librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-12 12:31:00	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-12 12:28:53	card stringlengths 11 1.01M
abdelhamidmalki/taxi-v3-repo	abdelhamidmalki	2023-08-10T11:03:56Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T11:03:54Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3-repo 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="abdelhamidmalki/taxi-v3-repo", 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"]) ```
skshreyas714/lora-trained-xl-colab	skshreyas714	2023-08-10T11:01:49Z	0	1	diffusers	[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]	text-to-image	2023-08-10T08:58:39Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - skshreyas714/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. 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. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
geranio/q-Taxi-v3	geranio	2023-08-10T10:57:53Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T10:57:51Z	--- 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="geranio/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"]) ```
abdelhamidmalki/q-FrozenLake-v1-4x4-noSlippery	abdelhamidmalki	2023-08-10T10:57:17Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T10:57:14Z	--- 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="abdelhamidmalki/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"]) ```
ui-chope/distilbert-base-uncased-finetuned-ner	ui-chope	2023-08-10T10:56:37Z	482	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-31T03:21:56Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-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. --> # distilbert-base-uncased-finetuned-ner This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1660 - Precision: 0.9701 - Recall: 0.9679 - F1: 0.9690 - Accuracy: 0.9863 ## 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: 11 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0091 \| 1.0 \| 5372 \| 0.1034 \| 0.9693 \| 0.9649 \| 0.9671 \| 0.9858 \| \| 0.0052 \| 2.0 \| 10744 \| 0.1362 \| 0.9715 \| 0.9679 \| 0.9697 \| 0.9868 \| \| 0.0064 \| 3.0 \| 16116 \| 0.1415 \| 0.9715 \| 0.9657 \| 0.9686 \| 0.9844 \| \| 0.0026 \| 4.0 \| 21488 \| 0.1629 \| 0.9709 \| 0.9701 \| 0.9705 \| 0.9870 \| \| 0.0034 \| 5.0 \| 26860 \| 0.1345 \| 0.9737 \| 0.9687 \| 0.9712 \| 0.9851 \| \| 0.0019 \| 6.0 \| 32232 \| 0.1297 \| 0.9700 \| 0.9649 \| 0.9675 \| 0.9841 \| \| 0.0031 \| 7.0 \| 37604 \| 0.1543 \| 0.9716 \| 0.9701 \| 0.9709 \| 0.9868 \| \| 0.0021 \| 8.0 \| 42976 \| 0.0605 \| 0.9782 \| 0.9716 \| 0.9749 \| 0.9903 \| \| 0.0023 \| 9.0 \| 48348 \| 0.1506 \| 0.9731 \| 0.9701 \| 0.9716 \| 0.9877 \| \| 0.0021 \| 10.0 \| 53720 \| 0.1714 \| 0.9693 \| 0.9672 \| 0.9682 \| 0.9860 \| \| 0.0015 \| 11.0 \| 59092 \| 0.1660 \| 0.9701 \| 0.9679 \| 0.9690 \| 0.9863 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
shajahan123/my-pet-cat	shajahan123	2023-08-10T10:52:52Z	0	0	null	[ "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-08-10T10:49:39Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat Dreambooth model trained by shajahan123 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: VJCET91 Sample pictures of this concept: ![0](https://huggingface.co/shajahan123/my-pet-cat/resolve/main/sample_images/xgz_(1).jpg)
StofEzz/whisper-tiny-fr	StofEzz	2023-08-10T10:49:32Z	92	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-10T06:33:39Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-tiny-fr 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. --> # whisper-tiny-fr This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8198 - Wer: 0.8502 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 6250 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.6223 \| 1.0 \| 250 \| 0.7567 \| 0.7225 \| \| 0.475 \| 2.0 \| 500 \| 0.6213 \| 0.5461 \| \| 0.2938 \| 3.0 \| 750 \| 0.5860 \| 0.5383 \| \| 0.1613 \| 4.0 \| 1000 \| 0.5903 \| 0.4384 \| \| 0.1026 \| 5.0 \| 1250 \| 0.5992 \| 0.4451 \| \| 0.0615 \| 6.0 \| 1500 \| 0.6322 \| 0.5383 \| \| 0.0422 \| 7.0 \| 1750 \| 0.6398 \| 0.4373 \| \| 0.019 \| 8.0 \| 2000 \| 0.6682 \| 0.5239 \| \| 0.0125 \| 9.0 \| 2250 \| 0.6980 \| 0.6681 \| \| 0.0069 \| 10.0 \| 2500 \| 0.7335 \| 0.8679 \| \| 0.0039 \| 11.0 \| 2750 \| 0.7354 \| 0.6238 \| \| 0.0026 \| 12.0 \| 3000 \| 0.7458 \| 0.6315 \| \| 0.0021 \| 13.0 \| 3250 \| 0.7599 \| 0.6715 \| \| 0.0018 \| 14.0 \| 3500 \| 0.7682 \| 0.7103 \| \| 0.0015 \| 15.0 \| 3750 \| 0.7750 \| 0.7081 \| \| 0.0013 \| 16.0 \| 4000 \| 0.7846 \| 0.7125 \| \| 0.0012 \| 17.0 \| 4250 \| 0.7897 \| 0.7114 \| \| 0.001 \| 18.0 \| 4500 \| 0.7962 \| 0.9345 \| \| 0.0009 \| 19.0 \| 4750 \| 0.8001 \| 0.7170 \| \| 0.0009 \| 20.0 \| 5000 \| 0.8074 \| 0.8335 \| \| 0.0008 \| 21.0 \| 5250 \| 0.8107 \| 0.8424 \| \| 0.0007 \| 22.0 \| 5500 \| 0.8152 \| 0.8402 \| \| 0.0007 \| 23.0 \| 5750 \| 0.8181 \| 0.8446 \| \| 0.0007 \| 24.0 \| 6000 \| 0.8187 \| 0.8479 \| \| 0.0007 \| 25.0 \| 6250 \| 0.8198 \| 0.8502 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
GrazittiInteractive/llama-2-13b	GrazittiInteractive	2023-08-10T10:49:14Z	8	1	transformers	[ "transformers", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-2", "en", "dataset:meta-llama/Llama-2-13b", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-08-01T07:39:26Z	--- inference: false language: - en pipeline_tag: text-generation datasets: - meta-llama/Llama-2-13b tags: - facebook - meta - pytorch - llama - llama-2 model_type: llama license: other --- # Meta's Llama 2 13B GGML A 4 Bit GGML format quantized version of base mode Llama-2-13b taken from https://huggingface.co/meta-llama/Llama-2-13b, reduced from 24.2 GB to 7.37GB These files are GGML format model files for [Meta's Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend. * [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server. ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| ggml-model-q4_0.bin \| q4_0 \| 4 \| 6.85 GB\| 9.118 GB \| Original quant method, 4-bit. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` we used langchain with llama-cpp-python, adjust for your tastes and needs: How to use this Llama-2-13b model from Python code and langchain First, make sure you have langchain and llama-cpp installed: ``` pip install llama-cpp-python ``` ``` # Make sure the model path is correct for your system! llm = LlamaCpp( model_path="/Users/rlm/Desktop/Code/llama/llama-2-13b-ggml/ggml-model-q4_0.bin", input={"temperature": 0.75, "max_length": 2000, "top_p": 1}, callback_manager=callback_manager, verbose=True, ) ``` # Original model card: Meta's Llama 2 13B --- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|
albagon/q-Taxi-v3	albagon	2023-08-10T10:48:16Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T10:47:24Z	--- 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: 7.52 +/- 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="albagon/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"]) ```
MrD05/otherhalf-pt	MrD05	2023-08-10T10:47:56Z	7	0	transformers	[ "transformers", "pytorch", "gptj", "text-generation", "text generation", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-08-10T10:20:35Z	--- license: creativeml-openrail-m language: - en thumbnail: null tags: - text generation ---
morell23/anyamelfisa	morell23	2023-08-10T10:47:00Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-10T10:46:32Z	--- license: creativeml-openrail-m ---
Ian-14/model_test	Ian-14	2023-08-10T10:46:45Z	156	0	transformers	[ "transformers", "pytorch", "chatglm", "text-generation", "custom_code", "zh", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2023-08-10T01:03:03Z	--- pipeline_tag: text-generation license: apache-2.0 language: - zh widget: - text: "你好啊，O(∩_∩)O哈哈~" example_title: "Sentiment analysis" - text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ..." example_title: "向量化" - text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book ..." example_title: "Logic puzzles" - text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night ..." example_title: "Reading comprehension" --- ### How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True) model = AutoModel.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True).half().cuda() model = model.eval() text = "你好" response, history = model.chat(tokenizer, text, history=[]) response ```
jordyvl/dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd	jordyvl	2023-08-10T10:45:34Z	164	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-31T13:04:43Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd 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. --> # dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5198 - Accuracy: 0.833 - Brier Loss: 0.2560 - Nll: 1.1465 - F1 Micro: 0.833 - F1 Macro: 0.8328 - Ece: 0.0719 - Aurc: 0.0425 ## 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: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 125 \| 1.0816 \| 0.622 \| 0.5119 \| 2.5026 \| 0.622 \| 0.6194 \| 0.0740 \| 0.1657 \| \| No log \| 2.0 \| 250 \| 0.8028 \| 0.715 \| 0.3936 \| 2.1454 \| 0.715 \| 0.7158 \| 0.0651 \| 0.1017 \| \| No log \| 3.0 \| 375 \| 0.7104 \| 0.7505 \| 0.3455 \| 2.0393 \| 0.7505 \| 0.7464 \| 0.0456 \| 0.0765 \| \| 0.9841 \| 4.0 \| 500 \| 0.6747 \| 0.7682 \| 0.3267 \| 1.9784 \| 0.7682 \| 0.7703 \| 0.0455 \| 0.0682 \| \| 0.9841 \| 5.0 \| 625 \| 0.6619 \| 0.7782 \| 0.3169 \| 1.9299 \| 0.7782 \| 0.7752 \| 0.0391 \| 0.0649 \| \| 0.9841 \| 6.0 \| 750 \| 0.6416 \| 0.7897 \| 0.3058 \| 1.8240 \| 0.7897 \| 0.7923 \| 0.0483 \| 0.0683 \| \| 0.9841 \| 7.0 \| 875 \| 0.6481 \| 0.786 \| 0.3016 \| 1.8855 \| 0.786 \| 0.7855 \| 0.0501 \| 0.0640 \| \| 0.259 \| 8.0 \| 1000 \| 0.6273 \| 0.7963 \| 0.2970 \| 1.7135 \| 0.7963 \| 0.7970 \| 0.0454 \| 0.0633 \| \| 0.259 \| 9.0 \| 1125 \| 0.6484 \| 0.7927 \| 0.3044 \| 1.7079 \| 0.7927 \| 0.7911 \| 0.0601 \| 0.0647 \| \| 0.259 \| 10.0 \| 1250 \| 0.6504 \| 0.7925 \| 0.3046 \| 1.8241 \| 0.7925 \| 0.7931 \| 0.0577 \| 0.0674 \| \| 0.259 \| 11.0 \| 1375 \| 0.6137 \| 0.7975 \| 0.2914 \| 1.6742 \| 0.7975 \| 0.7996 \| 0.0567 \| 0.0675 \| \| 0.133 \| 12.0 \| 1500 \| 0.6092 \| 0.7993 \| 0.2928 \| 1.6077 \| 0.7993 \| 0.8023 \| 0.0600 \| 0.0654 \| \| 0.133 \| 13.0 \| 1625 \| 0.5905 \| 0.805 \| 0.2842 \| 1.5790 \| 0.805 \| 0.8074 \| 0.0589 \| 0.0623 \| \| 0.133 \| 14.0 \| 1750 \| 0.5794 \| 0.8077 \| 0.2797 \| 1.4947 \| 0.8077 \| 0.8090 \| 0.0533 \| 0.0579 \| \| 0.133 \| 15.0 \| 1875 \| 0.5683 \| 0.8075 \| 0.2777 \| 1.4518 \| 0.8075 \| 0.8076 \| 0.0594 \| 0.0565 \| \| 0.1032 \| 16.0 \| 2000 \| 0.5762 \| 0.8125 \| 0.2794 \| 1.3998 \| 0.8125 \| 0.8146 \| 0.0633 \| 0.0551 \| \| 0.1032 \| 17.0 \| 2125 \| 0.5529 \| 0.8115 \| 0.2748 \| 1.3595 \| 0.8115 \| 0.8126 \| 0.0638 \| 0.0519 \| \| 0.1032 \| 18.0 \| 2250 \| 0.5669 \| 0.8133 \| 0.2759 \| 1.3803 \| 0.8133 \| 0.8143 \| 0.0603 \| 0.0547 \| \| 0.1032 \| 19.0 \| 2375 \| 0.5549 \| 0.8177 \| 0.2716 \| 1.3258 \| 0.8178 \| 0.8186 \| 0.0625 \| 0.0527 \| \| 0.0832 \| 20.0 \| 2500 \| 0.5576 \| 0.8147 \| 0.2737 \| 1.3814 \| 0.8148 \| 0.8183 \| 0.0627 \| 0.0513 \| \| 0.0832 \| 21.0 \| 2625 \| 0.5336 \| 0.8247 \| 0.2609 \| 1.2941 \| 0.8247 \| 0.8243 \| 0.0626 \| 0.0476 \| \| 0.0832 \| 22.0 \| 2750 \| 0.5276 \| 0.8257 \| 0.2595 \| 1.2491 \| 0.8257 \| 0.8262 \| 0.0633 \| 0.0455 \| \| 0.0832 \| 23.0 \| 2875 \| 0.5313 \| 0.8193 \| 0.2603 \| 1.2685 \| 0.8193 \| 0.8198 \| 0.0618 \| 0.0466 \| \| 0.0715 \| 24.0 \| 3000 \| 0.5208 \| 0.826 \| 0.2575 \| 1.2280 \| 0.826 \| 0.8266 \| 0.0644 \| 0.0468 \| \| 0.0715 \| 25.0 \| 3125 \| 0.5205 \| 0.8233 \| 0.2591 \| 1.2235 \| 0.8233 \| 0.8235 \| 0.0615 \| 0.0459 \| \| 0.0715 \| 26.0 \| 3250 \| 0.5067 \| 0.8293 \| 0.2536 \| 1.2028 \| 0.8293 \| 0.8298 \| 0.0630 \| 0.0433 \| \| 0.0715 \| 27.0 \| 3375 \| 0.5207 \| 0.8245 \| 0.2591 \| 1.2148 \| 0.8245 \| 0.8256 \| 0.0692 \| 0.0449 \| \| 0.0647 \| 28.0 \| 3500 \| 0.5197 \| 0.824 \| 0.2596 \| 1.1765 \| 0.824 \| 0.8242 \| 0.0690 \| 0.0469 \| \| 0.0647 \| 29.0 \| 3625 \| 0.5086 \| 0.8315 \| 0.2531 \| 1.1762 \| 0.8315 \| 0.8319 \| 0.0704 \| 0.0428 \| \| 0.0647 \| 30.0 \| 3750 \| 0.5025 \| 0.8313 \| 0.2509 \| 1.1560 \| 0.8313 \| 0.8314 \| 0.0687 \| 0.0439 \| \| 0.0647 \| 31.0 \| 3875 \| 0.5073 \| 0.832 \| 0.2527 \| 1.1743 \| 0.832 \| 0.8323 \| 0.0662 \| 0.0426 \| \| 0.0618 \| 32.0 \| 4000 \| 0.5068 \| 0.8303 \| 0.2526 \| 1.1644 \| 0.8303 \| 0.8304 \| 0.0679 \| 0.0422 \| \| 0.0618 \| 33.0 \| 4125 \| 0.5086 \| 0.8325 \| 0.2526 \| 1.1658 \| 0.8325 \| 0.8326 \| 0.0671 \| 0.0415 \| \| 0.0618 \| 34.0 \| 4250 \| 0.5114 \| 0.833 \| 0.2540 \| 1.1694 \| 0.833 \| 0.8326 \| 0.0649 \| 0.0440 \| \| 0.0618 \| 35.0 \| 4375 \| 0.5104 \| 0.8305 \| 0.2541 \| 1.1399 \| 0.8305 \| 0.8309 \| 0.0666 \| 0.0426 \| \| 0.0601 \| 36.0 \| 4500 \| 0.5122 \| 0.8307 \| 0.2547 \| 1.1755 \| 0.8308 \| 0.8309 \| 0.0689 \| 0.0435 \| \| 0.0601 \| 37.0 \| 4625 \| 0.5122 \| 0.8323 \| 0.2543 \| 1.1448 \| 0.8323 \| 0.8326 \| 0.0698 \| 0.0429 \| \| 0.0601 \| 38.0 \| 4750 \| 0.5144 \| 0.8307 \| 0.2554 \| 1.1444 \| 0.8308 \| 0.8308 \| 0.0699 \| 0.0414 \| \| 0.0601 \| 39.0 \| 4875 \| 0.5155 \| 0.8307 \| 0.2553 \| 1.1524 \| 0.8308 \| 0.8308 \| 0.0722 \| 0.0430 \| \| 0.0593 \| 40.0 \| 5000 \| 0.5132 \| 0.8315 \| 0.2543 \| 1.1554 \| 0.8315 \| 0.8318 \| 0.0721 \| 0.0423 \| \| 0.0593 \| 41.0 \| 5125 \| 0.5153 \| 0.8335 \| 0.2551 \| 1.1557 \| 0.8335 \| 0.8332 \| 0.0700 \| 0.0423 \| \| 0.0593 \| 42.0 \| 5250 \| 0.5141 \| 0.8313 \| 0.2545 \| 1.1530 \| 0.8313 \| 0.8314 \| 0.0728 \| 0.0419 \| \| 0.0593 \| 43.0 \| 5375 \| 0.5159 \| 0.8313 \| 0.2551 \| 1.1434 \| 0.8313 \| 0.8312 \| 0.0756 \| 0.0425 \| \| 0.0587 \| 44.0 \| 5500 \| 0.5164 \| 0.833 \| 0.2548 \| 1.1469 \| 0.833 \| 0.8329 \| 0.0688 \| 0.0428 \| \| 0.0587 \| 45.0 \| 5625 \| 0.5170 \| 0.8325 \| 0.2553 \| 1.1486 \| 0.8325 \| 0.8324 \| 0.0723 \| 0.0426 \| \| 0.0587 \| 46.0 \| 5750 \| 0.5188 \| 0.8325 \| 0.2559 \| 1.1478 \| 0.8325 \| 0.8324 \| 0.0731 \| 0.0423 \| \| 0.0587 \| 47.0 \| 5875 \| 0.5188 \| 0.8325 \| 0.2557 \| 1.1515 \| 0.8325 \| 0.8323 \| 0.0702 \| 0.0424 \| \| 0.0583 \| 48.0 \| 6000 \| 0.5195 \| 0.8327 \| 0.2559 \| 1.1477 \| 0.8327 \| 0.8325 \| 0.0702 \| 0.0427 \| \| 0.0583 \| 49.0 \| 6125 \| 0.5194 \| 0.8325 \| 0.2559 \| 1.1464 \| 0.8325 \| 0.8324 \| 0.0713 \| 0.0426 \| \| 0.0583 \| 50.0 \| 6250 \| 0.5198 \| 0.833 \| 0.2560 \| 1.1465 \| 0.833 \| 0.8328 \| 0.0719 \| 0.0425 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
Seappa/xlm-roberta-base-finetuned-panx-all	Seappa	2023-08-10T10:40:11Z	103	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	2023-08-10T10:26:46Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all 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-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1764 - F1: 0.8572 ## 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: 24 - eval_batch_size: 24 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.297 \| 1.0 \| 835 \| 0.1950 \| 0.8093 \| \| 0.1555 \| 2.0 \| 1670 \| 0.1687 \| 0.8455 \| \| 0.1 \| 3.0 \| 2505 \| 0.1764 \| 0.8572 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
morell23/ghblistloff	morell23	2023-08-10T10:38:02Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-10T10:35:45Z	--- license: creativeml-openrail-m ---
sd-concepts-library/animal-toy	sd-concepts-library	2023-08-10T10:33:22Z	0	2	null	[ "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:mit", "region:us" ]	null	2023-08-10T10:10:16Z	--- license: mit base_model: stabilityai/stable-diffusion-2 --- ### animal-toy on Stable Diffusion This is the `<animal-toy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<animal-toy> 0](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/2.jpg) ![<animal-toy> 1](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/0.jpeg) ![<animal-toy> 2](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/3.jpg) ![<animal-toy> 3](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/4.jpg) ![<animal-toy> 4](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/2.jpeg) ![<animal-toy> 5](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/3.jpeg) ![<animal-toy> 6](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/1.jpg) ![<animal-toy> 7](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/1.jpeg) ![<animal-toy> 8](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/4.jpeg) ![<animal-toy> 9](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/0.jpg)
DrishtiSharma/wav2vec2-base-finetuned-gtzan-bs-16	DrishtiSharma	2023-08-10T10:30:05Z	159	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-08-08T02:35:30Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan-bs-16 results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.88 --- <!-- 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. --> # wav2vec2-base-finetuned-gtzan-bs-16 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5497 - Accuracy: 0.88 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0557 \| 1.0 \| 57 \| 1.9783 \| 0.34 \| \| 1.6173 \| 2.0 \| 114 \| 1.6407 \| 0.55 \| \| 1.3884 \| 3.0 \| 171 \| 1.2228 \| 0.65 \| \| 1.1082 \| 4.0 \| 228 \| 1.0989 \| 0.7 \| \| 0.9112 \| 5.0 \| 285 \| 0.8724 \| 0.8 \| \| 0.7985 \| 6.0 \| 342 \| 0.8715 \| 0.76 \| \| 0.5456 \| 7.0 \| 399 \| 0.6832 \| 0.82 \| \| 0.4842 \| 8.0 \| 456 \| 0.6566 \| 0.85 \| \| 0.3419 \| 9.0 \| 513 \| 0.6485 \| 0.84 \| \| 0.5821 \| 10.0 \| 570 \| 0.5636 \| 0.85 \| \| 0.2112 \| 11.0 \| 627 \| 0.4572 \| 0.89 \| \| 0.2005 \| 12.0 \| 684 \| 0.5405 \| 0.87 \| \| 0.1314 \| 13.0 \| 741 \| 0.4695 \| 0.9 \| \| 0.0866 \| 14.0 \| 798 \| 0.5545 \| 0.88 \| \| 0.0594 \| 15.0 \| 855 \| 0.5497 \| 0.88 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
aura-tfn/q-FrozenLake-v1-4x4-noSlippery	aura-tfn	2023-08-10T10:27:11Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T10:27:09Z	--- 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="aura-tfn/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"]) ```
Sunny98/LunarLander-v2	Sunny98	2023-08-10T10:15:37Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T10:15:17Z	--- 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: 260.70 +/- 25.46 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 ... ```
RyangRyang/distilbert-base-uncased-finetuned-emotion	RyangRyang	2023-08-10T10:10:13Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-10T08:29:26Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: F1 type: f1 value: 0.9192696693027332 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2198 - Accuacy: 0.9195 - F1: 0.9193 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuacy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\| \| 0.8401 \| 1.0 \| 250 \| 0.3257 \| 0.906 \| 0.9036 \| \| 0.2584 \| 2.0 \| 500 \| 0.2198 \| 0.9195 \| 0.9193 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
pknayak/whisper-small-dv	pknayak	2023-08-10T10:06:47Z	74	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-09T14:31:45Z	--- language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - pkn results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: 'Common Voice 13 - pkn ' type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.290677052543728 --- <!-- 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. --> # Whisper Small Dv - pkn This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 - pkn dataset. It achieves the following results on the evaluation set: - Loss: 0.1689 - Wer Ortho: 62.8317 - Wer: 13.2907 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.1252 \| 1.63 \| 500 \| 0.1689 \| 62.8317 \| 13.2907 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
rishabguha/lora-trained-xl-colab_agsts_cat	rishabguha	2023-08-10T10:01:01Z	11	1	diffusers	[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]	text-to-image	2023-08-09T19:43:38Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of agsts cat tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - rishabguha/lora-trained-xl-colab_agsts_cat These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of agsts cat using [DreamBooth](https://dreambooth.github.io/). 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.
yezune/xlm-roberta-base-finetuned-panx-en	yezune	2023-08-10T09:55:26Z	106	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-10T09:54:05Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.en split: validation args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6877426511369938 --- <!-- 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-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3900 - F1: 0.6877 ## 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: 24 - eval_batch_size: 24 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1495 \| 1.0 \| 50 \| 0.5817 \| 0.4923 \| \| 0.5096 \| 2.0 \| 100 \| 0.4302 \| 0.6313 \| \| 0.3706 \| 3.0 \| 150 \| 0.3900 \| 0.6877 \| ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3
iliyaML/t5-small-billsum	iliyaML	2023-08-10T09:52:25Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "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	2023-08-10T09:42:37Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: t5-small-billsum 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: 0.1528 --- <!-- 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. --> # t5-small-billsum 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: 2.5246 - Rouge1: 0.1528 - Rouge2: 0.0586 - Rougel: 0.1291 - Rougelsum: 0.1292 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.8551 \| 0.1284 \| 0.0348 \| 0.1081 \| 0.1085 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.6404 \| 0.1373 \| 0.0453 \| 0.1147 \| 0.1147 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5665 \| 0.1423 \| 0.0494 \| 0.1195 \| 0.1192 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.5342 \| 0.149 \| 0.055 \| 0.1259 \| 0.1257 \| 19.0 \| \| No log \| 5.0 \| 310 \| 2.5246 \| 0.1528 \| 0.0586 \| 0.1291 \| 0.1292 \| 19.0 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
yezune/xlm-roberta-base-finetuned-panx-fr	yezune	2023-08-10T09:52:25Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-10T09:50:25Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.fr split: validation args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8341708542713568 --- <!-- 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-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2671 - F1: 0.8342 ## 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: 24 - eval_batch_size: 24 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5836 \| 1.0 \| 191 \| 0.3316 \| 0.7831 \| \| 0.26 \| 2.0 \| 382 \| 0.2738 \| 0.8256 \| \| 0.1681 \| 3.0 \| 573 \| 0.2671 \| 0.8342 \| ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3
chunwoolee0/t5_small_billsum	chunwoolee0	2023-08-10T09:52:24Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "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	2023-08-10T09:43:49Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: t5_small_billsum 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: 0.1508 --- <!-- 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. --> # t5_small_billsum 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: 2.3947 - Rouge1: 0.1508 - Rouge2: 0.0616 - Rougel: 0.1266 - Rougelsum: 0.1266 - 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 \| 2.5233 \| 0.1285 \| 0.0432 \| 0.1093 \| 0.109 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.4402 \| 0.1379 \| 0.0519 \| 0.1165 \| 0.1161 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.4054 \| 0.1477 \| 0.0592 \| 0.1242 \| 0.1242 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.3947 \| 0.1508 \| 0.0616 \| 0.1266 \| 0.1266 \| 19.0 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
stoyky/q-FrozenLake-v1-4x4-noSlippery	stoyky	2023-08-10T09:32:21Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T09:32:18Z	--- 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="stoyky/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"]) ```
trieudemo11/llama_7b_attributes_prompt_alpaca_2_stable_tested	trieudemo11	2023-08-10T09:31:48Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:44:36Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0
WinSenX/sd-class-butterflies-32	WinSenX	2023-08-10T09:27:17Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-08-10T09:26:59Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('WinSenX/sd-class-butterflies-32') image = pipeline().images[0] image ```
tmeskuti/distilbase-trained-sts-uncased	tmeskuti	2023-08-10T09:23:28Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-08-10T09:18:44Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 144, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
MRNH/proximal-policy-optimization-LunarLander-v2	MRNH	2023-08-10T09:15:05Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T08:27:37Z	--- 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: 275.81 +/- 26.46 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 ... ```
KeKu/llama2-french	KeKu	2023-08-10T09:06:12Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-10T09:06:06Z	--- library_name: peft --- ## 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
Dytorch/textual_inversion_cat	Dytorch	2023-08-10T08:50:46Z	3	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-09T02:43:24Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - Dytorch/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
rainishere/llama2-qlora-finetunined-french-test-rainishere	rainishere	2023-08-10T08:45:03Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-10T08:44:54Z	--- library_name: peft --- ## 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
NickTheSickDick/David-Draiman-V1-RVC	NickTheSickDick	2023-08-10T08:44:36Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-08-06T18:30:29Z	--- license: openrail --- The first version of my David Draiman model. Trained in RVC on a custom dataset, 220 Epochs, mangio-crepe
TigerResearch/tigerbot-7b-sft-v1-4bit	TigerResearch	2023-08-10T08:43:46Z	7	6	transformers	[ "transformers", "bloom", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-01T03:38:20Z	--- license: apache-2.0 --- <div style="width: 100%;"> <img src="https://github.com/TigerResearch/TigerBot/blob/main/image/logo_core.png" alt="TigerBot" style="width: 20%; display: block; margin: auto;"> </div> <p align="center"> <font face="黑体" size=5"> A cutting-edge foundation for your very own LLM. </font> </p> <p align="center"> 🌐 <a href="https://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a> </p> This is a 4-bit GPTQ version of the [Tigerbot 7B sft](https://huggingface.co/TigerResearch/tigerbot-7b-sft). It was quantized to 4bit using: https://github.com/TigerResearch/TigerBot/tree/main/gptq ## How to download and use this model in github: https://github.com/TigerResearch/TigerBot Here are commands to clone the TigerBot and install. ``` conda create --name tigerbot python=3.8 conda activate tigerbot conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia git clone https://github.com/TigerResearch/TigerBot cd TigerBot pip install -r requirements.txt ``` Inference with command line interface ``` cd TigerBot/gptq CUDA_VISIBLE_DEVICES=0 python tigerbot_infer.py TigerResearch/tigerbot-7b-sft-4bit-128g --wbits 4 --groupsize 128 --load TigerResearch/tigerbot-7b-sft-4bit-128g/tigerbot-7b-4bit-128g.pt ```
iliyaML/falcon-7b-openassistant-guanaco	iliyaML	2023-08-10T08:42:24Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2023-08-10T05:08:03Z	--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: falcon-7b-openassistant-guanaco 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. --> # falcon-7b-openassistant-guanaco This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
smangrul/peft-lora-starcoderbase3b-personal-copilot-A100-40GB-colab	smangrul	2023-08-10T08:35:19Z	15	0	peft	[ "peft", "generated_from_trainer", "base_model:bigcode/starcoderbase-3b", "base_model:adapter:bigcode/starcoderbase-3b", "license:bigcode-openrail-m", "region:us" ]	null	2023-08-09T20:17:40Z	--- license: bigcode-openrail-m base_model: bigcode/starcoderbase-3b tags: - generated_from_trainer model-index: - name: peft-lora-starcoderbase3b-personal-copilot-A100-40GB-colab results: [] library_name: peft --- <!-- 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. --> # peft-lora-starcoderbase3b-personal-copilot-A100-40GB-colab This model is a fine-tuned version of [bigcode/starcoderbase-3b](https://huggingface.co/bigcode/starcoderbase-3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5038 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## 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 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 30 - training_steps: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.8168 \| 0.05 \| 100 \| 0.7807 \| \| 0.7961 \| 0.1 \| 200 \| 0.7197 \| \| 0.7837 \| 0.15 \| 300 \| 0.6603 \| \| 0.7053 \| 0.2 \| 400 \| 0.6371 \| \| 0.6132 \| 0.25 \| 500 \| 0.6282 \| \| 0.6584 \| 0.3 \| 600 \| 0.6107 \| \| 0.621 \| 0.35 \| 700 \| 0.5934 \| \| 0.6961 \| 0.4 \| 800 \| 0.5877 \| \| 0.592 \| 0.45 \| 900 \| 0.5833 \| \| 0.6967 \| 0.5 \| 1000 \| 0.5746 \| \| 0.6382 \| 0.55 \| 1100 \| 0.5563 \| \| 0.6815 \| 0.6 \| 1200 \| 0.5436 \| \| 0.5483 \| 0.65 \| 1300 \| 0.5439 \| \| 0.7172 \| 0.7 \| 1400 \| 0.5401 \| \| 0.5479 \| 0.75 \| 1500 \| 0.5390 \| \| 0.9422 \| 0.8 \| 1600 \| 0.5357 \| \| 0.5503 \| 0.85 \| 1700 \| 0.5303 \| \| 0.5928 \| 0.9 \| 1800 \| 0.5322 \| \| 0.5513 \| 0.95 \| 1900 \| 0.5176 \| \| 0.6314 \| 1.0 \| 2000 \| 0.5038 \| ### Framework versions - PEFT 0.5.0.dev0 - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Hemanth-thunder/stable_diffusion_lora	Hemanth-thunder	2023-08-10T08:21:14Z	3	1	diffusers	[ "diffusers", "tensorboard", "text-to-image", "autotrain", "base_model:SG161222/Realistic_Vision_V1.4", "base_model:finetune:SG161222/Realistic_Vision_V1.4", "region:us" ]	text-to-image	2023-08-06T05:52:45Z	--- base_model: SG161222/Realistic_Vision_V1.4 instance_prompt: hmat tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Test enoder was trained.
MochaPixel/Lia	MochaPixel	2023-08-10T08:19:50Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T11:55:18Z	--- license: creativeml-openrail-m ---
ThuyNT03/distilbert-base-uncased-multil-cls-legal	ThuyNT03	2023-08-10T08:05:47Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "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	2023-08-10T00:09:04Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-multil-cls-legal 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. --> # distilbert-base-uncased-multil-cls-legal 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: 0.5448 - Accuracy: 0.9022 - F1: 0.9015 ## 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: 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 2.67 \| 1.0 \| 396 \| 1.9327 \| 0.5209 \| 0.4806 \| \| 1.5362 \| 2.0 \| 792 \| 1.0998 \| 0.7061 \| 0.6869 \| \| 0.8991 \| 3.0 \| 1188 \| 0.7546 \| 0.8013 \| 0.7975 \| \| 0.5899 \| 4.0 \| 1584 \| 0.6136 \| 0.8403 \| 0.8392 \| \| 0.4082 \| 5.0 \| 1980 \| 0.5527 \| 0.8601 \| 0.8589 \| \| 0.2874 \| 6.0 \| 2376 \| 0.5200 \| 0.8736 \| 0.8731 \| \| 0.2136 \| 7.0 \| 2772 \| 0.4991 \| 0.8831 \| 0.8815 \| \| 0.1564 \| 8.0 \| 3168 \| 0.4946 \| 0.8853 \| 0.8843 \| \| 0.1123 \| 9.0 \| 3564 \| 0.4814 \| 0.8928 \| 0.8920 \| \| 0.0866 \| 10.0 \| 3960 \| 0.4959 \| 0.8912 \| 0.8908 \| \| 0.0685 \| 11.0 \| 4356 \| 0.5060 \| 0.8928 \| 0.8923 \| \| 0.0508 \| 12.0 \| 4752 \| 0.5114 \| 0.8997 \| 0.8989 \| \| 0.037 \| 13.0 \| 5148 \| 0.5199 \| 0.8978 \| 0.8971 \| \| 0.0316 \| 14.0 \| 5544 \| 0.5236 \| 0.9003 \| 0.8993 \| \| 0.0243 \| 15.0 \| 5940 \| 0.5253 \| 0.9022 \| 0.9015 \| \| 0.021 \| 16.0 \| 6336 \| 0.5385 \| 0.9025 \| 0.9019 \| \| 0.0177 \| 17.0 \| 6732 \| 0.5396 \| 0.9038 \| 0.9032 \| \| 0.014 \| 18.0 \| 7128 \| 0.5449 \| 0.9025 \| 0.9018 \| \| 0.014 \| 19.0 \| 7524 \| 0.5467 \| 0.9010 \| 0.9002 \| \| 0.0103 \| 20.0 \| 7920 \| 0.5448 \| 0.9022 \| 0.9015 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
jubanbhura/lora-trained-xl-colab	jubanbhura	2023-08-10T08:02:11Z	2	1	diffusers	[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]	text-to-image	2023-08-10T06:14:27Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: digital badge designs tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - jubanbhura/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on digital badge designs using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Geotrend/distilbert-base-en-es-zh-cased	Geotrend	2023-08-10T08:02:08Z	142	0	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "fill-mask", "multilingual", "en", "es", "zh", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: - multilingual - en - es - zh datasets: wikipedia license: apache-2.0 --- # distilbert-base-en-es-zh-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-es-zh-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-es-zh-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.
perion/ai-avatar	perion	2023-08-10T07:55:27Z	11	5	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-02-22T16:05:50Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- Test prompt: Portrait of perion man as thomas shelby in peaky blinders, highly detailed digital painting, artstation, concept art, smooth, sharp focus, illustration Sample images: ![banner-large.jpeg](https://cdn-uploads.huggingface.co/production/uploads/63ba89ded90985e7acd775df/k-WNzB5XMlVvHj9afE1Y6.jpeg)
FYP19/t5-small-finetuned-sql3	FYP19	2023-08-10T07:43:28Z	16	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-02-24T15:24:43Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-sql3 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. --> # t5-small-finetuned-sql3 This model is a fine-tuned version of [FYP19/t5-small-finetuned-wikisql](https://huggingface.co/FYP19/t5-small-finetuned-wikisql) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0703 - Rouge2 Precision: 0.6901 - Rouge2 Recall: 0.4476 - Rouge2 Fmeasure: 0.5124 ## 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: 10 - eval_batch_size: 10 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.0456 \| 1.0 \| 916 \| 0.0626 \| 0.5941 \| 0.3852 \| 0.4383 \| \| 0.0277 \| 2.0 \| 1832 \| 0.0629 \| 0.6361 \| 0.4106 \| 0.469 \| \| 0.0191 \| 3.0 \| 2748 \| 0.0654 \| 0.6731 \| 0.4436 \| 0.5048 \| \| 0.0142 \| 4.0 \| 3664 \| 0.0678 \| 0.6767 \| 0.4365 \| 0.5001 \| \| 0.01 \| 5.0 \| 4580 \| 0.0703 \| 0.6901 \| 0.4476 \| 0.5124 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
jakezou/rl_course_vizdoom_health_gathering_supreme	jakezou	2023-08-10T07:41:19Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T07:41:13Z	--- 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: 10.63 +/- 5.23 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 jakezou/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.ipykernel_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.ipykernel_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.
HeejaeShin/sd-class-butterflies-32	HeejaeShin	2023-08-10T07:40:49Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-08-10T07:40:37Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('HeejaeShin/sd-class-butterflies-32') image = pipeline().images[0] image ```
newronai/llama-2-7b-Chat-QLoRA-Trial1	newronai	2023-08-10T07:32:04Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-10T07:31:16Z	--- library_name: peft --- ## 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
rossevine/wav2vec2_indonesia_6	rossevine	2023-08-10T07:27:07Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-10T05:34:45Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2_Indonesia_6 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. --> # wav2vec2_Indonesia_6 This model is a fine-tuned version of [facebook/wav2vec2-base-100h](https://huggingface.co/facebook/wav2vec2-base-100h) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.7559 - Wer: 1.0232 ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.1807 \| 3.23 \| 400 \| 1.3655 \| 1.0052 \| \| 0.5608 \| 6.45 \| 800 \| 1.3604 \| 1.0312 \| \| 0.3302 \| 9.68 \| 1200 \| 1.3724 \| 1.0355 \| \| 0.2405 \| 12.9 \| 1600 \| 1.4350 \| 1.0142 \| \| 0.1883 \| 16.13 \| 2000 \| 1.5079 \| 1.0213 \| \| 0.1535 \| 19.35 \| 2400 \| 1.5038 \| 1.0251 \| \| 0.1307 \| 22.58 \| 2800 \| 1.7026 \| 1.0189 \| \| 0.1104 \| 25.81 \| 3200 \| 1.7072 \| 1.0090 \| \| 0.0921 \| 29.03 \| 3600 \| 1.7559 \| 1.0232 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
kuangyebinbaba/sd	kuangyebinbaba	2023-08-10T07:21:30Z	0	0	null	[ "region:us" ]	null	2023-08-10T07:20:57Z	https://50f27352367144b604.gradio.live
yyyy1992/my_awesome_wnut_model	yyyy1992	2023-08-10T06:58:22Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-10T06:51:33Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: my_awesome_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.5096660808435852 - name: Recall type: recall value: 0.26876737720111216 - name: F1 type: f1 value: 0.35194174757281554 - name: Accuracy type: accuracy value: 0.9392501389423282 --- <!-- 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_wnut_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.0772 - Precision: 0.5097 - Recall: 0.2688 - F1: 0.3519 - Accuracy: 0.9393 ## 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 213 \| 0.0816 \| 0.4192 \| 0.1779 \| 0.2498 \| 0.9351 \| \| No log \| 2.0 \| 426 \| 0.0772 \| 0.5097 \| 0.2688 \| 0.3519 \| 0.9393 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.11.0 - Tokenizers 0.13.3
weiren119/traditional_chinese_qlora_llama2_13b_adapter	weiren119	2023-08-10T06:57:43Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-10T06:56:58Z	--- library_name: peft --- ## 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 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 - PEFT 0.4.0
reinhardfr/bloomz-560m_PROMPT_TUNING_CAUSAL_LM	reinhardfr	2023-08-10T06:53:25Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-10T05:52:22Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
leahsuperb/Reinforce-CartPole-v1	leahsuperb	2023-08-10T06:53:10Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T06:53:01Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 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
cuixing/textual_inversion_object_style_vangogh08101212-newstyle	cuixing	2023-08-10T06:51:27Z	4	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-10T04:12:51Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - cuixing/textual_inversion_object_style_vangogh08101212-newstyle These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
jakezou/a2c-PandaReachDense-v3	jakezou	2023-08-10T06:38:05Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T06:31:17Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.23 +/- 0.13 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
kasperchen/q-Taxi-v3	kasperchen	2023-08-10T06:36:12Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T06:36:10Z	--- 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: 7.48 +/- 2.79 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="kasperchen/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"]) ```
dminhk/dog-example-sdxl-lora	dminhk	2023-08-10T06:35:42Z	5	1	diffusers	[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]	text-to-image	2023-08-10T05:43:30Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - dminhk/dog-example-sdxl-lora These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. 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. LoRA for the text encoder was enabled: False. Special VAE used for training: None. Data set: https://huggingface.co/datasets/diffusers/dog-example Example images: ![sks dog sample 1](./sks_dog_example_1.png) ![sks dog sample 2](./sks_dog_example_2.png) ![sks dog sample 3](./sks_dog_example_3.png) ![sks dog sample 4](./sks_dog_example_4.png)
Roy61/textual_inversion_H3D	Roy61	2023-08-10T06:35:31Z	11	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-08T08:31:19Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - Roy61/textual_inversion_H3D These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. 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)
openerotica/mpt-7b-8k-GPTQ	openerotica	2023-08-10T06:30:59Z	5	0	transformers	[ "transformers", "pytorch", "mpt", "text-generation", "Composer", "MosaicML", "llm-foundry", "StreamingDatasets", "custom_code", "dataset:mc4", "dataset:c4", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:bigcode/the-stack", "dataset:allenai/s2orc", "arxiv:2108.12409", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2010.04245", "arxiv:1909.08053", "arxiv:2302.06675", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-08-10T06:08:21Z	--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - mc4 - c4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack - allenai/s2orc inference: false --- # MPT-7B-8k MPT-7B-8k is a decoder-style transformer pretrained starting from MPT-7B, but updating the sequence length to 8k and training for an additional 500B tokens, resulting in a total of 1.5T tokens of text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-7B-8k is part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-7B-8k is * Licensed for the possibility of commercial use. * Trained on a large amount of data (1.5T tokens like [XGen](https://huggingface.co/Salesforce/xgen-7b-8k-base) vs. 1T for [LLaMA](https://arxiv.org/abs/2302.13971), 1T for [MPT-7B](https://www.mosaicml.com/blog/mpt-7b), 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * Prepared to handle long inputs thanks to [ALiBi](https://arxiv.org/abs/2108.12409). With ALiBi, the model can extrapolate beyond the 8k training sequence length to up to 10k, and with a few million tokens it can be finetuned to extrapolate much further. * Capable of fast training and inference via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) * Equipped with highly efficient open-source training code via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-7B-8k: The following models are finetuned on MPT-7B-8k: * [MPT-7B-8k-Instruct](https://huggingface.co/mosaicml/mpt-7b-8k-instruct): a model for long-form instruction following (especially summarization and question-answering). Built by finetuning MPT-7B-8k on several carefully curated datasets. * License: _CC-BY-SA-3.0_ * [MPT-7B-8k-Chat](https://huggingface.co/mosaicml/mpt-7b-8k-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-7B-8k on approximately 1.5B tokens of chat data. * License: _CC-By-NC-SA-4.0_ ## Model Date July 18, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: MPT-7B-8k](https://www.mosaicml.com/blog/long-context-mpt-7b-8k) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b-8k', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-7b-8k' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-7b-8k' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 10000 # (input + output) tokens can now be up to 10000 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the MPT-7B-8k tokenizer which is identical to the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-7b-8k') ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline with torch.autocast('cuda', dtype=torch.bfloat16): inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda') outputs = model.generate(*inputs, max_new_tokens=100) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # or using the HF pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases \| Hyperparameter \| Value \| \|----------------\|-------\| \|n_parameters \| 6.7B \| \|n_layers \| 32 \| \| n_heads \| 32 \| \| d_model \| 4096 \| \| vocab size \| 50432 \| \| sequence length \| 2048 \| ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for ___T tokens. First it was trained for 1T tokens (with batch size 1760 and sequence length 2048) on the following data mix: #### Data Mix for Original 1T Tokens Used to Train MPT-7B \| Data Source \| Number of Tokens in Source \| Proportion \| Effective Number of Tokens \| Epochs \| \|-------------\|----------------------------\|------------\|----------------------------\|--------\| \| mC4 3.1.0 - English \| 417.99 B \| 0.33 \| 330 B \| 0.14 \| \| C4 - English - SemDedup 80% \| 100.42 B \| 0.299 \| 299 B \| 2.98 \| \| RedPajama - CommonCrawl \| 878.45 B \| 0.1 \| 100 B \| 0.11 \| \| The Stack - Selected Languages \| 463.78 B \| 0.1 \| 100 B \| 0.22 \| \| RedPajama - Wikipedia - En \| 4.87 B \| 0.04 \| 40 B \| 8.21 \| \| The Stack - Markdown \| 107.07 B \| 0.035 \| 35 B \| 0.33 \| \| S2ORC \| 48.85 B \| 0.033 \| 33 B \| 0.68 \| \| RedPajama - Books \| 26.02 B \| 0.03 \| 30B \| 1.15 \| \| RedPajama - arXiv \| 28.10 B \| 0.019 \| 19 B \| 0.68 \| \| RedPajama - StackExchange \| 20.54 B \| 0.014 \| 14 B \|0.68 \| #### Data Mix for Additional 500B Tokens Used to Further Train MPT-7B-8k We took 80B tokens from document samples that were longer than 4096 tokens, and 120B tokens with varying document sample lengths that matched the "baseline" length distribution for a total of 200B tokens in a single dataset. We then trained MPT-7B for 500B tokens with a maximum sequence length of 8192, resulting in MPT-7B-8k. Since we trained for 500B tokens using 200B tokens, nearly every subset was trained on for exactly 2.5 epochs. \| Sequence Length Distribution \| Number of Tokens in Source (Billion) \| Proportion \| Effective Number of Tokens (Billion) \| Epochs \| \|---\|---\|---\|---\|---\| \| mC4 3.1.0 - English (200+ words) - Baseline \| 33.60 \| 16.80% \| 84.00 \| 2.50 \| \| mC4 3.1.0 - English (200+ words) - ≥4096 tokens \| 23.04 \| 11.52% \| 57.60 \| 2.50 \| \| c4 - English - SemDedup 80% - Baseline \| 30.12 \| 15.06% \| 75.30 \| 2.50 \| \| c4 - English - SemDedup 80% - ≥4096 tokens \| 0.92 \| 0.46% \| 2.30 \| 2.50 \| \| RedPajama - CommonCrawl - Baseline \| 8.52 \| 4.26% \| 21.30 \| 2.50 \| \| RedPajama - CommonCrawl - ≥4096 tokens \| 12.80 \| 6.40% \| 32.00 \| 2.50 \| \| The Stack - Selected Languages - Baseline \| 30.00 \| 15.00% \| 75.00 \| 2.50 \| \| The Stack - Selected Languages - ≥4096 tokens \| 10.00 \| 5.00% \| 25.00 \| 2.50 \| \| RedPajama - Wikipedia - Baseline \| 3.60 \| 1.80% \| 9.00 \| 2.50 \| \| RedPajama - Wikipedia - ≥4096 tokens \| 1.04 \| 0.52% \| 2.60 \| 2.50 \| \| The Stack - Markdown - Baseline \| 4.50 \| 2.25% \| 11.25 \| 2.50 \| \| The Stack - Markdown - ≥4096 tokens \| 8.00 \| 4.00% \| 20.00 \| 2.50 \| \| Semantic Scholar ORC - Baseline \| 3.30 \| 1.65% \| 8.25 \| 2.50 \| \| Semantic Scholar ORC - ≥4096 tokens \| 8.00 \| 4.00% \| 20.00 \| 2.50 \| \| RedPajama - Books - Baseline \| 3.00 \| 1.50% \| 7.50 \| 2.50 \| \| RedPajama - Books - ≥4096 tokens \| 8.00 \| 4.00% \| 20.00 \| 2.50 \| \| RedPajama - arXiv - Baseline \| 1.92 \| 0.96% \| 4.80 \| 2.50 \| \| RedPajama - arXiv - ≥4096 tokens \| 5.40 \| 2.70% \| 13.50 \| 2.50 \| \| RedPajama - StackExchange - Baseline \| 1.44 \| 0.72% \| 3.60 \| 2.50 \| \| RedPajama - StackExchange - ≥4096 tokens \| 1.52 \| 1.40% \| 7.00 \| 4.60 \| \| N Training Tokens \| 200 \| 100.00% \| \| 2.5 epochs * 200B = 500B tokens \| Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points. ### Training Configuration This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B-8k is not intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-7B-8k can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B-8k was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://www.mosaicml.com/get-started?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b-8k). ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, ly Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-03-28}, % change this date urldate = {2023-03-28} % change this date } ```
deepvk/bert-base-uncased	deepvk	2023-08-10T06:23:07Z	756	1	transformers	[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "ru", "en", "license:apache-2.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-02-07T14:51:11Z	--- license: apache-2.0 language: - ru - en library_name: transformers pipeline_tag: feature-extraction --- # BERT-base <!-- Provide a quick summary of what the model is/does. --> Pretrained bidirectional encoder for russian language. The model was trained using standard MLM objective on large text corpora including open social data. See `Training Details` section for more information. ⚠️ This model contains only the encoder part without any pretrained head. - Developed by: [deepvk](https://vk.com/deepvk) - Model type: BERT - Languages: Mostly russian and small fraction of other languages - License: Apache 2.0 ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deepvk/bert-base-uncased") model = AutoModel.from_pretrained("deepvk/bert-base-uncased") text = "Привет, мир!" inputs = tokenizer(text, return_tensors='pt') predictions = model(inputs) ``` ## Training Details The model was trained using the NVIDIA source code. See the [pretraining documentation](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/README.md#training-process) for details. ### Training Data 250 GB of filtered texts in total. A mix of the following data: Wikipedia, Books and Social corpus. ### Architecture details \| Argument \| Value \| \|-------------------------\|----------------\| \|Encoder layers \| 12 \| \|Encoder attention heads \| 12 \| \|Encoder embed dim \| 768 \| \|Encoder ffn embed dim \| 3,072 \| \|Activation function \| GeLU \| \|Attention dropout \| 0.1 \| \|Dropout \| 0.1 \| \|Max positions \| 512 \| \|Vocab size \| 36000 \| \|Tokenizer type \| BertTokenizer \| ## Evaluation We evaluated the model on [Russian Super Glue](https://russiansuperglue.com/) dev set. The best result in each task is marked in bold. All models have the same size except the distilled version of DeBERTa. \| Model \| RCB \| PARus \| MuSeRC \| TERRa \| RUSSE \| RWSD \| DaNetQA \| Score \| \|------------------------------------------------------------------------\|-----------\|--------\|---------\|-------\|---------\|---------\|---------\|-----------\| \| [vk-deberta-distill](https://huggingface.co/deepvk/deberta-v1-distill) \| 0.433 \| 0.56 \| 0.625 \| 0.59 \| 0.943 \| 0.569 \| 0.726 \| 0.635 \| \| [vk-roberta-base](https://huggingface.co/deepvk/roberta-base) \| 0.46 \| 0.56 \| 0.679 \| 0.769 \| 0.960 \| 0.569 \| 0.658 \| 0.665 \| \| [vk-deberta-base](https://huggingface.co/deepvk/deberta-v1-base) \| 0.450 \|0.61\|0.722\| 0.704 \| 0.948 \| 0.578 \|0.76 \|0.682 \| \| [vk-bert-base](https://huggingface.co/deepvk/bert-base-uncased) \| 0.467 \| 0.57 \| 0.587 \| 0.704 \| 0.953 \|0.583\| 0.737 \| 0.657 \| \| [sber-bert-base](https://huggingface.co/ai-forever/ruBert-base) \| 0.491 \|0.61\| 0.663 \| 0.769 \|0.962**\| 0.574 \| 0.678 \| 0.678 \|
nullday/immersiveL-exp	nullday	2023-08-10T06:21:53Z	64	4	transformers	[ "transformers", "pytorch", "safetensors", "bloom", "text-generation", "translation", "gpt-style", "chinese", "english", "zh", "en", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2023-08-07T07:30:25Z	--- language: - zh - en tags: - translation - gpt-style - chinese - english license: "bigscience-bloom-rail-1.0" --- ## English: ### ImmersiveL Model on Hugging Face This model, available on Hugging Face under `funstoryai/immersiveL-exp`, is a GPT-like model designed specifically for English-Chinese and Chinese-English translations. Recommended Prompts: For English to Chinese: ``` 下面是一段英文文本，请将它翻译成中文。 {terms} #英文文本: {input} #中文翻译: ``` For Chinese to English: ``` 下面是一段中文文本，请将它翻译成英文。 {terms} #中文文本: {input} #英文翻译: ``` For the corresponding GitHub project, please visit: [ImmersiveL on GitHub](https://github.com/immersive-translate/ImmersiveL). <https://github.com/immersive-translate/ImmersiveL> --- ## 中文: ### Hugging Face 上的 ImmersiveL 模型此模型在 Hugging Face 的 `funstoryai/immersiveL-exp` 下可用，是专为英汉和汉英翻译设计的类GPT模型。推荐提示词: 英译中: ``` 下面是一段英文文本，请将它翻译成中文。 {terms} #英文文本: {input} #中文翻译: ``` 中译英: ``` 下面是一段中文文本，请将它翻译成英文。 {terms} #中文文本: {input} #英文翻译: ``` 对应的 GitHub 项目地址为: [ImmersiveL on GitHub](https://github.com/immersive-translate/ImmersiveL). <https://github.com/immersive-translate/ImmersiveL>
TheTravellingEngineer/llama2-7b-chat-hf-v3	TheTravellingEngineer	2023-08-10T06:21:28Z	1,536	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-10T06:03:25Z	The base model is meta's Llama-2-7b-chat-hf. It was finetuned using SFT and the Anthropic/hh-rlhf dataset and the model prompt is similar to the original Guanaco model. This repo contains the merged fp16 model. Legal Disclaimer: This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind. --- - license: - llama2 <br> - datasets: - Anthropic/hh-rlhf <br> - language: - en <br> - reference: https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da ---
AshutoshShrivastava/sdxl-db-lionelmessi	AshutoshShrivastava	2023-08-10T06:17:43Z	2	3	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-08-10T06:17:36Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks leoandresmessi tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Test enoder was not trained.
Bastian1111/dqn-SpaceInvadersNoFrameskip-v4	Bastian1111	2023-08-10T05:52:53Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-06T04:19:13Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 762.50 +/- 300.08 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Bastian1111 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Bastian1111 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Bastian1111 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
DAMO-NLP-MT/polylm-13b	DAMO-NLP-MT	2023-08-10T05:50:39Z	1,615	53	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "custom_code", "zh", "en", "es", "fr", "pt", "ru", "de", "it", "ar", "ja", "ko", "th", "vi", "id", "nl", "pl", "tr", "he", "arxiv:2307.06018", "arxiv:2104.09864", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-13T13:48:44Z	--- language: - zh - en - es - fr - pt - ru - de - it - ar - ja - ko - th - vi - id - nl - pl - tr - he tags: - text-generation license: apache-2.0 --- # Model Card for PolyLM (a polyglot large language model) ## Table of Contents 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 5. [Next Steps](#next-steps) 6. [Citation](#citation) # Model Details ## Abstract > Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been primarily focused on high-resource languages, such as English, thereby limiting their applicability and research in other languages. Consequently, we present PolyLM, a multilingual LLM trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B. To enhance its multilingual capabilities, we 1) integrate bilingual data into training data; and 2) adopt a curriculum learning strategy that increases the proportion of non-English data from 30% in the first stage to 60% in the final stage during pre-training. Further, we propose a multilingual self-instruct method which automatically generates 132.7K diverse multilingual instructions for model fine-tuning. To assess the model's performance, we collect several existing multilingual tasks, including multilingual understanding, question answering, generation, and translation. Extensive experiments show that PolyLM surpasses other open-source models such as LLaMA and BLOOM on multilingual tasks while maintaining comparable performance in English. ## Model Description - Model type: Decoder-only Language model - Language(s) (NLP): Chinese, English, Spanish, German, French, Portuguese, Russian, Italian, Arabic, Japanese, Korean, Thai, Vietnamese, Indonesian, Polish, Turkish, Dutch, Hebrew - License: Apache 2.0 - Original Checkpoints: [Modelscope DAMO PolyLM-13B](https://www.modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary) - Link to paper: [here](https://arxiv.org/pdf/2307.06018.pdf) - Number fotmat: bf16 - Total seen tokens: 640 billion tokens - Version: Version 1.0 / 12 July 2023 # Usage Find below some example scripts on how to use the model in `transformers`: <details> <summary> Click to expand </summary> ```python # pip install accelerate from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-MT/polylm-13b", legacy=False, use_fast=False) model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-MT/polylm-13b", device_map="auto", trust_remote_code=True) model.eval() input_doc = f"Beijing is the capital of China.\nTranslate this sentence from English to Chinese." inputs = tokenizer(input_doc, return_tensors="pt") generate_ids = model.generate( inputs.input_ids, attention_mask=inputs.attention_mask, do_sample=False, num_beams=4, max_length=128, early_stopping=True ) decoded = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(f">>> {decoded}") ### results ### Beijing is the capital of China.\nTranslate this sentence from English to Chinese.\\n北京是中华人民共和国的首都。\n ... ``` </details> # Uses ## Direct Use and Downstream Use > The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models See the [research paper](https://arxiv.org/pdf/2307.06018.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2307.06018.pdf): > Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. # Next Steps We are continuously enhancing the capabilities of PolyLM by focusing on the following aspects: 1. Replacement of absolute position embeddings with RoPE, as outlined in the research paper [here](https://arxiv.org/abs/2104.09864). 2. Expansion of window size to more than 10,000. 3. Verification of lightweight techniques to quickly enhance multilingual quality, especially for low-resource languages. # Citation BibTeX: ```bibtex @misc{wei2023polylm, title={PolyLM: An Open Source Polyglot Large Language Model}, author={Xiangpeng Wei and Haoran Wei and Huan Lin and Tianhao Li and Pei Zhang and Xingzhang Ren and Mei Li and Yu Wan and Zhiwei Cao and Binbin Xie and Tianxiang Hu and Shangjie Li and Binyuan Hui and Bowen Yu and Dayiheng Liu and Baosong Yang and Fei Huang and Jun Xie}, year={2023}, eprint={2307.06018}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
mchablani/Llama-2-7b-chat-hf-mini-lawyer-chat	mchablani	2023-08-10T05:36:12Z	2	0	peft	[ "peft", "pytorch", "llama", "region:us" ]	null	2023-08-05T03:54:19Z	--- library_name: peft --- ## 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: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
jonalkw/Reinforce-pixelcopter	jonalkw	2023-08-10T05:25:14Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T05:25:11Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 9.60 +/- 12.56 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . 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
nanirudh/qa_model_v3	nanirudh	2023-08-10T05:23:57Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-10T05:23:48Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
rodriguezj314/path_to_folder	rodriguezj314	2023-08-10T05:05:40Z	23	0	diffusers	[ "diffusers", "tensorboard", "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	2023-08-10T04:51:00Z	--- 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 - rodriguezj314/path_to_folder 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.
calvpang/distilhubert-finetuned-gtzan-finetuned-gtzan	calvpang	2023-08-10T05:04:13Z	160	0	transformers	[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:VinayHajare/distilhubert-finetuned-gtzan", "base_model:finetune:VinayHajare/distilhubert-finetuned-gtzan", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-08-10T03:51:48Z	--- license: apache-2.0 base_model: VinayHajare/distilhubert-finetuned-gtzan tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-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: 0.89 --- <!-- 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-finetuned-gtzan This model is a fine-tuned version of [VinayHajare/distilhubert-finetuned-gtzan](https://huggingface.co/VinayHajare/distilhubert-finetuned-gtzan) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5147 - Accuracy: 0.89 ## 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-07 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4687 \| 1.0 \| 113 \| 0.5210 \| 0.89 \| \| 0.5003 \| 2.0 \| 226 \| 0.5186 \| 0.89 \| \| 0.3839 \| 3.0 \| 339 \| 0.5186 \| 0.89 \| \| 0.4082 \| 4.0 \| 452 \| 0.5183 \| 0.89 \| \| 0.4479 \| 5.0 \| 565 \| 0.5183 \| 0.89 \| \| 0.4078 \| 6.0 \| 678 \| 0.5171 \| 0.89 \| \| 0.3089 \| 7.0 \| 791 \| 0.5156 \| 0.89 \| \| 0.3432 \| 8.0 \| 904 \| 0.5152 \| 0.89 \| \| 0.4122 \| 9.0 \| 1017 \| 0.5148 \| 0.89 \| \| 0.4231 \| 10.0 \| 1130 \| 0.5147 \| 0.89 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
kasperchen/q-FrozenLake-v1-4x4-noSlippery	kasperchen	2023-08-10T05:00:06Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T04:11:41Z	--- 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="kasperchen/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"]) ```
bookbot/byt5-small-wikipron-eng-latn-us-broad-p2g	bookbot	2023-08-10T04:54:35Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-04-19T04:07:16Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: byt5-small-wikipron-eng-latn-us-broad-p2g 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. --> # byt5-small-wikipron-eng-latn-us-broad-p2g This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2595 - Per: 0.4628 - Gen Len: 8.4996 ## 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: 128 - eval_batch_size: 32 - 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: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Per \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| 2.4797 \| 1.0 \| 382 \| 0.4371 \| 0.6951 \| 8.4302 \| \| 0.4823 \| 2.0 \| 764 \| 0.3543 \| 0.5974 \| 8.4338 \| \| 0.3878 \| 3.0 \| 1146 \| 0.3081 \| 0.545 \| 8.4394 \| \| 0.3378 \| 4.0 \| 1528 \| 0.2904 \| 0.518 \| 8.449 \| \| 0.3061 \| 5.0 \| 1910 \| 0.2736 \| 0.5004 \| 8.4612 \| \| 0.2823 \| 6.0 \| 2292 \| 0.2664 \| 0.4893 \| 8.4734 \| \| 0.265 \| 7.0 \| 2674 \| 0.2626 \| 0.4747 \| 8.4721 \| \| 0.2502 \| 8.0 \| 3056 \| 0.2612 \| 0.4697 \| 8.4945 \| \| 0.2388 \| 9.0 \| 3438 \| 0.2592 \| 0.4633 \| 8.489 \| \| 0.231 \| 10.0 \| 3820 \| 0.2595 \| 0.4628 \| 8.4996 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.1.dev0 - Tokenizers 0.13.2
nicbull/DialoGPT-medium-leric	nicbull	2023-08-10T04:37:18Z	150	0	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "chat", "conversational", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-10T04:25:26Z	--- language: - en pipeline_tag: conversational tags: - chat ---
jonalkw/Reinforce-CartPole	jonalkw	2023-08-10T04:24:00Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T04:23:47Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole 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
chunwoolee0/keti-air-ke-t5-base-en-to-ko	chunwoolee0	2023-08-10T04:00:42Z	114	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "base_model:KETI-AIR/ke-t5-base", "base_model:finetune:KETI-AIR/ke-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2023-08-10T03:27:30Z	--- license: apache-2.0 base_model: KETI-AIR/ke-t5-base tags: - translation - generated_from_trainer datasets: - kde4 model-index: - name: keti-air-ke-t5-base-en-to-ko 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. --> # keti-air-ke-t5-base-en-to-ko This model is a fine-tuned version of [KETI-AIR/ke-t5-base](https://huggingface.co/KETI-AIR/ke-t5-base) on the kde4 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: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
debjxt/tlx-bzx-btz	debjxt	2023-08-10T03:45:14Z	1	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-10T03:32:22Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### tlx_bzx_btz Dreambooth model trained by debjxt with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
Pixel390/NEWKUA	Pixel390	2023-08-10T03:27:24Z	5	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:Meina/MeinaMix_V10", "base_model:adapter:Meina/MeinaMix_V10", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-08-10T03:07:57Z	--- license: creativeml-openrail-m base_model: Meina/MeinaMix_V10 instance_prompt: a uxz man tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Pixel390/NEWKUA These are LoRA adaption weights for Meina/MeinaMix_V10. The weights were trained on a uxz man using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True.
dangkhoadl/AudioResNet	dangkhoadl	2023-08-10T03:21:17Z	38	0	transformers	[ "transformers", "pytorch", "resnet", "endpoints_compatible", "region:us" ]	null	2023-08-08T01:50:29Z	# Input tensor shape [batch_size, Cin, num_feats, num_frames]
EkoMickA/distilroberta-base-finetuned-wikitext2	EkoMickA	2023-08-10T03:14:19Z	162	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-08-10T03:03:02Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: distilroberta-base-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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8251 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0909 \| 1.0 \| 2406 \| 1.9271 \| \| 1.9984 \| 2.0 \| 4812 \| 1.8671 \| \| 1.941 \| 3.0 \| 7218 \| 1.8546 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
EkoMickA/distilgpt2-finetuned-wikitext2	EkoMickA	2023-08-10T02:55:06Z	216	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-10T02:31:24Z	--- license: apache-2.0 base_model: distilgpt2 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: 3.6423 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7553 \| 1.0 \| 2334 \| 3.6644 \| \| 3.6393 \| 2.0 \| 4668 \| 3.6485 \| \| 3.5938 \| 3.0 \| 7002 \| 3.6423 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
rriverar75/vit-model	rriverar75	2023-08-10T02:34:32Z	193	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:beans", "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	2023-08-10T02:08:37Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - beans metrics: - accuracy widget: - src: >- https://huggingface.co/rriverar75/vit-model/resolve/main/healthy.jpeg example_title: Healthy - src: >- https://huggingface.co/rriverar75/vit-model/resolve/main/bean_rust.jpeg example_title: Bean Rust model-index: - name: vit-model results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- 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-model 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0189 - 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: 0.0002 - 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1527 \| 3.85 \| 500 \| 0.0189 \| 1.0 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Phaaarus/QLoRA_replica_8rank_QKadap_1epoch	Phaaarus	2023-08-10T02:33:13Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-10T02:32:51Z	--- library_name: peft --- ## 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 0.5.0.dev0
wangxso/q-taxi-v3	wangxso	2023-08-10T02:28:47Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T02:28:44Z	--- 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: 7.52 +/- 2.70 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="wangxso/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"]) ```
TheTravellingEngineer/bloom-1b1-RLHF-v2	TheTravellingEngineer	2023-08-10T01:39:33Z	1,662	0	transformers	[ "transformers", "pytorch", "bloom", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-10T01:30:21Z	The base model is bigscience/bloom-1b1. It was finetuned using RLHF and the dataset and the model prompt is similar to the original model. This repo contains the merged fp16 model. Legal Disclaimer: This model is bound by the usage restrictions of the original BLOOM model. And comes with no warranty or gurantees of any kind. --- - license: - bigscience-bloom-rail-1.0 <br> - datasets: - Anthropic/hh-rlhf <br> - language: - en <br> - reference: https://github.com/hiyouga/LLaMA-Efficient-Tuning/tree/main ---
tianpf/llama2-qlora-finetunined-law	tianpf	2023-08-10T01:38:54Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-10T01:38:51Z	--- library_name: peft --- ## 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
Kappa7077/clip-finetuned-lora-organ	Kappa7077	2023-08-10T01:21:03Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-10T01:17:51Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
dana11235/ppo-Huggy	dana11235	2023-08-10T01:16:01Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-08-10T01:15:51Z	--- 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: dana11235/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
mani05/Taxi-v3	mani05	2023-08-10T01:08:38Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T01:08:34Z	--- 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: 7.56 +/- 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="mani05/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"]) ```
mani05/q-FrozenLake-v1-4x4-noSlippery	mani05	2023-08-10T01:06:06Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T01:06:02Z	--- 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="mani05/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"]) ```
toastedshibe/lora-trained-xl-colab	toastedshibe	2023-08-10T01:04:48Z	5	1	diffusers	[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]	text-to-image	2023-08-09T23:49:50Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - toastedshibe/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. 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. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
thomaspries/ppo-LunarLander-v2	thomaspries	2023-08-10T00:41:07Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-10T00:40:44Z	--- 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: 253.92 +/- 19.01 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 ... ```
Notespeak/ariadnetestn	Notespeak	2023-08-10T00:35:42Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-08-10T00:28:25Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [ai-forever/ruGPT-3.5-13B](https://huggingface.co/ai-forever/ruGPT-3.5-13B) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.31.0 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCES_TOKEN>) ``` - Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="Notespeak/ariadnetestn", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?</s><\|answer\|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "Notespeak/ariadnetestn", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "Notespeak/ariadnetestn", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Notespeak/ariadnetestn" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?</s><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` GPT2LMHeadModel( (transformer): GPT2Model( (wte): Embedding(50272, 5120) (wpe): Embedding(2048, 5120) (drop): Dropout(p=0.1, inplace=False) (h): ModuleList( (0-39): 40 x GPT2Block( (ln_1): LayerNorm((5120,), eps=1e-05, elementwise_affine=True) (attn): GPT2Attention( (c_attn): Conv1D() (c_proj): Conv1D() (attn_dropout): Dropout(p=0.1, inplace=False) (resid_dropout): Dropout(p=0.1, inplace=False) ) (ln_2): LayerNorm((5120,), eps=1e-05, elementwise_affine=True) (mlp): GPT2MLP( (c_fc): Conv1D() (c_proj): Conv1D() (act): NewGELUActivation() (dropout): Dropout(p=0.1, inplace=False) ) ) ) (ln_f): LayerNorm((5120,), eps=1e-05, elementwise_affine=True) ) (lm_head): Linear(in_features=5120, out_features=50272, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
Precious1/Clinical-Biomedical-Named-Entity-Recognition-Using-Scispacy	Precious1	2023-08-10T00:30:41Z	0	1	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-08-10T00:25:26Z	--- license: bigscience-openrail-m ---
off-topic-team/llama-2-13b-off-topic	off-topic-team	2023-08-10T00:17:48Z	0	1	null	[ "llama", "eleutherai", "llama-2", "region:us" ]	null	2023-08-09T23:44:20Z	--- tags: - llama - eleutherai - llama-2 --- It had to be done. Sample of data: ``` Hyperion: when it's dysfunctional you just yolo deploy into prod Fleetwood: remove the middle clause - fuck loyalty to ~~ companies~~ most companies Zippy: nono- like- it's more that- it works, but- there needs to be a dude who understands it and can move it to new servers, or make small patches for new functionality, etc-... ⭐ 1 <:sus:869276863414014052> 1 <:guilty:745897670534627369> 2 AI_WAIFU: oh and if you fuck up the wrong system you may get dragged to washington DC to explain what happened ``` also sorry about the reaction formatting having the IDs in it I didn't realize how annoying that would be with Llama's tokenizer :c
Pixel390/GIRLKAY	Pixel390	2023-08-09T23:53:42Z	1	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:Meina/MeinaMix_V10", "base_model:adapter:Meina/MeinaMix_V10", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-08-09T23:09:34Z	--- license: creativeml-openrail-m base_model: Meina/MeinaMix_V10 instance_prompt: a uxz girl tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Pixel390/GIRLKAY These are LoRA adaption weights for Meina/MeinaMix_V10. The weights were trained on a uxz girl using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True.
tingchih/pretrain_doc_concat	tingchih	2023-08-09T23:38:40Z	105	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-31T05:04:43Z	This is a pre-train baseline model for summarization. Input is to concatenate all articles in one cluster. the example.json is the example result. pipeline: input -> sum tokenizer -> perceiver -> sum model -> summary
agustinl/ppo-Pyramids	agustinl	2023-08-09T23:36:42Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-08-09T23:13:56Z	--- 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: agustinl/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
cjohlmacher/ppo-Pyramids	cjohlmacher	2023-08-09T23:30:56Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-08-09T21:01:25Z	--- 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: cjohlmacher/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
good-gaming/distilbert-base-uncased-finetuned-emotion	good-gaming	2023-08-09T23:21:58Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T22:48:26Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.927 - name: F1 type: f1 value: 0.9272353554627635 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2133 - Accuracy: 0.927 - F1: 0.9272 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8118 \| 1.0 \| 250 \| 0.3108 \| 0.905 \| 0.9056 \| \| 0.2485 \| 2.0 \| 500 \| 0.2133 \| 0.927 \| 0.9272 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.13.3

abdelhamidmalki/taxi-v3-repo

abdelhamidmalki

2023-08-10T11:03:56Z

0

null

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

reinforcement-learning

2023-08-10T11:03:54Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3-repo 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="abdelhamidmalki/taxi-v3-repo", 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"]) ```

skshreyas714/lora-trained-xl-colab

skshreyas714

2023-08-10T11:01:49Z

0

1

diffusers

[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]

text-to-image

2023-08-10T08:58:39Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - skshreyas714/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. 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. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

geranio/q-Taxi-v3

geranio

2023-08-10T10:57:53Z

0

null

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

reinforcement-learning

2023-08-10T10:57:51Z

--- 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="geranio/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"]) ```

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

abdelhamidmalki

2023-08-10T10:57:17Z

0

null

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

reinforcement-learning

2023-08-10T10:57:14Z

--- 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="abdelhamidmalki/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"]) ```

ui-chope/distilbert-base-uncased-finetuned-ner

ui-chope

2023-08-10T10:56:37Z

482

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-05-31T03:21:56Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] ---  # distilbert-base-uncased-finetuned-ner This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1660 - Precision: 0.9701 - Recall: 0.9679 - F1: 0.9690 - Accuracy: 0.9863 ## 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: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0091 | 1.0 | 5372 | 0.1034 | 0.9693 | 0.9649 | 0.9671 | 0.9858 | | 0.0052 | 2.0 | 10744 | 0.1362 | 0.9715 | 0.9679 | 0.9697 | 0.9868 | | 0.0064 | 3.0 | 16116 | 0.1415 | 0.9715 | 0.9657 | 0.9686 | 0.9844 | | 0.0026 | 4.0 | 21488 | 0.1629 | 0.9709 | 0.9701 | 0.9705 | 0.9870 | | 0.0034 | 5.0 | 26860 | 0.1345 | 0.9737 | 0.9687 | 0.9712 | 0.9851 | | 0.0019 | 6.0 | 32232 | 0.1297 | 0.9700 | 0.9649 | 0.9675 | 0.9841 | | 0.0031 | 7.0 | 37604 | 0.1543 | 0.9716 | 0.9701 | 0.9709 | 0.9868 | | 0.0021 | 8.0 | 42976 | 0.0605 | 0.9782 | 0.9716 | 0.9749 | 0.9903 | | 0.0023 | 9.0 | 48348 | 0.1506 | 0.9731 | 0.9701 | 0.9716 | 0.9877 | | 0.0021 | 10.0 | 53720 | 0.1714 | 0.9693 | 0.9672 | 0.9682 | 0.9860 | | 0.0015 | 11.0 | 59092 | 0.1660 | 0.9701 | 0.9679 | 0.9690 | 0.9863 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

shajahan123/my-pet-cat

shajahan123

2023-08-10T10:52:52Z

0

null

[ "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-08-10T10:49:39Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat Dreambooth model trained by shajahan123 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: VJCET91 Sample pictures of this concept: ![0](https://huggingface.co/shajahan123/my-pet-cat/resolve/main/sample_images/xgz_(1).jpg)

StofEzz/whisper-tiny-fr

StofEzz

2023-08-10T10:49:32Z

92

0

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-10T06:33:39Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-tiny-fr results: [] ---  # whisper-tiny-fr This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8198 - Wer: 0.8502 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 6250 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6223 | 1.0 | 250 | 0.7567 | 0.7225 | | 0.475 | 2.0 | 500 | 0.6213 | 0.5461 | | 0.2938 | 3.0 | 750 | 0.5860 | 0.5383 | | 0.1613 | 4.0 | 1000 | 0.5903 | 0.4384 | | 0.1026 | 5.0 | 1250 | 0.5992 | 0.4451 | | 0.0615 | 6.0 | 1500 | 0.6322 | 0.5383 | | 0.0422 | 7.0 | 1750 | 0.6398 | 0.4373 | | 0.019 | 8.0 | 2000 | 0.6682 | 0.5239 | | 0.0125 | 9.0 | 2250 | 0.6980 | 0.6681 | | 0.0069 | 10.0 | 2500 | 0.7335 | 0.8679 | | 0.0039 | 11.0 | 2750 | 0.7354 | 0.6238 | | 0.0026 | 12.0 | 3000 | 0.7458 | 0.6315 | | 0.0021 | 13.0 | 3250 | 0.7599 | 0.6715 | | 0.0018 | 14.0 | 3500 | 0.7682 | 0.7103 | | 0.0015 | 15.0 | 3750 | 0.7750 | 0.7081 | | 0.0013 | 16.0 | 4000 | 0.7846 | 0.7125 | | 0.0012 | 17.0 | 4250 | 0.7897 | 0.7114 | | 0.001 | 18.0 | 4500 | 0.7962 | 0.9345 | | 0.0009 | 19.0 | 4750 | 0.8001 | 0.7170 | | 0.0009 | 20.0 | 5000 | 0.8074 | 0.8335 | | 0.0008 | 21.0 | 5250 | 0.8107 | 0.8424 | | 0.0007 | 22.0 | 5500 | 0.8152 | 0.8402 | | 0.0007 | 23.0 | 5750 | 0.8181 | 0.8446 | | 0.0007 | 24.0 | 6000 | 0.8187 | 0.8479 | | 0.0007 | 25.0 | 6250 | 0.8198 | 0.8502 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

GrazittiInteractive/llama-2-13b

GrazittiInteractive

2023-08-10T10:49:14Z

8

1

transformers

[ "transformers", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-2", "en", "dataset:meta-llama/Llama-2-13b", "license:other", "autotrain_compatible", "region:us" ]

text-generation

2023-08-01T07:39:26Z

--- inference: false language: - en pipeline_tag: text-generation datasets: - meta-llama/Llama-2-13b tags: - facebook - meta - pytorch - llama - llama-2 model_type: llama license: other --- # Meta's Llama 2 13B GGML A 4 Bit GGML format quantized version of base mode Llama-2-13b taken from https://huggingface.co/meta-llama/Llama-2-13b, reduced from 24.2 GB to 7.37GB These files are GGML format model files for [Meta's Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend. * [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server. ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | ggml-model-q4_0.bin | q4_0 | 4 | 6.85 GB| 9.118 GB | Original quant method, 4-bit. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` we used langchain with llama-cpp-python, adjust for your tastes and needs: How to use this Llama-2-13b model from Python code and langchain First, make sure you have langchain and llama-cpp installed: ``` pip install llama-cpp-python ``` ``` # Make sure the model path is correct for your system! llm = LlamaCpp( model_path="/Users/rlm/Desktop/Code/llama/llama-2-13b-ggml/ggml-model-q4_0.bin", input={"temperature": 0.75, "max_length": 2000, "top_p": 1}, callback_manager=callback_manager, verbose=True, ) ``` # Original model card: Meta's Llama 2 13B --- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO2eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO2 emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

albagon/q-Taxi-v3

albagon

2023-08-10T10:48:16Z

0

null

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

reinforcement-learning

2023-08-10T10:47:24Z

--- 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: 7.52 +/- 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="albagon/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"]) ```

MrD05/otherhalf-pt

MrD05

2023-08-10T10:47:56Z

7

0

transformers

[ "transformers", "pytorch", "gptj", "text-generation", "text generation", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-08-10T10:20:35Z

--- license: creativeml-openrail-m language: - en thumbnail: null tags: - text generation ---

morell23/anyamelfisa

morell23

2023-08-10T10:47:00Z

0

null

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

null

2023-08-10T10:46:32Z

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

Ian-14/model_test

Ian-14

2023-08-10T10:46:45Z

156

0

transformers

[ "transformers", "pytorch", "chatglm", "text-generation", "custom_code", "zh", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-generation

2023-08-10T01:03:03Z

--- pipeline_tag: text-generation license: apache-2.0 language: - zh widget: - text: "你好啊，O(∩_∩)O哈哈~" example_title: "Sentiment analysis" - text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ..." example_title: "向量化" - text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book ..." example_title: "Logic puzzles" - text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night ..." example_title: "Reading comprehension" --- ### How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True) model = AutoModel.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True).half().cuda() model = model.eval() text = "你好" response, history = model.chat(tokenizer, text, history=[]) response ```

jordyvl/dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd

jordyvl

2023-08-10T10:45:34Z

164

0

transformers

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

image-classification

2023-07-31T13:04:43Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd results: [] ---  # dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5198 - Accuracy: 0.833 - Brier Loss: 0.2560 - Nll: 1.1465 - F1 Micro: 0.833 - F1 Macro: 0.8328 - Ece: 0.0719 - Aurc: 0.0425 ## 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: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - 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: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 125 | 1.0816 | 0.622 | 0.5119 | 2.5026 | 0.622 | 0.6194 | 0.0740 | 0.1657 | | No log | 2.0 | 250 | 0.8028 | 0.715 | 0.3936 | 2.1454 | 0.715 | 0.7158 | 0.0651 | 0.1017 | | No log | 3.0 | 375 | 0.7104 | 0.7505 | 0.3455 | 2.0393 | 0.7505 | 0.7464 | 0.0456 | 0.0765 | | 0.9841 | 4.0 | 500 | 0.6747 | 0.7682 | 0.3267 | 1.9784 | 0.7682 | 0.7703 | 0.0455 | 0.0682 | | 0.9841 | 5.0 | 625 | 0.6619 | 0.7782 | 0.3169 | 1.9299 | 0.7782 | 0.7752 | 0.0391 | 0.0649 | | 0.9841 | 6.0 | 750 | 0.6416 | 0.7897 | 0.3058 | 1.8240 | 0.7897 | 0.7923 | 0.0483 | 0.0683 | | 0.9841 | 7.0 | 875 | 0.6481 | 0.786 | 0.3016 | 1.8855 | 0.786 | 0.7855 | 0.0501 | 0.0640 | | 0.259 | 8.0 | 1000 | 0.6273 | 0.7963 | 0.2970 | 1.7135 | 0.7963 | 0.7970 | 0.0454 | 0.0633 | | 0.259 | 9.0 | 1125 | 0.6484 | 0.7927 | 0.3044 | 1.7079 | 0.7927 | 0.7911 | 0.0601 | 0.0647 | | 0.259 | 10.0 | 1250 | 0.6504 | 0.7925 | 0.3046 | 1.8241 | 0.7925 | 0.7931 | 0.0577 | 0.0674 | | 0.259 | 11.0 | 1375 | 0.6137 | 0.7975 | 0.2914 | 1.6742 | 0.7975 | 0.7996 | 0.0567 | 0.0675 | | 0.133 | 12.0 | 1500 | 0.6092 | 0.7993 | 0.2928 | 1.6077 | 0.7993 | 0.8023 | 0.0600 | 0.0654 | | 0.133 | 13.0 | 1625 | 0.5905 | 0.805 | 0.2842 | 1.5790 | 0.805 | 0.8074 | 0.0589 | 0.0623 | | 0.133 | 14.0 | 1750 | 0.5794 | 0.8077 | 0.2797 | 1.4947 | 0.8077 | 0.8090 | 0.0533 | 0.0579 | | 0.133 | 15.0 | 1875 | 0.5683 | 0.8075 | 0.2777 | 1.4518 | 0.8075 | 0.8076 | 0.0594 | 0.0565 | | 0.1032 | 16.0 | 2000 | 0.5762 | 0.8125 | 0.2794 | 1.3998 | 0.8125 | 0.8146 | 0.0633 | 0.0551 | | 0.1032 | 17.0 | 2125 | 0.5529 | 0.8115 | 0.2748 | 1.3595 | 0.8115 | 0.8126 | 0.0638 | 0.0519 | | 0.1032 | 18.0 | 2250 | 0.5669 | 0.8133 | 0.2759 | 1.3803 | 0.8133 | 0.8143 | 0.0603 | 0.0547 | | 0.1032 | 19.0 | 2375 | 0.5549 | 0.8177 | 0.2716 | 1.3258 | 0.8178 | 0.8186 | 0.0625 | 0.0527 | | 0.0832 | 20.0 | 2500 | 0.5576 | 0.8147 | 0.2737 | 1.3814 | 0.8148 | 0.8183 | 0.0627 | 0.0513 | | 0.0832 | 21.0 | 2625 | 0.5336 | 0.8247 | 0.2609 | 1.2941 | 0.8247 | 0.8243 | 0.0626 | 0.0476 | | 0.0832 | 22.0 | 2750 | 0.5276 | 0.8257 | 0.2595 | 1.2491 | 0.8257 | 0.8262 | 0.0633 | 0.0455 | | 0.0832 | 23.0 | 2875 | 0.5313 | 0.8193 | 0.2603 | 1.2685 | 0.8193 | 0.8198 | 0.0618 | 0.0466 | | 0.0715 | 24.0 | 3000 | 0.5208 | 0.826 | 0.2575 | 1.2280 | 0.826 | 0.8266 | 0.0644 | 0.0468 | | 0.0715 | 25.0 | 3125 | 0.5205 | 0.8233 | 0.2591 | 1.2235 | 0.8233 | 0.8235 | 0.0615 | 0.0459 | | 0.0715 | 26.0 | 3250 | 0.5067 | 0.8293 | 0.2536 | 1.2028 | 0.8293 | 0.8298 | 0.0630 | 0.0433 | | 0.0715 | 27.0 | 3375 | 0.5207 | 0.8245 | 0.2591 | 1.2148 | 0.8245 | 0.8256 | 0.0692 | 0.0449 | | 0.0647 | 28.0 | 3500 | 0.5197 | 0.824 | 0.2596 | 1.1765 | 0.824 | 0.8242 | 0.0690 | 0.0469 | | 0.0647 | 29.0 | 3625 | 0.5086 | 0.8315 | 0.2531 | 1.1762 | 0.8315 | 0.8319 | 0.0704 | 0.0428 | | 0.0647 | 30.0 | 3750 | 0.5025 | 0.8313 | 0.2509 | 1.1560 | 0.8313 | 0.8314 | 0.0687 | 0.0439 | | 0.0647 | 31.0 | 3875 | 0.5073 | 0.832 | 0.2527 | 1.1743 | 0.832 | 0.8323 | 0.0662 | 0.0426 | | 0.0618 | 32.0 | 4000 | 0.5068 | 0.8303 | 0.2526 | 1.1644 | 0.8303 | 0.8304 | 0.0679 | 0.0422 | | 0.0618 | 33.0 | 4125 | 0.5086 | 0.8325 | 0.2526 | 1.1658 | 0.8325 | 0.8326 | 0.0671 | 0.0415 | | 0.0618 | 34.0 | 4250 | 0.5114 | 0.833 | 0.2540 | 1.1694 | 0.833 | 0.8326 | 0.0649 | 0.0440 | | 0.0618 | 35.0 | 4375 | 0.5104 | 0.8305 | 0.2541 | 1.1399 | 0.8305 | 0.8309 | 0.0666 | 0.0426 | | 0.0601 | 36.0 | 4500 | 0.5122 | 0.8307 | 0.2547 | 1.1755 | 0.8308 | 0.8309 | 0.0689 | 0.0435 | | 0.0601 | 37.0 | 4625 | 0.5122 | 0.8323 | 0.2543 | 1.1448 | 0.8323 | 0.8326 | 0.0698 | 0.0429 | | 0.0601 | 38.0 | 4750 | 0.5144 | 0.8307 | 0.2554 | 1.1444 | 0.8308 | 0.8308 | 0.0699 | 0.0414 | | 0.0601 | 39.0 | 4875 | 0.5155 | 0.8307 | 0.2553 | 1.1524 | 0.8308 | 0.8308 | 0.0722 | 0.0430 | | 0.0593 | 40.0 | 5000 | 0.5132 | 0.8315 | 0.2543 | 1.1554 | 0.8315 | 0.8318 | 0.0721 | 0.0423 | | 0.0593 | 41.0 | 5125 | 0.5153 | 0.8335 | 0.2551 | 1.1557 | 0.8335 | 0.8332 | 0.0700 | 0.0423 | | 0.0593 | 42.0 | 5250 | 0.5141 | 0.8313 | 0.2545 | 1.1530 | 0.8313 | 0.8314 | 0.0728 | 0.0419 | | 0.0593 | 43.0 | 5375 | 0.5159 | 0.8313 | 0.2551 | 1.1434 | 0.8313 | 0.8312 | 0.0756 | 0.0425 | | 0.0587 | 44.0 | 5500 | 0.5164 | 0.833 | 0.2548 | 1.1469 | 0.833 | 0.8329 | 0.0688 | 0.0428 | | 0.0587 | 45.0 | 5625 | 0.5170 | 0.8325 | 0.2553 | 1.1486 | 0.8325 | 0.8324 | 0.0723 | 0.0426 | | 0.0587 | 46.0 | 5750 | 0.5188 | 0.8325 | 0.2559 | 1.1478 | 0.8325 | 0.8324 | 0.0731 | 0.0423 | | 0.0587 | 47.0 | 5875 | 0.5188 | 0.8325 | 0.2557 | 1.1515 | 0.8325 | 0.8323 | 0.0702 | 0.0424 | | 0.0583 | 48.0 | 6000 | 0.5195 | 0.8327 | 0.2559 | 1.1477 | 0.8327 | 0.8325 | 0.0702 | 0.0427 | | 0.0583 | 49.0 | 6125 | 0.5194 | 0.8325 | 0.2559 | 1.1464 | 0.8325 | 0.8324 | 0.0713 | 0.0426 | | 0.0583 | 50.0 | 6250 | 0.5198 | 0.833 | 0.2560 | 1.1465 | 0.833 | 0.8328 | 0.0719 | 0.0425 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

Seappa/xlm-roberta-base-finetuned-panx-all

Seappa

2023-08-10T10:40:11Z

103

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

2023-08-10T10:26:46Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] ---  # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1764 - F1: 0.8572 ## 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: 24 - eval_batch_size: 24 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.297 | 1.0 | 835 | 0.1950 | 0.8093 | | 0.1555 | 2.0 | 1670 | 0.1687 | 0.8455 | | 0.1 | 3.0 | 2505 | 0.1764 | 0.8572 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

morell23/ghblistloff

morell23

2023-08-10T10:38:02Z

0

null

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

null

2023-08-10T10:35:45Z

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

sd-concepts-library/animal-toy

sd-concepts-library

2023-08-10T10:33:22Z

0

2

null

[ "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:mit", "region:us" ]

null

2023-08-10T10:10:16Z

--- license: mit base_model: stabilityai/stable-diffusion-2 --- ### animal-toy on Stable Diffusion This is the `<animal-toy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<animal-toy> 0](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/2.jpg) ![<animal-toy> 1](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/0.jpeg) ![<animal-toy> 2](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/3.jpg) ![<animal-toy> 3](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/4.jpg) ![<animal-toy> 4](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/2.jpeg) ![<animal-toy> 5](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/3.jpeg) ![<animal-toy> 6](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/1.jpg) ![<animal-toy> 7](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/1.jpeg) ![<animal-toy> 8](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/4.jpeg) ![<animal-toy> 9](https://huggingface.co/sd-concepts-library/animal-toy/resolve/main/concept_images/0.jpg)

DrishtiSharma/wav2vec2-base-finetuned-gtzan-bs-16

DrishtiSharma

2023-08-10T10:30:05Z

159

0

transformers

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

audio-classification

2023-08-08T02:35:30Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan-bs-16 results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.88 ---  # wav2vec2-base-finetuned-gtzan-bs-16 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5497 - Accuracy: 0.88 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0557 | 1.0 | 57 | 1.9783 | 0.34 | | 1.6173 | 2.0 | 114 | 1.6407 | 0.55 | | 1.3884 | 3.0 | 171 | 1.2228 | 0.65 | | 1.1082 | 4.0 | 228 | 1.0989 | 0.7 | | 0.9112 | 5.0 | 285 | 0.8724 | 0.8 | | 0.7985 | 6.0 | 342 | 0.8715 | 0.76 | | 0.5456 | 7.0 | 399 | 0.6832 | 0.82 | | 0.4842 | 8.0 | 456 | 0.6566 | 0.85 | | 0.3419 | 9.0 | 513 | 0.6485 | 0.84 | | 0.5821 | 10.0 | 570 | 0.5636 | 0.85 | | 0.2112 | 11.0 | 627 | 0.4572 | 0.89 | | 0.2005 | 12.0 | 684 | 0.5405 | 0.87 | | 0.1314 | 13.0 | 741 | 0.4695 | 0.9 | | 0.0866 | 14.0 | 798 | 0.5545 | 0.88 | | 0.0594 | 15.0 | 855 | 0.5497 | 0.88 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3

aura-tfn/q-FrozenLake-v1-4x4-noSlippery

aura-tfn

2023-08-10T10:27:11Z

0

null

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

reinforcement-learning

2023-08-10T10:27:09Z

--- 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="aura-tfn/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"]) ```

Sunny98/LunarLander-v2

Sunny98

2023-08-10T10:15:37Z

0

stable-baselines3

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

reinforcement-learning

2023-08-10T10:15:17Z

--- 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: 260.70 +/- 25.46 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 ... ```

RyangRyang/distilbert-base-uncased-finetuned-emotion

RyangRyang

2023-08-10T10:10:13Z

105

0

transformers

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

text-classification

2023-08-10T08:29:26Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: F1 type: f1 value: 0.9192696693027332 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2198 - Accuacy: 0.9195 - F1: 0.9193 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuacy | F1 | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | 0.8401 | 1.0 | 250 | 0.3257 | 0.906 | 0.9036 | | 0.2584 | 2.0 | 500 | 0.2198 | 0.9195 | 0.9193 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

pknayak/whisper-small-dv

pknayak

2023-08-10T10:06:47Z

74

0

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-09T14:31:45Z

--- language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - pkn results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: 'Common Voice 13 - pkn ' type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.290677052543728 ---  # Whisper Small Dv - pkn This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 - pkn dataset. It achieves the following results on the evaluation set: - Loss: 0.1689 - Wer Ortho: 62.8317 - Wer: 13.2907 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.1252 | 1.63 | 500 | 0.1689 | 62.8317 | 13.2907 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

rishabguha/lora-trained-xl-colab_agsts_cat

rishabguha

2023-08-10T10:01:01Z

11

1

diffusers

[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]

text-to-image

2023-08-09T19:43:38Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of agsts cat tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - rishabguha/lora-trained-xl-colab_agsts_cat These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of agsts cat using [DreamBooth](https://dreambooth.github.io/). 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.

yezune/xlm-roberta-base-finetuned-panx-en

yezune

2023-08-10T09:55:26Z

106

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-10T09:54:05Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.en split: validation args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6877426511369938 ---  # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3900 - F1: 0.6877 ## 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: 24 - eval_batch_size: 24 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1495 | 1.0 | 50 | 0.5817 | 0.4923 | | 0.5096 | 2.0 | 100 | 0.4302 | 0.6313 | | 0.3706 | 3.0 | 150 | 0.3900 | 0.6877 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3

iliyaML/t5-small-billsum

iliyaML

2023-08-10T09:52:25Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "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

2023-08-10T09:42:37Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: t5-small-billsum 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: 0.1528 ---  # t5-small-billsum 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: 2.5246 - Rouge1: 0.1528 - Rouge2: 0.0586 - Rougel: 0.1291 - Rougelsum: 0.1292 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8551 | 0.1284 | 0.0348 | 0.1081 | 0.1085 | 19.0 | | No log | 2.0 | 124 | 2.6404 | 0.1373 | 0.0453 | 0.1147 | 0.1147 | 19.0 | | No log | 3.0 | 186 | 2.5665 | 0.1423 | 0.0494 | 0.1195 | 0.1192 | 19.0 | | No log | 4.0 | 248 | 2.5342 | 0.149 | 0.055 | 0.1259 | 0.1257 | 19.0 | | No log | 5.0 | 310 | 2.5246 | 0.1528 | 0.0586 | 0.1291 | 0.1292 | 19.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

yezune/xlm-roberta-base-finetuned-panx-fr

yezune

2023-08-10T09:52:25Z

105

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-10T09:50:25Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.fr split: validation args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8341708542713568 ---  # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2671 - F1: 0.8342 ## 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: 24 - eval_batch_size: 24 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5836 | 1.0 | 191 | 0.3316 | 0.7831 | | 0.26 | 2.0 | 382 | 0.2738 | 0.8256 | | 0.1681 | 3.0 | 573 | 0.2671 | 0.8342 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3

chunwoolee0/t5_small_billsum

chunwoolee0

2023-08-10T09:52:24Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "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

2023-08-10T09:43:49Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: t5_small_billsum 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: 0.1508 ---  # t5_small_billsum 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: 2.3947 - Rouge1: 0.1508 - Rouge2: 0.0616 - Rougel: 0.1266 - Rougelsum: 0.1266 - 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 | 2.5233 | 0.1285 | 0.0432 | 0.1093 | 0.109 | 19.0 | | No log | 2.0 | 124 | 2.4402 | 0.1379 | 0.0519 | 0.1165 | 0.1161 | 19.0 | | No log | 3.0 | 186 | 2.4054 | 0.1477 | 0.0592 | 0.1242 | 0.1242 | 19.0 | | No log | 4.0 | 248 | 2.3947 | 0.1508 | 0.0616 | 0.1266 | 0.1266 | 19.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

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

stoyky

2023-08-10T09:32:21Z

0

null

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

reinforcement-learning

2023-08-10T09:32:18Z

--- 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="stoyky/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"]) ```

trieudemo11/llama_7b_attributes_prompt_alpaca_2_stable_tested

trieudemo11

2023-08-10T09:31:48Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:44:36Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0

WinSenX/sd-class-butterflies-32

WinSenX

2023-08-10T09:27:17Z

30

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-08-10T09:26:59Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('WinSenX/sd-class-butterflies-32') image = pipeline().images[0] image ```

tmeskuti/distilbase-trained-sts-uncased

tmeskuti

2023-08-10T09:23:28Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-08-10T09:18:44Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 144, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

MRNH/proximal-policy-optimization-LunarLander-v2

MRNH

2023-08-10T09:15:05Z

0

stable-baselines3

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

reinforcement-learning

2023-08-10T08:27:37Z

--- 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: 275.81 +/- 26.46 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 ... ```

KeKu/llama2-french

KeKu

2023-08-10T09:06:12Z

0

peft

[ "peft", "region:us" ]

null

2023-08-10T09:06:06Z

--- library_name: peft --- ## 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

Dytorch/textual_inversion_cat

Dytorch

2023-08-10T08:50:46Z

3

0

diffusers

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

text-to-image

2023-08-09T02:43:24Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - Dytorch/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.

rainishere/llama2-qlora-finetunined-french-test-rainishere

rainishere

2023-08-10T08:45:03Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-10T08:44:54Z

--- library_name: peft --- ## 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

NickTheSickDick/David-Draiman-V1-RVC

NickTheSickDick

2023-08-10T08:44:36Z

0

null

[ "license:openrail", "region:us" ]

null

2023-08-06T18:30:29Z

--- license: openrail --- The first version of my David Draiman model. Trained in RVC on a custom dataset, 220 Epochs, mangio-crepe

TigerResearch/tigerbot-7b-sft-v1-4bit

TigerResearch

2023-08-10T08:43:46Z

7

6

transformers

[ "transformers", "bloom", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-06-01T03:38:20Z

--- license: apache-2.0 --- <div style="width: 100%;"> <img src="https://github.com/TigerResearch/TigerBot/blob/main/image/logo_core.png" alt="TigerBot" style="width: 20%; display: block; margin: auto;"> </div> A cutting-edge foundation for your very own LLM. 🌐 <a href="https://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a> This is a 4-bit GPTQ version of the [Tigerbot 7B sft](https://huggingface.co/TigerResearch/tigerbot-7b-sft). It was quantized to 4bit using: https://github.com/TigerResearch/TigerBot/tree/main/gptq ## How to download and use this model in github: https://github.com/TigerResearch/TigerBot Here are commands to clone the TigerBot and install. ``` conda create --name tigerbot python=3.8 conda activate tigerbot conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia git clone https://github.com/TigerResearch/TigerBot cd TigerBot pip install -r requirements.txt ``` Inference with command line interface ``` cd TigerBot/gptq CUDA_VISIBLE_DEVICES=0 python tigerbot_infer.py TigerResearch/tigerbot-7b-sft-4bit-128g --wbits 4 --groupsize 128 --load TigerResearch/tigerbot-7b-sft-4bit-128g/tigerbot-7b-4bit-128g.pt ```

iliyaML/falcon-7b-openassistant-guanaco

iliyaML

2023-08-10T08:42:24Z

0

null

[ "tensorboard", "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "region:us" ]

null

2023-08-10T05:08:03Z

--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: falcon-7b-openassistant-guanaco results: [] ---  # falcon-7b-openassistant-guanaco This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

smangrul/peft-lora-starcoderbase3b-personal-copilot-A100-40GB-colab

smangrul

2023-08-10T08:35:19Z

15

0

peft

[ "peft", "generated_from_trainer", "base_model:bigcode/starcoderbase-3b", "base_model:adapter:bigcode/starcoderbase-3b", "license:bigcode-openrail-m", "region:us" ]

null

2023-08-09T20:17:40Z

--- license: bigcode-openrail-m base_model: bigcode/starcoderbase-3b tags: - generated_from_trainer model-index: - name: peft-lora-starcoderbase3b-personal-copilot-A100-40GB-colab results: [] library_name: peft ---  # peft-lora-starcoderbase3b-personal-copilot-A100-40GB-colab This model is a fine-tuned version of [bigcode/starcoderbase-3b](https://huggingface.co/bigcode/starcoderbase-3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5038 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## 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 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 30 - training_steps: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8168 | 0.05 | 100 | 0.7807 | | 0.7961 | 0.1 | 200 | 0.7197 | | 0.7837 | 0.15 | 300 | 0.6603 | | 0.7053 | 0.2 | 400 | 0.6371 | | 0.6132 | 0.25 | 500 | 0.6282 | | 0.6584 | 0.3 | 600 | 0.6107 | | 0.621 | 0.35 | 700 | 0.5934 | | 0.6961 | 0.4 | 800 | 0.5877 | | 0.592 | 0.45 | 900 | 0.5833 | | 0.6967 | 0.5 | 1000 | 0.5746 | | 0.6382 | 0.55 | 1100 | 0.5563 | | 0.6815 | 0.6 | 1200 | 0.5436 | | 0.5483 | 0.65 | 1300 | 0.5439 | | 0.7172 | 0.7 | 1400 | 0.5401 | | 0.5479 | 0.75 | 1500 | 0.5390 | | 0.9422 | 0.8 | 1600 | 0.5357 | | 0.5503 | 0.85 | 1700 | 0.5303 | | 0.5928 | 0.9 | 1800 | 0.5322 | | 0.5513 | 0.95 | 1900 | 0.5176 | | 0.6314 | 1.0 | 2000 | 0.5038 | ### Framework versions - PEFT 0.5.0.dev0 - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Hemanth-thunder/stable_diffusion_lora

Hemanth-thunder

2023-08-10T08:21:14Z

3

1

diffusers

[ "diffusers", "tensorboard", "text-to-image", "autotrain", "base_model:SG161222/Realistic_Vision_V1.4", "base_model:finetune:SG161222/Realistic_Vision_V1.4", "region:us" ]

text-to-image

2023-08-06T05:52:45Z

--- base_model: SG161222/Realistic_Vision_V1.4 instance_prompt: hmat tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Test enoder was trained.

MochaPixel/Lia

MochaPixel

2023-08-10T08:19:50Z

0

null

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

null

2023-06-18T11:55:18Z

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

ThuyNT03/distilbert-base-uncased-multil-cls-legal

ThuyNT03

2023-08-10T08:05:47Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "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

2023-08-10T00:09:04Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-multil-cls-legal results: [] ---  # distilbert-base-uncased-multil-cls-legal 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: 0.5448 - Accuracy: 0.9022 - F1: 0.9015 ## 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: 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 2.67 | 1.0 | 396 | 1.9327 | 0.5209 | 0.4806 | | 1.5362 | 2.0 | 792 | 1.0998 | 0.7061 | 0.6869 | | 0.8991 | 3.0 | 1188 | 0.7546 | 0.8013 | 0.7975 | | 0.5899 | 4.0 | 1584 | 0.6136 | 0.8403 | 0.8392 | | 0.4082 | 5.0 | 1980 | 0.5527 | 0.8601 | 0.8589 | | 0.2874 | 6.0 | 2376 | 0.5200 | 0.8736 | 0.8731 | | 0.2136 | 7.0 | 2772 | 0.4991 | 0.8831 | 0.8815 | | 0.1564 | 8.0 | 3168 | 0.4946 | 0.8853 | 0.8843 | | 0.1123 | 9.0 | 3564 | 0.4814 | 0.8928 | 0.8920 | | 0.0866 | 10.0 | 3960 | 0.4959 | 0.8912 | 0.8908 | | 0.0685 | 11.0 | 4356 | 0.5060 | 0.8928 | 0.8923 | | 0.0508 | 12.0 | 4752 | 0.5114 | 0.8997 | 0.8989 | | 0.037 | 13.0 | 5148 | 0.5199 | 0.8978 | 0.8971 | | 0.0316 | 14.0 | 5544 | 0.5236 | 0.9003 | 0.8993 | | 0.0243 | 15.0 | 5940 | 0.5253 | 0.9022 | 0.9015 | | 0.021 | 16.0 | 6336 | 0.5385 | 0.9025 | 0.9019 | | 0.0177 | 17.0 | 6732 | 0.5396 | 0.9038 | 0.9032 | | 0.014 | 18.0 | 7128 | 0.5449 | 0.9025 | 0.9018 | | 0.014 | 19.0 | 7524 | 0.5467 | 0.9010 | 0.9002 | | 0.0103 | 20.0 | 7920 | 0.5448 | 0.9022 | 0.9015 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

jubanbhura/lora-trained-xl-colab

jubanbhura

2023-08-10T08:02:11Z

2

1

diffusers

[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]

text-to-image

2023-08-10T06:14:27Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: digital badge designs tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - jubanbhura/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on digital badge designs using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

Geotrend/distilbert-base-en-es-zh-cased

Geotrend

2023-08-10T08:02:08Z

142

0

transformers

[ "transformers", "pytorch", "safetensors", "distilbert", "fill-mask", "multilingual", "en", "es", "zh", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

--- language: - multilingual - en - es - zh datasets: wikipedia license: apache-2.0 --- # distilbert-base-en-es-zh-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-es-zh-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-es-zh-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.

perion/ai-avatar

perion

2023-08-10T07:55:27Z

11

5

diffusers

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

text-to-image

2023-02-22T16:05:50Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- Test prompt: Portrait of perion man as thomas shelby in peaky blinders, highly detailed digital painting, artstation, concept art, smooth, sharp focus, illustration Sample images: ![banner-large.jpeg](https://cdn-uploads.huggingface.co/production/uploads/63ba89ded90985e7acd775df/k-WNzB5XMlVvHj9afE1Y6.jpeg)

FYP19/t5-small-finetuned-sql3

FYP19

2023-08-10T07:43:28Z

16

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-02-24T15:24:43Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-sql3 results: [] ---  # t5-small-finetuned-sql3 This model is a fine-tuned version of [FYP19/t5-small-finetuned-wikisql](https://huggingface.co/FYP19/t5-small-finetuned-wikisql) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0703 - Rouge2 Precision: 0.6901 - Rouge2 Recall: 0.4476 - Rouge2 Fmeasure: 0.5124 ## 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: 10 - eval_batch_size: 10 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.0456 | 1.0 | 916 | 0.0626 | 0.5941 | 0.3852 | 0.4383 | | 0.0277 | 2.0 | 1832 | 0.0629 | 0.6361 | 0.4106 | 0.469 | | 0.0191 | 3.0 | 2748 | 0.0654 | 0.6731 | 0.4436 | 0.5048 | | 0.0142 | 4.0 | 3664 | 0.0678 | 0.6767 | 0.4365 | 0.5001 | | 0.01 | 5.0 | 4580 | 0.0703 | 0.6901 | 0.4476 | 0.5124 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

jakezou/rl_course_vizdoom_health_gathering_supreme

jakezou

2023-08-10T07:41:19Z

0

sample-factory

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

reinforcement-learning

2023-08-10T07:41:13Z

--- 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: 10.63 +/- 5.23 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 jakezou/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.ipykernel_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.ipykernel_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.

HeejaeShin/sd-class-butterflies-32

HeejaeShin

2023-08-10T07:40:49Z

30

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-08-10T07:40:37Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('HeejaeShin/sd-class-butterflies-32') image = pipeline().images[0] image ```

newronai/llama-2-7b-Chat-QLoRA-Trial1

newronai

2023-08-10T07:32:04Z

0

peft

[ "peft", "region:us" ]

null

2023-08-10T07:31:16Z

--- library_name: peft --- ## 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

rossevine/wav2vec2_indonesia_6

rossevine

2023-08-10T07:27:07Z

109

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-10T05:34:45Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2_Indonesia_6 results: [] ---  # wav2vec2_Indonesia_6 This model is a fine-tuned version of [facebook/wav2vec2-base-100h](https://huggingface.co/facebook/wav2vec2-base-100h) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.7559 - Wer: 1.0232 ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1807 | 3.23 | 400 | 1.3655 | 1.0052 | | 0.5608 | 6.45 | 800 | 1.3604 | 1.0312 | | 0.3302 | 9.68 | 1200 | 1.3724 | 1.0355 | | 0.2405 | 12.9 | 1600 | 1.4350 | 1.0142 | | 0.1883 | 16.13 | 2000 | 1.5079 | 1.0213 | | 0.1535 | 19.35 | 2400 | 1.5038 | 1.0251 | | 0.1307 | 22.58 | 2800 | 1.7026 | 1.0189 | | 0.1104 | 25.81 | 3200 | 1.7072 | 1.0090 | | 0.0921 | 29.03 | 3600 | 1.7559 | 1.0232 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

kuangyebinbaba/sd

kuangyebinbaba

2023-08-10T07:21:30Z

0

null

[ "region:us" ]

null

2023-08-10T07:20:57Z

https://50f27352367144b604.gradio.live

yyyy1992/my_awesome_wnut_model

yyyy1992

2023-08-10T06:58:22Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-10T06:51:33Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: my_awesome_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.5096660808435852 - name: Recall type: recall value: 0.26876737720111216 - name: F1 type: f1 value: 0.35194174757281554 - name: Accuracy type: accuracy value: 0.9392501389423282 ---  # my_awesome_wnut_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.0772 - Precision: 0.5097 - Recall: 0.2688 - F1: 0.3519 - Accuracy: 0.9393 ## 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 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.0816 | 0.4192 | 0.1779 | 0.2498 | 0.9351 | | No log | 2.0 | 426 | 0.0772 | 0.5097 | 0.2688 | 0.3519 | 0.9393 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.11.0 - Tokenizers 0.13.3

weiren119/traditional_chinese_qlora_llama2_13b_adapter

weiren119

2023-08-10T06:57:43Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-10T06:56:58Z

--- library_name: peft --- ## 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 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 - PEFT 0.4.0

reinhardfr/bloomz-560m_PROMPT_TUNING_CAUSAL_LM

reinhardfr

2023-08-10T06:53:25Z

0

peft

[ "peft", "region:us" ]

null

2023-08-10T05:52:22Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0

leahsuperb/Reinforce-CartPole-v1

leahsuperb

2023-08-10T06:53:10Z

0

null

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

reinforcement-learning

2023-08-10T06:53:01Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 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

cuixing/textual_inversion_object_style_vangogh08101212-newstyle

cuixing

2023-08-10T06:51:27Z

4

0

diffusers

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

text-to-image

2023-08-10T04:12:51Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - cuixing/textual_inversion_object_style_vangogh08101212-newstyle These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.

jakezou/a2c-PandaReachDense-v3

jakezou

2023-08-10T06:38:05Z

1

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-10T06:31:17Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.23 +/- 0.13 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```

kasperchen/q-Taxi-v3

kasperchen

2023-08-10T06:36:12Z

0

null

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

reinforcement-learning

2023-08-10T06:36:10Z

--- 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: 7.48 +/- 2.79 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="kasperchen/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"]) ```

dminhk/dog-example-sdxl-lora

dminhk

2023-08-10T06:35:42Z

5

1

diffusers

[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]

text-to-image

2023-08-10T05:43:30Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - dminhk/dog-example-sdxl-lora These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. 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. LoRA for the text encoder was enabled: False. Special VAE used for training: None. Data set: https://huggingface.co/datasets/diffusers/dog-example Example images: ![sks dog sample 1](./sks_dog_example_1.png) ![sks dog sample 2](./sks_dog_example_2.png) ![sks dog sample 3](./sks_dog_example_3.png) ![sks dog sample 4](./sks_dog_example_4.png)

Roy61/textual_inversion_H3D

Roy61

2023-08-10T06:35:31Z

11

0

diffusers

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

text-to-image

2023-08-08T08:31:19Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - Roy61/textual_inversion_H3D These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. 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)

openerotica/mpt-7b-8k-GPTQ

openerotica

2023-08-10T06:30:59Z

5

0

transformers

[ "transformers", "pytorch", "mpt", "text-generation", "Composer", "MosaicML", "llm-foundry", "StreamingDatasets", "custom_code", "dataset:mc4", "dataset:c4", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:bigcode/the-stack", "dataset:allenai/s2orc", "arxiv:2108.12409", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2010.04245", "arxiv:1909.08053", "arxiv:2302.06675", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-08-10T06:08:21Z

--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - mc4 - c4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack - allenai/s2orc inference: false --- # MPT-7B-8k MPT-7B-8k is a decoder-style transformer pretrained starting from MPT-7B, but updating the sequence length to 8k and training for an additional 500B tokens, resulting in a total of 1.5T tokens of text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-7B-8k is part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-7B-8k is * **Licensed for the possibility of commercial use.** * **Trained on a large amount of data** (1.5T tokens like [XGen](https://huggingface.co/Salesforce/xgen-7b-8k-base) vs. 1T for [LLaMA](https://arxiv.org/abs/2302.13971), 1T for [MPT-7B](https://www.mosaicml.com/blog/mpt-7b), 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * **Prepared to handle long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409). With ALiBi, the model can extrapolate beyond the 8k training sequence length to up to 10k, and with a few million tokens it can be finetuned to extrapolate much further. * **Capable of fast training and inference** via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) * **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-7B-8k: The following models are finetuned on MPT-7B-8k: * [MPT-7B-8k-Instruct](https://huggingface.co/mosaicml/mpt-7b-8k-instruct): a model for long-form instruction following (especially summarization and question-answering). Built by finetuning MPT-7B-8k on several carefully curated datasets. * License: _CC-BY-SA-3.0_ * [MPT-7B-8k-Chat](https://huggingface.co/mosaicml/mpt-7b-8k-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-7B-8k on approximately 1.5B tokens of chat data. * License: _CC-By-NC-SA-4.0_ ## Model Date July 18, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: MPT-7B-8k](https://www.mosaicml.com/blog/long-context-mpt-7b-8k) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b-8k', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-7b-8k' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-7b-8k' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 10000 # (input + output) tokens can now be up to 10000 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the MPT-7B-8k tokenizer which is identical to the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-7b-8k') ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline with torch.autocast('cuda', dtype=torch.bfloat16): inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda') outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # or using the HF pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases | Hyperparameter | Value | |----------------|-------| |n_parameters | 6.7B | |n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 50432 | | sequence length | 2048 | ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for ___T tokens. First it was trained for 1T tokens (with batch size 1760 and sequence length 2048) on the following data mix: #### Data Mix for Original 1T Tokens Used to Train MPT-7B | Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs | |-------------|----------------------------|------------|----------------------------|--------| | mC4 3.1.0 - English | 417.99 B | 0.33 | 330 B | 0.14 | | C4 - English - SemDedup 80% | 100.42 B | 0.299 | 299 B | 2.98 | | RedPajama - CommonCrawl | 878.45 B | 0.1 | 100 B | 0.11 | | The Stack - Selected Languages | 463.78 B | 0.1 | 100 B | 0.22 | | RedPajama - Wikipedia - En | 4.87 B | 0.04 | 40 B | 8.21 | | The Stack - Markdown | 107.07 B | 0.035 | 35 B | 0.33 | | S2ORC | 48.85 B | 0.033 | 33 B | 0.68 | | RedPajama - Books | 26.02 B | 0.03 | 30B | 1.15 | | RedPajama - arXiv | 28.10 B | 0.019 | 19 B | 0.68 | | RedPajama - StackExchange | 20.54 B | 0.014 | 14 B |0.68 | #### Data Mix for Additional 500B Tokens Used to Further Train MPT-7B-8k We took 80B tokens from document samples that were longer than 4096 tokens, and 120B tokens with varying document sample lengths that matched the "baseline" length distribution for a total of 200B tokens in a single dataset. We then trained MPT-7B for 500B tokens with a maximum sequence length of 8192, resulting in MPT-7B-8k. Since we trained for 500B tokens using 200B tokens, nearly every subset was trained on for exactly 2.5 epochs. | Sequence Length Distribution | Number of Tokens in Source (Billion) | Proportion | Effective Number of Tokens (Billion) | Epochs | |---|---|---|---|---| | mC4 3.1.0 - English (200+ words) - Baseline | 33.60 | 16.80% | 84.00 | 2.50 | | mC4 3.1.0 - English (200+ words) - ≥4096 tokens | 23.04 | 11.52% | 57.60 | 2.50 | | c4 - English - SemDedup 80% - Baseline | 30.12 | 15.06% | 75.30 | 2.50 | | c4 - English - SemDedup 80% - ≥4096 tokens | 0.92 | 0.46% | 2.30 | 2.50 | | RedPajama - CommonCrawl - Baseline | 8.52 | 4.26% | 21.30 | 2.50 | | RedPajama - CommonCrawl - ≥4096 tokens | 12.80 | 6.40% | 32.00 | 2.50 | | The Stack - Selected Languages - Baseline | 30.00 | 15.00% | 75.00 | 2.50 | | The Stack - Selected Languages - ≥4096 tokens | 10.00 | 5.00% | 25.00 | 2.50 | | RedPajama - Wikipedia - Baseline | 3.60 | 1.80% | 9.00 | 2.50 | | RedPajama - Wikipedia - ≥4096 tokens | 1.04 | 0.52% | 2.60 | 2.50 | | The Stack - Markdown - Baseline | 4.50 | 2.25% | 11.25 | 2.50 | | The Stack - Markdown - ≥4096 tokens | 8.00 | 4.00% | 20.00 | 2.50 | | Semantic Scholar ORC - Baseline | 3.30 | 1.65% | 8.25 | 2.50 | | Semantic Scholar ORC - ≥4096 tokens | 8.00 | 4.00% | 20.00 | 2.50 | | RedPajama - Books - Baseline | 3.00 | 1.50% | 7.50 | 2.50 | | RedPajama - Books - ≥4096 tokens | 8.00 | 4.00% | 20.00 | 2.50 | | RedPajama - arXiv - Baseline | 1.92 | 0.96% | 4.80 | 2.50 | | RedPajama - arXiv - ≥4096 tokens | 5.40 | 2.70% | 13.50 | 2.50 | | RedPajama - StackExchange - Baseline | 1.44 | 0.72% | 3.60 | 2.50 | | RedPajama - StackExchange - ≥4096 tokens | 1.52 | 1.40% | 7.00 | 4.60 | | N Training Tokens | 200 | 100.00% | | 2.5 epochs * 200B = 500B tokens | Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points. ### Training Configuration This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B-8k is **not** intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-7B-8k can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B-8k was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://www.mosaicml.com/get-started?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b-8k). ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, ly Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-03-28}, % change this date urldate = {2023-03-28} % change this date } ```

deepvk/bert-base-uncased

deepvk

2023-08-10T06:23:07Z

756

1

transformers

[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "ru", "en", "license:apache-2.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2023-02-07T14:51:11Z

--- license: apache-2.0 language: - ru - en library_name: transformers pipeline_tag: feature-extraction --- # BERT-base  Pretrained bidirectional encoder for russian language. The model was trained using standard MLM objective on large text corpora including open social data. See `Training Details` section for more information. ⚠️ This model contains only the encoder part without any pretrained head. - **Developed by:** [deepvk](https://vk.com/deepvk) - **Model type:** BERT - **Languages:** Mostly russian and small fraction of other languages - **License:** Apache 2.0 ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deepvk/bert-base-uncased") model = AutoModel.from_pretrained("deepvk/bert-base-uncased") text = "Привет, мир!" inputs = tokenizer(text, return_tensors='pt') predictions = model(**inputs) ``` ## Training Details The model was trained using the NVIDIA source code. See the [pretraining documentation](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/README.md#training-process) for details. ### Training Data 250 GB of filtered texts in total. A mix of the following data: Wikipedia, Books and Social corpus. ### Architecture details | Argument | Value | |-------------------------|----------------| |Encoder layers | 12 | |Encoder attention heads | 12 | |Encoder embed dim | 768 | |Encoder ffn embed dim | 3,072 | |Activation function | GeLU | |Attention dropout | 0.1 | |Dropout | 0.1 | |Max positions | 512 | |Vocab size | 36000 | |Tokenizer type | BertTokenizer | ## Evaluation We evaluated the model on [Russian Super Glue](https://russiansuperglue.com/) dev set. The best result in each task is marked in bold. All models have the same size except the distilled version of DeBERTa. | Model | RCB | PARus | MuSeRC | TERRa | RUSSE | RWSD | DaNetQA | Score | |------------------------------------------------------------------------|-----------|--------|---------|-------|---------|---------|---------|-----------| | [vk-deberta-distill](https://huggingface.co/deepvk/deberta-v1-distill) | 0.433 | 0.56 | 0.625 | 0.59 | 0.943 | 0.569 | 0.726 | 0.635 | | [vk-roberta-base](https://huggingface.co/deepvk/roberta-base) | 0.46 | 0.56 | 0.679 | 0.769 | 0.960 | 0.569 | 0.658 | 0.665 | | [vk-deberta-base](https://huggingface.co/deepvk/deberta-v1-base) | 0.450 |**0.61**|**0.722**| 0.704 | 0.948 | 0.578 |**0.76** |**0.682** | | [vk-bert-base](https://huggingface.co/deepvk/bert-base-uncased) | 0.467 | 0.57 | 0.587 | 0.704 | 0.953 |**0.583**| 0.737 | 0.657 | | [sber-bert-base](https://huggingface.co/ai-forever/ruBert-base) | **0.491** |**0.61**| 0.663 | 0.769 |**0.962**| 0.574 | 0.678 | 0.678 |

nullday/immersiveL-exp

nullday

2023-08-10T06:21:53Z

64

4

transformers

[ "transformers", "pytorch", "safetensors", "bloom", "text-generation", "translation", "gpt-style", "chinese", "english", "zh", "en", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

translation

2023-08-07T07:30:25Z

--- language: - zh - en tags: - translation - gpt-style - chinese - english license: "bigscience-bloom-rail-1.0" --- ## English: ### ImmersiveL Model on Hugging Face This model, available on Hugging Face under `funstoryai/immersiveL-exp`, is a GPT-like model designed specifically for English-Chinese and Chinese-English translations. **Recommended Prompts:** For English to Chinese: ``` 下面是一段英文文本，请将它翻译成中文。 {terms} #英文文本: {input} #中文翻译: ``` For Chinese to English: ``` 下面是一段中文文本，请将它翻译成英文。 {terms} #中文文本: {input} #英文翻译: ``` For the corresponding GitHub project, please visit: [ImmersiveL on GitHub](https://github.com/immersive-translate/ImmersiveL). <https://github.com/immersive-translate/ImmersiveL> --- ## 中文: ### Hugging Face 上的 ImmersiveL 模型此模型在 Hugging Face 的 `funstoryai/immersiveL-exp` 下可用，是专为英汉和汉英翻译设计的类GPT模型。 **推荐提示词:** 英译中: ``` 下面是一段英文文本，请将它翻译成中文。 {terms} #英文文本: {input} #中文翻译: ``` 中译英: ``` 下面是一段中文文本，请将它翻译成英文。 {terms} #中文文本: {input} #英文翻译: ``` 对应的 GitHub 项目地址为: [ImmersiveL on GitHub](https://github.com/immersive-translate/ImmersiveL). <https://github.com/immersive-translate/ImmersiveL>

TheTravellingEngineer/llama2-7b-chat-hf-v3

TheTravellingEngineer

2023-08-10T06:21:28Z

1,536

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-10T06:03:25Z

The base model is meta's Llama-2-7b-chat-hf. It was finetuned using SFT and the Anthropic/hh-rlhf dataset and the model prompt is similar to the original Guanaco model. This repo contains the merged fp16 model. **Legal Disclaimer: This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.** --- - license: - llama2 - datasets: - Anthropic/hh-rlhf - language: - en - reference: https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da ---

AshutoshShrivastava/sdxl-db-lionelmessi

AshutoshShrivastava

2023-08-10T06:17:43Z

2

3

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-08-10T06:17:36Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks leoandresmessi tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Test enoder was not trained.

Bastian1111/dqn-SpaceInvadersNoFrameskip-v4

Bastian1111

2023-08-10T05:52:53Z

1

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-06T04:19:13Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 762.50 +/- 300.08 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Bastian1111 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Bastian1111 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Bastian1111 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

DAMO-NLP-MT/polylm-13b

DAMO-NLP-MT

2023-08-10T05:50:39Z

1,615

53

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "custom_code", "zh", "en", "es", "fr", "pt", "ru", "de", "it", "ar", "ja", "ko", "th", "vi", "id", "nl", "pl", "tr", "he", "arxiv:2307.06018", "arxiv:2104.09864", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-13T13:48:44Z

--- language: - zh - en - es - fr - pt - ru - de - it - ar - ja - ko - th - vi - id - nl - pl - tr - he tags: - text-generation license: apache-2.0 --- # Model Card for PolyLM (a polyglot large language model) ## Table of Contents 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 5. [Next Steps](#next-steps) 6. [Citation](#citation) # Model Details ## Abstract > Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been primarily focused on high-resource languages, such as English, thereby limiting their applicability and research in other languages. Consequently, we present PolyLM, a multilingual LLM trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B. To enhance its multilingual capabilities, we 1) integrate bilingual data into training data; and 2) adopt a curriculum learning strategy that increases the proportion of non-English data from 30% in the first stage to 60% in the final stage during pre-training. Further, we propose a multilingual self-instruct method which automatically generates 132.7K diverse multilingual instructions for model fine-tuning. To assess the model's performance, we collect several existing multilingual tasks, including multilingual understanding, question answering, generation, and translation. Extensive experiments show that PolyLM surpasses other open-source models such as LLaMA and BLOOM on multilingual tasks while maintaining comparable performance in English. ## Model Description - **Model type:** Decoder-only Language model - **Language(s) (NLP):** Chinese, English, Spanish, German, French, Portuguese, Russian, Italian, Arabic, Japanese, Korean, Thai, Vietnamese, Indonesian, Polish, Turkish, Dutch, Hebrew - **License:** Apache 2.0 - **Original Checkpoints:** [Modelscope DAMO PolyLM-13B](https://www.modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary) - **Link to paper:** [here](https://arxiv.org/pdf/2307.06018.pdf) - **Number fotmat:** bf16 - **Total seen tokens:** 640 billion tokens - **Version:** Version 1.0 / 12 July 2023 # Usage Find below some example scripts on how to use the model in `transformers`: <details> <summary> Click to expand </summary> ```python # pip install accelerate from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-MT/polylm-13b", legacy=False, use_fast=False) model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-MT/polylm-13b", device_map="auto", trust_remote_code=True) model.eval() input_doc = f"Beijing is the capital of China.\nTranslate this sentence from English to Chinese." inputs = tokenizer(input_doc, return_tensors="pt") generate_ids = model.generate( inputs.input_ids, attention_mask=inputs.attention_mask, do_sample=False, num_beams=4, max_length=128, early_stopping=True ) decoded = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(f">>> {decoded}") ### results ### Beijing is the capital of China.\nTranslate this sentence from English to Chinese.\\n北京是中华人民共和国的首都。\n ... ``` </details> # Uses ## Direct Use and Downstream Use > The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models See the [research paper](https://arxiv.org/pdf/2307.06018.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2307.06018.pdf): > Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. # Next Steps We are continuously enhancing the capabilities of PolyLM by focusing on the following aspects: 1. Replacement of absolute position embeddings with RoPE, as outlined in the research paper [here](https://arxiv.org/abs/2104.09864). 2. Expansion of window size to more than 10,000. 3. Verification of lightweight techniques to quickly enhance multilingual quality, especially for low-resource languages. # Citation **BibTeX:** ```bibtex @misc{wei2023polylm, title={PolyLM: An Open Source Polyglot Large Language Model}, author={Xiangpeng Wei and Haoran Wei and Huan Lin and Tianhao Li and Pei Zhang and Xingzhang Ren and Mei Li and Yu Wan and Zhiwei Cao and Binbin Xie and Tianxiang Hu and Shangjie Li and Binyuan Hui and Bowen Yu and Dayiheng Liu and Baosong Yang and Fei Huang and Jun Xie}, year={2023}, eprint={2307.06018}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

mchablani/Llama-2-7b-chat-hf-mini-lawyer-chat

mchablani

2023-08-10T05:36:12Z

2

0

peft

[ "peft", "pytorch", "llama", "region:us" ]

null

2023-08-05T03:54:19Z

--- library_name: peft --- ## 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: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0

jonalkw/Reinforce-pixelcopter

jonalkw

2023-08-10T05:25:14Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-08-10T05:25:11Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 9.60 +/- 12.56 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . 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

nanirudh/qa_model_v3

nanirudh

2023-08-10T05:23:57Z

0

peft

[ "peft", "region:us" ]

null

2023-08-10T05:23:48Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0

rodriguezj314/path_to_folder

rodriguezj314

2023-08-10T05:05:40Z

23

0

diffusers

[ "diffusers", "tensorboard", "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

2023-08-10T04:51:00Z

--- 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 - rodriguezj314/path_to_folder 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.

calvpang/distilhubert-finetuned-gtzan-finetuned-gtzan

calvpang

2023-08-10T05:04:13Z

160

0

transformers

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

audio-classification

2023-08-10T03:51:48Z

--- license: apache-2.0 base_model: VinayHajare/distilhubert-finetuned-gtzan tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-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: 0.89 ---  # distilhubert-finetuned-gtzan-finetuned-gtzan This model is a fine-tuned version of [VinayHajare/distilhubert-finetuned-gtzan](https://huggingface.co/VinayHajare/distilhubert-finetuned-gtzan) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5147 - Accuracy: 0.89 ## 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-07 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4687 | 1.0 | 113 | 0.5210 | 0.89 | | 0.5003 | 2.0 | 226 | 0.5186 | 0.89 | | 0.3839 | 3.0 | 339 | 0.5186 | 0.89 | | 0.4082 | 4.0 | 452 | 0.5183 | 0.89 | | 0.4479 | 5.0 | 565 | 0.5183 | 0.89 | | 0.4078 | 6.0 | 678 | 0.5171 | 0.89 | | 0.3089 | 7.0 | 791 | 0.5156 | 0.89 | | 0.3432 | 8.0 | 904 | 0.5152 | 0.89 | | 0.4122 | 9.0 | 1017 | 0.5148 | 0.89 | | 0.4231 | 10.0 | 1130 | 0.5147 | 0.89 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3

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

kasperchen

2023-08-10T05:00:06Z

0

null

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

reinforcement-learning

2023-08-10T04:11:41Z

--- 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="kasperchen/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"]) ```

bookbot/byt5-small-wikipron-eng-latn-us-broad-p2g

bookbot

2023-08-10T04:54:35Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-04-19T04:07:16Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: byt5-small-wikipron-eng-latn-us-broad-p2g results: [] ---  # byt5-small-wikipron-eng-latn-us-broad-p2g This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2595 - Per: 0.4628 - Gen Len: 8.4996 ## 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: 128 - eval_batch_size: 32 - 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: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Per | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 2.4797 | 1.0 | 382 | 0.4371 | 0.6951 | 8.4302 | | 0.4823 | 2.0 | 764 | 0.3543 | 0.5974 | 8.4338 | | 0.3878 | 3.0 | 1146 | 0.3081 | 0.545 | 8.4394 | | 0.3378 | 4.0 | 1528 | 0.2904 | 0.518 | 8.449 | | 0.3061 | 5.0 | 1910 | 0.2736 | 0.5004 | 8.4612 | | 0.2823 | 6.0 | 2292 | 0.2664 | 0.4893 | 8.4734 | | 0.265 | 7.0 | 2674 | 0.2626 | 0.4747 | 8.4721 | | 0.2502 | 8.0 | 3056 | 0.2612 | 0.4697 | 8.4945 | | 0.2388 | 9.0 | 3438 | 0.2592 | 0.4633 | 8.489 | | 0.231 | 10.0 | 3820 | 0.2595 | 0.4628 | 8.4996 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.1.dev0 - Tokenizers 0.13.2

nicbull/DialoGPT-medium-leric

nicbull

2023-08-10T04:37:18Z

150

0

transformers

[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "chat", "conversational", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-10T04:25:26Z

--- language: - en pipeline_tag: conversational tags: - chat ---

jonalkw/Reinforce-CartPole

jonalkw

2023-08-10T04:24:00Z

0

null

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

reinforcement-learning

2023-08-10T04:23:47Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole 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

chunwoolee0/keti-air-ke-t5-base-en-to-ko

chunwoolee0

2023-08-10T04:00:42Z

114

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "base_model:KETI-AIR/ke-t5-base", "base_model:finetune:KETI-AIR/ke-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

translation

2023-08-10T03:27:30Z

--- license: apache-2.0 base_model: KETI-AIR/ke-t5-base tags: - translation - generated_from_trainer datasets: - kde4 model-index: - name: keti-air-ke-t5-base-en-to-ko results: [] ---  # keti-air-ke-t5-base-en-to-ko This model is a fine-tuned version of [KETI-AIR/ke-t5-base](https://huggingface.co/KETI-AIR/ke-t5-base) on the kde4 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: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

debjxt/tlx-bzx-btz

debjxt

2023-08-10T03:45:14Z

1

0

diffusers

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

text-to-image

2023-08-10T03:32:22Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### tlx_bzx_btz Dreambooth model trained by debjxt with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

Pixel390/NEWKUA

Pixel390

2023-08-10T03:27:24Z

5

1

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:Meina/MeinaMix_V10", "base_model:adapter:Meina/MeinaMix_V10", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-08-10T03:07:57Z

--- license: creativeml-openrail-m base_model: Meina/MeinaMix_V10 instance_prompt: a uxz man tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Pixel390/NEWKUA These are LoRA adaption weights for Meina/MeinaMix_V10. The weights were trained on a uxz man using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True.

dangkhoadl/AudioResNet

dangkhoadl

2023-08-10T03:21:17Z

38

0

transformers

[ "transformers", "pytorch", "resnet", "endpoints_compatible", "region:us" ]

null

2023-08-08T01:50:29Z

# Input tensor shape [batch_size, Cin, num_feats, num_frames]

EkoMickA/distilroberta-base-finetuned-wikitext2

EkoMickA

2023-08-10T03:14:19Z

162

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-08-10T03:03:02Z

--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 results: [] ---  # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8251 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.0909 | 1.0 | 2406 | 1.9271 | | 1.9984 | 2.0 | 4812 | 1.8671 | | 1.941 | 3.0 | 7218 | 1.8546 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

EkoMickA/distilgpt2-finetuned-wikitext2

EkoMickA

2023-08-10T02:55:06Z

216

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-10T02:31:24Z

--- license: apache-2.0 base_model: distilgpt2 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: 3.6423 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.7553 | 1.0 | 2334 | 3.6644 | | 3.6393 | 2.0 | 4668 | 3.6485 | | 3.5938 | 3.0 | 7002 | 3.6423 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

rriverar75/vit-model

rriverar75

2023-08-10T02:34:32Z

193

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:beans", "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

2023-08-10T02:08:37Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - beans metrics: - accuracy widget: - src: >- https://huggingface.co/rriverar75/vit-model/resolve/main/healthy.jpeg example_title: Healthy - src: >- https://huggingface.co/rriverar75/vit-model/resolve/main/bean_rust.jpeg example_title: Bean Rust model-index: - name: vit-model results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 1.0 ---  # vit-model 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0189 - 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: 0.0002 - 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1527 | 3.85 | 500 | 0.0189 | 1.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Phaaarus/QLoRA_replica_8rank_QKadap_1epoch

Phaaarus

2023-08-10T02:33:13Z

0

peft

[ "peft", "region:us" ]

null

2023-08-10T02:32:51Z

--- library_name: peft --- ## 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 0.5.0.dev0

wangxso/q-taxi-v3

wangxso

2023-08-10T02:28:47Z

0

null

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

reinforcement-learning

2023-08-10T02:28:44Z

--- 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: 7.52 +/- 2.70 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="wangxso/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"]) ```

TheTravellingEngineer/bloom-1b1-RLHF-v2

TheTravellingEngineer

2023-08-10T01:39:33Z

1,662

0

transformers

[ "transformers", "pytorch", "bloom", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-10T01:30:21Z

The base model is bigscience/bloom-1b1. It was finetuned using RLHF and the dataset and the model prompt is similar to the original model. This repo contains the merged fp16 model. **Legal Disclaimer: This model is bound by the usage restrictions of the original BLOOM model. And comes with no warranty or gurantees of any kind.** --- - license: - bigscience-bloom-rail-1.0 - datasets: - Anthropic/hh-rlhf - language: - en - reference: https://github.com/hiyouga/LLaMA-Efficient-Tuning/tree/main ---

tianpf/llama2-qlora-finetunined-law

tianpf

2023-08-10T01:38:54Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-10T01:38:51Z

--- library_name: peft --- ## 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

Kappa7077/clip-finetuned-lora-organ

Kappa7077

2023-08-10T01:21:03Z

0

peft

[ "peft", "region:us" ]

null

2023-08-10T01:17:51Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0

dana11235/ppo-Huggy

dana11235

2023-08-10T01:16:01Z

1

0

ml-agents

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

reinforcement-learning

2023-08-10T01:15:51Z

--- 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: dana11235/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

mani05/Taxi-v3

mani05

2023-08-10T01:08:38Z

0

null

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

reinforcement-learning

2023-08-10T01:08:34Z

--- 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: 7.56 +/- 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="mani05/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"]) ```

mani05/q-FrozenLake-v1-4x4-noSlippery

mani05

2023-08-10T01:06:06Z

0

null

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

reinforcement-learning

2023-08-10T01:06:02Z

--- 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="mani05/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"]) ```

toastedshibe/lora-trained-xl-colab

toastedshibe

2023-08-10T01:04:48Z

5

1

diffusers

[ "diffusers", "tensorboard", "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:openrail++", "region:us" ]

text-to-image

2023-08-09T23:49:50Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - toastedshibe/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. 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. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

thomaspries/ppo-LunarLander-v2

thomaspries

2023-08-10T00:41:07Z

0

stable-baselines3

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

reinforcement-learning

2023-08-10T00:40:44Z

--- 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: 253.92 +/- 19.01 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 ... ```

Notespeak/ariadnetestn

Notespeak

2023-08-10T00:35:42Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-08-10T00:28:25Z

--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [ai-forever/ruGPT-3.5-13B](https://huggingface.co/ai-forever/ruGPT-3.5-13B) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.31.0 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCES_TOKEN>) ``` - Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="Notespeak/ariadnetestn", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <|prompt|>Why is drinking water so healthy?</s><|answer|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "Notespeak/ariadnetestn", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "Notespeak/ariadnetestn", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Notespeak/ariadnetestn" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<|prompt|>How are you?</s><|answer|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` GPT2LMHeadModel( (transformer): GPT2Model( (wte): Embedding(50272, 5120) (wpe): Embedding(2048, 5120) (drop): Dropout(p=0.1, inplace=False) (h): ModuleList( (0-39): 40 x GPT2Block( (ln_1): LayerNorm((5120,), eps=1e-05, elementwise_affine=True) (attn): GPT2Attention( (c_attn): Conv1D() (c_proj): Conv1D() (attn_dropout): Dropout(p=0.1, inplace=False) (resid_dropout): Dropout(p=0.1, inplace=False) ) (ln_2): LayerNorm((5120,), eps=1e-05, elementwise_affine=True) (mlp): GPT2MLP( (c_fc): Conv1D() (c_proj): Conv1D() (act): NewGELUActivation() (dropout): Dropout(p=0.1, inplace=False) ) ) ) (ln_f): LayerNorm((5120,), eps=1e-05, elementwise_affine=True) ) (lm_head): Linear(in_features=5120, out_features=50272, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.

Precious1/Clinical-Biomedical-Named-Entity-Recognition-Using-Scispacy

Precious1

2023-08-10T00:30:41Z

0

1

null

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

null

2023-08-10T00:25:26Z

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

off-topic-team/llama-2-13b-off-topic

off-topic-team

2023-08-10T00:17:48Z

0

1

null

[ "llama", "eleutherai", "llama-2", "region:us" ]

null

2023-08-09T23:44:20Z

--- tags: - llama - eleutherai - llama-2 --- It had to be done. Sample of data: ``` Hyperion: when it's dysfunctional you just yolo deploy into prod Fleetwood: remove the middle clause - fuck loyalty to ~~ companies~~ most companies Zippy: nono- like- it's more that- it works, but- there needs to be a dude who understands it and can move it to new servers, or make small patches for new functionality, etc-... ⭐ 1 <:sus:869276863414014052> 1 <:guilty:745897670534627369> 2 AI_WAIFU: oh and if you fuck up the wrong system you may get dragged to washington DC to explain what happened ``` also sorry about the reaction formatting having the IDs in it I didn't realize how annoying that would be with Llama's tokenizer :c

Pixel390/GIRLKAY

Pixel390

2023-08-09T23:53:42Z

1

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:Meina/MeinaMix_V10", "base_model:adapter:Meina/MeinaMix_V10", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-08-09T23:09:34Z

--- license: creativeml-openrail-m base_model: Meina/MeinaMix_V10 instance_prompt: a uxz girl tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Pixel390/GIRLKAY These are LoRA adaption weights for Meina/MeinaMix_V10. The weights were trained on a uxz girl using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True.

tingchih/pretrain_doc_concat

tingchih

2023-08-09T23:38:40Z

105

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-31T05:04:43Z

This is a pre-train baseline model for summarization. Input is to concatenate all articles in one cluster. the example.json is the example result. pipeline: input -> sum tokenizer -> perceiver -> sum model -> summary

agustinl/ppo-Pyramids

agustinl

2023-08-09T23:36:42Z

4

0

ml-agents

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

reinforcement-learning

2023-08-09T23:13:56Z

--- 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: agustinl/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

cjohlmacher/ppo-Pyramids

cjohlmacher

2023-08-09T23:30:56Z

1

0

ml-agents

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

reinforcement-learning

2023-08-09T21:01:25Z

--- 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: cjohlmacher/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

good-gaming/distilbert-base-uncased-finetuned-emotion

good-gaming

2023-08-09T23:21:58Z

103

0

transformers

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

text-classification

2023-08-09T22:48:26Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.927 - name: F1 type: f1 value: 0.9272353554627635 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2133 - Accuracy: 0.927 - F1: 0.9272 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8118 | 1.0 | 250 | 0.3108 | 0.905 | 0.9056 | | 0.2485 | 2.0 | 500 | 0.2133 | 0.927 | 0.9272 | ### Framework versions - Transformers 4.31.0 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.13.3