [here](https://replicate.com/account) ```bash pip install replicate export REPLICATE_API_TOKEN=r8_************************************* ``` ```py import replicate output = replicate.run( "linoy_lora@sha256:6e68d04d64a29ce25df2002570d535b6582310304dd4360f15517c95f89033a7", input={"prompt": "a hugging face emoji in the style of TOK, dressed as yoda"} ) print(output) ``` You may also do inference via the API with Node.js or curl, and locally with COG and Docker, [check out the Replicate API page for this model](https://replicate.com/linoytsaban/linoy_lora/api) ## Inference with 🧨 diffusers Replicate SDXL LoRAs are trained with Pivotal Tuning, which combines training a concept via Dreambooth LoRA with training a new token with Textual Inversion. As `diffusers` doesn't yet support textual inversion for SDXL, we will use cog-sdxl `TokenEmbeddingsHandler` class. The trigger tokens for your prompt will be `<s0><s1>` ```shell pip install diffusers transformers accelerate safetensors huggingface_hub git clone https://github.com/replicate/cog-sdxl cog_sdxl ``` ```py import torch from huggingface_hub import hf_hub_download from diffusers import DiffusionPipeline from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler from diffusers.models import AutoencoderKL pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", ).to("cuda") pipe.load_lora_weights("LinoyTsaban/huggy-lora-sdxl-v7", weight_name="lora.safetensors") text_encoders = [pipe.text_encoder, pipe.text_encoder_2] tokenizers = [pipe.tokenizer, pipe.tokenizer_2] embedding_path = hf_hub_download(repo_id="LinoyTsaban/huggy-lora-sdxl-v7", filename="embeddings.pti", repo_type="model") embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers) embhandler.load_embeddings(embedding_path) prompt="a hugging face emoji in the style of <s0><s1>, dressed as yoda" images = pipe( prompt, cross_attention_kwargs={"scale": 0.8}, ).images #your output image images[0] ```

zamankh/my_awesome_mind_model

zamankh

2023-09-21T10:49:14Z

160

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-09-21T10:44:31Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind_model results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.05309734513274336 ---  # my_awesome_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6510 - Accuracy: 0.0531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.8 | 3 | 2.6492 | 0.0442 | | No log | 1.87 | 7 | 2.6548 | 0.0531 | | 2.6331 | 2.93 | 11 | 2.6597 | 0.0708 | | 2.6331 | 4.0 | 15 | 2.6611 | 0.0531 | | 2.6331 | 4.8 | 18 | 2.6578 | 0.0531 | | 2.6244 | 5.87 | 22 | 2.6493 | 0.0619 | | 2.6244 | 6.93 | 26 | 2.6509 | 0.0619 | | 2.6149 | 8.0 | 30 | 2.6510 | 0.0531 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

CyberHarem/matsumoto_sarina_idolmastercinderellagirls

CyberHarem

2023-09-21T10:44:21Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/matsumoto_sarina_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-21T10:33:22Z

--- license: mit datasets: - CyberHarem/matsumoto_sarina_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of matsumoto_sarina_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5100, you need to download `5100/matsumoto_sarina_idolmastercinderellagirls.pt` as the embedding and `5100/matsumoto_sarina_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 5100**, with the score of 0.976. The trigger words are: 1. `matsumoto_sarina_idolmastercinderellagirls` 2. `long_hair, blue_eyes, smile, brown_hair, breasts, large_breasts, cleavage, blush` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | **5100** | **0.976** | [**Download**](5100/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | [<NSFW, click to see>](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.968 | [Download](4760/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | [<NSFW, click to see>](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.961 | [Download](4420/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | [<NSFW, click to see>](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.973 | [Download](4080/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | [<NSFW, click to see>](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.943 | [Download](3740/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | [<NSFW, click to see>](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.974 | [Download](3400/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | [<NSFW, click to see>](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.917 | [Download](3060/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | [<NSFW, click to see>](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.974 | [Download](2720/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | [<NSFW, click to see>](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.934 | [Download](2380/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | [<NSFW, click to see>](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.892 | [Download](2040/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | [<NSFW, click to see>](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.958 | [Download](1700/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | [<NSFW, click to see>](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.922 | [Download](1360/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | [<NSFW, click to see>](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.897 | [Download](1020/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | [<NSFW, click to see>](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.917 | [Download](680/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | [<NSFW, click to see>](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.882 | [Download](340/matsumoto_sarina_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | [<NSFW, click to see>](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

philolathai/philolathai

philolathai

2023-09-21T10:44:16Z

0

null

[ "region:us" ]

null

2023-09-21T10:43:50Z

Philola ได้รับการผสมสูตรโดยใช้ส่วนผสมจากธรรมชาติที่แสดงให้เห็นว่ามีประโยชน์ในการปรับปรุงสุขภาพโดยรวมของดวงตา Philola ซื้อเลย!! คลิกลิงก์ด้านล่างเพื่อดูข้อมูลเพิ่มเติมและรับส่วนลด 50% ทันที !! รีบหน่อย!! อ่านเพิ่มเติม: https://www.boxdrug.com/PhiloThail https://sites.google.com/view/philola-thailand/home ➤ ชื่อผลิตภัณฑ์ — Philola ➤ ใช้สำหรับ: สุขภาพดวงตา ➤ ประโยชน์หลัก — ปรับปรุงสายตา ➤ ส่วนประกอบ — สารประกอบอินทรีย์ธรรมชาติ ➤ ผลข้างเคียง—NA เรตติ้งสุดท้าย: — 4.7 ➤ มีจำหน่าย — ออนไลน์ ➤ข้อเสนอและส่วนลด; ประหยัดวันนี้! ช้อปตอนนี้เพื่อซื้อข้อเสนอพิเศษ!!! Philola คืออะไร? สำหรับผู้ที่ไม่คุ้นเคย Philola เป็นผลิตภัณฑ์เสริมอาหารที่ช่วยปรับปรุงการมองเห็นที่ลอยอยู่บนอินเทอร์เน็ตโดยอ้างว่าช่วยเพิ่มการมองเห็นของบุคคลโดยจัดการกับ 3 สาเหตุหลักของความบกพร่องทางการมองเห็น สาเหตุหลักคือการได้รับสารพิษบางชนิดที่อาจทำลายดวงตาอย่างรุนแรงได้

chendelong/ChatGLM-PSP

chendelong

2023-09-21T10:37:11Z

104

0

transformers

[ "transformers", "pytorch", "chatglm", "feature-extraction", "custom_code", "arxiv:2309.11000", "region:us" ]

feature-extraction

2023-09-19T04:14:39Z

<div align="center"> 🎙 [**Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model**](https://huggingface.co/papers/2309.11000) [Xinyu Zhou (周欣宇)](https://www.linkedin.com/in/xinyu-zhou2000/), [Delong Chen (陈德龙)](https://chendelong.world/), [Yudong Chen (陈玉东)](https://rwxy.cuc.edu.cn/2019/0730/c5134a133504/pagem.htm) [ArXiv](https://arxiv.org/abs/2309.11000) | [Poster](doc/YFRSW_Poster.pdf) | [Notebook](prosody_prediction.ipynb) | [Github](https://github.com/XinyuZhou2000/Spoken-Dialogue) </div> This project explores the potential of constructing an AI spoken dialogue system that *"thinks how to respond"* and *"thinks how to speak"* simultaneously, which more closely aligns with the human speech production process compared to the current cascade pipeline of independent chatbot and Text-to-Speech (TTS) modules. We hypothesize that *Large Language Models (LLMs)* with billions of parameters possess significant speech understanding capabilities and can jointly model dialogue responses and linguistic features. We investigate the task of Prosodic structure prediction (PSP), a typical front-end task in TTS, demonstrating the speech understanding ability of LLMs.

reeen115/lora_output

reeen115

2023-09-21T10:36:45Z

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-2-1", "base_model:adapter:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-09-20T08:39:18Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: cardboards, grayscale tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - reeen115/lora_output These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on cardboards, grayscale using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False.

OpenDILabCommunity/Hopper-v3-SAC

OpenDILabCommunity

2023-09-21T10:31:12Z

0

pytorch

[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "Hopper-v3", "en", "license:apache-2.0", "region:us" ]

reinforcement-learning

2023-04-14T08:16:42Z

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - Hopper-v3 benchmark_name: OpenAI/Gym/MuJoCo task_name: Hopper-v3 pipeline_tag: reinforcement-learning model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/MuJoCo-Hopper-v3 type: OpenAI/Gym/MuJoCo-Hopper-v3 metrics: - type: mean_reward value: 3899.4 +/- 362.09 name: mean_reward --- # Play **Hopper-v3** with **SAC** Policy ## Model Description  This is a simple **SAC** implementation to OpenAI/Gym/MuJoCo **Hopper-v3** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed sudo apt update -y && sudo apt install -y build-essential libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev libglfw3 libglfw3-dev libsdl2-dev libsdl2-image-dev libglm-dev libfreetype6-dev patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import SACAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = SACAgent(env_id="Hopper-v3", exp_name="Hopper-v3-SAC", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import SACAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/Hopper-v3-SAC") # Instantiate the agent agent = SACAgent(env_id="Hopper-v3", exp_name="Hopper-v3-SAC", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from ding.bonus import SACAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = SACAgent(env_id="Hopper-v3", exp_name="Hopper-v3-SAC") # Train the agent return_ = agent.train(step=int(10000000), collector_env_num=4, evaluator_env_num=4, debug=False) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/MuJoCo", task_name="Hopper-v3", algo_name="SAC", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/sac.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html", installation_guide=''' sudo apt update -y \ && sudo apt install -y \ build-essential \ libgl1-mesa-dev \ libgl1-mesa-glx \ libglew-dev \ libosmesa6-dev \ libglfw3 \ libglfw3-dev \ libsdl2-dev \ libsdl2-image-dev \ libglm-dev \ libfreetype6-dev \ patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ''', usage_file_by_git_clone="./sac/hopper_sac_deploy.py", usage_file_by_huggingface_ding="./sac/hopper_sac_download.py", train_file="./sac/hopper_sac.py", repo_id="OpenDILabCommunity/Hopper-v3-SAC", create_repo=False ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 6000, 'n_evaluator_episode': 8, 'env_id': 'Hopper-v3', 'collector_env_num': 8, 'evaluator_env_num': 8, 'env_wrapper': 'mujoco_default' }, 'policy': { 'model': { 'twin_critic': True, 'action_space': 'reparameterization', 'obs_shape': 11, 'action_shape': 3, 'actor_head_hidden_size': 256, 'critic_head_hidden_size': 256 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 1, 'batch_size': 256, 'learning_rate_q': 0.001, 'learning_rate_policy': 0.001, 'learning_rate_alpha': 0.0003, 'target_theta': 0.005, 'discount_factor': 0.99, 'alpha': 0.2, 'auto_alpha': False, 'log_space': True, 'target_entropy': None, 'ignore_done': False, 'init_w': 0.003, 'reparameterization': True }, 'collect': { 'collector': {}, 'n_sample': 1, 'unroll_len': 1, 'collector_logit': False }, 'eval': { 'evaluator': { 'eval_freq': 1000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 6000, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 1000000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'sac', 'priority': False, 'priority_IS_weight': False, 'random_collect_size': 10000, 'transition_with_policy_data': True, 'multi_agent': False, 'cfg_type': 'SACPolicyDict' }, 'exp_name': 'Hopper-v3-SAC', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> **Training Procedure**  - **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zjowowen/Hopper-v3-SAC) ## Model Information  - **Github Repository:** [repo link](https://github.com/opendilab/DI-engine) - **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/sac.html) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/Hopper-v3-SAC/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/Hopper-v3-SAC/blob/main/replay.mp4)  - **Parameters total size:** 1642.06 KB - **Last Update Date:** 2023-09-21 ## Environments  - **Benchmark:** OpenAI/Gym/MuJoCo - **Task:** Hopper-v3 - **Gym version:** 0.25.1 - **DI-engine version:** v0.4.9 - **PyTorch version:** 2.0.1+cu117 - **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html)

dss107/mini_lm_base

dss107

2023-09-21T10:28:16Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-09-21T10:27:59Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # dss107/mini_lm_base This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("dss107/mini_lm_base") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

sophiaaez/distilhubert-finetuned-gtzan

sophiaaez

2023-09-21T10:25:51Z

160

0

transformers

[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-09-15T09:07:00Z

--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.79 ---  # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7146 - Accuracy: 0.79 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0711 | 1.0 | 75 | 1.9438 | 0.49 | | 1.4944 | 2.0 | 150 | 1.4307 | 0.53 | | 1.2562 | 3.0 | 225 | 1.2180 | 0.65 | | 0.9436 | 4.0 | 300 | 1.0209 | 0.71 | | 0.7543 | 5.0 | 375 | 0.9073 | 0.73 | | 0.5742 | 6.0 | 450 | 0.8047 | 0.75 | | 0.4728 | 7.0 | 525 | 0.7736 | 0.78 | | 0.3622 | 8.0 | 600 | 0.7412 | 0.78 | | 0.2447 | 9.0 | 675 | 0.7117 | 0.79 | | 0.2692 | 10.0 | 750 | 0.7146 | 0.79 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

Wariano/bsc-bio-ehr-es-vih-10k

Wariano

2023-09-21T10:24:33Z

105

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-21T10:09:24Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: bsc-bio-ehr-es-vih-10k results: [] ---  # bsc-bio-ehr-es-vih-10k This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9958 - Positives Preds: 598 - Negative Preds: 402 - Positives Refs: 500 - Negative Refs: 500 - Tp: 411 - Fn: 89 - Fp: 187 - Tn: 313 - Accuracy: 0.724 - Precision: 0.6873 - Recall: 0.822 - F1: 0.7486 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Positives Preds | Negative Preds | Positives Refs | Negative Refs | Tp | Fn | Fp | Tn | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------------:|:--------------:|:--------------:|:-------------:|:---:|:---:|:---:|:---:|:--------:|:---------:|:------:|:------:| | 0.4309 | 1.0 | 250 | 0.4999 | 384 | 616 | 500 | 500 | 316 | 184 | 68 | 432 | 0.748 | 0.8229 | 0.632 | 0.7149 | | 0.2849 | 2.0 | 500 | 0.6391 | 546 | 454 | 500 | 500 | 396 | 104 | 150 | 350 | 0.746 | 0.7253 | 0.792 | 0.7572 | | 0.1931 | 3.0 | 750 | 0.7333 | 610 | 390 | 500 | 500 | 414 | 86 | 196 | 304 | 0.718 | 0.6787 | 0.828 | 0.7459 | | 0.1255 | 4.0 | 1000 | 0.8917 | 604 | 396 | 500 | 500 | 417 | 83 | 187 | 313 | 0.73 | 0.6904 | 0.834 | 0.7554 | | 0.0918 | 5.0 | 1250 | 0.9958 | 598 | 402 | 500 | 500 | 411 | 89 | 187 | 313 | 0.724 | 0.6873 | 0.822 | 0.7486 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

ghost9023/DEEPNOID-llama2-7b-PoC-Only

ghost9023

2023-09-21T10:16:48Z

6

0

peft

[ "peft", "region:us" ]

null

2023-09-21T02:26:54Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0

yejeekang/qlora-koalpaca-polyglot-12.8b-50step

yejeekang

2023-09-21T10:15:07Z

1

0

peft

[ "peft", "region:us" ]

null

2023-09-20T05:03:58Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0

aident-ai/bge-base-en-onnx

aident-ai

2023-09-21T10:10:14Z

13

2

sentence-transformers

[ "sentence-transformers", "pytorch", "onnx", "bert", "feature-extraction", "sentence-similarity", "transformers", "mteb", "en", "arxiv:2309.07597", "license:mit", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2023-09-06T12:25:41Z

--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb license: mit language: - en --- This is a fork from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) and exported to onnx for inference. ======= <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. ************* 🌟**Updates**🌟 ************* - 09/15/2023: Release [paper](https://arxiv.org/pdf/2309.07597.pdf) and [dataset](https://data.baai.ac.cn/details/BAAI-MTP). - 09/12/2023: New Release: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval\* | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient \** | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient \** | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | \*: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. \**: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary>  Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>  **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary>  For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details pelease refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). ## Citation If you find our work helpful, please cite us: ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

0xk1h0/pythia-6.9b-deduped-py150k-r20-LoRA

0xk1h0

2023-09-21T10:05:51Z

3

1

peft

[ "peft", "region:us" ]

null

2023-09-21T10:02:32Z

--- library_name: peft --- ## Model Usage ```python import torch import transformers from finetune_peft import get_peft_config, PEFTArguments from peft import get_peft_model model_path = 'EleutherAI/pythia-6.9b-deduped' # peft_path = 'models/codegen25_7b/checkpoint' peft_path = '0xk1h0/pythia-6.9b-deduped-py150k-r20-LoRA' # peft_path = 'models/alpaca-llama-7b-peft/params.p' torch.set_default_tensor_type(torch.cuda.HalfTensor) model = transformers.AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, cache_dir='models') peft_config = get_peft_config(peft_args=PEFTArguments(peft_mode="lora")) model = get_peft_model(model, peft_config) # model.load_state_dict(torch.load(peft_path), strict=False) torch.set_default_tensor_type(torch.cuda.FloatTensor) tokenizer = transformers.AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) batch = tokenizer(""" ### Generate AES MODE encrypt function. """, return_tensors="pt") with torch.no_grad(): out = model.generate( input_ids=batch["input_ids"], attention_mask=torch.ones_like(batch["input_ids"]), max_length=256, do_sample=True, temperature = 0.4, top_p=0.95 ) print(tokenizer.decode(out[0])) ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0

nikinetrahutama/afx-grouping-model

nikinetrahutama

2023-09-21T10:04:23Z

105

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-21T09:01:00Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: afx-grouping-model results: [] ---  # afx-grouping-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0154 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 0.9060 | 0.7674 | | No log | 2.0 | 12 | 0.7408 | 0.7674 | | No log | 3.0 | 18 | 0.6209 | 0.8023 | | No log | 4.0 | 24 | 0.5090 | 0.8023 | | No log | 5.0 | 30 | 0.4159 | 0.8488 | | No log | 6.0 | 36 | 0.3407 | 0.8837 | | No log | 7.0 | 42 | 0.2719 | 0.9535 | | No log | 8.0 | 48 | 0.2218 | 0.9535 | | No log | 9.0 | 54 | 0.1801 | 0.9535 | | No log | 10.0 | 60 | 0.1476 | 0.9535 | | No log | 11.0 | 66 | 0.1164 | 0.9767 | | No log | 12.0 | 72 | 0.0937 | 0.9884 | | No log | 13.0 | 78 | 0.0723 | 1.0 | | No log | 14.0 | 84 | 0.0604 | 1.0 | | No log | 15.0 | 90 | 0.0485 | 1.0 | | No log | 16.0 | 96 | 0.0395 | 1.0 | | No log | 17.0 | 102 | 0.0339 | 1.0 | | No log | 18.0 | 108 | 0.0307 | 1.0 | | No log | 19.0 | 114 | 0.0262 | 1.0 | | No log | 20.0 | 120 | 0.0240 | 1.0 | | No log | 21.0 | 126 | 0.0215 | 1.0 | | No log | 22.0 | 132 | 0.0200 | 1.0 | | No log | 23.0 | 138 | 0.0189 | 1.0 | | No log | 24.0 | 144 | 0.0178 | 1.0 | | No log | 25.0 | 150 | 0.0170 | 1.0 | | No log | 26.0 | 156 | 0.0164 | 1.0 | | No log | 27.0 | 162 | 0.0160 | 1.0 | | No log | 28.0 | 168 | 0.0157 | 1.0 | | No log | 29.0 | 174 | 0.0155 | 1.0 | | No log | 30.0 | 180 | 0.0154 | 1.0 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3

ldos/text_shortening_model_v46

ldos

2023-09-21T10:02:51Z

7

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-xsum", "base_model:finetune:facebook/bart-large-xsum", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-21T07:26:22Z

--- license: mit base_model: facebook/bart-large-xsum tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v46 results: [] ---  # text_shortening_model_v46 This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8536 - Rouge1: 0.485 - Rouge2: 0.271 - Rougel: 0.4374 - Rougelsum: 0.4371 - Bert precision: 0.8676 - Bert recall: 0.8761 - Average word count: 9.1032 - Max word count: 17 - Min word count: 4 - Average token count: 15.8254 - % shortened texts with length > 12: 9.5238 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:| | 1.8213 | 1.0 | 42 | 2.1030 | 0.4561 | 0.2433 | 0.4131 | 0.4131 | 0.8606 | 0.8724 | 9.0529 | 15 | 5 | 14.7249 | 7.4074 | | 0.8874 | 2.0 | 84 | 1.8034 | 0.4778 | 0.2609 | 0.4316 | 0.4316 | 0.8569 | 0.8787 | 10.6323 | 21 | 5 | 16.2963 | 19.3122 | | 0.603 | 3.0 | 126 | 1.6613 | 0.4749 | 0.2594 | 0.425 | 0.4253 | 0.8576 | 0.8796 | 10.5106 | 21 | 5 | 16.2751 | 23.0159 | | 0.5413 | 4.0 | 168 | 1.5975 | 0.4729 | 0.249 | 0.4258 | 0.4254 | 0.8635 | 0.8696 | 8.6481 | 16 | 4 | 14.3677 | 4.2328 | | 0.3393 | 5.0 | 210 | 1.6755 | 0.4959 | 0.28 | 0.4476 | 0.4473 | 0.8687 | 0.8772 | 8.8942 | 20 | 5 | 15.8915 | 8.4656 | | 0.2573 | 6.0 | 252 | 1.6908 | 0.4775 | 0.2589 | 0.4309 | 0.4307 | 0.866 | 0.873 | 8.9868 | 22 | 4 | 15.4339 | 10.3175 | | 0.173 | 7.0 | 294 | 1.8536 | 0.485 | 0.271 | 0.4374 | 0.4371 | 0.8676 | 0.8761 | 9.1032 | 17 | 4 | 15.8254 | 9.5238 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

irexyc/Qwen-VL-Chat

irexyc

2023-09-21T09:57:47Z

12

0

transformers

[ "transformers", "pytorch", "qwen", "text-generation", "custom_code", "zh", "en", "arxiv:2308.12966", "autotrain_compatible", "region:us" ]

text-generation

2023-09-21T09:57:47Z

--- language: - zh - en tags: - qwen pipeline_tag: text-generation inference: false --- # Qwen-VL-Chat <br> <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/logo_vl.jpg" width="400"/> <p> <br> <p align="center"> Qwen-VL <a href="https://modelscope.cn/models/qwen/Qwen-VL/summary">🤖 <a> | <a href="https://huggingface.co/Qwen/Qwen-VL">🤗</a>&nbsp ｜ Qwen-VL-Chat <a href="https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary">🤖 <a>| <a href="https://huggingface.co/Qwen/Qwen-VL-Chat">🤗</a>&nbsp ｜ Qwen-VL-Chat-Int4 <a href="https://huggingface.co/Qwen/Qwen-VL-Chat-Int4">🤗</a> <br> <a href="assets/wechat.png">WeChat</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/summary">Demo</a>&nbsp ｜ &nbsp<a href="https://arxiv.org/abs/2308.12966">Report</a> </p> <br> **Qwen-VL** 是阿里云研发的大规模视觉语言模型（Large Vision Language Model, LVLM）。Qwen-VL 可以以图像、文本、检测框作为输入，并以文本和检测框作为输出。Qwen-VL 系列模型性能强大，具备多语言对话、多图交错对话等能力，并支持中文开放域定位和细粒度图像识别与理解。 **Qwen-VL** (Qwen Large Vision Language Model) is the visual multimodal version of the large model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-VL accepts image, text, and bounding box as inputs, outputs text and bounding box. The features of Qwen-VL include: 目前，我们提供了Qwen-VL和Qwen-VL-Chat两个模型，分别为预训练模型和Chat模型。如果想了解更多关于模型的信息，请点击[链接](https://github.com/QwenLM/Qwen-VL/blob/master/visual_memo.md)查看我们的技术备忘录。本仓库为Qwen-VL-Chat仓库。 We release Qwen-VL and Qwen-VL-Chat, which are pretrained model and Chat model respectively. For more details about Qwen-VL, please refer to our [technical memo](https://github.com/QwenLM/Qwen-VL/blob/master/visual_memo.md). This repo is the one for Qwen-VL-Chat. <br> ## 安装要求 (Requirements) * python 3.8及以上版本 * pytorch 1.12及以上版本，推荐2.0及以上版本 * 建议使用CUDA 11.4及以上（GPU用户需考虑此选项） * python 3.8 and above * pytorch 1.12 and above, 2.0 and above are recommended * CUDA 11.4 and above are recommended (this is for GPU users) <br> ## 快速开始 (Quickstart) 我们提供简单的示例来说明如何利用 🤗 Transformers 快速使用Qwen-VL-Chat。在开始前，请确保你已经配置好环境并安装好相关的代码包。最重要的是，确保你满足上述要求，然后安装相关的依赖库。 Below, we provide simple examples to show how to use Qwen-VL-Chat with 🤗 Transformers. Before running the code, make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries. ```bash pip install -r requirements.txt ``` 接下来你可以开始使用Transformers来使用我们的模型。关于视觉模块的更多用法，请参考[教程](TUTORIAL.md)。 Now you can start with Transformers. More usage aboue vision encoder, please refer to [tutorial](TUTORIAL_zh.md). #### 🤗 Transformers To use Qwen-VL-Chat for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, **please make sure that you are using the latest code.** ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation import GenerationConfig import torch torch.manual_seed(1234) # Note: The default behavior now has injection attack prevention off. tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True) # use bf16 # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval() # use fp16 # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval() # use cpu only # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="cpu", trust_remote_code=True).eval() # use cuda device model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="cuda", trust_remote_code=True).eval() # Specify hyperparameters for generation (No need to do this if you are using transformers>=4.32.0) # model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True) # 1st dialogue turn query = tokenizer.from_list_format([ {'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'}, {'text': '这是什么'}, ]) response, history = model.chat(tokenizer, query=query, history=None) print(response) # 图中是一名年轻女子在沙滩上和她的狗玩耍，狗的品种可能是拉布拉多。她们坐在沙滩上，狗的前腿抬起来，似乎在和人类击掌。两人之间充满了信任和爱。 # 2nd dialogue turn response, history = model.chat(tokenizer, '输出"击掌"的检测框', history=history) print(response) # <ref>击掌</ref><box>(517,508),(589,611)</box> image = tokenizer.draw_bbox_on_latest_picture(response, history) if image: image.save('1.jpg') else: print("no box") ``` <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo_highfive.jpg" width="500"/> <p> <br> ## 量化 (Quantization) ### 用法 (Usage) 当前我们提供了基于[AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ)的量化方案，并提供了Qwen-VL-Chat的Int4量化版本Qwen-VL-Chat-Int4 [点击此处](https://huggingface.co/Qwen/Qwen-VL-Chat-Int4)。该模型在效果评测上几乎无损，并在显存占用和推理速度上具有明显优势。下文说明如何使用该量化模型。开始之前，请确保你满足要求（如torch2.0及以上、transformers 4.32.0及以上，等）并安装所需的代码库： We provide a new solution based on [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), and release an Int4 quantized model for Qwen-VL-Chat, Qwen-VL-Chat-Int4 [Click here](https://huggingface.co/Qwen/Qwen-VL-Chat-Int4), which achieves nearly lossless model effects but improved performance on both memory costs and inference speed. Here we demonstrate how to use our provided quantized models for inference. Before you start, make sure you meet the requirements (e.g., torch 2.0 and above, transformers 4.32.0 and above, etc.) and install the required packages: ```bash pip install optimum git clone https://github.com/JustinLin610/AutoGPTQ.git & cd AutoGPTQ pip install -v . ``` 如遇到安装 `auto-gptq` 的问题，建议您前往官方[repo](https://github.com/PanQiWei/AutoGPTQ) 寻找合适的wheel。随后你便可以按照上述用法，轻松调用量化模型： If you meet problems installing `auto-gptq`, we advise you to check out the official [repo](https://github.com/PanQiWei/AutoGPTQ) to find a wheel. Then you can load the quantized model easily and run inference as same as usual: ```python model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen-VL-Chat-Int4", device_map="auto", trust_remote_code=True ).eval() # Either a local path or an u[](https://)rl between <img></img> tags. image_path = 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg' response, history = model.chat(tokenizer, query=f'<img>{image_path}</img>这是什么', history=None) print(response) ``` ### 效果评测 (Performance) 我们列出不同精度下模型在评测基准 **[TouchStone](https://github.com/OFA-Sys/TouchStone)** 上的表现，并发现量化模型并没有显著性能损失。结果如下所示： We illustrate the model performance of both BF16 and Int4 models on the benchmark **[TouchStone](https://github.com/OFA-Sys/TouchStone)**, and we find that the quantized model does not suffer from significant performance degradation. Results are shown below: | Quantization | ZH. | EN | | ------------ | :--------: | :-----------: | | BF16 | 401.2 | 645.2 | | Int4 | 386.6 | 651.4 | ### 推理速度 (Inference Speed) 我们测算了在输入一张图片（即258个token）的条件下BF16和Int4的模型生成1792 (2048-258) 和 7934 (8192-258) 个token的平均速度。 We measured the average inference speed (tokens/s) of generating 1792 (2048-258) and 7934 (8192-258) tokens with the context of an image (which takes 258 tokens) under BF16 precision and Int4 quantization, respectively. | Quantization | Speed (2048 tokens) | Speed (8192 tokens) | | ------------ | :-----------------: | :-----------------: | | BF16 | 28.87 | 24.32 | | Int4 | 37.79 | 34.34 | 推理速度测算是在单卡 A100-SXM4-80G GPU上运行，使用PyTorch 2.0.1及CUDA 11.4。 The profiling runs on a single A100-SXM4-80G GPU with PyTorch 2.0.1 and CUDA 11.4. ### GPU显存占用 (GPU Memory Usage) 我们还测算了在一张图片输入的条件下BF16和Int4模型生成1792 (2048-258) 和 7934 (8192-258) 个token所需显存。结果如下所示： We also profile the peak GPU memory usage for encoding 1792 (2048-258) tokens (including an image) as context (and generating single token) and generating 7934 (8192-258) tokens (with an image as context) under BF16 or Int4 quantization level, respectively. The results are shown below. | Quantization | Peak Usage for Encoding 2048 Tokens | Peak Usage for Generating 8192 Tokens | | ------------ | :---------------------------------: | :-----------------------------------: | | BF16 | 22.60GB | 28.01GB | | Int4 | 11.82GB | 17.23GB | 上述速度和显存测算使用[此脚本](https://qianwen-res.oss-cn-beijing.aliyuncs.com/profile_mm.py)完成。 The above speed and memory profiling are conducted using [this script](https://qianwen-res.oss-cn-beijing.aliyuncs.com/profile_mm.py). <br> ## 评测我们从两个角度评测了两个模型的能力： 1. 在**英文标准 Benchmark** 上评测模型的基础任务能力。目前评测了四大类多模态任务： - Zero-shot Caption: 评测模型在未见过数据集上的零样本图片描述能力； - General VQA: 评测模型的通用问答能力，例如判断题、颜色、个数、类目等问答能力； - Text-based VQA：评测模型对于图片中文字相关的识别/问答能力，例如文档问答、图表问答、文字问答等； - Referring Expression Compression：评测模型给定物体描述画检测框的能力； 2. **试金石 (TouchStone)**：为了评测模型整体的图文对话能力和人类对齐水平。我们为此构建了一个基于 GPT4 打分来评测 LVLM 模型的 Benchmark：TouchStone。在 TouchStone-v0.1 中： - 评测基准总计涵盖 300+张图片、800+道题目、27个类别。包括基础属性问答、人物地标问答、影视作品问答、视觉推理、反事实推理、诗歌创作、故事写作，商品比较、图片解题等**尽可能广泛的类别**。 - 为了弥补目前 GPT4 无法直接读取图片的缺陷，我们给所有的带评测图片提供了**人工标注的充分详细描述**，并且将图片的详细描述、问题和模型的输出结果一起交给 GPT4 打分。 - 评测同时包含英文版本和中文版本。评测结果如下： We evaluated the model's ability from two perspectives: 1. **Standard Benchmarks**: We evaluate the model's basic task capabilities on four major categories of multimodal tasks: - Zero-shot Caption: Evaluate model's zero-shot image captioning ability on unseen datasets; - General VQA: Evaluate the general question-answering ability of pictures, such as the judgment, color, number, category, etc; - Text-based VQA: Evaluate the model's ability to recognize text in pictures, such as document QA, chart QA, etc; - Referring Expression Comprehension: Evaluate the ability to localize a target object in an image described by a referring expression. 2. **TouchStone**: To evaluate the overall text-image dialogue capability and alignment level with humans, we have constructed a benchmark called TouchStone, which is based on scoring with GPT4 to evaluate the LVLM model. - The TouchStone benchmark covers a total of 300+ images, 800+ questions, and 27 categories. Such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc; - In order to break the current limitation of GPT4 in terms of direct image input, TouchStone provides fine-grained image annotations by human labeling. These detailed annotations, along with the questions and the model's output, are then presented to GPT4 for scoring. - The benchmark includes both English and Chinese versions. The results of the evaluation are as follows: Qwen-VL outperforms current SOTA generalist models on multiple VL tasks and has a more comprehensive coverage in terms of capability range. <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/radar.png" width="600"/> <p> ### 零样本图像描述 & 通用视觉问答 (Zero-shot Captioning & General VQA) <table> <thead> <tr> <th rowspan="2">Model type</th> <th rowspan="2">Model</th> <th colspan="2">Zero-shot Captioning</th> <th colspan="5">General VQA</th> </tr> <tr> <th>NoCaps</th> <th>Flickr30K</th> <th>VQAv2<sup>dev</sup></th> <th>OK-VQA</th> <th>GQA</th> <th>SciQA-Img<br>(0-shot)</th> <th>VizWiz<br>(0-shot)</th> </tr> </thead> <tbody align="center"> <tr> <td rowspan="10">Generalist<br>Models</td> <td>Flamingo-9B</td> <td>-</td> <td>61.5</td> <td>51.8</td> <td>44.7</td> <td>-</td> <td>-</td> <td>28.8</td> </tr> <tr> <td>Flamingo-80B</td> <td>-</td> <td>67.2</td> <td>56.3</td> <td>50.6</td> <td>-</td> <td>-</td> <td>31.6</td> </tr> <tr> <td>Unified-IO-XL</td> <td>100.0</td> <td>-</td> <td>77.9</td> <td>54.0</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>Kosmos-1</td> <td>-</td> <td>67.1</td> <td>51.0</td> <td>-</td> <td>-</td> <td>-</td> <td>29.2</td> </tr> <tr> <td>Kosmos-2</td> <td>-</td> <td>66.7</td> <td>45.6</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>BLIP-2 (Vicuna-13B)</td> <td>103.9</td> <td>71.6</td> <td>65.0</td> <td>45.9</td> <td>32.3</td> <td>61.0</td> <td>19.6</td> </tr> <tr> <td>InstructBLIP (Vicuna-13B)</td> <td><strong>121.9</strong></td> <td>82.8</td> <td>-</td> <td>-</td> <td>49.5</td> <td>63.1</td> <td>33.4</td> </tr> <tr> <td>Shikra (Vicuna-13B)</td> <td>-</td> <td>73.9</td> <td>77.36</td> <td>47.16</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td><strong>Qwen-VL (Qwen-7B)</strong></td> <td>121.4</td> <td><b>85.8</b></td> <td><b>78.8</b></td> <td><b>58.6</b></td> <td><b>59.3</b></td> <td>67.1</td> <td>35.2</td> </tr>  <tr> <td>Qwen-VL-Chat</td> <td>120.2</td> <td>81.0</td> <td>78.2</td> <td>56.6</td> <td>57.5</td> <td><b>68.2</b></td> <td><b>38.9</b></td> </tr>  <tr> <td>Previous SOTA<br>(Per Task Fine-tuning)</td> <td>-</td> <td>127.0<br>(PALI-17B)</td> <td>84.5<br>(InstructBLIP<br>-FlanT5-XL)</td> <td>86.1<br>(PALI-X<br>-55B)</td> <td>66.1<br>(PALI-X<br>-55B)</td> <td>72.1<br>(CFR)</td> <td>92.53<br>(LLaVa+<br>GPT-4)</td> <td>70.9<br>(PALI-X<br>-55B)</td> </tr> </tbody> </table> - 在 Zero-shot Caption 中，Qwen-VL 在 Flickr30K 数据集上取得了 **SOTA** 的结果，并在 Nocaps 数据集上取得了和 InstructBlip 可竞争的结果。 - 在 General VQA 中，Qwen-VL 取得了 LVLM 模型同等量级和设定下 **SOTA** 的结果。 - For zero-shot image captioning, Qwen-VL achieves the **SOTA** on Flickr30K and competitive results on Nocaps with InstructBlip. - For general VQA, Qwen-VL achieves the **SOTA** under the same generalist LVLM scale settings. ### 文本导向的视觉问答 (Text-oriented VQA) <table> <thead> <tr> <th>Model type</th> <th>Model</th> <th>TextVQA</th> <th>DocVQA</th> <th>ChartQA</th> <th>AI2D</th> <th>OCR-VQA</th> </tr> </thead> <tbody align="center"> <tr> <td rowspan="5">Generalist Models</td> <td>BLIP-2 (Vicuna-13B)</td> <td>42.4</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>InstructBLIP (Vicuna-13B)</td> <td>50.7</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>mPLUG-DocOwl (LLaMA-7B)</td> <td>52.6</td> <td>62.2</td> <td>57.4</td> <td>-</td> <td>-</td> </tr> <tr> <td>Pic2Struct-Large (1.3B)</td> <td>-</td> <td><b>76.6</b></td> <td>58.6</td> <td>42.1</td> <td>71.3</td> </tr> <tr> <td>Qwen-VL (Qwen-7B)</td> <td><b>63.8</b></td> <td>65.1</td> <td><b>65.7</b></td> <td><b>62.3</b></td> <td><b>75.7</b></td> </tr> <tr> <td>Specialist SOTAs<br>(Specialist/Finetuned)</td> <td>PALI-X-55B (Single-task FT)<br>(Without OCR Pipeline)</td> <td>71.44</td> <td>80.0</td> <td>70.0</td> <td>81.2</td> <td>75.0</td> </tr> </tbody> </table> - 在文字相关的识别/问答评测上，取得了当前规模下通用 LVLM 达到的最好结果。 - 分辨率对上述某几个评测非常重要，大部分 224 分辨率的开源 LVLM 模型无法完成以上评测，或只能通过切图的方式解决。Qwen-VL 将分辨率提升到 448，可以直接以端到端的方式进行以上评测。Qwen-VL 在很多任务上甚至超过了 1024 分辨率的 Pic2Struct-Large 模型。 - In text-related recognition/QA evaluation, Qwen-VL achieves the SOTA under the generalist LVLM scale settings. - Resolution is important for several above evaluations. While most open-source LVLM models with 224 resolution are incapable of these evaluations or can only solve these by cutting images, Qwen-VL scales the resolution to 448 so that it can be evaluated end-to-end. Qwen-VL even outperforms Pic2Struct-Large models of 1024 resolution on some tasks. ### 细粒度视觉定位 (Referring Expression Comprehension) <table> <thead> <tr> <th rowspan="2">Model type</th> <th rowspan="2">Model</th> <th colspan="3">RefCOCO</th> <th colspan="3">RefCOCO+</th> <th colspan="2">RefCOCOg</th> <th>GRIT</th> </tr> <tr> <th>val</th> <th>test-A</th> <th>test-B</th> <th>val</th> <th>test-A</th> <th>test-B</th> <th>val-u</th> <th>test-u</th> <th>refexp</th> </tr> </thead> <tbody align="center"> <tr> <td rowspan="8">Generalist Models</td> <td>GPV-2</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>51.50</td> </tr> <tr> <td>OFA-L*</td> <td>79.96</td> <td>83.67</td> <td>76.39</td> <td>68.29</td> <td>76.00</td> <td>61.75</td> <td>67.57</td> <td>67.58</td> <td>61.70</td> </tr> <tr> <td>Unified-IO</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td><b>78.61</b></td> </tr> <tr> <td>VisionLLM-H</td> <td></td> <td>86.70</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>Shikra-7B</td> <td>87.01</td> <td>90.61</td> <td>80.24 </td> <td>81.60</td> <td>87.36</td> <td>72.12</td> <td>82.27</td> <td>82.19</td> <td>69.34</td> </tr> <tr> <td>Shikra-13B</td> <td>87.83 </td> <td>91.11</td> <td>81.81</td> <td>82.89</td> <td>87.79</td> <td>74.41</td> <td>82.64</td> <td>83.16</td> <td>69.03</td> </tr> <tr> <td>Qwen-VL-7B</td> <td><b>89.36</b></td> <td>92.26</td> <td><b>85.34</b></td> <td><b>83.12</b></td> <td>88.25</td> <td><b>77.21</b></td> <td>85.58</td> <td>85.48</td> <td>78.22</td> </tr> <tr> <td>Qwen-VL-7B-Chat</td> <td>88.55</td> <td><b>92.27</b></td> <td>84.51</td> <td>82.82</td> <td><b>88.59</b></td> <td>76.79</td> <td><b>85.96</b></td> <td><b>86.32</b></td> <td>-</td> <tr> <td rowspan="3">Specialist SOTAs<br>(Specialist/Finetuned)</td> <td>G-DINO-L</td> <td>90.56  </td> <td>93.19</td> <td>88.24</td> <td>82.75</td> <td>88.95</td> <td>75.92</td> <td>86.13</td> <td>87.02</td> <td>-</td> </tr> <tr> <td>UNINEXT-H</td> <td>92.64 </td> <td>94.33</td> <td>91.46</td> <td>85.24</td> <td>89.63</td> <td>79.79</td> <td>88.73</td> <td>89.37</td> <td>-</td> </tr> <tr> <td>ONE-PEACE</td> <td>92.58 </td> <td>94.18</td> <td>89.26</td> <td>88.77</td> <td>92.21</td> <td>83.23</td> <td>89.22</td> <td>89.27</td> <td>-</td> </tr> </tbody> </table> - 在定位任务上，Qwen-VL 全面超过 Shikra-13B，取得了目前 Generalist LVLM 模型上在 Refcoco 上的 **SOTA**。 - Qwen-VL 并没有在任何中文定位数据上训练过，但通过中文 Caption 数据和英文 Grounding 数据的训练，可以 Zero-shot 泛化出中文 Grounding 能力。我们提供了以上**所有**评测脚本以供复现我们的实验结果。请阅读 [eval/EVALUATION.md](eval/EVALUATION.md) 了解更多信息。 - Qwen-VL achieves the **SOTA** in all above referring expression comprehension benchmarks. - Qwen-VL has not been trained on any Chinese grounding data, but it can still generalize to the Chinese Grounding tasks in a zero-shot way by training Chinese Caption data and English Grounding data. We provide all of the above evaluation scripts for reproducing our experimental results. Please read [eval/EVALUATION.md](eval/EVALUATION.md) for more information. ### 闲聊能力测评 (Chat Evaluation) TouchStone 是一个基于 GPT4 打分来评测 LVLM 模型的图文对话能力和人类对齐水平的基准。它涵盖了 300+张图片、800+道题目、27个类别，包括基础属性、人物地标、视觉推理、诗歌创作、故事写作、商品比较、图片解题等**尽可能广泛的类别**。关于 TouchStone 的详细介绍，请参考[touchstone/README_CN.md](touchstone/README_CN.md)了解更多信息。 TouchStone is a benchmark based on scoring with GPT4 to evaluate the abilities of the LVLM model on text-image dialogue and alignment levels with humans. It covers a total of 300+ images, 800+ questions, and 27 categories, such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc. Please read [touchstone/README_CN.md](touchstone/README.md) for more information. #### 英语 (English) | Model | Score | |---------------|-------| | PandaGPT | 488.5 | | MiniGPT4 | 531.7 | | InstructBLIP | 552.4 | | LLaMA-AdapterV2 | 590.1 | | mPLUG-Owl | 605.4 | | LLaVA | 602.7 | | Qwen-VL-Chat | 645.2 | #### 中文 (Chinese) | Model | Score | |---------------|-------| | VisualGLM | 247.1 | | Qwen-VL-Chat | 401.2 | Qwen-VL-Chat 模型在中英文的对齐评测中均取得当前 LVLM 模型下的最好结果。 Qwen-VL-Chat has achieved the best results in both Chinese and English alignment evaluation. <br> ## 常见问题 (FAQ) 如遇到问题，敬请查阅 [FAQ](https://github.com/QwenLM/Qwen-VL/blob/master/FAQ_zh.md)以及issue区，如仍无法解决再提交issue。 If you meet problems, please refer to [FAQ](https://github.com/QwenLM/Qwen-VL/blob/master/FAQ.md) and the issues first to search a solution before you launch a new issue. <br> ## 使用协议 (License Agreement) 研究人员与开发者可使用Qwen-VL和Qwen-VL-Chat或进行二次开发。我们同样允许商业使用，具体细节请查看[LICENSE](https://github.com/QwenLM/Qwen-VL/blob/master/LICENSE)。如需商用，请填写[问卷](https://dashscope.console.aliyun.com/openModelApply/qianwen)申请。 Researchers and developers are free to use the codes and model weights of both Qwen-VL and Qwen-VL-Chat. We also allow their commercial use. Check our license at [LICENSE](LICENSE) for more details. <br> ## 引用 (Citation) 如果你觉得我们的论文和代码对你的研究有帮助，请考虑:star: 和引用 :pencil: :) If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :) ```BibTeX @article{Qwen-VL, title={Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities}, author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren}, journal={arXiv preprint arXiv:2308.12966}, year={2023} } ``` <br> ## 联系我们 (Contact Us) 如果你想给我们的研发团队和产品团队留言，请通过邮件（qianwen_opensource@alibabacloud.com）联系我们。 If you are interested to leave a message to either our research team or product team, feel free to send an email to qianwen_opensource@alibabacloud.com. ``` ```

loupzeur/Pyramids

loupzeur

2023-09-21T09:56:07Z

7

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-09-21T09:54:54Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: loupzeur/Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Yntec/animeTEN

Yntec

2023-09-21T09:53:29Z

384

4

diffusers

[ "diffusers", "safetensors", "Anime", "General Purpose", "Ctuhulo", "realisticElves", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-21T08:18:50Z

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Anime - General Purpose - Ctuhulo - realisticElves - text-to-image - stable-diffusion - stable-diffusion-diffusers - diffusers --- # animeTEN This model with the zVAE baken in. Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/JyhNOB6-JV54HHztUelPU.png) chibi character, breathtaking, 8 k resolution, pop corn, visible brushstrokes, extremely detailed, Cartoon Pretty CUTE LITTLE Girl, beautiful, establishing shot, artistic, dangelico pino, Iconic, DETAILED CHIBI EYES, 1949, sharp focus, beautiful face, octane render, cinematic lighting, dramatic lighting, A magic garden with vegetables, performing, a beautiful detailed legs, fruitcake, gorgeous detailed hair, Magazine ad, ritual Original page: https://civitai.com/models/144023?modelVersionId=160609

EnzoZacharias/starcoder-fine-tuned-plc_V1

EnzoZacharias

2023-09-21T09:41:57Z

0

null

[ "generated_from_trainer", "base_model:bigcode/starcoder", "base_model:finetune:bigcode/starcoder", "license:bigcode-openrail-m", "region:us" ]

null

2023-09-21T09:20:41Z

--- license: bigcode-openrail-m base_model: bigcode/starcoder tags: - generated_from_trainer model-index: - name: starcoder-fine-tuned-plc_V1 results: [] ---  # starcoder-fine-tuned-plc_V1 This model is a fine-tuned version of [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.1.0.dev20230823 - Datasets 2.14.4 - Tokenizers 0.13.3

hustvl/vitmatte-base-composition-1k

hustvl

2023-09-21T09:25:07Z

14,261

10

transformers

[ "transformers", "pytorch", "vitmatte", "vision", "arxiv:2305.15272", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2023-09-10T07:56:12Z

--- license: apache-2.0 tags: - vision --- # ViTMatte model ViTMatte model trained on Composition-1k. It was introduced in the paper [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Yao et al. and first released in [this repository](https://github.com/hustvl/ViTMatte). Disclaimer: The team releasing ViTMatte did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ViTMatte is a simple approach to image matting, the task of accurately estimating the foreground object in an image. The model consists of a Vision Transformer (ViT) with a lightweight head on top. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vitmatte_architecture.png" alt="drawing" width="600"/> <small> ViTMatte high-level overview. Taken from the <a href="https://arxiv.org/abs/2305.15272">original paper.</a> </small> ## Intended uses & limitations You can use the raw model for image matting. See the [model hub](https://huggingface.co/models?search=vitmatte) to look for other fine-tuned versions that may interest you. ### How to use We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/vitmatte#transformers.VitMatteForImageMatting.forward.example). ### BibTeX entry and citation info ```bibtex @misc{yao2023vitmatte, title={ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers}, author={Jingfeng Yao and Xinggang Wang and Shusheng Yang and Baoyuan Wang}, year={2023}, eprint={2305.15272}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

JoyboyXoXo/rl_course_vizdoom_health_gathering_supreme

JoyboyXoXo

2023-09-21T09:17:48Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-21T09:17:39Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.70 +/- 2.25 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r JoyboyXoXo/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

sebastiantrbl/test-DialoGPT-finetune

sebastiantrbl

2023-09-21T09:16:30Z

207

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "conversational", "dataset:daily_dialog", "base_model:microsoft/DialoGPT-medium", "base_model:finetune:microsoft/DialoGPT-medium", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-21T08:19:37Z

--- license: mit base_model: microsoft/DialoGPT-medium tags: - generated_from_trainer datasets: - daily_dialog model-index: - name: tmplo2wugb5 results: [] pipeline_tag: conversational ---  # tmplo2wugb5 This model is a fine-tuned version of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) on the daily_dialog dataset. It achieves the following results on the evaluation set: - Loss: 1.7233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

Charishma010997/Falcon7b_finetuned

Charishma010997

2023-09-21T09:04:56Z

0

peft

[ "peft", "falcon", "custom_code", "base_model:vilsonrodrigues/falcon-7b-instruct-sharded", "base_model:adapter:vilsonrodrigues/falcon-7b-instruct-sharded", "4-bit", "bitsandbytes", "region:us" ]

null

2023-09-17T05:40:57Z

--- library_name: peft base_model: vilsonrodrigues/falcon-7b-instruct-sharded --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0

JcKosmos74/my_awesome_billsum_model

JcKosmos74

2023-09-21T09:03:47Z

104

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-21T08:34:29Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1351 ---  # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4889 - Rouge1: 0.1351 - Rouge2: 0.0465 - Rougel: 0.1133 - Rougelsum: 0.1132 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.7805 | 0.1295 | 0.0394 | 0.1095 | 0.109 | 19.0 | | No log | 2.0 | 124 | 2.5686 | 0.1312 | 0.0443 | 0.1118 | 0.1115 | 19.0 | | No log | 3.0 | 186 | 2.5062 | 0.1351 | 0.045 | 0.1133 | 0.1132 | 19.0 | | No log | 4.0 | 248 | 2.4889 | 0.1351 | 0.0465 | 0.1133 | 0.1132 | 19.0 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

thiru1/distilgpt2-finetuned-wikitext2

thiru1

2023-09-21T09:02:53Z

187

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-21T08:22:01Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] ---  # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6421 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7602 | 1.0 | 2334 | 3.6669 | | 3.653 | 2.0 | 4668 | 3.6472 | | 3.6006 | 3.0 | 7002 | 3.6421 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

CyberHarem/ebihara_naho_idolmastercinderellagirls

CyberHarem

2023-09-21T09:00:22Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/ebihara_naho_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-21T08:45:07Z

--- license: mit datasets: - CyberHarem/ebihara_naho_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of ebihara_naho_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4080, you need to download `4080/ebihara_naho_idolmastercinderellagirls.pt` as the embedding and `4080/ebihara_naho_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4080**, with the score of 0.956. The trigger words are: 1. `ebihara_naho_idolmastercinderellagirls` 2. `black_hair, green_eyes, breasts, blush, large_breasts, smile, ponytail, cleavage, hair_ornament` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:----------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.861 | [Download](5100/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | [<NSFW, click to see>](5100/previews/pattern_4.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.946 | [Download](4760/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | [<NSFW, click to see>](4760/previews/pattern_4.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.913 | [Download](4420/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | [<NSFW, click to see>](4420/previews/pattern_4.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | **4080** | **0.956** | [**Download**](4080/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | [<NSFW, click to see>](4080/previews/pattern_4.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.948 | [Download](3740/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | [<NSFW, click to see>](3740/previews/pattern_4.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.914 | [Download](3400/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | [<NSFW, click to see>](3400/previews/pattern_4.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.937 | [Download](3060/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | [<NSFW, click to see>](3060/previews/pattern_4.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.845 | [Download](2720/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | [<NSFW, click to see>](2720/previews/pattern_4.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.904 | [Download](2380/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | [<NSFW, click to see>](2380/previews/pattern_4.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.904 | [Download](2040/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | [<NSFW, click to see>](2040/previews/pattern_4.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.926 | [Download](1700/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | [<NSFW, click to see>](1700/previews/pattern_4.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.940 | [Download](1360/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | [<NSFW, click to see>](1360/previews/pattern_4.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.942 | [Download](1020/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | [<NSFW, click to see>](1020/previews/pattern_4.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.924 | [Download](680/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | [<NSFW, click to see>](680/previews/pattern_4.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.912 | [Download](340/ebihara_naho_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | [<NSFW, click to see>](340/previews/pattern_4.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

boimbukanbaim/codeparrot-ds

boimbukanbaim

2023-09-21T09:00:12Z

132

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-21T04:25:46Z

--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: codeparrot-ds results: [] ---  # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4899 | 0.94 | 5000 | 1.6274 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

EnzoZacharias/replit-code-v1-3b-fine-tuned-plc_V1

EnzoZacharias

2023-09-21T08:45:48Z

0

null

[ "generated_from_trainer", "base_model:replit/replit-code-v1-3b", "base_model:finetune:replit/replit-code-v1-3b", "license:cc-by-sa-4.0", "region:us" ]

null

2023-09-21T08:41:16Z

--- license: cc-by-sa-4.0 base_model: replit/replit-code-v1-3b tags: - generated_from_trainer model-index: - name: replit-code-v1-3b-fine-tuned-plc_V1 results: [] ---  # replit-code-v1-3b-fine-tuned-plc_V1 This model is a fine-tuned version of [replit/replit-code-v1-3b](https://huggingface.co/replit/replit-code-v1-3b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.1.0.dev20230823 - Datasets 2.14.4 - Tokenizers 0.13.3

Aharneish/qa-model

Aharneish

2023-09-21T08:42:04Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-04-05T15:31:45Z

--- license: apache-2.0 tags: - generated_from_trainer base_model: distilbert-base-uncased model-index: - name: qa-model results: [] ---  # qa-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

CyberHarem/rikka_4ninwasorezoreusootsuku

CyberHarem

2023-09-21T08:36:34Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/rikka_4ninwasorezoreusootsuku", "license:mit", "region:us" ]

text-to-image

2023-09-21T03:22:14Z

--- license: mit datasets: - CyberHarem/rikka_4ninwasorezoreusootsuku pipeline_tag: text-to-image tags: - art --- # Lora of rikka_4ninwasorezoreusootsuku This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5400, you need to download `5400/rikka_4ninwasorezoreusootsuku.pt` as the embedding and `5400/rikka_4ninwasorezoreusootsuku.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 5400**, with the score of 0.944. The trigger words are: 1. `rikka_4ninwasorezoreusootsuku` 2. `twintails, blush, red_eyes, smile, ribbon, grey_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 9000 | 0.936 | [Download](9000/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-9000](9000/previews/pattern_1.png) | ![pattern_2-9000](9000/previews/pattern_2.png) | ![pattern_3-9000](9000/previews/pattern_3.png) | ![pattern_4-9000](9000/previews/pattern_4.png) | ![pattern_5-9000](9000/previews/pattern_5.png) | ![pattern_6-9000](9000/previews/pattern_6.png) | ![pattern_7-9000](9000/previews/pattern_7.png) | ![pattern_8-9000](9000/previews/pattern_8.png) | ![pattern_9-9000](9000/previews/pattern_9.png) | ![pattern_10-9000](9000/previews/pattern_10.png) | ![pattern_11-9000](9000/previews/pattern_11.png) | [<NSFW, click to see>](9000/previews/bikini.png) | [<NSFW, click to see>](9000/previews/bondage.png) | ![free-9000](9000/previews/free.png) | ![maid-9000](9000/previews/maid.png) | ![miko-9000](9000/previews/miko.png) | [<NSFW, click to see>](9000/previews/nude.png) | [<NSFW, click to see>](9000/previews/nude2.png) | ![suit-9000](9000/previews/suit.png) | ![yukata-9000](9000/previews/yukata.png) | | 8400 | 0.941 | [Download](8400/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-8400](8400/previews/pattern_1.png) | ![pattern_2-8400](8400/previews/pattern_2.png) | ![pattern_3-8400](8400/previews/pattern_3.png) | ![pattern_4-8400](8400/previews/pattern_4.png) | ![pattern_5-8400](8400/previews/pattern_5.png) | ![pattern_6-8400](8400/previews/pattern_6.png) | ![pattern_7-8400](8400/previews/pattern_7.png) | ![pattern_8-8400](8400/previews/pattern_8.png) | ![pattern_9-8400](8400/previews/pattern_9.png) | ![pattern_10-8400](8400/previews/pattern_10.png) | ![pattern_11-8400](8400/previews/pattern_11.png) | [<NSFW, click to see>](8400/previews/bikini.png) | [<NSFW, click to see>](8400/previews/bondage.png) | ![free-8400](8400/previews/free.png) | ![maid-8400](8400/previews/maid.png) | ![miko-8400](8400/previews/miko.png) | [<NSFW, click to see>](8400/previews/nude.png) | [<NSFW, click to see>](8400/previews/nude2.png) | ![suit-8400](8400/previews/suit.png) | ![yukata-8400](8400/previews/yukata.png) | | 7800 | 0.932 | [Download](7800/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-7800](7800/previews/pattern_1.png) | ![pattern_2-7800](7800/previews/pattern_2.png) | ![pattern_3-7800](7800/previews/pattern_3.png) | ![pattern_4-7800](7800/previews/pattern_4.png) | ![pattern_5-7800](7800/previews/pattern_5.png) | ![pattern_6-7800](7800/previews/pattern_6.png) | ![pattern_7-7800](7800/previews/pattern_7.png) | ![pattern_8-7800](7800/previews/pattern_8.png) | ![pattern_9-7800](7800/previews/pattern_9.png) | ![pattern_10-7800](7800/previews/pattern_10.png) | ![pattern_11-7800](7800/previews/pattern_11.png) | [<NSFW, click to see>](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) | ![free-7800](7800/previews/free.png) | ![maid-7800](7800/previews/maid.png) | ![miko-7800](7800/previews/miko.png) | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) | ![suit-7800](7800/previews/suit.png) | ![yukata-7800](7800/previews/yukata.png) | | 7200 | 0.934 | [Download](7200/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-7200](7200/previews/pattern_1.png) | ![pattern_2-7200](7200/previews/pattern_2.png) | ![pattern_3-7200](7200/previews/pattern_3.png) | ![pattern_4-7200](7200/previews/pattern_4.png) | ![pattern_5-7200](7200/previews/pattern_5.png) | ![pattern_6-7200](7200/previews/pattern_6.png) | ![pattern_7-7200](7200/previews/pattern_7.png) | ![pattern_8-7200](7200/previews/pattern_8.png) | ![pattern_9-7200](7200/previews/pattern_9.png) | ![pattern_10-7200](7200/previews/pattern_10.png) | ![pattern_11-7200](7200/previews/pattern_11.png) | [<NSFW, click to see>](7200/previews/bikini.png) | [<NSFW, click to see>](7200/previews/bondage.png) | ![free-7200](7200/previews/free.png) | ![maid-7200](7200/previews/maid.png) | ![miko-7200](7200/previews/miko.png) | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) | ![suit-7200](7200/previews/suit.png) | ![yukata-7200](7200/previews/yukata.png) | | 6600 | 0.939 | [Download](6600/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-6600](6600/previews/pattern_1.png) | ![pattern_2-6600](6600/previews/pattern_2.png) | ![pattern_3-6600](6600/previews/pattern_3.png) | ![pattern_4-6600](6600/previews/pattern_4.png) | ![pattern_5-6600](6600/previews/pattern_5.png) | ![pattern_6-6600](6600/previews/pattern_6.png) | ![pattern_7-6600](6600/previews/pattern_7.png) | ![pattern_8-6600](6600/previews/pattern_8.png) | ![pattern_9-6600](6600/previews/pattern_9.png) | ![pattern_10-6600](6600/previews/pattern_10.png) | ![pattern_11-6600](6600/previews/pattern_11.png) | [<NSFW, click to see>](6600/previews/bikini.png) | [<NSFW, click to see>](6600/previews/bondage.png) | ![free-6600](6600/previews/free.png) | ![maid-6600](6600/previews/maid.png) | ![miko-6600](6600/previews/miko.png) | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) | ![suit-6600](6600/previews/suit.png) | ![yukata-6600](6600/previews/yukata.png) | | 6000 | 0.939 | [Download](6000/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-6000](6000/previews/pattern_1.png) | ![pattern_2-6000](6000/previews/pattern_2.png) | ![pattern_3-6000](6000/previews/pattern_3.png) | ![pattern_4-6000](6000/previews/pattern_4.png) | ![pattern_5-6000](6000/previews/pattern_5.png) | ![pattern_6-6000](6000/previews/pattern_6.png) | ![pattern_7-6000](6000/previews/pattern_7.png) | ![pattern_8-6000](6000/previews/pattern_8.png) | ![pattern_9-6000](6000/previews/pattern_9.png) | ![pattern_10-6000](6000/previews/pattern_10.png) | ![pattern_11-6000](6000/previews/pattern_11.png) | [<NSFW, click to see>](6000/previews/bikini.png) | [<NSFW, click to see>](6000/previews/bondage.png) | ![free-6000](6000/previews/free.png) | ![maid-6000](6000/previews/maid.png) | ![miko-6000](6000/previews/miko.png) | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) | ![suit-6000](6000/previews/suit.png) | ![yukata-6000](6000/previews/yukata.png) | | **5400** | **0.944** | [**Download**](5400/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-5400](5400/previews/pattern_1.png) | ![pattern_2-5400](5400/previews/pattern_2.png) | ![pattern_3-5400](5400/previews/pattern_3.png) | ![pattern_4-5400](5400/previews/pattern_4.png) | ![pattern_5-5400](5400/previews/pattern_5.png) | ![pattern_6-5400](5400/previews/pattern_6.png) | ![pattern_7-5400](5400/previews/pattern_7.png) | ![pattern_8-5400](5400/previews/pattern_8.png) | ![pattern_9-5400](5400/previews/pattern_9.png) | ![pattern_10-5400](5400/previews/pattern_10.png) | ![pattern_11-5400](5400/previews/pattern_11.png) | [<NSFW, click to see>](5400/previews/bikini.png) | [<NSFW, click to see>](5400/previews/bondage.png) | ![free-5400](5400/previews/free.png) | ![maid-5400](5400/previews/maid.png) | ![miko-5400](5400/previews/miko.png) | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) | ![suit-5400](5400/previews/suit.png) | ![yukata-5400](5400/previews/yukata.png) | | 4800 | 0.935 | [Download](4800/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-4800](4800/previews/pattern_1.png) | ![pattern_2-4800](4800/previews/pattern_2.png) | ![pattern_3-4800](4800/previews/pattern_3.png) | ![pattern_4-4800](4800/previews/pattern_4.png) | ![pattern_5-4800](4800/previews/pattern_5.png) | ![pattern_6-4800](4800/previews/pattern_6.png) | ![pattern_7-4800](4800/previews/pattern_7.png) | ![pattern_8-4800](4800/previews/pattern_8.png) | ![pattern_9-4800](4800/previews/pattern_9.png) | ![pattern_10-4800](4800/previews/pattern_10.png) | ![pattern_11-4800](4800/previews/pattern_11.png) | [<NSFW, click to see>](4800/previews/bikini.png) | [<NSFW, click to see>](4800/previews/bondage.png) | ![free-4800](4800/previews/free.png) | ![maid-4800](4800/previews/maid.png) | ![miko-4800](4800/previews/miko.png) | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) | ![suit-4800](4800/previews/suit.png) | ![yukata-4800](4800/previews/yukata.png) | | 4200 | 0.939 | [Download](4200/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-4200](4200/previews/pattern_1.png) | ![pattern_2-4200](4200/previews/pattern_2.png) | ![pattern_3-4200](4200/previews/pattern_3.png) | ![pattern_4-4200](4200/previews/pattern_4.png) | ![pattern_5-4200](4200/previews/pattern_5.png) | ![pattern_6-4200](4200/previews/pattern_6.png) | ![pattern_7-4200](4200/previews/pattern_7.png) | ![pattern_8-4200](4200/previews/pattern_8.png) | ![pattern_9-4200](4200/previews/pattern_9.png) | ![pattern_10-4200](4200/previews/pattern_10.png) | ![pattern_11-4200](4200/previews/pattern_11.png) | [<NSFW, click to see>](4200/previews/bikini.png) | [<NSFW, click to see>](4200/previews/bondage.png) | ![free-4200](4200/previews/free.png) | ![maid-4200](4200/previews/maid.png) | ![miko-4200](4200/previews/miko.png) | [<NSFW, click to see>](4200/previews/nude.png) | [<NSFW, click to see>](4200/previews/nude2.png) | ![suit-4200](4200/previews/suit.png) | ![yukata-4200](4200/previews/yukata.png) | | 3600 | 0.944 | [Download](3600/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-3600](3600/previews/pattern_1.png) | ![pattern_2-3600](3600/previews/pattern_2.png) | ![pattern_3-3600](3600/previews/pattern_3.png) | ![pattern_4-3600](3600/previews/pattern_4.png) | ![pattern_5-3600](3600/previews/pattern_5.png) | ![pattern_6-3600](3600/previews/pattern_6.png) | ![pattern_7-3600](3600/previews/pattern_7.png) | ![pattern_8-3600](3600/previews/pattern_8.png) | ![pattern_9-3600](3600/previews/pattern_9.png) | ![pattern_10-3600](3600/previews/pattern_10.png) | ![pattern_11-3600](3600/previews/pattern_11.png) | [<NSFW, click to see>](3600/previews/bikini.png) | [<NSFW, click to see>](3600/previews/bondage.png) | ![free-3600](3600/previews/free.png) | ![maid-3600](3600/previews/maid.png) | ![miko-3600](3600/previews/miko.png) | [<NSFW, click to see>](3600/previews/nude.png) | [<NSFW, click to see>](3600/previews/nude2.png) | ![suit-3600](3600/previews/suit.png) | ![yukata-3600](3600/previews/yukata.png) | | 3000 | 0.929 | [Download](3000/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-3000](3000/previews/pattern_1.png) | ![pattern_2-3000](3000/previews/pattern_2.png) | ![pattern_3-3000](3000/previews/pattern_3.png) | ![pattern_4-3000](3000/previews/pattern_4.png) | ![pattern_5-3000](3000/previews/pattern_5.png) | ![pattern_6-3000](3000/previews/pattern_6.png) | ![pattern_7-3000](3000/previews/pattern_7.png) | ![pattern_8-3000](3000/previews/pattern_8.png) | ![pattern_9-3000](3000/previews/pattern_9.png) | ![pattern_10-3000](3000/previews/pattern_10.png) | ![pattern_11-3000](3000/previews/pattern_11.png) | [<NSFW, click to see>](3000/previews/bikini.png) | [<NSFW, click to see>](3000/previews/bondage.png) | ![free-3000](3000/previews/free.png) | ![maid-3000](3000/previews/maid.png) | ![miko-3000](3000/previews/miko.png) | [<NSFW, click to see>](3000/previews/nude.png) | [<NSFW, click to see>](3000/previews/nude2.png) | ![suit-3000](3000/previews/suit.png) | ![yukata-3000](3000/previews/yukata.png) | | 2400 | 0.936 | [Download](2400/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-2400](2400/previews/pattern_1.png) | ![pattern_2-2400](2400/previews/pattern_2.png) | ![pattern_3-2400](2400/previews/pattern_3.png) | ![pattern_4-2400](2400/previews/pattern_4.png) | ![pattern_5-2400](2400/previews/pattern_5.png) | ![pattern_6-2400](2400/previews/pattern_6.png) | ![pattern_7-2400](2400/previews/pattern_7.png) | ![pattern_8-2400](2400/previews/pattern_8.png) | ![pattern_9-2400](2400/previews/pattern_9.png) | ![pattern_10-2400](2400/previews/pattern_10.png) | ![pattern_11-2400](2400/previews/pattern_11.png) | [<NSFW, click to see>](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) | ![free-2400](2400/previews/free.png) | ![maid-2400](2400/previews/maid.png) | ![miko-2400](2400/previews/miko.png) | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) | ![suit-2400](2400/previews/suit.png) | ![yukata-2400](2400/previews/yukata.png) | | 1800 | 0.882 | [Download](1800/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-1800](1800/previews/pattern_1.png) | ![pattern_2-1800](1800/previews/pattern_2.png) | ![pattern_3-1800](1800/previews/pattern_3.png) | ![pattern_4-1800](1800/previews/pattern_4.png) | ![pattern_5-1800](1800/previews/pattern_5.png) | ![pattern_6-1800](1800/previews/pattern_6.png) | ![pattern_7-1800](1800/previews/pattern_7.png) | ![pattern_8-1800](1800/previews/pattern_8.png) | ![pattern_9-1800](1800/previews/pattern_9.png) | ![pattern_10-1800](1800/previews/pattern_10.png) | ![pattern_11-1800](1800/previews/pattern_11.png) | [<NSFW, click to see>](1800/previews/bikini.png) | [<NSFW, click to see>](1800/previews/bondage.png) | ![free-1800](1800/previews/free.png) | ![maid-1800](1800/previews/maid.png) | ![miko-1800](1800/previews/miko.png) | [<NSFW, click to see>](1800/previews/nude.png) | [<NSFW, click to see>](1800/previews/nude2.png) | ![suit-1800](1800/previews/suit.png) | ![yukata-1800](1800/previews/yukata.png) | | 1200 | 0.909 | [Download](1200/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-1200](1200/previews/pattern_1.png) | ![pattern_2-1200](1200/previews/pattern_2.png) | ![pattern_3-1200](1200/previews/pattern_3.png) | ![pattern_4-1200](1200/previews/pattern_4.png) | ![pattern_5-1200](1200/previews/pattern_5.png) | ![pattern_6-1200](1200/previews/pattern_6.png) | ![pattern_7-1200](1200/previews/pattern_7.png) | ![pattern_8-1200](1200/previews/pattern_8.png) | ![pattern_9-1200](1200/previews/pattern_9.png) | ![pattern_10-1200](1200/previews/pattern_10.png) | ![pattern_11-1200](1200/previews/pattern_11.png) | [<NSFW, click to see>](1200/previews/bikini.png) | [<NSFW, click to see>](1200/previews/bondage.png) | ![free-1200](1200/previews/free.png) | ![maid-1200](1200/previews/maid.png) | ![miko-1200](1200/previews/miko.png) | [<NSFW, click to see>](1200/previews/nude.png) | [<NSFW, click to see>](1200/previews/nude2.png) | ![suit-1200](1200/previews/suit.png) | ![yukata-1200](1200/previews/yukata.png) | | 600 | 0.741 | [Download](600/rikka_4ninwasorezoreusootsuku.zip) | ![pattern_1-600](600/previews/pattern_1.png) | ![pattern_2-600](600/previews/pattern_2.png) | ![pattern_3-600](600/previews/pattern_3.png) | ![pattern_4-600](600/previews/pattern_4.png) | ![pattern_5-600](600/previews/pattern_5.png) | ![pattern_6-600](600/previews/pattern_6.png) | ![pattern_7-600](600/previews/pattern_7.png) | ![pattern_8-600](600/previews/pattern_8.png) | ![pattern_9-600](600/previews/pattern_9.png) | ![pattern_10-600](600/previews/pattern_10.png) | ![pattern_11-600](600/previews/pattern_11.png) | [<NSFW, click to see>](600/previews/bikini.png) | [<NSFW, click to see>](600/previews/bondage.png) | ![free-600](600/previews/free.png) | ![maid-600](600/previews/maid.png) | ![miko-600](600/previews/miko.png) | [<NSFW, click to see>](600/previews/nude.png) | [<NSFW, click to see>](600/previews/nude2.png) | ![suit-600](600/previews/suit.png) | ![yukata-600](600/previews/yukata.png) |

TemporalGames/opt-1.3b-lambada_rmt_ms7_bptt7_sl2028_mt10_lTrue_LORA_cur2

TemporalGames

2023-09-21T08:28:42Z

0

peft

[ "peft", "region:us" ]

null

2023-09-21T08:28:39Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0

martinnnnn/ppo-Huggy

martinnnnn

2023-09-21T08:21:39Z

2

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-09-21T08:21:26Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: martinnnnn/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

McMilly/TNF-Milly

McMilly

2023-09-21T08:17:33Z

0

null

[ "license:bigscience-openrail-m", "region:us" ]

null

2023-09-21T08:17:33Z

--- license: bigscience-openrail-m ---

hihisu1231/mbti_230921_4

hihisu1231

2023-09-21T08:10:51Z

140

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-21T08:06:27Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: polyglot-1.3b-koalpaca-v1.1a-rtx3090__230921_4 results: [] ---  # polyglot-1.3b-koalpaca-v1.1a-rtx3090__230921_4 This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

EnzoZacharias/LLama2-70b-fine-tuned-plc_V2

EnzoZacharias

2023-09-21T08:09:34Z

0

null

[ "generated_from_trainer", "base_model:meta-llama/Llama-2-70b-chat-hf", "base_model:finetune:meta-llama/Llama-2-70b-chat-hf", "region:us" ]

null

2023-09-21T06:41:09Z

--- base_model: meta-llama/Llama-2-70b-chat-hf tags: - generated_from_trainer model-index: - name: LLama2-70b-fine-tuned-plc_V2 results: [] ---  # LLama2-70b-fine-tuned-plc_V2 This model is a fine-tuned version of [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.1.0.dev20230823 - Datasets 2.14.4 - Tokenizers 0.13.3

hbbz/cyberhbbz

hbbz

2023-09-21T08:02:30Z

29

0

diffusers

[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-21T07:59:13Z

--- license: creativeml-openrail-m ---

QWW/dreambooth_beacon

QWW

2023-09-21T07:55:28Z

29

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-21T07:42:22Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - QWW/dreambooth_beacon This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

loupzeur/ppo-SnowballTarget

loupzeur

2023-09-21T07:53:51Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-09-21T07:52:53Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: loupzeur/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Apptware/D_tell_market_falcon7b_sharded

Apptware

2023-09-21T07:47:05Z

4

0

peft

[ "peft", "region:us" ]

null

2023-09-21T07:47:02Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

yezituan/test

yezituan

2023-09-21T07:46:42Z

0

null

[ "en", "dataset:allenai/dolma", "license:openrail", "region:us" ]

null

2023-09-21T07:44:03Z

--- license: openrail datasets: - allenai/dolma language: - en metrics: - accuracy ---

andriydovgal/bert-base-banking77-pt2

andriydovgal

2023-09-21T07:36:07Z

106

1

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:banking77", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-13T12:11:42Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - banking77 metrics: - f1 model-index: - name: bert-base-banking77-pt2 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 config: default split: test args: default metrics: - name: F1 type: f1 value: 0.9292385279025629 ---  # bert-base-banking77-pt2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.2993 - F1: 0.9292 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0521 | 1.0 | 626 | 0.7762 | 0.8277 | | 0.3536 | 2.0 | 1252 | 0.3612 | 0.9208 | | 0.1678 | 3.0 | 1878 | 0.2993 | 0.9292 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

ArturoGL/practica2

ArturoGL

2023-09-21T07:34:32Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-20T20:41:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: practica2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8480392156862745 - name: F1 type: f1 value: 0.8888888888888888 ---  # practica2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5060 - Accuracy: 0.8480 - F1: 0.8889 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5475 | 1.09 | 500 | 0.6785 | 0.7598 | 0.8281 | | 0.3811 | 2.18 | 1000 | 0.5060 | 0.8480 | 0.8889 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

OpenDILabCommunity/PongNoFrameskip-v4-C51

OpenDILabCommunity

2023-09-21T07:24:11Z

0

pytorch

[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "PongNoFrameskip-v4", "en", "license:apache-2.0", "region:us" ]

reinforcement-learning

2023-05-18T09:52:30Z

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - PongNoFrameskip-v4 benchmark_name: OpenAI/Gym/Atari task_name: PongNoFrameskip-v4 pipeline_tag: reinforcement-learning model-index: - name: C51 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Atari-PongNoFrameskip-v4 type: OpenAI/Gym/Atari-PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.3 +/- 0.64 name: mean_reward --- # Play **PongNoFrameskip-v4** with **C51** Policy ## Model Description  This is a simple **C51** implementation to OpenAI/Gym/Atari **PongNoFrameskip-v4** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import C51Agent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = C51Agent( env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import C51Agent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/PongNoFrameskip-v4-C51") # Instantiate the agent agent = C51Agent( env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from ding.bonus import C51Agent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = C51Agent(env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-C51") # Train the agent return_ = agent.train(step=int(20000000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Atari", task_name="PongNoFrameskip-v4", algo_name="C51", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./c51/pong_c51_deploy.py", usage_file_by_huggingface_ding="./c51/pong_c51_download.py", train_file="./c51/pong_c51.py", repo_id="OpenDILabCommunity/PongNoFrameskip-v4-C51", create_repo=False ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 30, 'n_evaluator_episode': 8, 'collector_env_num': 8, 'evaluator_env_num': 8, 'env_id': 'PongNoFrameskip-v4', 'frame_stack': 4, 'env_wrapper': 'atari_default' }, 'policy': { 'model': { 'encoder_hidden_size_list': [128, 128, 512], 'v_min': -10, 'v_max': 10, 'n_atom': 51, 'obs_shape': [4, 84, 84], 'action_shape': 6 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 10, 'batch_size': 32, 'learning_rate': 0.0001, 'target_update_freq': 500, 'target_theta': 0.005, 'ignore_done': False }, 'collect': { 'collector': {}, 'n_sample': 100, 'unroll_len': 1 }, 'eval': { 'evaluator': { 'eval_freq': 4000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 30, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 100000 }, 'eps': { 'type': 'exp', 'start': 1.0, 'end': 0.05, 'decay': 250000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'c51', 'priority': False, 'priority_IS_weight': False, 'discount_factor': 0.99, 'nstep': 3, 'cfg_type': 'C51PolicyDict' }, 'exp_name': 'PongNoFrameskip-v4-C51', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> **Training Procedure**  - **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zjowowen/PongNoFrameskip-v4-C51) ## Model Information  - **Github Repository:** [repo link](https://github.com/opendilab/DI-engine) - **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-C51/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-C51/blob/main/replay.mp4)  - **Parameters total size:** 55276.2 KB - **Last Update Date:** 2023-09-21 ## Environments  - **Benchmark:** OpenAI/Gym/Atari - **Task:** PongNoFrameskip-v4 - **Gym version:** 0.25.1 - **DI-engine version:** v0.4.9 - **PyTorch version:** 2.0.1+cu117 - **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html)

CJ-gyuwonpark/filtering-platypus-13b

CJ-gyuwonpark

2023-09-21T07:10:45Z

1

0

peft

[ "peft", "llama", "4-bit", "bitsandbytes", "region:us" ]

null

2023-09-21T01:36:15Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0

CyberHarem/wakui_rumi_idolmastercinderellagirls

CyberHarem

2023-09-21T07:09:06Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/wakui_rumi_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-21T06:58:24Z

--- license: mit datasets: - CyberHarem/wakui_rumi_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of wakui_rumi_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/wakui_rumi_idolmastercinderellagirls.pt` as the embedding and `4760/wakui_rumi_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4760**, with the score of 0.939. The trigger words are: 1. `wakui_rumi_idolmastercinderellagirls` 2. `short_hair, blue_hair, jewelry, black_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.923 | [Download](5100/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![pattern_6-5100](5100/previews/pattern_6.png) | ![pattern_7-5100](5100/previews/pattern_7.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | **4760** | **0.939** | [**Download**](4760/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![pattern_7-4760](4760/previews/pattern_7.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.854 | [Download](4420/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![pattern_6-4420](4420/previews/pattern_6.png) | ![pattern_7-4420](4420/previews/pattern_7.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.820 | [Download](4080/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![pattern_7-4080](4080/previews/pattern_7.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.873 | [Download](3740/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![pattern_6-3740](3740/previews/pattern_6.png) | ![pattern_7-3740](3740/previews/pattern_7.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.766 | [Download](3400/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![pattern_7-3400](3400/previews/pattern_7.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.829 | [Download](3060/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![pattern_6-3060](3060/previews/pattern_6.png) | ![pattern_7-3060](3060/previews/pattern_7.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.758 | [Download](2720/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![pattern_7-2720](2720/previews/pattern_7.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.755 | [Download](2380/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![pattern_6-2380](2380/previews/pattern_6.png) | ![pattern_7-2380](2380/previews/pattern_7.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.911 | [Download](2040/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![pattern_7-2040](2040/previews/pattern_7.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.842 | [Download](1700/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![pattern_6-1700](1700/previews/pattern_6.png) | ![pattern_7-1700](1700/previews/pattern_7.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.824 | [Download](1360/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![pattern_7-1360](1360/previews/pattern_7.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.908 | [Download](1020/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![pattern_6-1020](1020/previews/pattern_6.png) | ![pattern_7-1020](1020/previews/pattern_7.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.903 | [Download](680/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![pattern_7-680](680/previews/pattern_7.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.898 | [Download](340/wakui_rumi_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![pattern_6-340](340/previews/pattern_6.png) | ![pattern_7-340](340/previews/pattern_7.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

mirfan899/urdu-roberta-ner

mirfan899

2023-09-21T06:52:08Z

101

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-09-21T06:50:23Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: urdu-roberta-ner results: [] ---  # urdu-roberta-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1387 - Precision: 0.7735 - Recall: 0.8129 - F1: 0.7927 - Accuracy: 0.9541 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.165 | 1.0 | 2272 | 0.1521 | 0.7204 | 0.7960 | 0.7564 | 0.9454 | | 0.1208 | 2.0 | 4544 | 0.1413 | 0.7577 | 0.8101 | 0.7830 | 0.9510 | | 0.0977 | 3.0 | 6816 | 0.1387 | 0.7735 | 0.8129 | 0.7927 | 0.9541 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3

martinnnnn/ppo-LunarLander-v2

martinnnnn

2023-09-21T06:47:04Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-21T06:46:44Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.64 +/- 19.86 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

turboderp/Llama2-13B-exl2

turboderp

2023-09-21T06:44:13Z

19

2

null

[ "region:us" ]

null

2023-09-21T06:42:13Z

EXL2 quants of Llama2-13B [2.50 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/2.5bpw) [3.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/3.0bpw) [3.50 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/3.5bpw) [4.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/4.0bpw) [4.65 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/4.65bpw) [5.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/5.0bpw) [6.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/6.0bpw) [8.00 bits per weight](https://huggingface.co/turboderp/Llama2-13B-exl2/tree/8.0bpw) [measurement.json](https://huggingface.co/turboderp/Llama2-13B-exl2/blob/main/measurement.json)

chenxiang204/sd-pokemon-model-lora-sdxl

chenxiang204

2023-09-21T06:37:45Z

1

0

diffusers

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-09-20T09:28:47Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: lambdalabs/pokemon-blip-captions tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - chenxiang204/sd-pokemon-model-lora-sdxl These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

Sandra26/Sandy

Sandra26

2023-09-21T06:29:44Z

109

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-20T21:04:51Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: Sandy results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8406862745098039 - name: F1 type: f1 value: 0.8820326678765881 ---  # Sandy This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6410 - Accuracy: 0.8407 - F1: 0.8820 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4994 | 1.09 | 500 | 0.7821 | 0.8211 | 0.8793 | | 0.3466 | 2.18 | 1000 | 0.6410 | 0.8407 | 0.8820 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

CyberHarem/tsuchiya_ako_idolmastercinderellagirls

CyberHarem

2023-09-21T06:19:56Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/tsuchiya_ako_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-21T06:09:58Z

--- license: mit datasets: - CyberHarem/tsuchiya_ako_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of tsuchiya_ako_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 3060, you need to download `3060/tsuchiya_ako_idolmastercinderellagirls.pt` as the embedding and `3060/tsuchiya_ako_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 3060**, with the score of 0.968. The trigger words are: 1. `tsuchiya_ako_idolmastercinderellagirls` 2. `brown_hair, short_hair, glasses, hair_ornament, mole, hairclip, ahoge, green_eyes, smile, mole_under_mouth, open_mouth` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:----------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-----------------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.960 | [Download](5100/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | [<NSFW, click to see>](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.953 | [Download](4760/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | [<NSFW, click to see>](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.950 | [Download](4420/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | [<NSFW, click to see>](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.950 | [Download](4080/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | [<NSFW, click to see>](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.953 | [Download](3740/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | [<NSFW, click to see>](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.961 | [Download](3400/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | [<NSFW, click to see>](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | **3060** | **0.968** | [**Download**](3060/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | [<NSFW, click to see>](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.957 | [Download](2720/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | [<NSFW, click to see>](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.925 | [Download](2380/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | [<NSFW, click to see>](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.917 | [Download](2040/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | [<NSFW, click to see>](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.880 | [Download](1700/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | [<NSFW, click to see>](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.942 | [Download](1360/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | [<NSFW, click to see>](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.908 | [Download](1020/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | [<NSFW, click to see>](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.915 | [Download](680/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | [<NSFW, click to see>](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.835 | [Download](340/tsuchiya_ako_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | [<NSFW, click to see>](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

li-ping/songgodv2

li-ping

2023-09-21T06:11:47Z

0

peft

[ "peft", "region:us" ]

null

2023-09-21T05:54:04Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

Spacetimetravel/autotrain-financial-conversation_financial-summary-bart-90558144325

Spacetimetravel

2023-09-21T06:00:57Z

113

1

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autotrain", "summarization", "unk", "dataset:Spacetimetravel/autotrain-data-financial-conversation_financial-summary-bart", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-09-21T05:59:10Z

--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain" datasets: - Spacetimetravel/autotrain-data-financial-conversation_financial-summary-bart co2_eq_emissions: emissions: 0.05543082382688346 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 90558144325 - CO2 Emissions (in grams): 0.0554 ## Validation Metrics - Loss: 1.555 - Rouge1: 61.365 - Rouge2: 33.249 - RougeL: 48.538 - RougeLsum: 51.545 - Gen Len: 72.500 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Spacetimetravel/autotrain-financial-conversation_financial-summary-bart-90558144325 ```

mirfan899/urdu-distilbert-ner

mirfan899

2023-09-21T05:56:53Z

115

0

transformers

[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-09-21T05:56:31Z

--- license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: urdu-distilbert-ner results: [] ---  # urdu-distilbert-ner This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1387 - Precision: 0.7575 - Recall: 0.8057 - F1: 0.7809 - Accuracy: 0.9535 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1637 | 1.0 | 2272 | 0.1505 | 0.7131 | 0.7800 | 0.7451 | 0.9457 | | 0.1159 | 2.0 | 4544 | 0.1390 | 0.7377 | 0.8037 | 0.7693 | 0.9507 | | 0.0882 | 3.0 | 6816 | 0.1387 | 0.7575 | 0.8057 | 0.7809 | 0.9535 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3

actualbrain/Reinforce-CartPolev1

actualbrain

2023-09-21T05:55:54Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-09-03T11:07:02Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPolev1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

OpenDILabCommunity/Hopper-v3-DDPG

OpenDILabCommunity

2023-09-21T05:49:02Z

0

pytorch

[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "Hopper-v3", "en", "license:apache-2.0", "region:us" ]

reinforcement-learning

2023-04-19T01:05:47Z

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - Hopper-v3 benchmark_name: OpenAI/Gym/MuJoCo task_name: Hopper-v3 pipeline_tag: reinforcement-learning model-index: - name: DDPG results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/MuJoCo-Hopper-v3 type: OpenAI/Gym/MuJoCo-Hopper-v3 metrics: - type: mean_reward value: 3784.92 +/- 29.08 name: mean_reward --- # Play **Hopper-v3** with **DDPG** Policy ## Model Description  This is a simple **DDPG** implementation to OpenAI/Gym/MuJoCo **Hopper-v3** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed sudo apt update -y && sudo apt install -y build-essential libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev libglfw3 libglfw3-dev libsdl2-dev libsdl2-image-dev libglm-dev libfreetype6-dev patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import DDPGAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = DDPGAgent(env_id="Hopper-v3", exp_name="Hopper-v3-DDPG", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import DDPGAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/Hopper-v3-DDPG") # Instantiate the agent agent = DDPGAgent(env_id="Hopper-v3", exp_name="Hopper-v3-DDPG", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from ding.bonus import DDPGAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = DDPGAgent(env_id="Hopper-v3", exp_name="Hopper-v3-DDPG") # Train the agent return_ = agent.train(step=int(10000000), collector_env_num=4, evaluator_env_num=4, debug=False) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/MuJoCo", task_name="Hopper-v3", algo_name="DDPG", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/ddpg.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html", installation_guide=''' sudo apt update -y \ && sudo apt install -y \ build-essential \ libgl1-mesa-dev \ libgl1-mesa-glx \ libglew-dev \ libosmesa6-dev \ libglfw3 \ libglfw3-dev \ libsdl2-dev \ libsdl2-image-dev \ libglm-dev \ libfreetype6-dev \ patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ''', usage_file_by_git_clone="./ddpg/hopper_ddpg_deploy.py", usage_file_by_huggingface_ding="./ddpg/hopper_ddpg_download.py", train_file="./ddpg/hopper_ddpg.py", repo_id="OpenDILabCommunity/Hopper-v3-DDPG", create_repo=False ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 6000, 'n_evaluator_episode': 8, 'env_id': 'Hopper-v3', 'norm_obs': { 'use_norm': False }, 'norm_reward': { 'use_norm': False }, 'collector_env_num': 1, 'evaluator_env_num': 8, 'env_wrapper': 'mujoco_default' }, 'policy': { 'model': { 'obs_shape': 11, 'action_shape': 3, 'twin_critic': False, 'actor_head_hidden_size': 256, 'critic_head_hidden_size': 256, 'action_space': 'regression' }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 1, 'batch_size': 256, 'learning_rate_actor': 0.001, 'learning_rate_critic': 0.001, 'ignore_done': False, 'target_theta': 0.005, 'discount_factor': 0.99, 'actor_update_freq': 1, 'noise': False }, 'collect': { 'collector': {}, 'unroll_len': 1, 'noise_sigma': 0.1, 'n_sample': 1 }, 'eval': { 'evaluator': { 'eval_freq': 5000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 6000, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 1000000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'ddpg', 'priority': False, 'priority_IS_weight': False, 'random_collect_size': 25000, 'transition_with_policy_data': False, 'action_space': 'continuous', 'reward_batch_norm': False, 'multi_agent': False, 'cfg_type': 'DDPGPolicyDict' }, 'exp_name': 'Hopper-v3-DDPG', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> **Training Procedure**  - **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zjowowen/Hopper-v3-DDPG) ## Model Information  - **Github Repository:** [repo link](https://github.com/opendilab/DI-engine) - **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/ddpg.html) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/Hopper-v3-DDPG/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/Hopper-v3-DDPG/blob/main/replay.mp4)  - **Parameters total size:** 1090.03 KB - **Last Update Date:** 2023-09-21 ## Environments  - **Benchmark:** OpenAI/Gym/MuJoCo - **Task:** Hopper-v3 - **Gym version:** 0.25.1 - **DI-engine version:** v0.4.9 - **PyTorch version:** 2.0.1+cu117 - **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html)

Spacetimetravel/autotrain-financial-conversation-backstory-bart-90555144323

Spacetimetravel

2023-09-21T05:46:48Z

123

0

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autotrain", "summarization", "unk", "dataset:Spacetimetravel/autotrain-data-financial-conversation-backstory-bart", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-09-21T05:45:14Z

--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain" datasets: - Spacetimetravel/autotrain-data-financial-conversation-backstory-bart co2_eq_emissions: emissions: 0.05137309412154303 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 90555144323 - CO2 Emissions (in grams): 0.0514 ## Validation Metrics - Loss: 2.399 - Rouge1: 32.368 - Rouge2: 4.298 - RougeL: 20.788 - RougeLsum: 28.288 - Gen Len: 71.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Spacetimetravel/autotrain-financial-conversation-backstory-bart-90555144323 ```

xizhn/output_model_dir

xizhn

2023-09-21T05:38:41Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-20T04:59:23Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dress tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - xizhn/output_model_dir This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dress using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

li-ping/songgod

li-ping

2023-09-21T05:36:00Z

0

peft

[ "peft", "region:us" ]

null

2023-09-21T05:29:53Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

Prakstar/Bloom_3b_1_fine_tuned

Prakstar

2023-09-21T05:25:30Z

0

null

[ "license:bigscience-openrail-m", "region:us" ]

null

2023-09-21T05:25:30Z

--- license: bigscience-openrail-m ---

masta-g3/phi-1_5-psychology

masta-g3

2023-09-21T05:08:14Z

59

0

transformers

[ "transformers", "pytorch", "mixformer-sequential", "text-generation", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "autotrain_compatible", "region:us" ]

text-generation

2023-09-21T03:53:42Z

--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer model-index: - name: phi-1_5-psychology results: [] ---  # phi-1_5-psychology This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7574 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8667 | 0.04 | 100 | 0.8554 | | 0.8401 | 0.09 | 200 | 0.8524 | | 0.8492 | 0.13 | 300 | 0.8437 | | 0.8563 | 0.18 | 400 | 0.8393 | | 0.8353 | 0.22 | 500 | 0.8367 | | 0.8232 | 0.26 | 600 | 0.8305 | | 0.8299 | 0.31 | 700 | 0.8226 | | 0.8307 | 0.35 | 800 | 0.8233 | | 0.8087 | 0.39 | 900 | 0.8170 | | 0.8124 | 0.44 | 1000 | 0.8160 | | 0.7943 | 0.48 | 1100 | 0.8103 | | 0.7924 | 0.53 | 1200 | 0.8076 | | 0.7918 | 0.57 | 1300 | 0.8026 | | 0.807 | 0.61 | 1400 | 0.8012 | | 0.788 | 0.66 | 1500 | 0.8034 | | 0.7946 | 0.7 | 1600 | 0.7946 | | 0.7959 | 0.75 | 1700 | 0.7926 | | 0.7878 | 0.79 | 1800 | 0.7921 | | 0.754 | 0.83 | 1900 | 0.7890 | | 0.7762 | 0.88 | 2000 | 0.7850 | | 0.7651 | 0.92 | 2100 | 0.7849 | | 0.7868 | 0.97 | 2200 | 0.7855 | | 0.7651 | 1.01 | 2300 | 0.7820 | | 0.7323 | 1.05 | 2400 | 0.7818 | | 0.7316 | 1.1 | 2500 | 0.7804 | | 0.7311 | 1.14 | 2600 | 0.7808 | | 0.7221 | 1.18 | 2700 | 0.7782 | | 0.722 | 1.23 | 2800 | 0.7736 | | 0.7217 | 1.27 | 2900 | 0.7780 | | 0.7226 | 1.32 | 3000 | 0.7730 | | 0.7305 | 1.36 | 3100 | 0.7731 | | 0.7237 | 1.4 | 3200 | 0.7712 | | 0.7127 | 1.45 | 3300 | 0.7710 | | 0.7252 | 1.49 | 3400 | 0.7699 | | 0.7076 | 1.54 | 3500 | 0.7687 | | 0.7185 | 1.58 | 3600 | 0.7672 | | 0.6921 | 1.62 | 3700 | 0.7639 | | 0.6882 | 1.67 | 3800 | 0.7642 | | 0.7184 | 1.71 | 3900 | 0.7633 | | 0.7048 | 1.76 | 4000 | 0.7601 | | 0.7136 | 1.8 | 4100 | 0.7598 | | 0.7063 | 1.84 | 4200 | 0.7591 | | 0.7054 | 1.89 | 4300 | 0.7589 | | 0.6945 | 1.93 | 4400 | 0.7564 | | 0.6955 | 1.97 | 4500 | 0.7544 | | 0.6869 | 2.02 | 4600 | 0.7536 | | 0.6477 | 2.06 | 4700 | 0.7566 | | 0.6593 | 2.11 | 4800 | 0.7568 | | 0.6441 | 2.15 | 4900 | 0.7562 | | 0.6527 | 2.19 | 5000 | 0.7574 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

0xk1h0/codegen2.5-7b-py150k-r20-LoRA

0xk1h0

2023-09-21T04:58:49Z

0

peft

[ "peft", "region:us" ]

null

2023-09-21T04:48:19Z

--- library_name: peft --- ## Model Usage ```python import torch import transformers from finetune_peft import get_peft_config, PEFTArguments from peft import get_peft_model model_path = 'Salesforce/codegen25-7b-mono' # peft_path = 'models/codegen25_7b/checkpoint' peft_path = '0xk1h0/codegen25-7b-py150k-r20' # peft_path = 'models/alpaca-llama-7b-peft/params.p' torch.set_default_tensor_type(torch.cuda.HalfTensor) model = transformers.AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, cache_dir='models') peft_config = get_peft_config(peft_args=PEFTArguments(peft_mode="lora")) model = get_peft_model(model, peft_config) # model.load_state_dict(torch.load(peft_path), strict=False) torch.set_default_tensor_type(torch.cuda.FloatTensor) tokenizer = transformers.AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) batch = tokenizer(""" ### Generate AES MODE encrypt function. """, return_tensors="pt") with torch.no_grad(): out = model.generate( input_ids=batch["input_ids"], attention_mask=torch.ones_like(batch["input_ids"]), max_length=256, do_sample=True, temperature = 0.4, top_p=0.95 ) print(tokenizer.decode(out[0])) ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0

awrysfab/emotion_classification

awrysfab

2023-09-21T04:48:06Z

193

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-21T04:34:56Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: emotion_classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6 ---  # emotion_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2383 - Accuracy: 0.6 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0769 | 1.0 | 10 | 2.0617 | 0.1812 | | 2.0383 | 2.0 | 20 | 2.0104 | 0.3 | | 1.9423 | 3.0 | 30 | 1.8932 | 0.425 | | 1.7923 | 4.0 | 40 | 1.7442 | 0.475 | | 1.6547 | 5.0 | 50 | 1.6047 | 0.4875 | | 1.5297 | 6.0 | 60 | 1.5184 | 0.5437 | | 1.4345 | 7.0 | 70 | 1.4392 | 0.5625 | | 1.337 | 8.0 | 80 | 1.3847 | 0.5875 | | 1.2722 | 9.0 | 90 | 1.3442 | 0.55 | | 1.217 | 10.0 | 100 | 1.3058 | 0.5625 | | 1.1497 | 11.0 | 110 | 1.2914 | 0.55 | | 1.0977 | 12.0 | 120 | 1.2377 | 0.6125 | | 1.0507 | 13.0 | 130 | 1.2253 | 0.5687 | | 1.0268 | 14.0 | 140 | 1.2269 | 0.5938 | | 0.967 | 15.0 | 150 | 1.2260 | 0.5938 | | 0.9269 | 16.0 | 160 | 1.2421 | 0.5687 | | 0.9102 | 17.0 | 170 | 1.2218 | 0.5687 | | 0.8883 | 18.0 | 180 | 1.2207 | 0.5687 | | 0.8633 | 19.0 | 190 | 1.1933 | 0.6062 | | 0.8557 | 20.0 | 200 | 1.1830 | 0.575 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

CyberHarem/nagatomi_hasumi_idolmastercinderellagirls

CyberHarem

2023-09-21T04:38:53Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/nagatomi_hasumi_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-21T04:26:55Z

--- license: mit datasets: - CyberHarem/nagatomi_hasumi_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of nagatomi_hasumi_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2380, you need to download `2380/nagatomi_hasumi_idolmastercinderellagirls.pt` as the embedding and `2380/nagatomi_hasumi_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 2380**, with the score of 0.979. The trigger words are: 1. `nagatomi_hasumi_idolmastercinderellagirls` 2. `brown_hair, brown_eyes, smile, short_hair, blush, hairband, open_mouth, bangs` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.958 | [Download](5100/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.937 | [Download](4760/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.869 | [Download](4420/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.955 | [Download](4080/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.920 | [Download](3740/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.936 | [Download](3400/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.945 | [Download](3060/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.938 | [Download](2720/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | **2380** | **0.979** | [**Download**](2380/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.937 | [Download](2040/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.929 | [Download](1700/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.933 | [Download](1360/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.909 | [Download](1020/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.892 | [Download](680/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.879 | [Download](340/nagatomi_hasumi_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

rmuema/orca_mini_3B_test_guanaco

rmuema

2023-09-21T04:28:15Z

2

1

peft

[ "peft", "region:us" ]

null

2023-09-15T01:45:50Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 - base_model: psmathur/orca_mini_3b ### Framework versions - PEFT 0.6.0.dev0

Pradeep016/GAN

Pradeep016

2023-09-21T04:27:14Z

0

keras

[ "keras", "license:mit", "region:us" ]

null

2023-09-21T04:19:15Z

--- license: mit library_name: keras ---

antphb/pretrain-vit5-large

antphb

2023-09-21T04:03:03Z

3

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-19T17:20:50Z

--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: pretrain-vit5-large results: [] ---  # pretrain-vit5-large This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2677 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4276 | 4.56 | 200 | 0.2848 | | 0.4608 | 9.12 | 400 | 0.2677 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

JoseVallar01/practica2009

JoseVallar01

2023-09-21T03:51:33Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-21T03:46:16Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: practica2009 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8504901960784313 - name: F1 type: f1 value: 0.8908765652951698 ---  # practica2009 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5264 - Accuracy: 0.8505 - F1: 0.8909 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5107 | 1.09 | 500 | 0.4968 | 0.8333 | 0.8832 | | 0.3606 | 2.18 | 1000 | 0.5264 | 0.8505 | 0.8909 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

Panchovix/airoboros-l2-70b-gpt4-1.4.1_2.5bpw-h6-exl2

Panchovix

2023-09-21T03:42:47Z

7

3

transformers

[ "transformers", "llama", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-09-15T18:37:34Z

--- license: other --- 2.5 bit quantization of airoboros 70b 1.4.1 (https://huggingface.co/jondurbin/airoboros-l2-70b-gpt4-1.4.1), using exllama2. Updated as 21 of September 2023, which should fix the bad ppl results. I suggest, if using Ubuntu, to use it with flash-attn. It reduces VRAM usage by a good margin, and is specially useful for this case (70B model on a single 24GB VRAM GPU)

nomsgadded/opt_RestaurantReview

nomsgadded

2023-09-21T03:38:05Z

0

adapter-transformers

[ "adapter-transformers", "safetensors", "opt", "code", "text-classification", "en", "region:us" ]

text-classification

2023-09-20T00:06:47Z

--- pipeline_tag: text-classification language: - en metrics: - accuracy library_name: adapter-transformers tags: - code --- This is the Finetune version of the facebook/opt-350m model Dataset is RestaurantReview from kaggle How to use? Input text must be in the form of ##Rating :{text} e.g. ##Rating :It was really nice to dine there, however the waiter is very mean. Then it will return the possible rating customer gave to the restaurant.

Arlethh/leth

Arlethh

2023-09-21T03:36:57Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-20T20:57:48Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: leth results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8504901960784313 - name: F1 type: f1 value: 0.8872458410351203 ---  # leth This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6024 - Accuracy: 0.8505 - F1: 0.8872 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5009 | 1.09 | 500 | 0.5683 | 0.8113 | 0.8693 | | 0.3177 | 2.18 | 1000 | 0.6024 | 0.8505 | 0.8872 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

zfox/finetuning-sentiment-model-3000-samples

zfox

2023-09-21T03:34:37Z

107

1

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "doi:10.57967/hf/1135", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-21T03:28:42Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8666666666666667 - name: F1 type: f1 value: 0.8692810457516339 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3195 - Accuracy: 0.8667 - F1: 0.8693 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

Yntec/level4

Yntec

2023-09-21T03:14:00Z

1,673

1

diffusers

[ "diffusers", "safetensors", "Photorealistic", "Beautiful", "Fantasy", "AreThoseLevel4Plates", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-20T22:17:37Z

--- library_name: diffusers pipeline_tag: text-to-image license: creativeml-openrail-m tags: - Photorealistic - Beautiful - Fantasy - AreThoseLevel4Plates - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # level 4 v3 Original page: https://civitai.com/models/17449?modelVersionId=21896 Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/gaFiXeJAWFV2zkQaXnSVH.png) Pretty cute girl. Detailed coffee table in the vaporwave mid century modern livingroom. highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, artgerm, tomasz alen kopera, peter mohrbacher, little girl, donato giancola, joseph christian leyendecker, boris vallejo, wlop

yudiwbs/marian-finetuned-kde4-en-to-id

yudiwbs

2023-09-21T03:06:04Z

67

0

transformers

[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-en-id", "base_model:finetune:Helsinki-NLP/opus-mt-en-id", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-30T04:58:11Z

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-id tags: - generated_from_keras_callback model-index: - name: yudiwbs/marian-finetuned-kde4-en-to-id results: [] ---  # yudiwbs/marian-finetuned-kde4-en-to-id Penjelasan: https://yudiwbs.wordpress.com/2023/09/01/fine-tune-model-machine-translation-inggris-indonesia-en-id/ This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-id](https://huggingface.co/Helsinki-NLP/opus-mt-en-id) on KDE dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5779 - Validation Loss: 0.6892 - Epoch: 2 ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1245, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0329 | 0.7683 | 0 | | 0.7086 | 0.7042 | 1 | | 0.5779 | 0.6892 | 2 | ### Framework versions - Transformers 4.32.1 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

daliapv/new_model

daliapv

2023-09-21T03:02:30Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-20T21:04:29Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: new_model results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.821078431372549 - name: F1 type: f1 value: 0.8620037807183365 ---  # new_model This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6106 - Accuracy: 0.8211 - F1: 0.8620 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5319 | 1.09 | 500 | 0.5276 | 0.8284 | 0.8822 | | 0.3708 | 2.18 | 1000 | 0.6106 | 0.8211 | 0.8620 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

lilucheng/sourcedetection

lilucheng

2023-09-21T03:02:25Z

0

null

[ "region:us" ]

null

2023-09-21T02:50:44Z

Basalt Source Insight Basalt Source Insight is the models which can detect the source lithology, temperature and pressure based on the major elements content. We obtained accuracy of approximately 95% on the testing dataset. Furthermore, we also employ an XGBoost regression model to predict the pressure and temperature conditions of generation of basalts from diverse sources. Our predictions of temperature exhibit remarkable precision, with mean absolute errors of about 49°C. Similarly, our pressure estimations have also a high level of accuracy, with mean absolute errors hovering at approximately 0.37 GPa for various lithologies.

OpenDILabCommunity/PongNoFrameskip-v4-DQN

OpenDILabCommunity

2023-09-21T02:58:27Z

0

pytorch

[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "PongNoFrameskip-v4", "en", "license:apache-2.0", "region:us" ]

reinforcement-learning

2023-06-14T11:55:23Z

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - PongNoFrameskip-v4 benchmark_name: OpenAI/Gym/Atari task_name: PongNoFrameskip-v4 pipeline_tag: reinforcement-learning model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Atari-PongNoFrameskip-v4 type: OpenAI/Gym/Atari-PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.7 +/- 0.46 name: mean_reward --- # Play **PongNoFrameskip-v4** with **DQN** Policy ## Model Description  This is a simple **DQN** implementation to OpenAI/Gym/Atari **PongNoFrameskip-v4** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import DQNAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = DQNAgent( env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-DQN", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import DQNAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/PongNoFrameskip-v4-DQN") # Instantiate the agent agent = DQNAgent( env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-DQN", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from ding.bonus import DQNAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = DQNAgent(env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-DQN") # Train the agent return_ = agent.train(step=int(20000000), collector_env_num=8, evaluator_env_num=8, debug=False) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Atari", task_name="PongNoFrameskip-v4", algo_name="DQN", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/dqn.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./dqn/pong_dqn_deploy.py", usage_file_by_huggingface_ding="./dqn/pong_dqn_download.py", train_file="./dqn/pong_dqn.py", repo_id="OpenDILabCommunity/PongNoFrameskip-v4-DQN", create_repo=False ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 30, 'n_evaluator_episode': 8, 'env_id': 'PongNoFrameskip-v4', 'collector_env_num': 8, 'evaluator_env_num': 8, 'fram_stack': 4, 'env_wrapper': 'atari_default' }, 'policy': { 'model': { 'encoder_hidden_size_list': [128, 128, 512], 'obs_shape': [4, 84, 84], 'action_shape': 6 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 10, 'batch_size': 32, 'learning_rate': 0.0001, 'target_update_freq': 500, 'target_theta': 0.005, 'ignore_done': False }, 'collect': { 'collector': {}, 'n_sample': 96, 'unroll_len': 1 }, 'eval': { 'evaluator': { 'eval_freq': 1000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 30, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 100000 }, 'eps': { 'type': 'exp', 'start': 1.0, 'end': 0.05, 'decay': 250000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'dqn', 'priority': False, 'priority_IS_weight': False, 'discount_factor': 0.99, 'nstep': 3, 'cfg_type': 'DQNPolicyDict' }, 'exp_name': 'PongNoFrameskip-v4-DQN', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> **Training Procedure**  - **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zjowowen/PongNoFrameskip-v4-DQN) ## Model Information  - **Github Repository:** [repo link](https://github.com/opendilab/DI-engine) - **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/dqn.html) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-DQN/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-DQN/blob/main/replay.mp4)  - **Parameters total size:** 55703.03 KB - **Last Update Date:** 2023-09-21 ## Environments  - **Benchmark:** OpenAI/Gym/Atari - **Task:** PongNoFrameskip-v4 - **Gym version:** 0.25.1 - **DI-engine version:** v0.4.9 - **PyTorch version:** 2.0.1+cu117 - **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html)

kcyu/Cifar100_LoRA_model_Vit-cifar_100

kcyu

2023-09-21T02:39:30Z

0

peft

[ "peft", "region:us" ]

null

2023-09-21T02:39:26Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

isashap/contexttrained-validationloss-waldomodel3

isashap

2023-09-21T01:41:15Z

31

0

peft

[ "peft", "text-generation", "region:us" ]

text-generation

2023-09-21T01:17:44Z

--- library_name: peft pipeline_tag: text-generation widget: - text: "Job: Skills: Resume Point" --- ## Training procedure ### Framework versions - PEFT 0.5.0

CyberHarem/kurokawa_chiaki_idolmastercinderellagirls

CyberHarem

2023-09-21T01:18:27Z

0

1

null

[ "art", "text-to-image", "dataset:CyberHarem/kurokawa_chiaki_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-21T01:05:33Z

--- license: mit datasets: - CyberHarem/kurokawa_chiaki_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of kurokawa_chiaki_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 3740, you need to download `3740/kurokawa_chiaki_idolmastercinderellagirls.pt` as the embedding and `3740/kurokawa_chiaki_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 3740**, with the score of 0.675. The trigger words are: 1. `kurokawa_chiaki_idolmastercinderellagirls` 2. `long_hair, black_hair, brown_eyes, bangs, smile, breasts, blunt_bangs, blush, medium_breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.555 | [Download](5100/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | [<NSFW, click to see>](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.614 | [Download](4760/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | [<NSFW, click to see>](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.661 | [Download](4420/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | [<NSFW, click to see>](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.543 | [Download](4080/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | [<NSFW, click to see>](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | **3740** | **0.675** | [**Download**](3740/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | [<NSFW, click to see>](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.489 | [Download](3400/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | [<NSFW, click to see>](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.575 | [Download](3060/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | [<NSFW, click to see>](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.576 | [Download](2720/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | [<NSFW, click to see>](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.564 | [Download](2380/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | [<NSFW, click to see>](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.541 | [Download](2040/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | [<NSFW, click to see>](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.548 | [Download](1700/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | [<NSFW, click to see>](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.493 | [Download](1360/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | [<NSFW, click to see>](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.533 | [Download](1020/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | [<NSFW, click to see>](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.408 | [Download](680/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | [<NSFW, click to see>](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.279 | [Download](340/kurokawa_chiaki_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | [<NSFW, click to see>](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

Spacetimetravel/autotrain-financial-conversation-goals-90496144312

Spacetimetravel

2023-09-21T01:07:33Z

115

1

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain", "summarization", "en", "dataset:Spacetimetravel/autotrain-data-financial-conversation-goals", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2023-09-21T01:06:38Z

--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain" datasets: - Spacetimetravel/autotrain-data-financial-conversation-goals co2_eq_emissions: emissions: 0.005787519308560734 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 90496144312 - CO2 Emissions (in grams): 0.0058 ## Validation Metrics - Loss: 3.149 - Rouge1: 6.000 - Rouge2: 0.000 - RougeL: 4.000 - RougeLsum: 4.000 - Gen Len: 19.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Spacetimetravel/autotrain-financial-conversation-goals-90496144312 ```