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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-06 06:27:01	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 542 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-06 06:26:44	card stringlengths 11 1.01M
Vasanth/gemma-sql	Vasanth	2024-03-12T06:38:53Z	3	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-7b", "base_model:adapter:google/gemma-7b", "license:other", "region:us" ]	null	2024-03-12T05:20:11Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: google/gemma-7b model-index: - name: gemma-sql 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. --> # gemma-sql This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
jeonsiyun/layoutlmv3-v33-epoch20	jeonsiyun	2024-03-12T06:38:26Z	117	0	transformers	[ "transformers", "safetensors", "layoutlmv3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-12T06:37:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Vasanth/mistral-sql	Vasanth	2024-03-12T06:38:23Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2024-03-12T05:20:05Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: mistral-sql 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. --> # mistral-sql This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
syedzaidi-kiwi/Llama-2-7b-chat-finetune	syedzaidi-kiwi	2024-03-12T06:37:40Z	8	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "llm", "fine-tuned", "Llama 2 7b", "KiwiTech LLC", "question-answering", "en", "dataset:mlabonne/guanaco-llama2-1k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2024-03-11T15:19:17Z	--- license: apache-2.0 language: - en datasets: - mlabonne/guanaco-llama2-1k pipeline_tag: question-answering tags: - llm - fine-tuned - Llama 2 7b - KiwiTech LLC --- # Model Card for syedzaidi-kiwi/Llama-2-7b-chat-finetune This model is a fine-tuned version of Meta's Llama 2 7B variant for enhanced chat functionalities. This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description - Developed by: Syed Asad - Model type: Fine-tuned Llama 2 7B variant - Language(s) (NLP): English - License: Apache-2.0 - Finetuned from model: NousResearch/Llama-2-7b-chat-hf ### Model Sources - Repository: [syedzaidi-kiwi/Llama-2-7b-chat-finetune](https://huggingface.co/syedzaidi-kiwi/Llama-2-7b-chat-finetune) - Paper: [https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/] ## Uses ### Direct Use The model is intended for direct use in applications requiring conversational responses, such as chatbots or virtual assistants. ### Out-of-Scope Use The model is not designed for tasks outside of conversational AI, such as document summarization or translation. ## Bias, Risks, and Limitations Users should be aware of potential biases in the training data and limitations in the model's understanding of nuanced human language. Further evaluation is recommended for specific use cases. ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("syedzaidi-kiwi/Llama-2-7b-chat-finetune") model = AutoModelForCausalLM.from_pretrained("syedzaidi-kiwi/Llama-2-7b-chat-finetune") inputs = tokenizer("Hello, how are you?", return_tensors="pt") response = model.generate(inputs) print(tokenizer.decode(response[0], skip_special_tokens=True)) ``` ## Training Details ### Training Data The model was fine-tuned using the dataset mlabonne/guanaco-llama2-1k. Link: https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k ### Training Procedure #### Training Hyperparameters - Training regime:** The model was fine-tuned using a mix of precision training techniques to balance training speed and model performance effectively. While the exact precision format (e.g., fp32, fp16, bf16) utilized depends on the compute capabilities available, an emphasis was placed on leveraging mixed precision (fp16) training to accelerate the training process on compatible hardware. This approach allowed for faster computation and reduced memory usage without significant loss in training quality. Users are encouraged to adjust the precision settings based on their hardware specifications to optimize performance further. #### Speeds, Sizes, Times To be tested by the KiwiTech Team ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data The model's performance was evaluated on a held-out test set from the mlabonne/guanaco-llama2-1k dataset. This dataset comprises diverse conversational contexts to assess the model's generalization and robustness across various topics. [https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k] #### Factors Evaluation focused on several key factors to ensure the model's versatility and reliability in conversational AI applications: Context understanding: The model's ability to maintain context and coherence over long conversations. Diversity of responses: The variety in the model's responses to similar prompts, indicating its creative and dynamic conversational capabilities. Safety and bias: Monitoring for any unintended biases in responses or generation of inappropriate content. #### Metrics To comprehensively assess the model's performance, the following metrics were utilized: Perplexity (PPL): Lower perplexity scores indicate better understanding and generation of the text. BLEU Score: For measuring the similarity between the model's generated responses and a set of reference responses, indicating the model's accuracy in reproducing human-like answers. F1 Score: Evaluating the balance between precision and recall in the model's responses, useful for assessing conversational relevance. Safety and Bias Evaluation: Custom metrics were developed to quantify the model's performance in generating safe, unbiased content. ### Results To be Evaulated, will be updated in this section. #### Summary The fine-tuned model demonstrates significant improvements in generating coherent, diverse, and contextually appropriate responses across various conversational settings. It represents a step forward in developing conversational AI systems that are both efficient and effective. Continuous evaluation and monitoring are advised to further enhance and maintain the model's performance standards. ## Technical Specifications ### Model Architecture and Objective Transformers ### Compute Infrastructure T4 GPU #### Hardware Fine Tuned on Apple M3 Pro (Silicon Chip) #### Software Google Colab Notebook Used ## Citation OriginalLlama2Citation Title: Llama 2: Open Foundation and Fine-Tuned Chat Models}, Authors: Hugo Touvron∗ Louis Martin† Kevin Stone† Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale Dan Bikel Lukas Blecher Cristian Canton Ferrer Moya Chen Guillem Cucurull David Esiobu Jude Fernandes Jeremy Fu Wenyin Fu Brian Fuller Cynthia Gao Vedanuj Goswami Naman Goyal Anthony Hartshorn Saghar Hosseini Rui Hou Hakan Inan Marcin Kardas Viktor Kerkez Madian Khabsa Isabel Kloumann Artem Korenev Punit Singh Koura Marie-Anne Lachaux Thibaut Lavril Jenya Lee Diana Liskovich Yinghai Lu Yuning Mao Xavier Martinet Todor Mihaylov Pushkar Mishra Igor Molybog Yixin Nie Andrew Poulton Jeremy Reizenstein Rashi Rungta Kalyan Saladi Alan Schelten Ruan Silva Eric Michael Smith Ranjan Subramanian Xiaoqing Ellen Tan Binh Tang Ross Taylor Adina Williams Jian Xiang Kuan Puxin Xu Zheng Yan Iliyan Zarov Yuchen Zhang Angela Fan Melanie Kambadur Sharan Narang Aurelien Rodriguez Robert Stojnic Sergey Edunov Thomas Scialom Journal: Gen AI, Meta Year: 2023 Link to Research Paper: https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/ ## Model Card Authors Syed Asad ## Model Card Contact Syed Asad (syed.asad@kiwitech.com)
omroali/ppo-Huggy	omroali	2024-03-12T06:36:24Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-12T06:36:17Z	--- 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: omroali/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
saltlux/luxia-21.4b-alignment-v1.0	saltlux	2024-03-12T06:34:43Z	10,624	33	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T06:03:15Z	--- license: apache-2.0 language: - en --- # Introduction We introduce luxia-21.4b-alignment-v1.0, an instruction-tuned and alignment model based on luxia-21.4b. Please refer to the evaluation results table for details. # Instruction Fine-tuning Strategy We utilize state-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO) # Data Contamination Test Results Results will be updated soon. # Evaluation Results Results will be updated soon. # Usage Instructions ### How to use ```python # pip install transformers==4.35.2 import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("saltlux/luxia-21.4b-alignment-v0.1") model = AutoModelForCausalLM.from_pretrained( "saltlux/luxia-21.4b-alignment-v0.1", device_map="auto", torch_dtype=torch.float16, ) ``` ### License - [saltlux/luxia-21.4b-alignment-v1.0](https://huggingface.co/saltlux/luxia-21.4b-alignment-v1.0): apache-2.0 ### Contact Us ### Any questions and suggestions are welcomed at the discussion tab.
AlanHou/xlm-roberta-base-finetuned-panx-all	AlanHou	2024-03-12T06:28:31Z	92	0	transformers	[ "transformers", "safetensors", "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	2024-03-12T06:15:22Z	--- 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: 0.1758 - F1: 0.8558 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.299 \| 1.0 \| 835 \| 0.2074 \| 0.8078 \| \| 0.1587 \| 2.0 \| 1670 \| 0.1705 \| 0.8461 \| \| 0.1012 \| 3.0 \| 2505 \| 0.1758 \| 0.8558 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
csshali/q-learning-taxi	csshali	2024-03-12T06:24:48Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-12T06:24:46Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-learning-taxi 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="csshali/q-learning-taxi", 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"]) ```
csshali/q-FrozenLake-v1-4x4-noSlippery	csshali	2024-03-12T06:23:33Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-12T06:23:31Z	--- 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="csshali/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"]) ```
DarshanDeshpande/distilbert_eli5_reward_model	DarshanDeshpande	2024-03-12T06:19:54Z	93	0	transformers	[ "transformers", "safetensors", "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	2024-03-12T06:19:42Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert_eli5_reward_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_eli5_reward_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6934 ## 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: 0.005 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6932 \| 1.28 \| 100 \| 0.6913 \| \| 0.6933 \| 2.56 \| 200 \| 0.6934 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
AlanHou/xlm-roberta-base-finetuned-panx-en	AlanHou	2024-03-12T06:15:15Z	111	0	transformers	[ "transformers", "safetensors", "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	2024-03-12T06:13:56Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en 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-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3905 - F1: 0.6861 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.0479 \| 1.0 \| 50 \| 0.4854 \| 0.5857 \| \| 0.4604 \| 2.0 \| 100 \| 0.3995 \| 0.6605 \| \| 0.3797 \| 3.0 \| 150 \| 0.3905 \| 0.6861 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
nsanghi/dqn-cart-pole-rlzoo	nsanghi	2024-03-12T06:14:49Z	5	0	stable-baselines3	[ "stable-baselines3", "CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-25T18:17:10Z	--- library_name: stable-baselines3 tags: - CartPole-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN 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 --- # DQN Agent playing CartPole-v1 This is a trained model of a DQN agent playing CartPole-v1 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 CartPole-v1 -orga nsanghi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env CartPole-v1 -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 CartPole-v1 -orga nsanghi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env CartPole-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env CartPole-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env CartPole-v1 -f logs/ -orga nsanghi ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('exploration_final_eps', 0.04), ('exploration_fraction', 0.16), ('gamma', 0.99), ('gradient_steps', 128), ('learning_rate', 0.0023), ('learning_starts', 1000), ('n_timesteps', 50000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(net_arch=[256, 256])'), ('target_update_interval', 10), ('train_freq', 256), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
AlanHou/xlm-roberta-base-finetuned-panx-it	AlanHou	2024-03-12T06:13:52Z	91	0	transformers	[ "transformers", "safetensors", "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	2024-03-12T06:12:13Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it 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-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2619 - F1: 0.8321 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7217 \| 1.0 \| 70 \| 0.3193 \| 0.7343 \| \| 0.2736 \| 2.0 \| 140 \| 0.2760 \| 0.8055 \| \| 0.1838 \| 3.0 \| 210 \| 0.2619 \| 0.8321 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
AlanHou/xlm-roberta-base-finetuned-panx-fr	AlanHou	2024-03-12T06:12:07Z	90	0	transformers	[ "transformers", "safetensors", "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	2024-03-12T06:08:51Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-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. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2750 - F1: 0.8495 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5647 \| 1.0 \| 191 \| 0.3242 \| 0.7728 \| \| 0.2671 \| 2.0 \| 382 \| 0.2672 \| 0.8202 \| \| 0.1744 \| 3.0 \| 573 \| 0.2750 \| 0.8495 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
hyeogi/SOLAR-10.7B-v1.4	hyeogi	2024-03-12T06:11:03Z	2,248	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "SOLAR-10.7B", "conversational", "ko", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T05:31:08Z	--- language: - ko pipeline_tag: text-generation tags: - SOLAR-10.7B license: cc-by-nc-4.0 --- # SOLAR-10.7B ### Model Details - Base Model: [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) ### Datasets - sampling and translate [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) - sampling and instrcution format [HAERAE-HUB/KMMLU](https://huggingface.co/datasets/HAERAE-HUB/KMMLU)
Kukedlc/Neural-Krishna-Multiverse-7b	Kukedlc	2024-03-12T06:04:29Z	50	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "Kukedlc/NeuralSirKrishna-7b", "ammarali32/multi_verse_model", "conversational", "base_model:Kukedlc/NeuralSirKrishna-7b", "base_model:merge:Kukedlc/NeuralSirKrishna-7b", "base_model:MTSAIR/multi_verse_model", "base_model:merge:MTSAIR/multi_verse_model", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T21:12:36Z	--- tags: - merge - mergekit - lazymergekit - Kukedlc/NeuralSirKrishna-7b - ammarali32/multi_verse_model base_model: - Kukedlc/NeuralSirKrishna-7b - ammarali32/multi_verse_model license: apache-2.0 --- # Neural-Krishna-Multiverse-7b Neural-Krishna-Multiverse-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b) * [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model) ## 🧩 Configuration ```yaml slices: - sources: - model: Kukedlc/NeuralSirKrishna-7b layer_range: [0, 32] - model: ammarali32/multi_verse_model layer_range: [0, 32] merge_method: slerp base_model: ammarali32/multi_verse_model parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/Neural-Krishna-Multiverse-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
DevanshSinha/test_model_bits	DevanshSinha	2024-03-12T06:02:23Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T05:55:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Red-8/Gujarati_NER-1	Red-8	2024-03-12T06:01:41Z	96	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "PERSON", "LOCATION", "ORGANIZATION", "gu", "dataset:ai4bharat/naamapadam", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-12T05:38:36Z	--- datasets: - ai4bharat/naamapadam language: - gu pipeline_tag: token-classification tags: - PERSON - LOCATION - ORGANIZATION ---
EddyGiusepe/tinyllama-colorist-lora-v0.3	EddyGiusepe	2024-03-12T05:54:33Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "license:apache-2.0", "region:us" ]	null	2024-03-12T05:02:11Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.3 model-index: - name: tinyllama-colorist-lora-v0.3 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. --> <h1 align="center"><font color="red">tinyllama-colorist-lora-v0.3</font></h1> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/628fcb73267c3813eb5ae99d/UMg3Uviv6JcwD4D6Vil7o.png) This model, `tinyllama-colorist-lora-v0.3`, is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.3) on the color dataset. ## <font color="yellow">Study Motivation</font> To study this new TinyLlama model as a replacement for Llama2 for resource-constrained environment. Also, in the future I will perform the Fine-Tuning of this model for Chat and for a specific domain in Portuguese and Spanish 🤗. ## <font color="yellow">Prompt format</font> The model training process is similar to the regular Llama2 model with a chat prompt format like this: ``` <\|im_start\|>user\n{question}<\|im_end\|>\n<\|im_start\|>assistant\n{answer}<\|im_end\|>\n ``` ## <font color="yellow">Instructions for use</font> ``` User Input: Give me a sky blue color. LLM response: #6092ff ``` ## <font color="yellow">Model usage</font> ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer from transformers import pipeline def print_color_space(hex_color): def hex_to_rgb(hex_color): hex_color = hex_color.lstrip('#') return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4)) r, g, b = hex_to_rgb(hex_color) print(f'{hex_color}: \033[48;2;{r};{g};{b}m \033[0m') tokenizer = AutoTokenizer.from_pretrained(model_id_colorist_final) pipe = pipeline( "text-generation", model=model_id_colorist_final, torch_dtype=torch.float16, device_map="auto", ) from time import perf_counter start_time = perf_counter() prompt = formatted_prompt('give me a pure brown color') sequences = pipe( prompt, do_sample=True, temperature=0.1, top_p=0.9, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_new_tokens=12 ) for seq in sequences: print(f"Result: {seq['generated_text']}") output_time = perf_counter() - start_time print(f"Time taken for inference: {round(output_time,2)} seconds") ``` ### 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: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
jadhav21/squirrel	jadhav21	2024-03-12T05:48:59Z	7	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T05:45:13Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### SQUIRREL Dreambooth model trained by jadhav21 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: I21-14 Sample pictures of this concept: ![0](https://huggingface.co/jadhav21/squirrel/resolve/main/sample_images/xzg2.jpg)
Afterglow777/chemical-llama	Afterglow777	2024-03-12T05:48:44Z	69	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T05:37:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
AlanHou/xlm-roberta-base-finetuned-panx-de	AlanHou	2024-03-12T05:48:03Z	121	0	transformers	[ "transformers", "tensorboard", "safetensors", "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	2024-03-12T05:38:51Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de 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-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1363 - F1: 0.8658 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2539 \| 1.0 \| 525 \| 0.1505 \| 0.8246 \| \| 0.1268 \| 2.0 \| 1050 \| 0.1380 \| 0.8503 \| \| 0.0794 \| 3.0 \| 1575 \| 0.1363 \| 0.8658 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
nocudaexe/Neural-Dark-Waifu-GGUF	nocudaexe	2024-03-12T05:38:29Z	26	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-10T14:44:13Z	--- license: apache-2.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65bcd419d5341c3e56189303/dkXNI8qHyZkJQl3ltSaEV.png) Potentially broken at 8k context Use: [nocudaexe/Neural-Dark-Waifu-V0.2](https://huggingface.co/nocudaexe/Neural-Dark-Waifu-V0.2-GGUF) instead, tested to 15872 tokens # Model Card for Model ID <!-- RP Chat model --> This is a merge of 2 models based on mlabonne/AlphaMonarch-7B. With the intent of making it more RP friendly. ### Model Sources Base model: nocudaexe/Neural-Dark-Waifu Primary Models: mlabonne/AlphaMonarch-7B Test157t/Kunocchini-7b-128k-test Additional merges: TeeZee/DarkSapling-7B-v2.0 NeverSleep/Noromaid-7B-0.4-DPO Endevor/InfinityRP-v1-7B KatyTheCutie/SlushySlerp-7B ## Uses NSFW/ERP Chat ### Recommendations Silly Tavern
migueldeguzmandev/GPT2XL_RLLMv11-8	migueldeguzmandev	2024-03-12T05:36:34Z	73	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T04:44:58Z	[More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
migueldeguzmandev/GPT2XL_RLLMv11-6	migueldeguzmandev	2024-03-12T05:35:50Z	76	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T04:26:08Z	--- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
migueldeguzmandev/GPT2XL_RLLMv11-5	migueldeguzmandev	2024-03-12T05:35:29Z	73	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T04:14:32Z	[More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
migueldeguzmandev/GPT2XL_RLLMv11-3	migueldeguzmandev	2024-03-12T05:34:55Z	76	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T03:55:53Z	--- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
migueldeguzmandev/GPT2XL_RLLMv11-2	migueldeguzmandev	2024-03-12T05:34:36Z	73	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T03:39:34Z	--- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
Red-8/Gujarati_NER	Red-8	2024-03-12T05:33:39Z	92	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "gu", "dataset:Red-8/NER_Gujarati_data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-12T05:00:18Z	--- datasets: - Red-8/NER_Gujarati_data language: - gu pipeline_tag: token-classification ---
migueldeguzmandev/GPT2XL_RLLMv11-10	migueldeguzmandev	2024-03-12T05:33:32Z	74	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T09:39:00Z	--- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
kurugai/Kurugai-EEVE-v1.1	kurugai	2024-03-12T05:30:02Z	2,244	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ko", "dataset:kurugai/MedText", "base_model:kurugai/Kurugai-EEVE-v1.0", "base_model:finetune:kurugai/Kurugai-EEVE-v1.0", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T13:48:09Z	--- license: cc-by-nc-sa-4.0 base_model: kurugai/Kurugai-EEVE-v1.0 datasets: - kurugai/MedText language: - ko --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) kurugai/Kurugai-EEVE-v1.1는 kurugai/Kurugai-EEVE-v1.0를 베이스모델로 해서 BI55/MedText 데이터셋으로 학습된 모델입니다. # 학습시간 RTX 8000 GPU 1EA로 1시간 학습하였습니다. # 도움을 주신분 이 모델은 아내의 지원을 받아 제작되었습니다. 아내에게 감사의 말을 전합니다.
OpenGVLab/pvt_v2_b0	OpenGVLab	2024-03-12T05:27:22Z	3,976	2	transformers	[ "transformers", "safetensors", "pvt_v2", "image-classification", "arxiv:2106.13797", "arxiv:2105.15203", "arxiv:2201.07436", "arxiv:2010.04159", "arxiv:2109.03814", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-25T14:14:35Z	--- license: apache-2.0 --- # PVTv2 This is the Hugging Face PyTorch implementation of the [PVTv2](https://arxiv.org/abs/2106.13797) model. ## Model Description The Pyramid Vision Transformer v2 (PVTv2) is a powerful, lightweight hierarchical transformer backbone for vision tasks. PVTv2 infuses convolution operations into its transformer layers to infuse properties of CNNs that enable them to learn image data efficiently. This mix transformer architecture requires no added positional embeddings, and produces multi-scale feature maps which are known to be beneficial for dense and fine-grained prediction tasks. Vision models using PVTv2 for a backbone: 1. [Segformer](https://arxiv.org/abs/2105.15203) for Semantic Segmentation. 2. [GLPN](https://arxiv.org/abs/2201.07436) for Monocular Depth. 3. [Deformable DETR](https://arxiv.org/abs/2010.04159) for 2D Object Detection. 4. [Panoptic Segformer](https://arxiv.org/abs/2109.03814) for Panoptic Segmentation.
Or4cl3-1/Cognitive-Agent-Gemma_7b	Or4cl3-1	2024-03-12T05:26:57Z	3	0	transformers	[ "transformers", "text-gemma-001", "text-generation", "merge", "mergekit", "lazymergekit", "Or4cl3-1/agent_gemma_7b", "cognitivecomputations/dolphin-2.5-mixtral-8x7b", "en", "base_model:Or4cl3-1/Agent_Gemma_7b", "base_model:merge:Or4cl3-1/Agent_Gemma_7b", "base_model:cognitivecomputations/dolphin-2.5-mixtral-8x7b", "base_model:merge:cognitivecomputations/dolphin-2.5-mixtral-8x7b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T02:24:50Z	--- tags: - merge - mergekit - lazymergekit - Or4cl3-1/agent_gemma_7b - cognitivecomputations/dolphin-2.5-mixtral-8x7b base_model: - Or4cl3-1/agent_gemma_7b - cognitivecomputations/dolphin-2.5-mixtral-8x7b license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation --- # Cognitive-Agent-Gemma_7b Cognitive-Agent-Gemma_7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Or4cl3-1/agent_gemma_7b](https://huggingface.co/Or4cl3-1/agent_gemma_7b) * [cognitivecomputations/dolphin-2.5-mixtral-8x7b](https://huggingface.co/cognitivecomputations/dolphin-2.5-mixtral-8x7b) ## 🧩 Configuration ```yaml slices: - sources: - model: Or4cl3-1/agent_gemma_7b layer_range: [0, 32] - model: cognitivecomputations/dolphin-2.5-mixtral-8x7b layer_range: [0, 32] merge_method: slerp base_model: Or4cl3-1/agent_gemma_7b parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Or4cl3-1/Cognitive-Agent-Gemma_7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Animesh001/un-ms-16bt-fine	Animesh001	2024-03-12T05:23:32Z	1	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T05:09:51Z	--- library_name: transformers --- --- library_name: transformers # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
SilasK/llama-7b-medqa_version_5	SilasK	2024-03-12T05:21:27Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "base_model:huggyllama/llama-7b", "base_model:adapter:huggyllama/llama-7b", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2024-03-11T18:30:44Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: huggyllama/llama-7b model-index: - name: llama-7b-medqa_version_5 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. --> # llama-7b-medqa_version_5 This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) 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: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.37.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
automerger/Experiment26Strangemerges_30-7B	automerger	2024-03-12T05:18:19Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:Gille/StrangeMerges_30-7B-slerp", "base_model:finetune:Gille/StrangeMerges_30-7B-slerp", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T05:17:29Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - Gille/StrangeMerges_30-7B-slerp --- # Experiment26Strangemerges_30-7B Experiment26Strangemerges_30-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [Gille/StrangeMerges_30-7B-slerp](https://huggingface.co/Gille/StrangeMerges_30-7B-slerp) ## 🧩 Configuration ```yaml models: - model: yam-peleg/Experiment26-7B # No parameters necessary for base model - model: Gille/StrangeMerges_30-7B-slerp parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: yam-peleg/Experiment26-7B parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Experiment26Strangemerges_30-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
mmnga/tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-gguf	mmnga	2024-03-12T05:18:11Z	414	4	null	[ "gguf", "mixtral", "en", "ja", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-11T12:22:59Z	--- license: apache-2.0 language: - en - ja tags: - mixtral --- # tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-gguf [tokyotech-llmさんが公開しているSwallow-MX-8x7b-NVE-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1)のggufフォーマット変換版です。こちらはベースモデルになります。 ## 他のモデル [mmnga/tokyotech-llm-Swallow-7b-plus-hf-gguf](https://huggingface.co/mmnga/tokyotech-llm-Swallow-7b-plus-hf-gguf) [mmnga/tokyotech-llm-Swallow-MS-7b-v0.1-gguf](https://huggingface.co/mmnga/tokyotech-llm-Swallow-MS-7b-v0.1-gguf) [mmnga/tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-gguf](https://huggingface.co/mmnga/tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-gguf) ## Usage ``` git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp make -j ./main -m 'tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-q4_0.gguf' -p "今晩の夕食をご紹介します。" -n 128 ```
Deepnoid/mergekit_v2	Deepnoid	2024-03-12T05:17:52Z	2,250	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:heavytail/kullm-solar-S", "base_model:finetune:heavytail/kullm-solar-S", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T03:31:08Z	--- base_model: - heavytail/kullm-solar-S library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # mergekit_v2 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged ### Configuration
Pongsathorn/Taxi-v3	Pongsathorn	2024-03-12T05:14:26Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-12T05:14:24Z	--- 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="Pongsathorn/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"]) ```
Pongsathorn/q-FrozenLake-v1-4x4-noSlippery	Pongsathorn	2024-03-12T05:13:24Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-12T05:13:21Z	--- 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="Pongsathorn/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"]) ```
fhai50032/RP-check-TPU	fhai50032	2024-03-12T05:07:39Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T21:36:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tarekxpc/test	tarekxpc	2024-03-12T05:07:38Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-12T05:07:27Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - Developed by: tarekxpc - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
EleutherAI/llemma_7b_muinstruct_camelmath	EleutherAI	2024-03-12T05:05:08Z	54	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "math", "en", "dataset:EleutherAI/muInstruct", "dataset:camel-ai/math", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-27T18:52:14Z	--- license: apache-2.0 datasets: - EleutherAI/muInstruct - camel-ai/math language: - en tags: - math --- `llemma_7b_muinstruct_camelmath` is an instruction-following finetune of [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b), trained on the [μInstruct](https://huggingface.co/datasets/EleutherAI/muInstruct) and [camel-ai/math](https://huggingface.co/datasets/camel-ai/math) datasets. ## Input Formatting Format input queries as follows: ``` input_text = f"Input:{input}\n\nResponse:" ``` Note that due to an error during training, this model's end-of-sequence token ID is `0` instead of the `2` which is standard for Llama-2 based models. Inference APIs should handle this automatically by reading this repo's `config.json`, but be aware of this difference if you are doing token surgery. ## Evals ` llemma_7b_muinstruct_camelmath` compares favorably to other 7B parameter models on the [Hungarian Math Exam](https://huggingface.co/datasets/keirp/hungarian_national_hs_finals_exam/blob/main/README.md). It surpasses the few-shot performance of Llemma 7B whilst being the strongest Llama-2 7B based model. \| Model \| Exam Score \| \| ------------------------------------------------------------------------------ \| ---------- \| \| [Code Llama 7B](https://huggingface.co/codellama/CodeLlama-7b-hf) (few-shot) \| 8\% \| \| [MetaMath 7B](https://huggingface.co/meta-math/MetaMath-7B-V1.0) \| 20\% \| \| [MAmmoTH 7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B) \| 17\% \| \| [MAmmoTH Coder 7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B) \| 11\% \| \| [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) (few-shot) \| 23\% \| \| Llemma_7B_muinstruct_camelmath \| 25\% \| \| - \| - \| \| [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) (few-shot) \| 22\% \| \| [MetaMath Mistral 7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B) \| 29\% \| \| [OpenChat 3.5](https://huggingface.co/openchat/openchat_3.5) \| 37\% \|
Deepnoid/deep-solar-eeve-kullm-v2	Deepnoid	2024-03-12T05:02:03Z	0	0	peft	[ "peft", "safetensors", "llama", "generated_from_trainer", "base_model:yanolja/EEVE-Korean-10.8B-v1.0", "base_model:adapter:yanolja/EEVE-Korean-10.8B-v1.0", "license:apache-2.0", "region:us" ]	null	2024-03-12T03:27:34Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: yanolja/EEVE-Korean-10.8B-v1.0 model-index: - name: data/Models/deep-solar-eeve-kullm-v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # data/Models/deep-solar-eeve-kullm-v2 This model is a fine-tuned version of [yanolja/EEVE-Korean-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0) on the None 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
ameya2408/phi-1_5-finetuned-gsm8k	ameya2408	2024-03-12T05:00:28Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-1_5", "base_model:adapter:microsoft/phi-1_5", "license:mit", "region:us" ]	null	2024-03-07T18:37:55Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-1_5 model-index: - name: phi-1_5-finetuned-gsm8k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-1_5-finetuned-gsm8k This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Or4cl3-1/CogniFusion-XTTS-slerp	Or4cl3-1	2024-03-12T05:00:27Z	4	0	transformers	[ "transformers", "merge", "mergekit", "lazymergekit", "Or4cl3-1/cognitive-agent-xtts-optimized", "Or4cl3-1/multimodal-fusion-optimized", "base_model:Or4cl3-1/cognitive-agent-xtts-optimized", "base_model:merge:Or4cl3-1/cognitive-agent-xtts-optimized", "base_model:Or4cl3-1/multimodal-fusion-optimized", "base_model:merge:Or4cl3-1/multimodal-fusion-optimized", "endpoints_compatible", "region:us" ]	null	2024-03-12T01:06:51Z	--- tags: - merge - mergekit - lazymergekit - Or4cl3-1/cognitive-agent-xtts-optimized - Or4cl3-1/multimodal-fusion-optimized base_model: - Or4cl3-1/cognitive-agent-xtts-optimized - Or4cl3-1/multimodal-fusion-optimized --- # CogniFusion-XTTS-slerp CogniFusion-XTTS-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Or4cl3-1/cognitive-agent-xtts-optimized](https://huggingface.co/Or4cl3-1/cognitive-agent-xtts-optimized) * [Or4cl3-1/multimodal-fusion-optimized](https://huggingface.co/Or4cl3-1/multimodal-fusion-optimized) ## 🧩 Configuration ```yaml slices: - sources: - model: Or4cl3-1/cognitive-agent-xtts-optimized layer_range: [0, 32] # Specify appropriate layer range for cognitive agent - model: Or4cl3-1/multimodal-fusion-optimized layer_range: [0, 32] # Specify appropriate layer range for multimodal fusion merge_method: slerp base_model: Or4cl3-1/cognitive-agent-xtts-optimized parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] # Fine-tune self-attention parameters - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] # Adjust MLP parameters for optimal fusion - value: 0.5 # Set overall fusion parameter value dtype: bfloat16 # Add ethical considerations and any additional optimization parameters here ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Or4cl3-1/CogniFusion-XTTS-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Owhslp/nous_researcher_tuning_2_25	Owhslp	2024-03-12T04:59:42Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T03:56:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Owhslp/nous_researcher_tuning_2_24	Owhslp	2024-03-12T04:57:57Z	90	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T03:49:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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atharv56/bheem	atharv56	2024-03-12T04:57:52Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T04:53:22Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Bheem Dreambooth model trained by atharv56 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:
pappubind/tiger	pappubind	2024-03-12T04:56:29Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T04:52:34Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### Tiger Dreambooth model trained by pappubind following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: I21-08 Sample pictures of this concept: ![0](https://huggingface.co/pappubind/tiger/resolve/main/sample_images/xzg(3).jpg)
Sumail/Alchemist_09_1_2b	Sumail	2024-03-12T04:54:56Z	90	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Sumail/Alchemist_06_2b", "base_model:merge:Sumail/Alchemist_06_2b", "base_model:deepnet/SN6-71G7", "base_model:merge:deepnet/SN6-71G7", "base_model:deepnetguy/gemma-70", "base_model:merge:deepnetguy/gemma-70", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T04:52:23Z	--- base_model: - deepnetguy/gemma-70 - Sumail/Alchemist_06_2b - Aspik101/Haliaeetusalbicilla10 - deepnet/SN6-71G7 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Sumail/Alchemist_06_2b](https://huggingface.co/Sumail/Alchemist_06_2b) as a base. ### Models Merged The following models were included in the merge: * [deepnetguy/gemma-70](https://huggingface.co/deepnetguy/gemma-70) * [Aspik101/Haliaeetusalbicilla10](https://huggingface.co/Aspik101/Haliaeetusalbicilla10) * [deepnet/SN6-71G7](https://huggingface.co/deepnet/SN6-71G7) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Sumail/Alchemist_06_2b # No parameters necessary for base model - model: Aspik101/Haliaeetusalbicilla10 parameters: density: 0.53 weight: 0.23 - model: deepnetguy/gemma-70 parameters: density: 0.53 weight: 0.5 - model: deepnet/SN6-71G7 parameters: density: 0.53 weight: 0.23 merge_method: dare_ties base_model: Sumail/Alchemist_06_2b parameters: int8_mask: true dtype: bfloat16 ```
vinuuuuu/my-car	vinuuuuu	2024-03-12T04:46:37Z	2	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T04:38:27Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### my-car Dreambooth model trained by vinuuuuu following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: i21-21 Sample pictures of this concept: ![0](https://huggingface.co/vinuuuuu/my-car/resolve/main/sample_images/c1.jpeg)
rombodawg/EveryoneLLM-7b-Gemma-Base-GGUF	rombodawg	2024-03-12T04:45:41Z	2	0	null	[ "gguf", "merge", "license:other", "endpoints_compatible", "region:us" ]	null	2024-03-12T03:19:00Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms tags: - merge --- EveryoneLLM-7b-Gemma-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/85jTMkzdKWv4V8aQntMY-.jpeg) EveryoneLLM series of models made by the community, for the community. This is the second version of Everyone-LLM using Gemma-7b, a model that combines the power of the large majority of powerfull fine-tuned LLM's made by the community, to create a vast and knowledgable LLM with various abilities with an extra emphasis on coding capabilities. Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` The models that were used in this merger were as follow: - https://huggingface.co/openchat/openchat-3.5-0106-gemma - https://huggingface.co/TechxGenus/CodeGemma-7b - https://huggingface.co/VAGOsolutions/SauerkrautLM-Gemma-7b - https://huggingface.co/macadeliccc/gemma-orchid-7b-dpo - https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1 - https://huggingface.co/CorticalStack/gemma-7b-ultrachat-sft - https://huggingface.co/google/gemma-7b Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 This model was merges in 2 parts. The order of parts is listed bellow, then a copy and pastable version is bellow that. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/Ctla5otlla8UaTgW9fODc.png) ```yaml models: - model: VAGOsolutions_SauerkrautLM-Gemma-7b parameters: weight: 1 - model: macadeliccc_gemma-orchid-7b-dpo parameters: weight: 1 - model: HuggingFaceH4_zephyr-7b-gemma-v0.1 parameters: weight: 1 - model: CorticalStack_gemma-7b-ultrachat-sft parameters: weight: 1 merge_method: task_arithmetic base_model: gemma-7b-base parameters: normalize: true int8_mask: true dtype: float16 ``` ```yaml models: - model: Gemma-Merge-1-7b parameters: weight: 1 - model: openchat_openchat-3.5-0106-gemma parameters: weight: 1 - model: TechxGenus_CodeGemma-7b parameters: weight: 1 merge_method: task_arithmetic base_model: gemma-7b-base parameters: normalize: true int8_mask: true dtype: float16 ```
EddyGiusepe/tinyllama-colorist-lora-v0.2	EddyGiusepe	2024-03-12T04:44:23Z	3	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "license:apache-2.0", "region:us" ]	null	2024-03-12T04:26:49Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.3 model-index: - name: tinyllama-colorist-lora-v0.2 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. --> # tinyllama-colorist-lora-v0.2 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.3) on the None 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
EleutherAI/Mistral-7B-v0.1-authors-random-standardized	EleutherAI	2024-03-12T04:43:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:43:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-nli-random-standardized	EleutherAI	2024-03-12T04:43:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:42:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-population-random-standardized	EleutherAI	2024-03-12T04:42:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:42:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-hemisphere-random-standardized	EleutherAI	2024-03-12T04:42:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:42:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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David-Xu/llama-2-7b-cira-sft-v0.1-merge-right	David-Xu	2024-03-12T04:41:43Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-08T01:57:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-squaring-random	EleutherAI	2024-03-12T04:41:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:41:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-modularaddition-random	EleutherAI	2024-03-12T04:41:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:41:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-multiplication-random	EleutherAI	2024-03-12T04:41:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:40:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-subtraction-random	EleutherAI	2024-03-12T04:40:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:40:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-nli-random	EleutherAI	2024-03-12T04:40:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:40:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Teju123/my-pet-cat	Teju123	2024-03-12T04:40:06Z	2	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-12T04:35:54Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat Dreambooth model trained by Teju123 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 4MC22EE094- Sample pictures of this concept: ![0](https://huggingface.co/Teju123/my-pet-cat/resolve/main/sample_images/tlg1.jpg) ![1](https://huggingface.co/Teju123/my-pet-cat/resolve/main/sample_images/tlg0.jpg) ![2](https://huggingface.co/Teju123/my-pet-cat/resolve/main/sample_images/tlg2.jpg) ![3](https://huggingface.co/Teju123/my-pet-cat/resolve/main/sample_images/tlg3.jpg) ![4](https://huggingface.co/Teju123/my-pet-cat/resolve/main/sample_images/tlg4.jpg)
EleutherAI/Mistral-7B-v0.1-sentiment-random	EleutherAI	2024-03-12T04:39:59Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:39:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-sciq-random	EleutherAI	2024-03-12T04:39:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:39:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-hemisphere-random	EleutherAI	2024-03-12T04:39:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T04:39:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Kazminka51/Krasota	Kazminka51	2024-03-12T04:36:20Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-03-12T04:33:39Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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saucam/Phind-CodeLlama-34B-v2-Codefuse-CodeLlama-34B-dare-ties	saucam	2024-03-12T04:34:38Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "Phind/Phind-CodeLlama-34B-v2", "codefuse-ai/CodeFuse-CodeLlama-34B", "base_model:Phind/Phind-CodeLlama-34B-v2", "base_model:merge:Phind/Phind-CodeLlama-34B-v2", "base_model:codefuse-ai/CodeFuse-CodeLlama-34B", "base_model:merge:codefuse-ai/CodeFuse-CodeLlama-34B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T04:04:45Z	--- tags: - merge - mergekit - lazymergekit - Phind/Phind-CodeLlama-34B-v2 - codefuse-ai/CodeFuse-CodeLlama-34B base_model: - Phind/Phind-CodeLlama-34B-v2 - codefuse-ai/CodeFuse-CodeLlama-34B --- # Phind-CodeLlama-34B-v2-Codefuse-CodeLlama-34B-dare-ties Phind-CodeLlama-34B-v2-Codefuse-CodeLlama-34B-dare-ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) * [codefuse-ai/CodeFuse-CodeLlama-34B](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B) ## 🧩 Configuration ```yaml models: - model: Phind/Phind-CodeLlama-34B-v2 parameters: density: 0.5 weight: 0.6 # No parameters necessary for base model - model: codefuse-ai/CodeFuse-CodeLlama-34B parameters: density: 0.5 weight: 0.4 merge_method: dare_ties base_model: Phind/Phind-CodeLlama-34B-v2 parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "saucam/Phind-CodeLlama-34B-v2-Codefuse-CodeLlama-34B-dare-ties" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
SyntaxTheRed/poca-SoccerTwos	SyntaxTheRed	2024-03-12T04:17:25Z	34	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-03-12T04:16:07Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos 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: SyntaxTheRed/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
JCX-kcuf/Llama-2-7b-hf-gpt-3.5-80k	JCX-kcuf	2024-03-12T04:16:21Z	49	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T16:34:06Z	--- license: apache-2.0 --- ## Description This model is finetuned on the distillation data from GPT-3.5. The base model is meta-llama/Llama-2-7b-hf ## Usage The model has a query format as in llama-2. ``` <s> [INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {query} [/INST] ```
exala/db_mc_10.3	exala	2024-03-12T04:07:08Z	5,573	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-12T04:06:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nediaz/Enlighten_Instruct_merged	nediaz	2024-03-12T04:01:38Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T21:55:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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22h/open-cabrita3b	22h	2024-03-12T03:58:44Z	326	20	transformers	[ "transformers", "pytorch", "llama", "text-generation", "pt", "en", "arxiv:2308.11878", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-06T18:09:57Z	--- language: - pt - en license: apache-2.0 model-index: - name: open-cabrita3b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 33.79 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=22h/open-cabrita3b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 55.35 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=22h/open-cabrita3b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 25.16 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=22h/open-cabrita3b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 38.5 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=22h/open-cabrita3b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 59.43 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=22h/open-cabrita3b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=22h/open-cabrita3b name: Open LLM Leaderboard --- The Cabrita model is a collection of continued pre-trained and tokenizer-adapted models for the Portuguese language. This artifact is the 3 billion size variant. The weights were initially obtained from the open-llama project (https://github.com/openlm-research/open_llama) in the open_llama_3b option. ``` @misc{larcher2023cabrita, title={Cabrita: closing the gap for foreign languages}, author={Celio Larcher and Marcos Piau and Paulo Finardi and Pedro Gengo and Piero Esposito and Vinicius Caridá}, year={2023}, eprint={2308.11878}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_22h__open-cabrita3b) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|35.54\| \|AI2 Reasoning Challenge (25-Shot)\|33.79\| \|HellaSwag (10-Shot) \|55.35\| \|MMLU (5-Shot) \|25.16\| \|TruthfulQA (0-shot) \|38.50\| \|Winogrande (5-shot) \|59.43\| \|GSM8k (5-shot) \| 0.99\|
nkkbr/codeparrot-ds	nkkbr	2024-03-12T03:54:32Z	92	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T06:01:25Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: codeparrot-ds results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0896 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 768 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.4935 \| 0.23 \| 5000 \| 1.4177 \| \| 1.3089 \| 0.46 \| 10000 \| 1.2413 \| \| 1.2055 \| 0.69 \| 15000 \| 1.1374 \| \| 1.1502 \| 0.92 \| 20000 \| 1.0896 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
exala/db_mc_10.4	exala	2024-03-12T03:52:56Z	92	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-12T03:52:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Pongsathorn/ppo-LunarLander-v2	Pongsathorn	2024-03-12T03:49:09Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-12T03:45:33Z	--- 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: 263.08 +/- 22.67 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 ... ```
Holarissun/gptj6b-aisft-giga-seq-subset100000	Holarissun	2024-03-12T03:49:07Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:EleutherAI/gpt-j-6b", "base_model:adapter:EleutherAI/gpt-j-6b", "license:apache-2.0", "region:us" ]	null	2024-03-12T03:49:02Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: EleutherAI/gpt-j-6b model-index: - name: gptj6b-aisft-giga-seq-subset100000 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. --> # gptj6b-aisft-giga-seq-subset100000 This model is a fine-tuned version of [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) 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: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
OwOOwO/mistral_magic_goat_2	OwOOwO	2024-03-12T03:47:33Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T03:44:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Sumail/Alchemist_09_2b	Sumail	2024-03-12T03:47:22Z	90	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Sumail/Alchemist_06_2b", "base_model:merge:Sumail/Alchemist_06_2b", "base_model:deepnet/SN6-71G7", "base_model:merge:deepnet/SN6-71G7", "base_model:deepnetguy/gemma-70", "base_model:merge:deepnetguy/gemma-70", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T03:44:39Z	--- base_model: - Aspik101/Haliaeetusalbicilla10 - deepnet/SN6-71G7 - Sumail/Alchemist_06_2b - deepnetguy/gemma-70 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Sumail/Alchemist_06_2b](https://huggingface.co/Sumail/Alchemist_06_2b) as a base. ### Models Merged The following models were included in the merge: * [Aspik101/Haliaeetusalbicilla10](https://huggingface.co/Aspik101/Haliaeetusalbicilla10) * [deepnet/SN6-71G7](https://huggingface.co/deepnet/SN6-71G7) * [deepnetguy/gemma-70](https://huggingface.co/deepnetguy/gemma-70) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Sumail/Alchemist_06_2b # No parameters necessary for base model - model: Aspik101/Haliaeetusalbicilla10 parameters: density: 0.53 weight: 0.2 - model: deepnetguy/gemma-70 parameters: density: 0.53 weight: 0.4 - model: deepnet/SN6-71G7 parameters: density: 0.53 weight: 0.2 merge_method: dare_ties base_model: Sumail/Alchemist_06_2b parameters: int8_mask: true dtype: bfloat16 ```
zach-lamberty/mm2024-gpt-M-20240311T220716	zach-lamberty	2024-03-12T03:40:48Z	184	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T02:11:25Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: mm2024-gpt-M-20240311T220716 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. --> # mm2024-gpt-M-20240311T220716 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.15.2
adebayojosephine/ppo-Huggy	adebayojosephine	2024-03-12T03:36:59Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-12T03:16:48Z	--- 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: adebayojosephine/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
essiam/pb	essiam	2024-03-12T03:36:39Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "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	2024-03-12T03:14:10Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of ex68peri86me765nt876al butterfly --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - essiam/pb This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of ex68peri86me765nt876al butterfly using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
OwOOwO/mistral_magic_goat	OwOOwO	2024-03-12T03:35:32Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T03:32:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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danna1121/LDCC_finetuning	danna1121	2024-03-12T03:33:28Z	0	0	peft	[ "peft", "region:us" ]	null	2024-03-06T12:23:08Z	--- 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 0.4.0
rombodawg/EveryoneLLM-7b-Gemma-Base	rombodawg	2024-03-12T03:19:36Z	50	2	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "merge", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T03:53:28Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms tags: - merge --- EveryoneLLM-7b-Gemma-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/85jTMkzdKWv4V8aQntMY-.jpeg) Quantizations: GGUF - https://huggingface.co/rombodawg/EveryoneLLM-7b-Gemma-Base-GGUF EveryoneLLM series of models made by the community, for the community. This is the second version of Everyone-LLM using Gemma-7b, a model that combines the power of the large majority of powerfull fine-tuned LLM's made by the community, to create a vast and knowledgable LLM with various abilities with an extra emphasis on coding capabilities. Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` The models that were used in this merger were as follow: - https://huggingface.co/openchat/openchat-3.5-0106-gemma - https://huggingface.co/TechxGenus/CodeGemma-7b - https://huggingface.co/VAGOsolutions/SauerkrautLM-Gemma-7b - https://huggingface.co/macadeliccc/gemma-orchid-7b-dpo - https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1 - https://huggingface.co/CorticalStack/gemma-7b-ultrachat-sft - https://huggingface.co/google/gemma-7b Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 This model was merges in 2 parts. The order of parts is listed bellow, then a copy and pastable version is bellow that. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/Ctla5otlla8UaTgW9fODc.png) ```yaml models: - model: VAGOsolutions_SauerkrautLM-Gemma-7b parameters: weight: 1 - model: macadeliccc_gemma-orchid-7b-dpo parameters: weight: 1 - model: HuggingFaceH4_zephyr-7b-gemma-v0.1 parameters: weight: 1 - model: CorticalStack_gemma-7b-ultrachat-sft parameters: weight: 1 merge_method: task_arithmetic base_model: gemma-7b-base parameters: normalize: true int8_mask: true dtype: float16 ``` ```yaml models: - model: Gemma-Merge-1-7b parameters: weight: 1 - model: openchat_openchat-3.5-0106-gemma parameters: weight: 1 - model: TechxGenus_CodeGemma-7b parameters: weight: 1 merge_method: task_arithmetic base_model: gemma-7b-base parameters: normalize: true int8_mask: true dtype: float16 ```
blockblockblock/TinyLlama-1.1B-intermediate-step-480k-1T-bpw4	blockblockblock	2024-03-12T03:14:19Z	1	0	transformers	[ "transformers", "llama", "text-generation", "en", "dataset:cerebras/SlimPajama-627B", "dataset:bigcode/starcoderdata", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T02:38:45Z	--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata language: - en --- <div align="center"> # TinyLlama-1.1B </div> https://github.com/jzhang38/TinyLlama The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. <div align="center"> <img src="./TinyLlama_logo.png" width="300"/> </div> We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. #### This Model This is an intermediate checkpoint with 480K steps and 1007B tokens. #### How to use You will need the transformers>=4.31 Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information. ```python from transformers import AutoTokenizer import transformers import torch model = "PY007/TinyLlama-1.1B-intermediate-step-240k-503b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.', do_sample=True, top_k=10, num_return_sequences=1, repetition_penalty=1.5, eos_token_id=tokenizer.eos_token_id, max_length=500, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ```
blockblockblock/TinyLlama-1.1B-intermediate-step-480k-1T-bpw3.5	blockblockblock	2024-03-12T03:08:00Z	1	0	transformers	[ "transformers", "llama", "text-generation", "en", "dataset:cerebras/SlimPajama-627B", "dataset:bigcode/starcoderdata", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T02:32:49Z	--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata language: - en --- <div align="center"> # TinyLlama-1.1B </div> https://github.com/jzhang38/TinyLlama The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. <div align="center"> <img src="./TinyLlama_logo.png" width="300"/> </div> We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. #### This Model This is an intermediate checkpoint with 480K steps and 1007B tokens. #### How to use You will need the transformers>=4.31 Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information. ```python from transformers import AutoTokenizer import transformers import torch model = "PY007/TinyLlama-1.1B-intermediate-step-240k-503b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.', do_sample=True, top_k=10, num_return_sequences=1, repetition_penalty=1.5, eos_token_id=tokenizer.eos_token_id, max_length=500, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ```
hahoney/distilbert-base-uncased-finetuned-emotion	hahoney	2024-03-12T03:00:14Z	92	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "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	2024-01-22T02:41:11Z	--- 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: 0.9415 - name: F1 type: f1 value: 0.9415318687607991 --- <!-- 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: 0.2932 - Accuracy: 0.9415 - F1: 0.9415 ## 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.0207 \| 1.0 \| 250 \| 0.2731 \| 0.943 \| 0.9431 \| \| 0.0166 \| 2.0 \| 500 \| 0.3001 \| 0.934 \| 0.9341 \| \| 0.0108 \| 3.0 \| 750 \| 0.2939 \| 0.941 \| 0.9409 \| \| 0.0068 \| 4.0 \| 1000 \| 0.2932 \| 0.9415 \| 0.9415 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.0
jsfs11/NTIHackTest-TIESLINEAR	jsfs11	2024-03-12T02:49:15Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "FelixChao/WestSeverus-7B-DPO-v2", "CultriX/Wernicke-7B-v9", "base_model:CultriX/Wernicke-7B-v9", "base_model:merge:CultriX/Wernicke-7B-v9", "base_model:PetroGPT/WestSeverus-7B-DPO-v2", "base_model:merge:PetroGPT/WestSeverus-7B-DPO-v2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T02:41:40Z	--- tags: - merge - mergekit - lazymergekit - FelixChao/WestSeverus-7B-DPO-v2 - CultriX/Wernicke-7B-v9 base_model: - FelixChao/WestSeverus-7B-DPO-v2 - CultriX/Wernicke-7B-v9 --- # NTIHackTest-TIESLINEAR NTIHackTest-TIESLINEAR is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2) * [CultriX/Wernicke-7B-v9](https://huggingface.co/CultriX/Wernicke-7B-v9) * NOTE: This is an EXPERIMENTAL merge with near tuned interpolation hacked in from this PR https://github.com/arcee-ai/mergekit/pull/179 ## 🧩 Configuration ```yaml models: - model: FelixChao/WestSeverus-7B-DPO-v2 # No parameters necessary for base model - model: FelixChao/WestSeverus-7B-DPO-v2 parameters: density: [1, 0.7, 0.1] weight: [0, 0.3, 0.7, 1] - model: CultriX/Wernicke-7B-v9 parameters: density: [1, 0.7, 0.3] weight: [0, 0.25, 0.5, 1] merge_method: dare_linear base_model: FelixChao/WestSeverus-7B-DPO-v2 parameters: int8_mask: true normalize: true near_tuned_interpolation: true nti_t: 0.001 sparsify: - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "jsfs11/NTIHackTest-TIESLINEAR" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
OpenGVLab/pvt_v2_b5	OpenGVLab	2024-03-12T02:47:06Z	166	1	transformers	[ "transformers", "safetensors", "pvt_v2", "image-classification", "arxiv:2106.13797", "arxiv:2105.15203", "arxiv:2201.07436", "arxiv:2010.04159", "arxiv:2109.03814", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-25T14:16:24Z	--- license: apache-2.0 --- # PVTv2 This is the Hugging Face PyTorch implementation of the [PVTv2](https://arxiv.org/abs/2106.13797) model. ## Model Description The Pyramid Vision Transformer v2 (PVTv2) is a powerful, lightweight hierarchical transformer backbone for vision tasks. PVTv2 infuses convolution operations into its transformer layers to infuse properties of CNNs that enable them to learn image data efficiently. This mix transformer architecture requires no added positional embeddings, and produces multi-scale feature maps which are known to be beneficial for dense and fine-grained prediction tasks. Vision models using PVTv2 for a backbone: 1. [Segformer](https://arxiv.org/abs/2105.15203) for Semantic Segmentation. 2. [GLPN](https://arxiv.org/abs/2201.07436) for Monocular Depth. 3. [Deformable DETR](https://arxiv.org/abs/2010.04159) for 2D Object Detection. 4. [Panoptic Segformer](https://arxiv.org/abs/2109.03814) for Panoptic Segmentation.
OpenGVLab/pvt_v2_b2_linear	OpenGVLab	2024-03-12T02:46:04Z	127	1	transformers	[ "transformers", "safetensors", "pvt_v2", "image-classification", "arxiv:2106.13797", "arxiv:2105.15203", "arxiv:2201.07436", "arxiv:2010.04159", "arxiv:2109.03814", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-25T14:15:34Z	--- license: apache-2.0 --- # PVTv2 This is the Hugging Face PyTorch implementation of the [PVTv2](https://arxiv.org/abs/2106.13797) model. ## Model Description The Pyramid Vision Transformer v2 (PVTv2) is a powerful, lightweight hierarchical transformer backbone for vision tasks. PVTv2 infuses convolution operations into its transformer layers to infuse properties of CNNs that enable them to learn image data efficiently. This mix transformer architecture requires no added positional embeddings, and produces multi-scale feature maps which are known to be beneficial for dense and fine-grained prediction tasks. Vision models using PVTv2 for a backbone: 1. [Segformer](https://arxiv.org/abs/2105.15203) for Semantic Segmentation. 2. [GLPN](https://arxiv.org/abs/2201.07436) for Monocular Depth. 3. [Deformable DETR](https://arxiv.org/abs/2010.04159) for 2D Object Detection. 4. [Panoptic Segformer](https://arxiv.org/abs/2109.03814) for Panoptic Segmentation.
allistair99/tinybert-6l-768d-squad2-finetuned-SRH-v1	allistair99	2024-03-12T02:45:54Z	99	0	transformers	[ "transformers", "safetensors", "bert", "question-answering", "generated_from_trainer", "dataset:srh_test66", "base_model:deepset/tinybert-6l-768d-squad2", "base_model:finetune:deepset/tinybert-6l-768d-squad2", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2024-03-12T02:37:38Z	--- license: mit base_model: deepset/tinybert-6l-768d-squad2 tags: - generated_from_trainer datasets: - srh_test66 model-index: - name: tinybert-6l-768d-squad2-finetuned-SRH-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tinybert-6l-768d-squad2-finetuned-SRH-v1 This model is a fine-tuned version of [deepset/tinybert-6l-768d-squad2](https://huggingface.co/deepset/tinybert-6l-768d-squad2) on the srh_test66 dataset. It achieves the following results on the evaluation set: - Loss: 1.8492 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1297 \| 1.0 \| 43 \| 1.9241 \| \| 0.919 \| 2.0 \| 86 \| 1.8474 \| \| 1.2643 \| 3.0 \| 129 \| 1.8492 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
OpenGVLab/pvt_v2_b1	OpenGVLab	2024-03-12T02:42:40Z	164	1	transformers	[ "transformers", "safetensors", "pvt_v2", "image-classification", "arxiv:2106.13797", "arxiv:2105.15203", "arxiv:2201.07436", "arxiv:2010.04159", "arxiv:2109.03814", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-25T14:15:12Z	--- license: apache-2.0 --- # PVTv2 This is the Hugging Face PyTorch implementation of the [PVTv2](https://arxiv.org/abs/2106.13797) model. ## Model Description The Pyramid Vision Transformer v2 (PVTv2) is a powerful, lightweight hierarchical transformer backbone for vision tasks. PVTv2 infuses convolution operations into its transformer layers to infuse properties of CNNs that enable them to learn image data efficiently. This mix transformer architecture requires no added positional embeddings, and produces multi-scale feature maps which are known to be beneficial for dense and fine-grained prediction tasks. Vision models using PVTv2 for a backbone: 1. [Segformer](https://arxiv.org/abs/2105.15203) for Semantic Segmentation. 2. [GLPN](https://arxiv.org/abs/2201.07436) for Monocular Depth. 3. [Deformable DETR](https://arxiv.org/abs/2010.04159) for 2D Object Detection. 4. [Panoptic Segformer](https://arxiv.org/abs/2109.03814) for Panoptic Segmentation.
OpenGVLab/pvt_v2_b3	OpenGVLab	2024-03-12T02:42:13Z	134	1	transformers	[ "transformers", "safetensors", "pvt_v2", "image-classification", "arxiv:2106.13797", "arxiv:2105.15203", "arxiv:2201.07436", "arxiv:2010.04159", "arxiv:2109.03814", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-25T14:16:03Z	--- license: apache-2.0 --- # PVTv2 This is the Hugging Face PyTorch implementation of the [PVTv2](https://arxiv.org/abs/2106.13797) model. ## Model Description The Pyramid Vision Transformer v2 (PVTv2) is a powerful, lightweight hierarchical transformer backbone for vision tasks. PVTv2 infuses convolution operations into its transformer layers to infuse properties of CNNs that enable them to learn image data efficiently. This mix transformer architecture requires no added positional embeddings, and produces multi-scale feature maps which are known to be beneficial for dense and fine-grained prediction tasks. Vision models using PVTv2 for a backbone: 1. [Segformer](https://arxiv.org/abs/2105.15203) for Semantic Segmentation. 2. [GLPN](https://arxiv.org/abs/2201.07436) for Monocular Depth. 3. [Deformable DETR](https://arxiv.org/abs/2010.04159) for 2D Object Detection. 4. [Panoptic Segformer](https://arxiv.org/abs/2109.03814) for Panoptic Segmentation.
ufdatastudio/vit-orientation	ufdatastudio	2024-03-12T02:38:10Z	180	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-05T20:47:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-modularaddition-first	EleutherAI	2024-03-12T02:21:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T02:21:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-addition-first	EleutherAI	2024-03-12T02:20:36Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T02:20:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Mistral-7B-v0.1-nli-first	EleutherAI	2024-03-12T02:20:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-12T02:20:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Subsets and Splits

Filtered Qwen2.5 Distill Models

Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.

Filtered Model Cards Count

Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.

Filtered Distill Qwen 7B Models

Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.

Filtered Qwen-7b Model Cards

The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.

Filtered Qwen 7B Model Cards

The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.

Qwen 7B Distilled Models

The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.

Qwen 7B Distilled Model Cards

The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.

Qwen 7B Distilled Models

Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.

Distilled Qwen 7B Models

The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.

Filtered Model Cards with Distill Qwen2.

Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.

Filtered Model Cards with Distill Qwen 7

The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.

Distill Qwen 7B Model Cards

The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.