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maisonmargela/granite-8b-final	maisonmargela	2024-05-18T00:13:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T00:12:20Z	--- 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]
dingjunyu888/dqn-SpaceInvadersNoFrameskip-v4	dingjunyu888	2024-05-18T00:07:27Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T00:07:00Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 254.50 +/- 113.35 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dingjunyu888 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dingjunyu888 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dingjunyu888 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 1000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
alvinwmtan/whisper-dl3-xty	alvinwmtan	2024-05-18T00:05:52Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T23:04:38Z	--- 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]
baseten/Meta-Llama-3-Instruct-tokenizer	baseten	2024-05-17T23:59:01Z	0	0	null	[ "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "conversational", "en", "license:llama3", "region:us" ]	text-generation	2024-05-17T23:57:09Z	--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3 extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Meta Llama 3" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. "Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. 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Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy) #### Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. 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Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit widget: - example_title: Hello messages: - role: user content: Hey my name is Julien! How are you? - example_title: Winter holidays messages: - role: system content: You are a helpful and honest assistant. Please, respond concisely and truthfully. - role: user content: Can you recommend a good destination for Winter holidays? - example_title: Programming assistant messages: - role: system content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully. - role: user content: Write a function that computes the nth fibonacci number. inference: parameters: max_new_tokens: 300 stop: - <\|end_of_text\|> - <\|eot_id\|> --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Meta Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date April 18, 2024. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both. #### Transformers pipeline ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` #### Transformers AutoModelForCausalLM ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate( input_ids, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/" --local-dir Meta-Llama-3-8B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software Training Factors* We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
LEVI-Project/sft-data	LEVI-Project	2024-05-17T23:47:44Z	0	6	null	[ "region:us" ]	null	2024-04-10T06:40:51Z	# Instruction for downloading data from the sft-data repository. First, you would want to log in and access the huggingface data through using ```py from huggingface_hub import login login() ``` Then, you could either download the zip file of the all the sft data folders, which would look like ```py from huggingface_hub import hf_hub_download hf_hub_download(repo_id="LEVI-Project/sft-data", filename="sft-data.zip") ``` Notice that the `sft-data.zip` file above has the following structure: ``` sft-data ├── README.md # This README file. ├── alf # Folder for ALFWORLD. │ ├── alfworld.json # The JSON file for ALFWORLD. │ └── alf_data_folder # Folder for the ALFWORLD environment. │ ├── alf_image_id_0 # Folder 0 for ALFWORLD image data. │ ├── alf_image_id_1 # Folder 1 for ALFWORLD image data. │ ├── alf_image_id_2 # Folder 2 for ALFWORLD image data. │ ├── alf_image_id_3 # Folder 3 for ALFWORLD image data. │ └── alf_image_id_4 # Folder 4 for ALFWORLD image data. ├── blackjack # Folder for blackjack environment in the `gym_cards`. │ ├── blackjack_data_folder # Folder for blackjack image data. │ └── blackjack.json # The JSON file for blackjack. ├── ezpoints # Folder for ezpoints environment in the `gym_cards`. │ ├── ezpoints_data_folder # Folder for ezpoints image data. │ └── ezpoints.json # The JSON file for ezpoints. ├── points24 # Folder for points24 environment in the `gym_cards`. │ ├── points24_data_folder # Folder for points24 image data. │ └── points24.json # The JSON file for points24. └── numberline # Folder for numberline environment in the `gym_cards`. ├── numberline_data_folder # Folder for numberline image data. └── numberline.json # The JSON file for numberline. ``` Also, you could choose to download the files for any environment out of the five ones. For example, you should be using the following code for downloading data from blackjack. ```py from huggingface_hub import hf_hub_download hf_hub_download(repo_id="LEVI-Project/sft-data", filename="blackjack.zip") # zip folder for image data folder hf_hub_download(repo_id="LEVI-Project/sft-data", filename="blackjack.json") # JSON file ``` For ALFWORLD, notice that the zip file for the image data folder is `alf_data_folder.zip`.
impossibleexchange/diptral-1.5b	impossibleexchange	2024-05-17T23:47:32Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T22:48:21Z	--- base_model: - mistralai/Mistral-7B-Instruct-v0.2 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 SLERP merge method. ### Models Merged The following models were included in the merge: * [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.2 slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.2 layer_range: [0, 32] - model: mistralai/Mistral-7B-Instruct-v0.2 layer_range: [0, 32] 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 ```
Jonny00/falcon-rw-1b-biome-definition	Jonny00	2024-05-17T23:44:10Z	1	0	peft	[ "peft", "safetensors", "pcgml", "pcg", "biome", "terrain", "en", "base_model:tiiuae/falcon-rw-1b", "base_model:adapter:tiiuae/falcon-rw-1b", "license:apache-2.0", "region:us" ]	null	2024-05-17T08:47:14Z	--- library_name: peft base_model: tiiuae/falcon-rw-1b license: apache-2.0 language: - en tags: - pcgml - pcg - biome - terrain --- # Model Card for Model ID Part of bachelor thesis focusing on generating 3d terrain from text.<br> Biome Definition is a json formatted string detailing the biomes and their features. <br> Together with SD Heightmap Generation v3.0, 3d terrain can be procedurally generated. <!-- Provide a quick summary of what the model is/does. --> ## Uses Example Inference:<br> <prompt> Alpine mountains and a forest adorned with red flowers. Result:<br> <prompt> Alpine mountains and a forest adorned with red flowers.<br> <biomes> [{'Entities': {'Boulders': [], 'BouldersSpawnrate': 0.0, 'Grass': [{'BaseTint': '#ff0000', 'Id': 'flowers'}], 'GrassSpawnrate': 0.0, 'Trees': [], 'TreesSpawnrate': 0.0}, 'Name': 'Red Flowers', 'SpawnCondition': {'HeightRange': [0.0, 0.1], 'SlopeRange': [0.0, 0.1]}, 'Texturing': {'GroundTexture': 'grass', 'GroundTextureTint': '#7cfc00', 'SlopeTexture': 'grass', 'SlopeTextureTint': '#7cfc00'}}, {'Entities': {'Boulders': [], 'BouldersSpawnrate': 0.0, 'Grass': [], 'GrassSpawnrate': 0.0, 'Trees': [{'BaseTint': '#ffffff', 'Id': 'tree_medium'}], 'TreesSpawnrate': 0.8}, 'Name': 'Mountain Range', 'SpawnCondition': {'HeightRange': [0.0, 0.7], 'SlopeRange': [0.0, 0.5]}, 'Texturing': {'GroundTexture':'stone', 'GroundTextureTint': '#ffffff', 'SlopeTexture':'stone_smooth', 'SlopeTextureTint': '#ffffff'}}] <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Requires: ~8GB VRAM Training Loss: 2% Validation Loss: 4% ## Bias, Risks, and Limitations Model is biased to certain ids like: * textures: grass, dirt, mud, mud_dry, stone, stone_smooth * boulders: boulder_small, boulder_medium, boulder_large * trees: tree_small, tree_medium, tree_large, tree_conifer_small, ... * grass: grass_small, grass_medium, grass_large, grass_dry, flowers Model also has trouble overadjusting tints, leading to oversaturated colorations. Model sometimes continues output. Just end prompt at "}}]". Model sometimes creates incomplete biome definitions or places entities on wrong biome. <!-- This section is meant to convey both technical and sociotechnical limitations. --> ## Model Details Finetuned on custom dataset of size 538 json formatted biome definitions consisting of: * Name * SpawnCondition * HeightRange * SlopeRange * Entities * Trees * Id * BaseTint * Boulders * Id * BaseTint * Grass * Id * BaseTint * TreesSpawnrate * BouldersSpawnrate * GrassSpawnrate * Texturing * GroundTexture * GroundTextureTint * SlopeTexture * SlopeTextureTint ### Model Description <!-- Provide a longer summary of what this model is. --> - Model type: [PEFT Adapter] - Language(s) (NLP): [English] - License: [Apache 2.0] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Paper [optional]: [tba] [More Information Needed] ### Framework versions - PEFT 0.11.0
fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-51550	fine-tuned	2024-05-17T23:42:33Z	10	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Argumentation", "Corpus", "Research", "Quality", "Dataset", "custom_code", "en", "dataset:fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-51550", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-17T23:42:19Z	--- license: apache-2.0 datasets: - fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-51550 - allenai/c4 language: - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Argumentation - Corpus - Research - Quality - Dataset --- This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: academic research data retrieval ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-51550', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
ahmed792002/alzheimers_memory_support_ai	ahmed792002	2024-05-17T23:37:50Z	152	2	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	2024-05-17T13:46:44Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: custom_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. --> # custom_model 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: 1.7420 ## 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: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.4865 \| 1.0 \| 369 \| 1.3129 \| \| 1.2021 \| 2.0 \| 738 \| 1.1809 \| \| 1.0322 \| 3.0 \| 1107 \| 1.1234 \| \| 0.9008 \| 4.0 \| 1476 \| 1.0991 \| \| 0.8134 \| 5.0 \| 1845 \| 1.0827 \| \| 0.7293 \| 6.0 \| 2214 \| 1.0923 \| \| 0.6539 \| 7.0 \| 2583 \| 1.0942 \| \| 0.5962 \| 8.0 \| 2952 \| 1.1175 \| \| 0.546 \| 9.0 \| 3321 \| 1.1365 \| \| 0.4915 \| 10.0 \| 3690 \| 1.1490 \| \| 0.4523 \| 11.0 \| 4059 \| 1.1860 \| \| 0.4204 \| 12.0 \| 4428 \| 1.1977 \| \| 0.3831 \| 13.0 \| 4797 \| 1.2311 \| \| 0.357 \| 14.0 \| 5166 \| 1.2499 \| \| 0.3378 \| 15.0 \| 5535 \| 1.2674 \| \| 0.3203 \| 16.0 \| 5904 \| 1.2902 \| \| 0.2943 \| 17.0 \| 6273 \| 1.3226 \| \| 0.2796 \| 18.0 \| 6642 \| 1.3355 \| \| 0.2679 \| 19.0 \| 7011 \| 1.3618 \| \| 0.2479 \| 20.0 \| 7380 \| 1.3775 \| \| 0.2361 \| 21.0 \| 7749 \| 1.3995 \| \| 0.2274 \| 22.0 \| 8118 \| 1.4151 \| \| 0.2102 \| 23.0 \| 8487 \| 1.4315 \| \| 0.1994 \| 24.0 \| 8856 \| 1.4490 \| \| 0.1943 \| 25.0 \| 9225 \| 1.4714 \| \| 0.1777 \| 26.0 \| 9594 \| 1.4906 \| \| 0.1697 \| 27.0 \| 9963 \| 1.5078 \| \| 0.1602 \| 28.0 \| 10332 \| 1.5293 \| \| 0.1497 \| 29.0 \| 10701 \| 1.5457 \| \| 0.1403 \| 30.0 \| 11070 \| 1.5652 \| \| 0.1315 \| 31.0 \| 11439 \| 1.5814 \| \| 0.124 \| 32.0 \| 11808 \| 1.5987 \| \| 0.1142 \| 33.0 \| 12177 \| 1.6151 \| \| 0.1057 \| 34.0 \| 12546 \| 1.6354 \| \| 0.1002 \| 35.0 \| 12915 \| 1.6508 \| \| 0.093 \| 36.0 \| 13284 \| 1.6641 \| \| 0.0867 \| 37.0 \| 13653 \| 1.6808 \| \| 0.081 \| 38.0 \| 14022 \| 1.6866 \| \| 0.076 \| 39.0 \| 14391 \| 1.7061 \| \| 0.0716 \| 40.0 \| 14760 \| 1.7150 \| \| 0.067 \| 41.0 \| 15129 \| 1.7232 \| \| 0.0638 \| 42.0 \| 15498 \| 1.7322 \| \| 0.0598 \| 43.0 \| 15867 \| 1.7388 \| \| 0.0575 \| 44.0 \| 16236 \| 1.7446 \| \| 0.0539 \| 45.0 \| 16605 \| 1.7524 \| \| 0.0525 \| 46.0 \| 16974 \| 1.7580 \| \| 0.0505 \| 47.0 \| 17343 \| 1.7609 \| \| 0.0479 \| 48.0 \| 17712 \| 1.7612 \| \| 0.0473 \| 49.0 \| 18081 \| 1.7642 \| \| 0.0462 \| 50.0 \| 18450 \| 1.7644 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
felixml/CodeLlama-7b-hf_sql-create-context	felixml	2024-05-17T23:30:04Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	2024-05-17T23:09:55Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: codellama/CodeLlama-7b-hf datasets: - generator model-index: - name: CodeLlama-7b-hf_sql-create-context 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. --> # CodeLlama-7b-hf_sql-create-context This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) 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: 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: 1 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
KomeijiForce/Meta-Llama-3-8B-AutoPersona-Chinese	KomeijiForce	2024-05-17T23:27:57Z	20	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text2text-generation", "zh", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-16T16:01:30Z	--- library_name: transformers license: mit language: - zh pipeline_tag: text2text-generation --- # AutoPersona 这是一个基于[萌娘百科](https://zh.moegirl.org.cn/%E8%90%8C%E5%A8%98)的原始文本从[GPT-4o](https://openai.com/index/hello-gpt-4o/)中蒸馏下来的自动从未预处理的脏数据中总结角色人设的中文[llama-3模型](https://huggingface.co/KomeijiForce/Meta-Llama-3-8B-AutoPersona-Chinese)，使用该模型可以免费地从大量未经预处理的脏文本中迅速地抽取有用的人设信息，可以帮助构建大规模的人设数据。 ## 使用本工具非常容易使用，功能被完全封装在这个[Repo](https://github.com/KomeijiForce/AutoPersona)的```auto_persona(character, passage)```这个function之中，以下是一个使用例： ```python from autopersona import AutoPersona model = AutoPersona("KomeijiForce/Meta-Llama-3-8B-AutoPersona-Chinese") character = "古明地恋" passage = '''{\| style="text-align:left" \| """能力""" \|\| 操纵无意识程度的能力 \|- \| """危险度""" \|\| 不明 \|- \| """人类友好度""" \|\| 完全没有 \|- \| """主要活动场所""" \|\| 不明 \|} 跟姐姐觉一样的妖怪，觉。只不过她是一个把自己的内心关闭了，没办法读出他人的内心的觉。''' print(auto_persona(character, passage)) ``` 你将会看到如下的输出 ``` 古明地恋是一位拥有操纵无意识程度能力的妖怪，跟姐姐觉一样的觉。然而，她的内心已被关闭，无法读出他人的内心。她的危险度不明，人类友好度为完全没有，主要活动场所不明。 ``` 当模型认为```passage```无法被转化为人设信息时，会固定输出“无用信息。” ```python character = "古明地恋" passage = '''== 经历 ==''' print(model.auto_persona(character, passage)) ``` 你将会看到“无用信息。”作为输出 ## 应用你可以在这个萌娘百科的原始文本[milashkaarshif/MoeGirlPedia_wikitext_raw_archive](https://huggingface.co/datasets/milashkaarshif/MoeGirlPedia_wikitext_raw_archive)上使用AutoPersona，但是我在使用load_dataset的时候遇到了问题，所以你可以使用我从该repo中的.gz文件中得到的2024年5月的萌娘百科的原始文本[(KomeijiForce/moe_girl_wiki)](KomeijiForce/moe_girl_wiki)上使用。
jswing/my_awesome_qa_model	jswing	2024-05-17T23:23:59Z	64	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-17T20:16:04Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: jswing/my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # jswing/my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5245 - Validation Loss: 1.7007 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.3392 \| 1.9259 \| 0 \| \| 1.7237 \| 1.7007 \| 1 \| \| 1.5245 \| 1.7007 \| 2 \| ### Framework versions - Transformers 4.40.2 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
KevStrider/LunarLander_by_foot	KevStrider	2024-05-17T23:20:31Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T23:20:25Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -170.54 +/- 84.43 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 100000 'learning_rate': 0.00027 'num_envs': 6 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 5 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'KevStrider/LunarLander_by_foot' 'batch_size': 768 'minibatch_size': 192} ```
yasminsur/distilbert-kazakh-ner	yasminsur	2024-05-17T23:14:13Z	133	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-17T23:13:52Z	--- 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. 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CMU-AIR2/math-phi-1-5-FULL-ArithHard-lr-1-1e-6	CMU-AIR2	2024-05-17T23:13:32Z	6	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T18:12:39Z	--- 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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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. 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apwic/sentiment-lora-r8a0d0.05-0	apwic	2024-05-17T23:13:22Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T22:40:13Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r8a0d0.05-0 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. --> # sentiment-lora-r8a0d0.05-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3260 - Accuracy: 0.8622 - Precision: 0.8319 - Recall: 0.8400 - F1: 0.8357 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5609 \| 1.0 \| 122 \| 0.5086 \| 0.7193 \| 0.6580 \| 0.6514 \| 0.6543 \| \| 0.4986 \| 2.0 \| 244 \| 0.4855 \| 0.7494 \| 0.7127 \| 0.7427 \| 0.7201 \| \| 0.4593 \| 3.0 \| 366 \| 0.4238 \| 0.7694 \| 0.7249 \| 0.7394 \| 0.7309 \| \| 0.3957 \| 4.0 \| 488 \| 0.3916 \| 0.8070 \| 0.7670 \| 0.7735 \| 0.7700 \| \| 0.3658 \| 5.0 \| 610 \| 0.4266 \| 0.7995 \| 0.7641 \| 0.7981 \| 0.7744 \| \| 0.3345 \| 6.0 \| 732 \| 0.3666 \| 0.8371 \| 0.8028 \| 0.8072 \| 0.8049 \| \| 0.3237 \| 7.0 \| 854 \| 0.3714 \| 0.8396 \| 0.8045 \| 0.8265 \| 0.8136 \| \| 0.304 \| 8.0 \| 976 \| 0.3537 \| 0.8421 \| 0.8083 \| 0.8158 \| 0.8119 \| \| 0.3027 \| 9.0 \| 1098 \| 0.3531 \| 0.8446 \| 0.8111 \| 0.8201 \| 0.8153 \| \| 0.2962 \| 10.0 \| 1220 \| 0.3382 \| 0.8521 \| 0.8220 \| 0.8204 \| 0.8212 \| \| 0.2721 \| 11.0 \| 1342 \| 0.3490 \| 0.8496 \| 0.8162 \| 0.8311 \| 0.8229 \| \| 0.2693 \| 12.0 \| 1464 \| 0.3502 \| 0.8546 \| 0.8220 \| 0.8372 \| 0.8288 \| \| 0.2745 \| 13.0 \| 1586 \| 0.3284 \| 0.8571 \| 0.8289 \| 0.8239 \| 0.8264 \| \| 0.2712 \| 14.0 \| 1708 \| 0.3297 \| 0.8596 \| 0.8299 \| 0.8332 \| 0.8315 \| \| 0.256 \| 15.0 \| 1830 \| 0.3357 \| 0.8647 \| 0.8346 \| 0.8442 \| 0.8391 \| \| 0.2504 \| 16.0 \| 1952 \| 0.3346 \| 0.8571 \| 0.8255 \| 0.8364 \| 0.8306 \| \| 0.2487 \| 17.0 \| 2074 \| 0.3242 \| 0.8571 \| 0.8281 \| 0.8264 \| 0.8272 \| \| 0.2514 \| 18.0 \| 2196 \| 0.3309 \| 0.8622 \| 0.8314 \| 0.8425 \| 0.8365 \| \| 0.2451 \| 19.0 \| 2318 \| 0.3243 \| 0.8622 \| 0.8333 \| 0.8350 \| 0.8341 \| \| 0.2461 \| 20.0 \| 2440 \| 0.3260 \| 0.8622 \| 0.8319 \| 0.8400 \| 0.8357 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
antinoice/Tamarin_XL_v1	antinoice	2024-05-17T22:57:21Z	0	0	null	[ "region:us" ]	null	2024-05-17T22:11:51Z	![01142-320208653.jpeg](https://cdn-uploads.huggingface.co/production/uploads/6647d3e22b528039ed63d236/b_IbTabAs11FvlsgrI_-l.jpeg) Tamarin_XL ---------------------------------------------------------- + You will be pleasantly surprised by the excellent results. ---------------------------------------------------------- + To create Tamarin_XL model, only the best, in my opinion, models were used. + The refiner is unnecessary, and VAE is included. + All in one. Best model ever for everyone! + If you leave the negative field blank, you can also achieve good quality of the generated image. ----------------------------------------------------------- + Works great with various LoRa and embeddings. ----------------------------------------------------------- + Good luck to everyone!
somosnlp/bertin_base_climate_detection_spa	somosnlp	2024-05-17T22:53:55Z	126	2	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "es", "dataset:somosnlp/spa_climate_detection", "base_model:bertin-project/bertin-roberta-base-spanish", "base_model:finetune:bertin-project/bertin-roberta-base-spanish", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-07T16:52:35Z	--- license: cc-by-4.0 base_model: bertin-project/bertin-roberta-base-spanish tags: - generated_from_trainer metrics: - accuracy model-index: - name: bertin_base_climate_detection_spa results: [] datasets: - somosnlp/spa_climate_detection language: - es widget: - text: > El uso excesivo de fertilizantes nitrogenados -un fenómeno frecuente en la agricultura- da lugar a la producción de óxido nitroso, un potente gas de efecto invernadero. Un uso más juicioso de los fertilizantes puede frenar estas emisiones y reducir la producción de fertilizantes, que consume mucha energía. pipeline_tag: text-classification --- # Model Card for bertin_base_climate_detection_spa_v2 README Spanish Version: [README_ES](https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/blob/main/README_ES.md) <p align="center"> <img src="https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/resolve/main/model_image_repo_380.jpg" alt="Model Illustration" width="500"> </p> This model is a fine-tuning version of the model: [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) using the dataset [somosnlp/spa_climate_detection](https://huggingface.co/datasets/somosnlp/spa_climate_detection). The model is focused on the identification of texts on topics related to climate change and sustainability. This project was based on the English version of [climatebert/distilroberta-base-climate-detector](https://huggingface.co/climatebert/distilroberta-base-climate-detector). The motivation of the project was to create a repository in Spanish on information or resources on topics such as: climate change, sustainability, global warming, energy, etc; the idea is to give visibility to solutions, examples of good environmental practices or news that help us to combat the effects of climate change; in a way similar to what the project [Drawdown](https://drawdown.org/solutions/table-of-solutions) does but providing examples of solutions or new research on each topic. To achieve this In order to achieve this objective, we consider that the identification of texts that speak about these topics is the first step. Some of the direct applications are: classification of papers and scientific publications, news, opinions. Future steps: - We intend to create an advanced model that classifies texts related to climate change based on sectors (token classification), for example: classify based on electricity, agriculture, industry, transport, etc. - Publish a sector-based dataset. - Realize a Q/A model that can provide relevant information to the user on the topic of climate change. ## Model Details ### Model Description - Developed by: [Gerardo Huerta](https://huggingface.co/Gerard-1705) [Gabriela Zuñiga](https://huggingface.co/Gabrielaz) - Funded by: SomosNLP, HuggingFace - Model type: Language model, instruction tuned, text classification - Language(s): es-ES, es-PE - License: cc-by-nc-sa-4.0 - Fine-tuned from model: [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) - Dataset used: [somosnlp/spa_climate_detection](https://huggingface.co/datasets/somosnlp/spa_climate_detection) ### Model resurces: - Repository: [somosnlp/bertin_base_climate_detection_spa](https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/tree/main) <!-- Enlace al `main` del repo donde tengáis los scripts, i.e.: o del mismo repo del modelo en HuggingFace o a GitHub. --> - Demo: [identificacion de textos sobre cambio climatico y sustentabilidad](https://huggingface.co/spaces/somosnlp/Identificacion_de_textos_sobre_sustentabilidad_cambio_climatico) - Video presentation: [Proyecto BERTIN-ClimID](https://www.youtube.com/watch?v=sfXLUP9Ei-o) ## Uses ### Direct Use - News classification: With this model it is possible to classify news headlines related to the areas of climate change. - Paper classification: The identification of scientific texts that disclose solutions and/or effects of climate change. For this use, the abstract of each paper can be used for identification. ### Indirect Use - For the creation of information repositories regarding climate issues. - This model can serve as a basis for creating new classification systems for climate solutions to disseminate new efforts to combat climate change in different sectors. - Creation of new datasets that address the issue. ### Out-of-Scope Use - The use for text classification of unverifiable or unreliable sources and their dissemination, e.g., fake news or disinformation. ## Bias, Risks, and Limitations No specific studies on biases and limitations have been carried out at this point, however, we make the following points based on previous experience and model tests: - It inherits the biases and limitations of the base model with which it was trained, for more details see: [BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6403). However, they are not so obvious to find because of the type of task in which the model is being implemented, such as text classification. - Direct biases such as the majority use of high-level language in the dataset due to the use of texts extracted from news, legal documentation of companies that can complicate the identification of texts with low-level language (e.g. colloquial). To mitigate these biases, diverse opinions on climate change issues extracted from sources such as social networks were included in the dataset, in addition to a rebalancing of the labels. - The dataset inherits other limitations such as: the model loses performance on short texts, this is due to the fact that most of the texts used in the dataset have a long length between 200 - 500 words. Again, we tried to mitigate these limitations by including short texts. ### Recommendations - As we have mentioned, the model tends to lower performance in short texts, so it is advisable to establish a selection criterion for long texts whose subject matter needs to be identified. ## How to Get Started with the Model ```python ## Asumiendo tener instalados transformers, torch from transformers import AutoModelForSequenceClassification import torch from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("somosnlp/bertin_base_climate_detection_spa") model = AutoModelForSequenceClassification.from_pretrained("somosnlp/bertin_base_climate_detection_spa") # Traduccion del label id2label = {0: "NEGATIVE", 1: "POSITIVE"} label2id = {"NEGATIVE": 0, "POSITIVE": 1} # Funcion de inferencia def inference_fun(Texto): inputs = tokenizer(Texto, return_tensors="pt") with torch.no_grad(): logits = model(inputs).logits predicted_class_id = logits.argmax().item() output_tag = model.config.id2label[predicted_class_id] return output_tag input_text = "El uso excesivo de fertilizantes nitrogenados -un fenómeno frecuente en la agricultura- da lugar a la producción de óxido nitroso, un potente gas de efecto invernadero. Un uso más juicioso de los fertilizantes puede frenar estas emisiones y reducir la producción de fertilizantes, que consume mucha energía." print(inference_fun(input_text)) ``` ## Training Details ### Training Data The training data were obtained from the dataset [somosnlp/spa_climate_detection](https://huggingface.co/datasets/somosnlp/spa_climate_detection). The training data represent about 79% of the total data in the dataset. The labels are represented as follows: Labels 1s 1000 - data on paragraphs extracted from company reports on the subject. 600 - data on various opinions, mostly short texts. Labels 0s 300 - data on paragraphs extracted from business reports not related to the subject. 500 - data on news on various topics unrelated to the subject. 500 - data on opinions on various topics unrelated to the subject. ### Training Procedure You can check our Google Colab to review the training procedure we take: [Colab Entrenamiento](https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/blob/main/entrenamiento_del_modelo.ipynb) The accelerate configuration is as follows: In which compute environment are you running?: 0 Which type of machine are you using?: No distributed training Do you want to run your training on CPU only (even if a GPU / Apple Silicon / Ascend NPU device is available)? [yes/NO]:NO Do you wish to optimize your script with torch dynamo?[yes/NO]:NO Do you want to use DeepSpeed? [yes/NO]: NO What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:all Do you wish to use FP16 or BF16 (mixed precision)?: no #### Training Hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 #### Speeds, Sizes, Times The model was trained in 2 epochs with a total training duration of 14.22 minutes, 'train_runtime': 853.6759. Additional information: No mixed precision (FP16 or BF16) was used. #### Resultados del entrenamiento: \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 182 \| 0.1964 \| 0.9551 \| \| No log \| 2.0 \| 364 \| 0.1592 \| 0.9705 \| ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data The assessment data were obtained from the dataset [somosnlp/spa_climate_detection](https://huggingface.co/datasets/somosnlp/spa_climate_detection). The assessment data represent about 21% of the total data in the dataset. The labels are represented as follows: Labels 1s 320 - data on paragraphs extracted from company reports on the subject. 160 - data on various opinions, mostly short texts. Labels 0s 80 - data on paragraphs extracted from business reports not related to the subject. 120 - data on news on various topics unrelated to the subject. 100 - data on opinions on various topics unrelated to the subject. Model reached the following results on evaluation dataset: - Loss: 0.1592 - Accuracy: 0.9705 #### Metrics The metric was precision. ### Results Look at the Inference section of Colab: [entrenamiento_del_modelo](https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/blob/main/entrenamiento_del_modelo.ipynb) Accuracy 0.95 Precision 0.916 Recall 0.99 F1 score 0.951 ## Environmental Impact Using the tool [ML CO2 IMPACT](https://mlco2.github.io/impact/#co2eq) we estimate the following environmental impact due to training: - Type of hardware: T4 - Total Hours for iterations and tests: 4 horas - Cloud provider Google Cloud (colab) - Computational region us-east - Carbon footprint 0.1kg CO2 ## Technical Specifications #### Software - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2 #### Hardware - GPU equivalent to T4 - For reference, the model was trained on the free version of Google Colab ## License cc-by-nc-sa-4.0 Due to inheritance of the data used in the dataset. ## Citation BibTeX: ``` @software{BERTIN-ClimID, author = {Gerardo Huerta, Gabriela Zuñiga}, title = {BERTIN-ClimID: BERTIN-Base Climate-related text Identification}, month = Abril, year = 2024, url = {https://huggingface.co/somosnlp/bertin_base_climate_detection_spa} } ``` ## More Information This project was developed during the [Hackathon #Somos600M](https://somosnlp.org/hackathon) organized by SomosNLP. We thank all event organizers and sponsors for their support during the event. Team:** - [Gerardo Huerta](https://huggingface.co/Gerard-1705) - [Gabriela Zuñiga](https://huggingface.co/Gabrielaz) ## Contact - [email protected] - [email protected]
nuebaek/mss-a_jh_llama3_ft2	nuebaek	2024-05-17T22:53:47Z	79	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T22:31:46Z	--- 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]
benredmond/llama-3-kto-lora-q5_k_m	benredmond	2024-05-17T22:51:47Z	10	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T22:49:42Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: benredmond - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama 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)
RichardErkhov/KnutJaegersberg_-_webMistral-7B-4bits	RichardErkhov	2024-05-17T22:46:44Z	50	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T22:41:56Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) webMistral-7B - bnb 4bits - Model creator: https://huggingface.co/KnutJaegersberg/ - Original model: https://huggingface.co/KnutJaegersberg/webMistral-7B/ Original model description: --- license: cc-by-nc-4.0 --- Prompt Example: ``` ## Question: What is slowing down our internet speeds? ### Google Search Results: - Are my other devices slowing down my connection? Gadgets with slower internet technology can slow down speeds for all your other devices. Everyone knows the feeling: you’re hard at work and then suddenly the Internet seems to slow down. Why is that? From background programs to cheap routers, here are a few reasons why your Internet might be a concern. When working outside your home, here’s how to keep your information safe on public Wi-Fi. If your internet slows down only when too many other people are online simultaneously, you’re probably using more bandwidth than your plan allows. Use our internet speed test to see if you’re getting the speed advertised by your ISP. If your results are close to your plan speed, consider upgrading. Generally, your modem or router (or both) will create a speed bottleneck if not working properly—the same goes with wireless gateways. If your equipment is too old, it may not support important internet protocols. Equipment damage, such as bad ports or components, can also cause slowdowns. Is your internet suddenly moving slowly? It could be due to an outdated router or a less-than-ideal router location. Your connection issues may need only an easy fix, like upgrading to a mesh network (which also has to be set up in the right spot) or simply restarting your modem and router. But if you've already attempted many of the tried-and-true methods and your internet speeds are still subpar, the issue might be something your internet service provider is intentionally doing: bandwidth throttling. ### Response: There are several factors that can slow down internet speeds. These include having gadgets with slower internet technology, running background programs[2], using more bandwidth than your plan allows[3], equipment damage[4], an outdated router or a less-than-ideal router location[5], and bandwidth throttling by the internet service provider[5]. ``` # [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_KnutJaegersberg__webMistral-7B) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 47.08 \| \| ARC (25-shot) \| 59.04 \| \| HellaSwag (10-shot) \| 80.89 \| \| MMLU (5-shot) \| 59.0 \| \| TruthfulQA (0-shot) \| 39.71 \| \| Winogrande (5-shot) \| 76.32 \| \| GSM8K (5-shot) \| 8.87 \| \| DROP (3-shot) \| 5.75 \|
gbenson/dom-tokenizer-10k	gbenson	2024-05-17T22:46:26Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-16T07:40:18Z	--- license: apache-2.0 ---
mnoukhov/pythia410m-dpo2-tldr	mnoukhov	2024-05-17T22:45:45Z	3	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:mnoukhov/pythia410m-sft-tldr", "base_model:adapter:mnoukhov/pythia410m-sft-tldr", "license:apache-2.0", "region:us" ]	null	2024-05-12T18:36:16Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mnoukhov/pythia410m-sft-tldr model-index: - name: pythia410m-dpo2-tldr 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. --> # pythia410m-dpo2-tldr This model is a fine-tuned version of [mnoukhov/pythia410m-sft-tldr](https://huggingface.co/mnoukhov/pythia410m-sft-tldr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6073 - Rewards/chosen: -1.2728 - Rewards/rejected: -1.5670 - Rewards/accuracies: 0.6761 - Rewards/margins: 0.2942 - Logps/rejected: -91.1163 - Logps/chosen: -91.1163 - Logps/ref Rejected: -59.5615 - Logps/ref Chosen: -65.6594 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logps/ref Rejected \| Logps/ref Chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:------------------:\|:----------------:\| \| 0.6681 \| 0.1999 \| 335 \| 0.6376 \| -0.2343 \| -0.3789 \| 0.6615 \| 0.1446 \| -70.3464 \| -70.3464 \| -59.5615 \| -65.6594 \| \| 0.6485 \| 0.3999 \| 670 \| 0.6171 \| -0.9421 \| -1.1796 \| 0.6678 \| 0.2375 \| -84.5023 \| -84.5023 \| -59.5615 \| -65.6594 \| \| 0.6362 \| 0.5998 \| 1005 \| 0.6095 \| -1.1035 \| -1.3785 \| 0.6743 \| 0.2750 \| -87.7290 \| -87.7290 \| -59.5615 \| -65.6594 \| \| 0.6342 \| 0.7998 \| 1340 \| 0.6063 \| -1.2460 \| -1.5415 \| 0.6768 \| 0.2955 \| -90.5797 \| -90.5797 \| -59.5615 \| -65.6594 \| \| 0.6299 \| 0.9997 \| 1675 \| 0.6073 \| -1.2728 \| -1.5670 \| 0.6761 \| 0.2942 \| -91.1163 \| -91.1163 \| -59.5615 \| -65.6594 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
svjack/Mistral7B_v2_inst_sharegpt_common_lora_small	svjack	2024-05-17T22:45:34Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:other", "region:us" ]	null	2024-05-17T22:29:16Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: train_2024-05-17-13-21-02 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. --> # Install ```bash pip install peft transformers bitsandbytes ``` # Run by transformers ```python from transformers import TextStreamer, AutoTokenizer, AutoModelForCausalLM from peft import PeftModel tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2",) mis_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", load_in_4bit = True) mis_model = PeftModel.from_pretrained(mis_model, "svjack/Mistral7B_v2_inst_sharegpt_common_lora_small") mis_model = mis_model.eval() streamer = TextStreamer(tokenizer) def mistral_hf_predict(messages, mis_model = mis_model, tokenizer = tokenizer, streamer = streamer, do_sample = True, top_p = 0.95, top_k = 40, max_new_tokens = 512, max_input_length = 3500, temperature = 0.9, repetition_penalty = 1.0, device = "cuda"): encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) generated_ids = mis_model.generate(model_inputs, max_new_tokens=max_new_tokens, do_sample=do_sample, streamer = streamer, top_p = top_p, top_k = top_k, temperature = temperature, repetition_penalty = repetition_penalty, ) out = tokenizer.batch_decode(generated_ids)[0].split("[/INST]")[-1].replace("</s>", "").strip() return out out = mistral_hf_predict([ { "role": "user", "content": "写一份简单的Python排序程序。" } ], repetition_penalty = 1.1, temperature = 0.01, max_new_tokens=1024 ) print(out) ``` # Output 当然，这是一个简单的Python排序程序： ```python def sort_list(lst): """ 将列表按升序排序 :param lst: 要排序的列表 :return: 排序后的列表 """ return sorted(lst) #示例使用 numbers = [5, 2, 9, 1, 5, 6] print(sort_list(numbers)) ``` 该程序定义了一个名为`sort_list`的函数，它接受一个列表作为参数并返回排序后的列表。该函数使用内置的`sorted()`函数来对列表进行排序，默认情况下按升序排序。 ```python out = mistral_hf_predict([ { "role": "user", "content": "写一份简单的Python排序程序。" }, { "role": "assistant", "content": ''' 当然，这是一个简单的Python排序程序： ```python def sort_list(lst): """ 将列表按升序排序 :param lst: 要排序的列表 :return: 排序后的列表 """ return sorted(lst) #示例使用 numbers = [5, 2, 9, 1, 5, 6] print(sort_list(numbers)) ``` 该程序定义了一个名为`sort_list`的函数，它接受一个列表作为参数并返回排序后的列表。该函数使用内置的`sorted()`函数来对列表进行排序，默认情况下按升序排序。 ''' }, { "role": "user", "content": "将上面的语言翻译成C++。" } ], repetition_penalty = 1.1, temperature = 0.01, max_new_tokens=1024 ) print(out) ``` # Output 当然，这是一个相同功能的C++版本： ```c++ #include <algorithm> #include <vector> using namespace std; void sortList(vector<int>& nums) { //使用标准库中的sort函数对列表进行排序 sort(nums.begin(), nums.end()); } //示例使用 int main() { vector<int> numbers = {5, 2, 9, 1, 5, 6}; sortList(numbers); for (int i : numbers) { cout << i << " "; } cout << endl; return 0; } ``` 该程序定义了一个名为`sortList`的函数，它接受一个整型向量（`std::vector<int>`）作为参数并对其进行排序。该函数使用标准库中的`std::sort`函数对向量进行排序。请注意，C++不支持Python中的元组类型，因此我们在C++中使用了`std::vector`而不是Python中的元组。此外，C++中的`std::cout`与Python中的`print`函数类似，可以用于输出到控制台。 # train_2024-05-17-13-21-02 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the sharegpt_common_zh 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.0 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
apwic/sentiment-lora-r4a2d0.15-0	apwic	2024-05-17T22:39:55Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T22:06:44Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a2d0.15-0 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. --> # sentiment-lora-r4a2d0.15-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3513 - Accuracy: 0.8471 - Precision: 0.8147 - Recall: 0.8193 - F1: 0.8169 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5621 \| 1.0 \| 122 \| 0.5100 \| 0.7218 \| 0.6593 \| 0.6482 \| 0.6527 \| \| 0.5049 \| 2.0 \| 244 \| 0.4890 \| 0.7343 \| 0.6945 \| 0.7195 \| 0.7011 \| \| 0.4776 \| 3.0 \| 366 \| 0.4480 \| 0.7594 \| 0.7150 \| 0.7323 \| 0.7216 \| \| 0.4422 \| 4.0 \| 488 \| 0.4104 \| 0.7945 \| 0.7524 \| 0.7446 \| 0.7482 \| \| 0.4146 \| 5.0 \| 610 \| 0.4257 \| 0.7594 \| 0.7202 \| 0.7473 \| 0.7283 \| \| 0.3828 \| 6.0 \| 732 \| 0.3869 \| 0.8246 \| 0.7880 \| 0.7909 \| 0.7894 \| \| 0.3697 \| 7.0 \| 854 \| 0.3959 \| 0.8145 \| 0.7766 \| 0.7988 \| 0.7854 \| \| 0.3486 \| 8.0 \| 976 \| 0.3808 \| 0.8321 \| 0.7961 \| 0.8087 \| 0.8018 \| \| 0.3437 \| 9.0 \| 1098 \| 0.3738 \| 0.8271 \| 0.7904 \| 0.8001 \| 0.7949 \| \| 0.3317 \| 10.0 \| 1220 \| 0.3643 \| 0.8471 \| 0.8159 \| 0.8143 \| 0.8151 \| \| 0.3114 \| 11.0 \| 1342 \| 0.3683 \| 0.8271 \| 0.7902 \| 0.8051 \| 0.7968 \| \| 0.3035 \| 12.0 \| 1464 \| 0.3660 \| 0.8346 \| 0.7988 \| 0.8155 \| 0.8061 \| \| 0.3117 \| 13.0 \| 1586 \| 0.3518 \| 0.8471 \| 0.8167 \| 0.8118 \| 0.8142 \| \| 0.3048 \| 14.0 \| 1708 \| 0.3533 \| 0.8446 \| 0.8115 \| 0.8176 \| 0.8144 \| \| 0.2916 \| 15.0 \| 1830 \| 0.3570 \| 0.8421 \| 0.8083 \| 0.8158 \| 0.8119 \| \| 0.2832 \| 16.0 \| 1952 \| 0.3579 \| 0.8471 \| 0.8138 \| 0.8243 \| 0.8187 \| \| 0.284 \| 17.0 \| 2074 \| 0.3496 \| 0.8471 \| 0.8153 \| 0.8168 \| 0.8160 \| \| 0.2906 \| 18.0 \| 2196 \| 0.3537 \| 0.8446 \| 0.8111 \| 0.8201 \| 0.8153 \| \| 0.2805 \| 19.0 \| 2318 \| 0.3505 \| 0.8496 \| 0.8186 \| 0.8186 \| 0.8186 \| \| 0.2815 \| 20.0 \| 2440 \| 0.3513 \| 0.8471 \| 0.8147 \| 0.8193 \| 0.8169 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
antitheft159/nonexsitentsky.195	antitheft159	2024-05-17T22:37:59Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-05-17T22:36:05Z	--- license: cc-by-nc-sa-4.0 ---
RichardErkhov/Menouar_-_saqr-7b-merged-8bits	RichardErkhov	2024-05-17T22:37:02Z	106	0	transformers	[ "transformers", "safetensors", "falcon", "text-generation", "conversational", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T22:31:09Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) saqr-7b-merged - bnb 8bits - Model creator: https://huggingface.co/Menouar/ - Original model: https://huggingface.co/Menouar/saqr-7b-merged/ Original model description: --- library_name: transformers tags: - saqr-7b-instrcut - Pytorch license: apache-2.0 datasets: - HuggingFaceH4/ultrachat_200k - openbmb/UltraFeedback - gsm8k language: - en pipeline_tag: text-generation --- # saqr-7b-merged This model is a merged version of [saqr-7b-instruct](https://huggingface.co/Menouar/saqr-7b-instruct) with LoRA Adapters. <img src="https://huggingface.co/Menouar/saqr-7b-instruct/resolve/main/saqr.jpg" alt="Saqr Logo" width="800" style="margin-left:auto; margin-right:auto; display:block;"/>
jswing/dqn-pong2	jswing	2024-05-17T22:24:27Z	0	0	stable-baselines3	[ "stable-baselines3", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T17:51:54Z	--- library_name: stable-baselines3 tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: -21.00 +/- 0.00 name: mean_reward verified: false --- # DQN Agent playing PongNoFrameskip-v4 This is a trained model of a DQN agent playing PongNoFrameskip-v4 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 ... ```
henriksound/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF	henriksound	2024-05-17T22:21:54Z	0	0	null	[ "gguf", "finetuned", "llama-cpp", "gguf-my-repo", "text-generation", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-17T22:21:43Z	--- license: apache-2.0 tags: - finetuned - llama-cpp - gguf-my-repo pipeline_tag: text-generation inference: true widget: - messages: - role: user content: What is your favorite condiment? --- # henriksound/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF This model was converted to GGUF format from [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo henriksound/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF --model mistral-7b-instruct-v0.2.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo henriksound/Mistral-7B-Instruct-v0.2-Q4_K_M-GGUF --model mistral-7b-instruct-v0.2.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-7b-instruct-v0.2.Q4_K_M.gguf -n 128 ```
moiz1/Mistral-7b-Instruct-v0.2-finetune-classification-10k-alpaca-style	moiz1	2024-05-17T22:18:50Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T21:26:28Z	--- 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. 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Mullerjo/Reinforce-v2pixelcopter	Mullerjo	2024-05-17T22:07:13Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T22:07:10Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-v2pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 28.50 +/- 23.54 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
apwic/sentiment-lora-r4a2d0.1-0	apwic	2024-05-17T22:06:27Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T21:33:17Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a2d0.1-0 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. --> # sentiment-lora-r4a2d0.1-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3483 - Accuracy: 0.8446 - Precision: 0.8111 - Recall: 0.8201 - F1: 0.8153 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5617 \| 1.0 \| 122 \| 0.5117 \| 0.7193 \| 0.6580 \| 0.6514 \| 0.6543 \| \| 0.5046 \| 2.0 \| 244 \| 0.4917 \| 0.7419 \| 0.7042 \| 0.7324 \| 0.7112 \| \| 0.4798 \| 3.0 \| 366 \| 0.4466 \| 0.7594 \| 0.7129 \| 0.7248 \| 0.7179 \| \| 0.4374 \| 4.0 \| 488 \| 0.3994 \| 0.8195 \| 0.7866 \| 0.7648 \| 0.7741 \| \| 0.4037 \| 5.0 \| 610 \| 0.4150 \| 0.7845 \| 0.7480 \| 0.7800 \| 0.7575 \| \| 0.3741 \| 6.0 \| 732 \| 0.3737 \| 0.8371 \| 0.8028 \| 0.8072 \| 0.8049 \| \| 0.3574 \| 7.0 \| 854 \| 0.3776 \| 0.8221 \| 0.7845 \| 0.7991 \| 0.7909 \| \| 0.3387 \| 8.0 \| 976 \| 0.3654 \| 0.8446 \| 0.8120 \| 0.8151 \| 0.8135 \| \| 0.3293 \| 9.0 \| 1098 \| 0.3627 \| 0.8371 \| 0.8021 \| 0.8122 \| 0.8068 \| \| 0.3209 \| 10.0 \| 1220 \| 0.3553 \| 0.8371 \| 0.8032 \| 0.8047 \| 0.8040 \| \| 0.2967 \| 11.0 \| 1342 \| 0.3674 \| 0.8346 \| 0.7989 \| 0.8130 \| 0.8052 \| \| 0.2928 \| 12.0 \| 1464 \| 0.3707 \| 0.8321 \| 0.7960 \| 0.8112 \| 0.8027 \| \| 0.2967 \| 13.0 \| 1586 \| 0.3514 \| 0.8471 \| 0.8153 \| 0.8168 \| 0.8160 \| \| 0.2934 \| 14.0 \| 1708 \| 0.3507 \| 0.8421 \| 0.8083 \| 0.8158 \| 0.8119 \| \| 0.2811 \| 15.0 \| 1830 \| 0.3553 \| 0.8346 \| 0.7991 \| 0.8105 \| 0.8043 \| \| 0.2738 \| 16.0 \| 1952 \| 0.3555 \| 0.8421 \| 0.8077 \| 0.8208 \| 0.8136 \| \| 0.2717 \| 17.0 \| 2074 \| 0.3468 \| 0.8496 \| 0.8174 \| 0.8236 \| 0.8204 \| \| 0.278 \| 18.0 \| 2196 \| 0.3510 \| 0.8421 \| 0.8080 \| 0.8183 \| 0.8127 \| \| 0.2701 \| 19.0 \| 2318 \| 0.3471 \| 0.8471 \| 0.8142 \| 0.8218 \| 0.8178 \| \| 0.2722 \| 20.0 \| 2440 \| 0.3483 \| 0.8446 \| 0.8111 \| 0.8201 \| 0.8153 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
raiyan007/whisper-base-bn-f	raiyan007	2024-05-17T22:04:35Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "bn", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-09T21:37:14Z	--- language: - bn license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Base Bn - Raiyan Ahmed results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: bn split: None args: 'config: bn, split: test' metrics: - name: Wer type: wer value: 33.54106242324475 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Base Bn - Raiyan Ahmed This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2026 - Wer: 33.5411 ## 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: 3.75e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 16000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:-------:\| \| 0.2369 \| 0.6365 \| 1000 \| 0.2433 \| 62.1881 \| \| 0.1242 \| 1.2731 \| 2000 \| 0.1734 \| 49.4369 \| \| 0.1022 \| 1.9096 \| 3000 \| 0.1197 \| 39.0531 \| \| 0.046 \| 2.5461 \| 4000 \| 0.1067 \| 34.5497 \| \| 0.0777 \| 3.1827 \| 5000 \| 0.1440 \| 43.2194 \| \| 0.0649 \| 3.8192 \| 6000 \| 0.1266 \| 38.6232 \| \| 0.0367 \| 4.4558 \| 7000 \| 0.1288 \| 38.0392 \| \| 0.0126 \| 5.0923 \| 8000 \| 0.1382 \| 35.0226 \| \| 0.0108 \| 5.7288 \| 9000 \| 0.1416 \| 34.5340 \| \| 0.0038 \| 6.3654 \| 10000 \| 0.1611 \| 33.3921 \| \| 0.0023 \| 7.0019 \| 11000 \| 0.1744 \| 33.4875 \| \| 0.0133 \| 7.6384 \| 12000 \| 0.1625 \| 36.0534 \| \| 0.0066 \| 8.2750 \| 13000 \| 0.1801 \| 35.3936 \| \| 0.004 \| 8.9115 \| 14000 \| 0.1781 \| 34.1577 \| \| 0.0009 \| 9.5481 \| 15000 \| 0.1918 \| 33.6939 \| \| 0.0003 \| 10.1846 \| 16000 \| 0.2026 \| 33.5411 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
nuebaek/draft_mistral_ft_mss_2	nuebaek	2024-05-17T21:59:00Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T21:49:51Z	--- 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. 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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. 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galkowskim/longformer_base_4096_QA_SQUAD	galkowskim	2024-05-17T21:54:18Z	110	0	transformers	[ "transformers", "safetensors", "longformer", "question-answering", "generated_from_trainer", "base_model:allenai/longformer-base-4096", "base_model:finetune:allenai/longformer-base-4096", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-17T05:50:20Z	--- license: apache-2.0 base_model: allenai/longformer-base-4096 tags: - generated_from_trainer model-index: - name: longformer_base_4096_QA_SQUAD 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. --> # longformer_base_4096_QA_SQUAD This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1 - Datasets 2.19.0 - Tokenizers 0.19.1
mradermacher/Mahou-1.2-mistral-7B-GGUF	mradermacher	2024-05-17T21:50:46Z	8	0	transformers	[ "transformers", "gguf", "en", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "base_model:flammenai/Mahou-1.2-mistral-7B", "base_model:quantized:flammenai/Mahou-1.2-mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T17:43:34Z	--- base_model: flammenai/Mahou-1.2-mistral-7B datasets: - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/flammenai/Mahou-1.2-mistral-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.2-mistral-7B-GGUF/resolve/main/Mahou-1.2-mistral-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
DarwinAnim8or/gpt2-grugged-6-Q4_K_M-GGUF	DarwinAnim8or	2024-05-17T21:50:24Z	2	0	null	[ "gguf", "autotrain", "text-generation", "llama-cpp", "gguf-my-repo", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-17T21:50:13Z	--- license: other tags: - autotrain - text-generation - llama-cpp - gguf-my-repo widget: - text: 'I love AutoTrain because ' --- # DarwinAnim8or/gpt2-grugged-6-Q4_K_M-GGUF This model was converted to GGUF format from [`DarwinAnim8or/gpt2-grugged-6`](https://huggingface.co/DarwinAnim8or/gpt2-grugged-6) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/DarwinAnim8or/gpt2-grugged-6) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DarwinAnim8or/gpt2-grugged-6-Q4_K_M-GGUF --model gpt2-grugged-6.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DarwinAnim8or/gpt2-grugged-6-Q4_K_M-GGUF --model gpt2-grugged-6.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m gpt2-grugged-6.Q4_K_M.gguf -n 128 ```
hyp1231/blair-games-roberta-base	hyp1231	2024-05-17T21:47:39Z	110	0	transformers	[ "transformers", "safetensors", "roberta", "feature-extraction", "en", "arxiv:2403.03952", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-17T21:41:48Z	--- license: mit language: - en base_model: FacebookAI/roberta-base --- # BLaIR-Games-roberta-base A checkpoint for ablation study in [BLaIR paper](https://arxiv.org/abs/2403.03952). Please refer to [🤗 blair-roberta-base](https://huggingface.co/hyp1231/blair-roberta-base) for more details about using this checkpoint.
BuroIdentidadDigital/Ine_ReversoV2	BuroIdentidadDigital	2024-05-17T21:39:14Z	50	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-17T21:29:26Z	--- 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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omar8/bpm_test01	omar8	2024-05-17T21:30:07Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T21:28:45Z	--- library_name: transformers tags: - unsloth --- # 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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RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf	RichardErkhov	2024-05-17T21:06:22Z	44	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-17T19:41:10Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-7B-Discord-0.1 - GGUF - Model creator: https://huggingface.co/BlouseJury/ - Original model: https://huggingface.co/BlouseJury/Mistral-7B-Discord-0.1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Mistral-7B-Discord-0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [Mistral-7B-Discord-0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [Mistral-7B-Discord-0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [Mistral-7B-Discord-0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [Mistral-7B-Discord-0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [Mistral-7B-Discord-0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [Mistral-7B-Discord-0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [Mistral-7B-Discord-0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [Mistral-7B-Discord-0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [Mistral-7B-Discord-0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [Mistral-7B-Discord-0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [Mistral-7B-Discord-0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [Mistral-7B-Discord-0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [Mistral-7B-Discord-0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [Mistral-7B-Discord-0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [Mistral-7B-Discord-0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [Mistral-7B-Discord-0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [Mistral-7B-Discord-0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [Mistral-7B-Discord-0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [Mistral-7B-Discord-0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [Mistral-7B-Discord-0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [Mistral-7B-Discord-0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-gguf/blob/main/Mistral-7B-Discord-0.1.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- language: - en license: apache-2.0 tags: - finetune pipeline_tag: text-generation model-index: - name: Mistral-7B-Discord-0.1 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: 60.24 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 83.13 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 62.82 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 44.1 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 78.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 32.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 name: Open LLM Leaderboard --- # Mistral-7B-Discord-0.1 This model is a finetune of [Mistral-7B-0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on ~20 Million tokens worth of mostly not formatted, anonymized discord messages for 4 Epochs. This is a base model. ## Model Details - Finetuned from model : mistralai/Mistral-7B-v0.1 # [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_BlouseJury__Mistral-7B-Discord-0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|60.28\| \|AI2 Reasoning Challenge (25-Shot)\|60.24\| \|HellaSwag (10-Shot) \|83.13\| \|MMLU (5-Shot) \|62.82\| \|TruthfulQA (0-shot) \|44.10\| \|Winogrande (5-shot) \|78.93\| \|GSM8k (5-shot) \|32.45\|
nuebaek/draft_mistral_ft_insta_2	nuebaek	2024-05-17T21:05:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T21:05:19Z	--- 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]
starBot/LunarLander-v2	starBot	2024-05-17T21:04:36Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T21:04:16Z	--- 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: 245.68 +/- 16.73 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 ... ```
alexandro767/stable-diffusion-v1-5-finetuned_5e_v1	alexandro767	2024-05-17T21:03:16Z	29	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-17T20:59:10Z	--- library_name: diffusers --- # 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 🧨 diffusers model that has been pushed on the Hub. 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apwic/sentiment-lora-r4a1d0.15-0	apwic	2024-05-17T20:59:36Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T20:26:28Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a1d0.15-0 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. --> # sentiment-lora-r4a1d0.15-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3513 - Accuracy: 0.8471 - Precision: 0.8147 - Recall: 0.8193 - F1: 0.8169 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5621 \| 1.0 \| 122 \| 0.5100 \| 0.7218 \| 0.6593 \| 0.6482 \| 0.6527 \| \| 0.5049 \| 2.0 \| 244 \| 0.4890 \| 0.7343 \| 0.6945 \| 0.7195 \| 0.7011 \| \| 0.4776 \| 3.0 \| 366 \| 0.4480 \| 0.7594 \| 0.7150 \| 0.7323 \| 0.7216 \| \| 0.4422 \| 4.0 \| 488 \| 0.4104 \| 0.7945 \| 0.7524 \| 0.7446 \| 0.7482 \| \| 0.4146 \| 5.0 \| 610 \| 0.4257 \| 0.7594 \| 0.7202 \| 0.7473 \| 0.7283 \| \| 0.3828 \| 6.0 \| 732 \| 0.3869 \| 0.8246 \| 0.7880 \| 0.7909 \| 0.7894 \| \| 0.3697 \| 7.0 \| 854 \| 0.3959 \| 0.8145 \| 0.7766 \| 0.7988 \| 0.7854 \| \| 0.3486 \| 8.0 \| 976 \| 0.3808 \| 0.8321 \| 0.7961 \| 0.8087 \| 0.8018 \| \| 0.3437 \| 9.0 \| 1098 \| 0.3738 \| 0.8271 \| 0.7904 \| 0.8001 \| 0.7949 \| \| 0.3317 \| 10.0 \| 1220 \| 0.3643 \| 0.8471 \| 0.8159 \| 0.8143 \| 0.8151 \| \| 0.3114 \| 11.0 \| 1342 \| 0.3683 \| 0.8271 \| 0.7902 \| 0.8051 \| 0.7968 \| \| 0.3035 \| 12.0 \| 1464 \| 0.3660 \| 0.8346 \| 0.7988 \| 0.8155 \| 0.8061 \| \| 0.3117 \| 13.0 \| 1586 \| 0.3518 \| 0.8471 \| 0.8167 \| 0.8118 \| 0.8142 \| \| 0.3048 \| 14.0 \| 1708 \| 0.3533 \| 0.8446 \| 0.8115 \| 0.8176 \| 0.8144 \| \| 0.2916 \| 15.0 \| 1830 \| 0.3570 \| 0.8421 \| 0.8083 \| 0.8158 \| 0.8119 \| \| 0.2832 \| 16.0 \| 1952 \| 0.3579 \| 0.8471 \| 0.8138 \| 0.8243 \| 0.8187 \| \| 0.284 \| 17.0 \| 2074 \| 0.3496 \| 0.8471 \| 0.8153 \| 0.8168 \| 0.8160 \| \| 0.2906 \| 18.0 \| 2196 \| 0.3537 \| 0.8446 \| 0.8111 \| 0.8201 \| 0.8153 \| \| 0.2805 \| 19.0 \| 2318 \| 0.3505 \| 0.8496 \| 0.8186 \| 0.8186 \| 0.8186 \| \| 0.2815 \| 20.0 \| 2440 \| 0.3513 \| 0.8471 \| 0.8147 \| 0.8193 \| 0.8169 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
OwOpeepeepoopoo/DancingElaine	OwOpeepeepoopoo	2024-05-17T20:55:19Z	5	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T11:20:06Z	--- 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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wassemgtk/FC	wassemgtk	2024-05-17T20:53:14Z	9	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "conversational", "base_model:meetkai/functionary-medium-v2.4", "base_model:finetune:meetkai/functionary-medium-v2.4", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T20:36:21Z	--- base_model: - meetkai/functionary-medium-v2.4 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 passthrough merge method. ### Models Merged The following models were included in the merge: * [meetkai/functionary-medium-v2.4](https://huggingface.co/meetkai/functionary-medium-v2.4) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - layer_range: [0, 10] model: meetkai/functionary-medium-v2.4 - sources: - layer_range: [5, 20] model: meetkai/functionary-medium-v2.4 - sources: - layer_range: [25, 30] model: meetkai/functionary-medium-v2.4 merge_method: passthrough dtype: float16 ```
hi000000/Insta_llama2_blackup	hi000000	2024-05-17T20:53:13Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T20:40:26Z	--- 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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hi000000/DRAFT_Insta_llama2_FT_att	hi000000	2024-05-17T20:51:57Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T18:49:52Z	--- 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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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. 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Viku06/ppo-LunarLander-v2	Viku06	2024-05-17T20:47:29Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T20:47:03Z	--- 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: 263.39 +/- 16.02 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 ... ```
redponike/MAmmoTH2-8B-Plus-GGUF	redponike	2024-05-17T20:47:09Z	5	3	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T19:38:39Z	GGUF quants of [TIGER-Lab/MAmmoTH2-8B-Plus](https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus)
berquetR/phi15_second_run_non_quantized-Q4_K_M-GGUF	berquetR	2024-05-17T20:45:00Z	5	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T20:44:52Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo --- # berquetR/phi15_second_run_non_quantized-Q4_K_M-GGUF This model was converted to GGUF format from [`berquetR/phi15_second_run_non_quantized`](https://huggingface.co/berquetR/phi15_second_run_non_quantized) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/berquetR/phi15_second_run_non_quantized) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo berquetR/phi15_second_run_non_quantized-Q4_K_M-GGUF --model phi15_second_run_non_quantized.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo berquetR/phi15_second_run_non_quantized-Q4_K_M-GGUF --model phi15_second_run_non_quantized.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m phi15_second_run_non_quantized.Q4_K_M.gguf -n 128 ```
RichardErkhov/dreamgen_-_opus-v1.2-7b-8bits	RichardErkhov	2024-05-17T20:43:30Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T20:37:37Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) opus-v1.2-7b - bnb 8bits - Model creator: https://huggingface.co/dreamgen/ - Original model: https://huggingface.co/dreamgen/opus-v1.2-7b/ Original model description: --- language: - en pipeline_tag: text-generation tags: - unsloth - axolotl license: cc-by-nc-nd-4.0 --- # DreamGen Opus V1 <div style="display: flex; flex-direction: row; align-items: center;"> <img src="/dreamgen/opus-v1.2-7b/resolve/main/images/logo-1024.png" alt="model logo" style=" border-radius: 12px; margin-right: 12px; margin-top: 0px; margin-bottom: 0px; max-width: 100px; height: auto; "/> Models for (steerable) story-writing and role-playing. <br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31). </div> ## Resources - [Opus V1 prompting guide](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy. - [Google Colab](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`. - [Python code](example/prompt/format.py) to format the prompt correctly. - Join the community on [Discord](https://dreamgen.com/discord) to get early access to new models. <img src="/dreamgen/opus-v1.2-7b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style=" padding: 12px; border-radius: 12px; border: 2px solid #f9a8d4; background: rgb(9, 9, 11); "/> ## Prompting <details> <summary>The models use an extended version of ChatML.</summary> ``` <\|im_start\|>system (Story description in the right format here) (Typically consists of plot description, style description and characters)<\|im_end\|> <\|im_start\|>user (Your instruction on how the story should continue)<\|im_end\|> <\|im_start\|>text names= Alice (Continuation of the story from the Alice character)<\|im_end\|> <\|im_start\|>text (Continuation of the story from no character in particular (pure narration))<\|im_end\|> <\|im_start\|>user (Your instruction on how the story should continue)<\|im_end\|> <\|im_start\|>text names= Bob (Continuation of the story from the Bob character)<\|im_end\|> ``` The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names. Pay attention to the following: - The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play. - There can be multiple subsequent message with a `text` role, especially if names are involved. - There can be multiple names attached to a message. - The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names. </details> While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance. Here's how you can prompt the model for the following tasks ### Steerable Story-writing and Role-playing: - [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing) - [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing) - Input: - System prompt: You provide story / role-play description, which consists of: - Plot description - Style description - Characters and their descriptions - Conversation turns: - Text / message turn: This represents part of the story or role play - Instruction: This tells the model what should happen next - Output: Continuation of the story / role-play. ### Story plot summarization - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description) - Input: A story, or a few chapters of a story. - Output: A description of the story or chapters. #### Examples: <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary> <blockquote> Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic. </blockquote> </details> <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary> <blockquote> The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth. One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic. As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary> <blockquote> The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary> <blockquote> A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses. </blockquote> </details> ### Story character description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description) - Input: A story, or a few chapters of a story, set of characters. - Output: A description of the characters. #### Examples: <details> <summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary> <blockquote> Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui. </blockquote> </details> <details> <summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary> <blockquote> The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision. </blockquote> </details> ### Story style description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description) - Input: A story, or a few chapters of a story. - Output: A description the style of the story. <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary> <blockquote> The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose. </blockquote> </details> <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary> <blockquote> The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory. </blockquote> </details> ### Story description to chapters - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions) - Input: A brief plot description and the desired number of chapters. - Output: A description for each chapter. ### And more... ## Sampling params For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`. You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures. ## Dataset The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long. All story-writing and role-playing examples were based on human-written text. ![token count distribution](images/token_count_cum__token_bucket.png) ## Running the model The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization. I recommend using these model versions: - 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b) - 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq) - 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq) ### Running on DreamGen.com (free) You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api). ### Running Locally - Make sure your prompt is as close as possible to the Opus V1 - Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly. - [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1) - [Read the prompt formatting code](example/prompt/format.py) - Make sure `<\|im_start\|>` and `<\|im_end\|>` are tokenized correctly - vLLM - [Google Colab](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU. - [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario. - SillyTavern - [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models - SillyTavern (staging) comes with built-in DreamGen preset for RP - Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-7b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti - Make sure to unselect `Skip special tokens`, otherwise it won't work - This is just an attempt at approximating the Opus V1 prompt, it won't be perfect - Character cards specifically rewritten for the built-in DreamGen preset: - [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card) - [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony) - LM Studio - [Config](configs/lmstudio/preset.json) - Just like ChatML, just changed "assistant" to "text" role. - There's a bug in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280). - HuggingFace - [Chat template](tokenizer_config.json#L51) - Just like ChatML, just changed "assistant" to "text" role. ## Known Issues - 34B repetition: - The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes. - GGUF: - The tokenization might be messed up. Some users reported that `<\|im_start\|>` and `<\|im_end\|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer). ## License - This model is intended for personal use only, other use is not permitted.
berquetR/phi15_second_run_non_quantized	berquetR	2024-05-17T20:42:05Z	7	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T20:33:06Z	--- 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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nuebaek/draft_mistral_ft_insta	nuebaek	2024-05-17T20:35:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T20:34:13Z	--- 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. 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(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]
Dabococo/OWAI_02	Dabococo	2024-05-17T20:34:21Z	0	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T17:54:22Z	--- license: apache-2.0 ---
RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf	RichardErkhov	2024-05-17T20:28:57Z	48	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T18:11:46Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephykor-ko-beta-7b-chang - GGUF - Model creator: https://huggingface.co/lcw99/ - Original model: https://huggingface.co/lcw99/zephykor-ko-beta-7b-chang/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [zephykor-ko-beta-7b-chang.Q2_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q2_K.gguf) \| Q2_K \| 2.7GB \| \| [zephykor-ko-beta-7b-chang.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.IQ3_XS.gguf) \| IQ3_XS \| 2.99GB \| \| [zephykor-ko-beta-7b-chang.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.IQ3_S.gguf) \| IQ3_S \| 3.14GB \| \| [zephykor-ko-beta-7b-chang.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q3_K_S.gguf) \| Q3_K_S \| 3.13GB \| \| [zephykor-ko-beta-7b-chang.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.IQ3_M.gguf) \| IQ3_M \| 3.24GB \| \| [zephykor-ko-beta-7b-chang.Q3_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q3_K.gguf) \| Q3_K \| 3.46GB \| \| [zephykor-ko-beta-7b-chang.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q3_K_M.gguf) \| Q3_K_M \| 3.46GB \| \| [zephykor-ko-beta-7b-chang.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q3_K_L.gguf) \| Q3_K_L \| 3.74GB \| \| [zephykor-ko-beta-7b-chang.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.IQ4_XS.gguf) \| IQ4_XS \| 3.87GB \| \| [zephykor-ko-beta-7b-chang.Q4_0.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q4_0.gguf) \| Q4_0 \| 4.02GB \| \| [zephykor-ko-beta-7b-chang.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.IQ4_NL.gguf) \| IQ4_NL \| 4.07GB \| \| [zephykor-ko-beta-7b-chang.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q4_K_S.gguf) \| Q4_K_S \| 4.05GB \| \| [zephykor-ko-beta-7b-chang.Q4_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q4_K.gguf) \| Q4_K \| 4.27GB \| \| [zephykor-ko-beta-7b-chang.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q4_K_M.gguf) \| Q4_K_M \| 4.27GB \| \| [zephykor-ko-beta-7b-chang.Q4_1.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q4_1.gguf) \| Q4_1 \| 4.45GB \| \| [zephykor-ko-beta-7b-chang.Q5_0.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q5_0.gguf) \| Q5_0 \| 4.87GB \| \| [zephykor-ko-beta-7b-chang.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q5_K_S.gguf) \| Q5_K_S \| 4.87GB \| \| [zephykor-ko-beta-7b-chang.Q5_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q5_K.gguf) \| Q5_K \| 4.99GB \| \| [zephykor-ko-beta-7b-chang.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q5_K_M.gguf) \| Q5_K_M \| 4.99GB \| \| [zephykor-ko-beta-7b-chang.Q5_1.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q5_1.gguf) \| Q5_1 \| 5.29GB \| \| [zephykor-ko-beta-7b-chang.Q6_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q6_K.gguf) \| Q6_K \| 5.77GB \| \| [zephykor-ko-beta-7b-chang.Q8_0.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-gguf/blob/main/zephykor-ko-beta-7b-chang.Q8_0.gguf) \| Q8_0 \| 7.47GB \| Original model description: --- language: - ko - en --- * Under construction, be carefull.
mradermacher/Llama-3-8B-Synthia-v3.5-GGUF	mradermacher	2024-05-17T20:26:10Z	13	0	transformers	[ "transformers", "gguf", "en", "base_model:migtissera/Llama-3-8B-Synthia-v3.5", "base_model:quantized:migtissera/Llama-3-8B-Synthia-v3.5", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T19:58:21Z	--- base_model: migtissera/Llama-3-8B-Synthia-v3.5 language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Synthia-v3.5-GGUF/resolve/main/Llama-3-8B-Synthia-v3.5.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
abbenedek/whisper-base.en-finetuning3-D3K	abbenedek	2024-05-17T20:20:30Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T20:20:15Z	--- 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]
abbenedek/abbenedekwhisper-base.en-finetuning3-D3K	abbenedek	2024-05-17T20:20:28Z	124	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-base.en", "base_model:finetune:openai/whisper-base.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-17T16:54:46Z	--- license: apache-2.0 base_model: openai/whisper-base.en tags: - generated_from_trainer metrics: - wer model-index: - name: abbenedekwhisper-base.en-finetuning3-D3K 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. --> # abbenedekwhisper-base.en-finetuning3-D3K This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3880 - Cer: 68.1692 - Wer: 115.5629 - Ser: 100.0 - Cer Clean: 3.6171 - Wer Clean: 6.2914 - Ser Clean: 7.0175 ## 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-08 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \| Ser \| Cer Clean \| Wer Clean \| Ser Clean \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:--------:\|:-----:\|:---------:\|:---------:\|:---------:\| \| 7.3491 \| 1.06 \| 200 \| 6.1358 \| 64.7746 \| 122.5166 \| 100.0 \| 3.2832 \| 5.6291 \| 7.0175 \| \| 6.162 \| 2.13 \| 400 \| 5.2935 \| 64.2181 \| 119.8675 \| 100.0 \| 3.7284 \| 6.6225 \| 7.8947 \| \| 5.3192 \| 3.19 \| 600 \| 4.7534 \| 64.6633 \| 119.2053 \| 100.0 \| 3.5058 \| 6.2914 \| 7.0175 \| \| 4.7266 \| 4.26 \| 800 \| 4.3761 \| 65.1085 \| 118.2119 \| 100.0 \| 3.2832 \| 5.9603 \| 6.1404 \| \| 4.2728 \| 5.32 \| 1000 \| 4.0472 \| 65.9432 \| 117.2185 \| 100.0 \| 3.2276 \| 5.9603 \| 6.1404 \| \| 3.9248 \| 6.38 \| 1200 \| 3.7904 \| 66.7223 \| 116.2252 \| 100.0 \| 3.2276 \| 5.9603 \| 6.1404 \| \| 3.6714 \| 7.45 \| 1400 \| 3.6008 \| 67.8909 \| 117.2185 \| 100.0 \| 3.1720 \| 5.9603 \| 6.1404 \| \| 3.499 \| 8.51 \| 1600 \| 3.4790 \| 69.0595 \| 118.2119 \| 100.0 \| 3.1720 \| 5.9603 \| 6.1404 \| \| 3.393 \| 9.57 \| 1800 \| 3.4106 \| 68.9482 \| 117.5497 \| 100.0 \| 3.1720 \| 5.9603 \| 6.1404 \| \| 3.3491 \| 10.64 \| 2000 \| 3.3880 \| 68.1692 \| 115.5629 \| 100.0 \| 3.6171 \| 6.2914 \| 7.0175 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.14.5 - Tokenizers 0.15.2
qunfengd/esm2_t12_35M_UR50D-finetuned-AMP_Classification_AntiGramPositive	qunfengd	2024-05-17T20:20:21Z	61	0	transformers	[ "transformers", "tf", "esm", "text-classification", "generated_from_keras_callback", "base_model:facebook/esm2_t12_35M_UR50D", "base_model:finetune:facebook/esm2_t12_35M_UR50D", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T20:20:13Z	--- license: mit tags: - generated_from_keras_callback base_model: facebook/esm2_t12_35M_UR50D model-index: - name: esm2_t12_35M_UR50D-finetuned-AMP_Classification_AntiGramPositive results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # esm2_t12_35M_UR50D-finetuned-AMP_Classification_AntiGramPositive This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3822 - Train Accuracy: 0.8324 - Validation Loss: 0.4433 - Validation Accuracy: 0.8020 - Epoch: 2 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.5531 \| 0.7238 \| 0.4958 \| 0.7804 \| 0 \| \| 0.4654 \| 0.7885 \| 0.4547 \| 0.7921 \| 1 \| \| 0.3822 \| 0.8324 \| 0.4433 \| 0.8020 \| 2 \| ### Framework versions - Transformers 4.40.2 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
neovalle/ArmoniosaAnthea	neovalle	2024-05-17T20:16:36Z	11	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T20:11:10Z	--- 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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DrNicefellow/microscopic-mamba-2.1B-hf-7.8ksteps	DrNicefellow	2024-05-17T20:14:43Z	4	0	transformers	[ "transformers", "pytorch", "mamba", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T23:05:41Z	--- license: apache-2.0 --- Self trained microscopic Mamba. Around 2.1G parameters. The tokenizer is the one from https://huggingface.co/state-spaces/mamba-2.8b-hf. It is being trained on around 400B tokens and this is step 7.8k. The evaluation is being conducted now. ## License This model is available under the Apache 2.0 License. ## Discord Server Join our Discord server [here](https://discord.gg/xhcBDEM3). ## Feeling Generous? 😊 Eager to buy me a cup of 2$ coffe or iced tea?🍵☕ Sure, here is the link: [https://ko-fi.com/drnicefellow](https://ko-fi.com/drnicefellow). Please add a note on which one you want me to drink?
jeongmi/insta-A_attr_solar_ft	jeongmi	2024-05-17T20:14:17Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T20:02:22Z	--- 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. 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. 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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. 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macostaplata/finetuned_sentiment_analysis_model_yelp	macostaplata	2024-05-17T20:10:27Z	110	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "simplification", "generated_from_trainer", "base_model:distilbert/distilbert-base-cased", "base_model:finetune:distilbert/distilbert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T20:09:59Z	--- license: apache-2.0 base_model: distilbert-base-cased tags: - simplification - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: finetuned_sentiment_analysis_model_yelp 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. --> # finetuned_sentiment_analysis_model_yelp This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8819 - Precision: 0.6435 - Recall: 0.6438 - F1: 0.6435 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.8693 \| 1.0 \| 3657 \| 0.8631 \| 0.6183 \| 0.6197 \| 0.6183 \| \| 0.7493 \| 2.0 \| 7314 \| 0.8451 \| 0.6358 \| 0.6361 \| 0.6350 \| \| 0.5914 \| 3.0 \| 10971 \| 0.8819 \| 0.6435 \| 0.6438 \| 0.6435 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mradermacher/Seamaiiza-7B-v3-32k-GGUF	mradermacher	2024-05-17T20:10:14Z	48	2	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "grimjim/kunoichi-lemon-royale-v2-32K-7B", "AlekseiPravdin/Seamaiiza-7B-v1", "Nitral-AI/Nyanade_Stunna-Maid-7B", "en", "base_model:AlekseiPravdin/Seamaiiza-7B-v3-32k", "base_model:quantized:AlekseiPravdin/Seamaiiza-7B-v3-32k", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T19:07:21Z	--- base_model: AlekseiPravdin/Seamaiiza-7B-v3-32k language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - grimjim/kunoichi-lemon-royale-v2-32K-7B - AlekseiPravdin/Seamaiiza-7B-v1 - Nitral-AI/Nyanade_Stunna-Maid-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/AlekseiPravdin/Seamaiiza-7B-v3-32k <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Seamaiiza-7B-v3-32k-GGUF/resolve/main/Seamaiiza-7B-v3-32k.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
amaye15/microsoft-swinv2-base-patch4-window16-256-batch32-lr5e-05-standford-dogs	amaye15	2024-05-17T20:07:02Z	156	0	transformers	[ "transformers", "safetensors", "swinv2", "image-classification", "generated_from_trainer", "dataset:stanford-dogs", "base_model:microsoft/swinv2-base-patch4-window16-256", "base_model:finetune:microsoft/swinv2-base-patch4-window16-256", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-17T20:06:39Z	--- license: apache-2.0 base_model: microsoft/swinv2-base-patch4-window16-256 tags: - generated_from_trainer datasets: - stanford-dogs metrics: - accuracy - f1 - precision - recall model-index: - name: microsoft-swinv2-base-patch4-window16-256-batch32-lr5e-05-standford-dogs results: - task: name: Image Classification type: image-classification dataset: name: stanford-dogs type: stanford-dogs config: default split: full args: default metrics: - name: Accuracy type: accuracy value: 0.9467930029154519 - name: F1 type: f1 value: 0.9450299849824627 - name: Precision type: precision value: 0.9479779072439513 - name: Recall type: recall value: 0.9453246844288115 --- <!-- 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. --> # microsoft-swinv2-base-patch4-window16-256-batch32-lr5e-05-standford-dogs This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the stanford-dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.1856 - Accuracy: 0.9468 - F1: 0.9450 - Precision: 0.9480 - Recall: 0.9453 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 4.7437 \| 0.0777 \| 10 \| 4.6395 \| 0.0862 \| 0.0519 \| 0.0499 \| 0.0821 \| \| 4.5551 \| 0.1553 \| 20 \| 4.3696 \| 0.1713 \| 0.1162 \| 0.1608 \| 0.1573 \| \| 4.2151 \| 0.2330 \| 30 \| 3.8252 \| 0.3188 \| 0.2681 \| 0.4133 \| 0.3021 \| \| 3.5619 \| 0.3107 \| 40 \| 2.8929 \| 0.6368 \| 0.5785 \| 0.6552 \| 0.6211 \| \| 2.6253 \| 0.3883 \| 50 \| 1.8693 \| 0.7850 \| 0.7538 \| 0.7906 \| 0.7733 \| \| 1.8818 \| 0.4660 \| 60 \| 1.1203 \| 0.8542 \| 0.8406 \| 0.8667 \| 0.8468 \| \| 1.3652 \| 0.5437 \| 70 \| 0.7330 \| 0.8880 \| 0.8780 \| 0.9039 \| 0.8850 \| \| 1.0456 \| 0.6214 \| 80 \| 0.5269 \| 0.9084 \| 0.9015 \| 0.9101 \| 0.9050 \| \| 0.9039 \| 0.6990 \| 90 \| 0.4139 \| 0.9181 \| 0.9093 \| 0.9213 \| 0.9150 \| \| 0.7965 \| 0.7767 \| 100 \| 0.3441 \| 0.9249 \| 0.9181 \| 0.9315 \| 0.9221 \| \| 0.7053 \| 0.8544 \| 110 \| 0.3184 \| 0.9225 \| 0.9163 \| 0.9320 \| 0.9208 \| \| 0.6907 \| 0.9320 \| 120 \| 0.2870 \| 0.9283 \| 0.9261 \| 0.9324 \| 0.9270 \| \| 0.6293 \| 1.0097 \| 130 \| 0.2760 \| 0.9276 \| 0.9245 \| 0.9329 \| 0.9260 \| \| 0.5564 \| 1.0874 \| 140 \| 0.2517 \| 0.9339 \| 0.9308 \| 0.9362 \| 0.9320 \| \| 0.5902 \| 1.1650 \| 150 \| 0.2500 \| 0.9351 \| 0.9308 \| 0.9371 \| 0.9328 \| \| 0.5269 \| 1.2427 \| 160 \| 0.2429 \| 0.9334 \| 0.9307 \| 0.9370 \| 0.9317 \| \| 0.5148 \| 1.3204 \| 170 \| 0.2358 \| 0.9393 \| 0.9368 \| 0.9407 \| 0.9373 \| \| 0.4998 \| 1.3981 \| 180 \| 0.2451 \| 0.9310 \| 0.9270 \| 0.9357 \| 0.9283 \| \| 0.4797 \| 1.4757 \| 190 \| 0.2425 \| 0.9325 \| 0.9287 \| 0.9377 \| 0.9315 \| \| 0.4933 \| 1.5534 \| 200 \| 0.2360 \| 0.9281 \| 0.9257 \| 0.9333 \| 0.9266 \| \| 0.4414 \| 1.6311 \| 210 \| 0.2201 \| 0.9371 \| 0.9343 \| 0.9398 \| 0.9351 \| \| 0.4401 \| 1.7087 \| 220 \| 0.2248 \| 0.9346 \| 0.9327 \| 0.9375 \| 0.9337 \| \| 0.4023 \| 1.7864 \| 230 \| 0.2199 \| 0.9344 \| 0.9282 \| 0.9381 \| 0.9317 \| \| 0.4723 \| 1.8641 \| 240 \| 0.2071 \| 0.9419 \| 0.9389 \| 0.9437 \| 0.9401 \| \| 0.4593 \| 1.9417 \| 250 \| 0.2123 \| 0.9402 \| 0.9371 \| 0.9421 \| 0.9382 \| \| 0.4544 \| 2.0194 \| 260 \| 0.2191 \| 0.9385 \| 0.9347 \| 0.9396 \| 0.9371 \| \| 0.3871 \| 2.0971 \| 270 \| 0.2158 \| 0.9395 \| 0.9372 \| 0.9401 \| 0.9378 \| \| 0.4162 \| 2.1748 \| 280 \| 0.2073 \| 0.9385 \| 0.9353 \| 0.9396 \| 0.9364 \| \| 0.3774 \| 2.2524 \| 290 \| 0.1981 \| 0.9397 \| 0.9387 \| 0.9422 \| 0.9387 \| \| 0.3895 \| 2.3301 \| 300 \| 0.2008 \| 0.9400 \| 0.9361 \| 0.9395 \| 0.9376 \| \| 0.3804 \| 2.4078 \| 310 \| 0.2018 \| 0.9431 \| 0.9396 \| 0.9443 \| 0.9412 \| \| 0.3783 \| 2.4854 \| 320 \| 0.2038 \| 0.9422 \| 0.9384 \| 0.9439 \| 0.9403 \| \| 0.4376 \| 2.5631 \| 330 \| 0.1968 \| 0.9419 \| 0.9404 \| 0.9459 \| 0.9414 \| \| 0.3696 \| 2.6408 \| 340 \| 0.2011 \| 0.9441 \| 0.9422 \| 0.9464 \| 0.9430 \| \| 0.3954 \| 2.7184 \| 350 \| 0.1997 \| 0.9417 \| 0.9379 \| 0.9430 \| 0.9399 \| \| 0.3651 \| 2.7961 \| 360 \| 0.1952 \| 0.9434 \| 0.9392 \| 0.9407 \| 0.9415 \| \| 0.3646 \| 2.8738 \| 370 \| 0.2045 \| 0.9429 \| 0.9391 \| 0.9459 \| 0.9413 \| \| 0.3532 \| 2.9515 \| 380 \| 0.1991 \| 0.9427 \| 0.9394 \| 0.9455 \| 0.9413 \| \| 0.342 \| 3.0291 \| 390 \| 0.1958 \| 0.9410 \| 0.9399 \| 0.9441 \| 0.9404 \| \| 0.3706 \| 3.1068 \| 400 \| 0.2010 \| 0.9419 \| 0.9401 \| 0.9442 \| 0.9406 \| \| 0.3031 \| 3.1845 \| 410 \| 0.2013 \| 0.9424 \| 0.9407 \| 0.9449 \| 0.9410 \| \| 0.3345 \| 3.2621 \| 420 \| 0.2022 \| 0.9414 \| 0.9399 \| 0.9438 \| 0.9406 \| \| 0.3356 \| 3.3398 \| 430 \| 0.1927 \| 0.9470 \| 0.9451 \| 0.9500 \| 0.9453 \| \| 0.3538 \| 3.4175 \| 440 \| 0.1927 \| 0.9446 \| 0.9422 \| 0.9472 \| 0.9430 \| \| 0.3505 \| 3.4951 \| 450 \| 0.1909 \| 0.9480 \| 0.9461 \| 0.9498 \| 0.9466 \| \| 0.3398 \| 3.5728 \| 460 \| 0.1917 \| 0.9453 \| 0.9419 \| 0.9475 \| 0.9436 \| \| 0.3303 \| 3.6505 \| 470 \| 0.1895 \| 0.9483 \| 0.9453 \| 0.9506 \| 0.9464 \| \| 0.3685 \| 3.7282 \| 480 \| 0.1883 \| 0.9458 \| 0.9442 \| 0.9468 \| 0.9445 \| \| 0.3125 \| 3.8058 \| 490 \| 0.1926 \| 0.9441 \| 0.9422 \| 0.9462 \| 0.9426 \| \| 0.3857 \| 3.8835 \| 500 \| 0.1911 \| 0.9446 \| 0.9426 \| 0.9473 \| 0.9430 \| \| 0.3407 \| 3.9612 \| 510 \| 0.1825 \| 0.9470 \| 0.9454 \| 0.9486 \| 0.9459 \| \| 0.3545 \| 4.0388 \| 520 \| 0.1919 \| 0.9444 \| 0.9428 \| 0.9448 \| 0.9432 \| \| 0.306 \| 4.1165 \| 530 \| 0.1901 \| 0.9466 \| 0.9437 \| 0.9471 \| 0.9450 \| \| 0.2511 \| 4.1942 \| 540 \| 0.2026 \| 0.9431 \| 0.9388 \| 0.9448 \| 0.9410 \| \| 0.3233 \| 4.2718 \| 550 \| 0.1950 \| 0.9453 \| 0.9433 \| 0.9470 \| 0.9438 \| \| 0.2793 \| 4.3495 \| 560 \| 0.1973 \| 0.9453 \| 0.9437 \| 0.9466 \| 0.9444 \| \| 0.3035 \| 4.4272 \| 570 \| 0.1944 \| 0.9470 \| 0.9454 \| 0.9491 \| 0.9459 \| \| 0.2776 \| 4.5049 \| 580 \| 0.2030 \| 0.9412 \| 0.9393 \| 0.9445 \| 0.9398 \| \| 0.3204 \| 4.5825 \| 590 \| 0.1959 \| 0.9441 \| 0.9417 \| 0.9468 \| 0.9428 \| \| 0.2868 \| 4.6602 \| 600 \| 0.1959 \| 0.9429 \| 0.9413 \| 0.9437 \| 0.9414 \| \| 0.3325 \| 4.7379 \| 610 \| 0.1991 \| 0.9414 \| 0.9389 \| 0.9435 \| 0.9401 \| \| 0.3255 \| 4.8155 \| 620 \| 0.1894 \| 0.9441 \| 0.9425 \| 0.9448 \| 0.9431 \| \| 0.2744 \| 4.8932 \| 630 \| 0.1915 \| 0.9434 \| 0.9411 \| 0.9434 \| 0.9421 \| \| 0.2945 \| 4.9709 \| 640 \| 0.1932 \| 0.9453 \| 0.9415 \| 0.9468 \| 0.9436 \| \| 0.253 \| 5.0485 \| 650 \| 0.1928 \| 0.9448 \| 0.9423 \| 0.9465 \| 0.9435 \| \| 0.2614 \| 5.1262 \| 660 \| 0.1942 \| 0.9451 \| 0.9441 \| 0.9478 \| 0.9444 \| \| 0.2699 \| 5.2039 \| 670 \| 0.1924 \| 0.9468 \| 0.9433 \| 0.9479 \| 0.9451 \| \| 0.2839 \| 5.2816 \| 680 \| 0.1894 \| 0.9461 \| 0.9442 \| 0.9475 \| 0.9447 \| \| 0.2353 \| 5.3592 \| 690 \| 0.1947 \| 0.9427 \| 0.9407 \| 0.9435 \| 0.9410 \| \| 0.2627 \| 5.4369 \| 700 \| 0.1964 \| 0.9419 \| 0.9405 \| 0.9440 \| 0.9409 \| \| 0.2592 \| 5.5146 \| 710 \| 0.1893 \| 0.9456 \| 0.9440 \| 0.9468 \| 0.9441 \| \| 0.2634 \| 5.5922 \| 720 \| 0.1918 \| 0.9458 \| 0.9431 \| 0.9473 \| 0.9443 \| \| 0.294 \| 5.6699 \| 730 \| 0.1922 \| 0.9446 \| 0.9417 \| 0.9457 \| 0.9428 \| \| 0.2565 \| 5.7476 \| 740 \| 0.1907 \| 0.9456 \| 0.9432 \| 0.9469 \| 0.9439 \| \| 0.2657 \| 5.8252 \| 750 \| 0.1902 \| 0.9453 \| 0.9415 \| 0.9464 \| 0.9434 \| \| 0.2945 \| 5.9029 \| 760 \| 0.1872 \| 0.9453 \| 0.9427 \| 0.9457 \| 0.9439 \| \| 0.2758 \| 5.9806 \| 770 \| 0.1855 \| 0.9444 \| 0.9432 \| 0.9460 \| 0.9430 \| \| 0.226 \| 6.0583 \| 780 \| 0.1867 \| 0.9470 \| 0.9456 \| 0.9488 \| 0.9457 \| \| 0.2105 \| 6.1359 \| 790 \| 0.1866 \| 0.9470 \| 0.9446 \| 0.9482 \| 0.9451 \| \| 0.2524 \| 6.2136 \| 800 \| 0.1891 \| 0.9456 \| 0.9441 \| 0.9470 \| 0.9441 \| \| 0.2987 \| 6.2913 \| 810 \| 0.1879 \| 0.9463 \| 0.9442 \| 0.9472 \| 0.9447 \| \| 0.2393 \| 6.3689 \| 820 \| 0.1876 \| 0.9456 \| 0.9439 \| 0.9467 \| 0.9442 \| \| 0.2779 \| 6.4466 \| 830 \| 0.1870 \| 0.9473 \| 0.9460 \| 0.9486 \| 0.9463 \| \| 0.3117 \| 6.5243 \| 840 \| 0.1866 \| 0.9470 \| 0.9450 \| 0.9483 \| 0.9455 \| \| 0.2574 \| 6.6019 \| 850 \| 0.1853 \| 0.9468 \| 0.9449 \| 0.9481 \| 0.9454 \| \| 0.2307 \| 6.6796 \| 860 \| 0.1886 \| 0.9463 \| 0.9441 \| 0.9475 \| 0.9447 \| \| 0.2771 \| 6.7573 \| 870 \| 0.1878 \| 0.9456 \| 0.9437 \| 0.9464 \| 0.9440 \| \| 0.2575 \| 6.8350 \| 880 \| 0.1868 \| 0.9458 \| 0.9440 \| 0.9465 \| 0.9443 \| \| 0.2422 \| 6.9126 \| 890 \| 0.1857 \| 0.9463 \| 0.9447 \| 0.9466 \| 0.9448 \| \| 0.2564 \| 6.9903 \| 900 \| 0.1861 \| 0.9451 \| 0.9434 \| 0.9458 \| 0.9437 \| \| 0.222 \| 7.0680 \| 910 \| 0.1866 \| 0.9461 \| 0.9442 \| 0.9471 \| 0.9445 \| \| 0.2467 \| 7.1456 \| 920 \| 0.1862 \| 0.9456 \| 0.9438 \| 0.9464 \| 0.9441 \| \| 0.2412 \| 7.2233 \| 930 \| 0.1860 \| 0.9463 \| 0.9449 \| 0.9474 \| 0.9451 \| \| 0.2518 \| 7.3010 \| 940 \| 0.1857 \| 0.9458 \| 0.9442 \| 0.9466 \| 0.9445 \| \| 0.2811 \| 7.3786 \| 950 \| 0.1857 \| 0.9463 \| 0.9446 \| 0.9472 \| 0.9448 \| \| 0.2255 \| 7.4563 \| 960 \| 0.1856 \| 0.9468 \| 0.9451 \| 0.9477 \| 0.9453 \| \| 0.2425 \| 7.5340 \| 970 \| 0.1857 \| 0.9466 \| 0.9449 \| 0.9478 \| 0.9451 \| \| 0.2352 \| 7.6117 \| 980 \| 0.1856 \| 0.9468 \| 0.9450 \| 0.9480 \| 0.9453 \| \| 0.2328 \| 7.6893 \| 990 \| 0.1855 \| 0.9468 \| 0.9450 \| 0.9480 \| 0.9453 \| \| 0.2353 \| 7.7670 \| 1000 \| 0.1856 \| 0.9468 \| 0.9450 \| 0.9480 \| 0.9453 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
wendy41/kogpt-insta-2	wendy41	2024-05-17T20:00:24Z	78	0	transformers	[ "transformers", "safetensors", "gptj", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T19:58:43Z	--- 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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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. 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DizzyDozze/my_awesome_qa_model	DizzyDozze	2024-05-17T19:58:22Z	62	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-17T01:31:52Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: DizzyDozze/my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # DizzyDozze/my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.9734 - Validation Loss: 4.3852 - Epoch: 2 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.6834 \| 4.4291 \| 0 \| \| 4.1255 \| 4.3852 \| 1 \| \| 3.9734 \| 4.3852 \| 2 \| ### Framework versions - Transformers 4.40.2 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
tomasonjo/text2cypher-demo-8bit-gguf	tomasonjo	2024-05-17T19:54:41Z	7	1	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:tomasonjo/text2cypher-demo-16bit", "base_model:quantized:tomasonjo/text2cypher-demo-16bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T19:50:27Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: tomasonjo/text2cypher-demo-16bit --- # Uploaded model - Developed by: tomasonjo - License: apache-2.0 - Finetuned from model : tomasonjo/text2cypher-demo-16bit This llama 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)
apwic/sentiment-lora-r4a1d0.05-0	apwic	2024-05-17T19:52:46Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T19:19:18Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a1d0.05-0 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. --> # sentiment-lora-r4a1d0.05-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3486 - Accuracy: 0.8396 - Precision: 0.8055 - Recall: 0.8115 - F1: 0.8084 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5619 \| 1.0 \| 122 \| 0.5127 \| 0.7168 \| 0.6536 \| 0.6446 \| 0.6484 \| \| 0.5059 \| 2.0 \| 244 \| 0.4967 \| 0.7343 \| 0.6956 \| 0.7220 \| 0.7022 \| \| 0.4822 \| 3.0 \| 366 \| 0.4506 \| 0.7469 \| 0.7006 \| 0.7159 \| 0.7065 \| \| 0.4402 \| 4.0 \| 488 \| 0.3984 \| 0.8195 \| 0.7876 \| 0.7623 \| 0.7728 \| \| 0.4068 \| 5.0 \| 610 \| 0.4136 \| 0.7870 \| 0.7473 \| 0.7718 \| 0.7561 \| \| 0.3791 \| 6.0 \| 732 \| 0.3771 \| 0.8321 \| 0.7972 \| 0.7987 \| 0.7979 \| \| 0.3635 \| 7.0 \| 854 \| 0.3916 \| 0.8195 \| 0.7822 \| 0.8048 \| 0.7912 \| \| 0.3433 \| 8.0 \| 976 \| 0.3799 \| 0.8296 \| 0.7934 \| 0.8019 \| 0.7974 \| \| 0.3379 \| 9.0 \| 1098 \| 0.3714 \| 0.8271 \| 0.7903 \| 0.8026 \| 0.7959 \| \| 0.3296 \| 10.0 \| 1220 \| 0.3635 \| 0.8371 \| 0.8032 \| 0.8047 \| 0.8040 \| \| 0.3105 \| 11.0 \| 1342 \| 0.3652 \| 0.8296 \| 0.7933 \| 0.8044 \| 0.7984 \| \| 0.3024 \| 12.0 \| 1464 \| 0.3702 \| 0.8346 \| 0.7988 \| 0.8180 \| 0.8069 \| \| 0.309 \| 13.0 \| 1586 \| 0.3512 \| 0.8371 \| 0.8032 \| 0.8047 \| 0.8040 \| \| 0.3021 \| 14.0 \| 1708 \| 0.3505 \| 0.8396 \| 0.8060 \| 0.8090 \| 0.8075 \| \| 0.2903 \| 15.0 \| 1830 \| 0.3553 \| 0.8421 \| 0.8077 \| 0.8208 \| 0.8136 \| \| 0.2834 \| 16.0 \| 1952 \| 0.3530 \| 0.8396 \| 0.8046 \| 0.8215 \| 0.8119 \| \| 0.2811 \| 17.0 \| 2074 \| 0.3471 \| 0.8446 \| 0.8120 \| 0.8151 \| 0.8135 \| \| 0.288 \| 18.0 \| 2196 \| 0.3505 \| 0.8446 \| 0.8107 \| 0.8226 \| 0.8161 \| \| 0.277 \| 19.0 \| 2318 \| 0.3479 \| 0.8396 \| 0.8055 \| 0.8115 \| 0.8084 \| \| 0.2775 \| 20.0 \| 2440 \| 0.3486 \| 0.8396 \| 0.8055 \| 0.8115 \| 0.8084 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
Tales-Alves/Meta-Llama-3-8B-Q6_K-GGUF	Tales-Alves	2024-05-17T19:52:00Z	2	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T19:51:41Z	--- language: - en license: llama3 tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo pipeline_tag: text-generation extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\ \ 3\" means the foundational large language models and software and algorithms,\ \ including machine-learning model code, trained model weights, inference-enabling\ \ code, training-enabling code, fine-tuning enabling code and other elements of\ \ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n\ \ \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted\ \ a non-exclusive, worldwide, non-transferable and royalty-free limited license\ \ under Meta’s intellectual property or other rights owned by Meta embodied in the\ \ Llama Materials to use, reproduce, distribute, copy, create derivative works of,\ \ and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni.\ \ If you distribute or make available the Llama Materials (or any derivative works\ \ thereof), or a product or service that uses any of them, including another AI\ \ model, you shall (A) provide a copy of this Agreement with any such Llama Materials;\ \ and (B) prominently display “Built with Meta Llama 3” on a related website, user\ \ interface, blogpost, about page, or product documentation. 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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # Tales-Alves/Meta-Llama-3-8B-Q6_K-GGUF This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Tales-Alves/Meta-Llama-3-8B-Q6_K-GGUF --model meta-llama-3-8b.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Tales-Alves/Meta-Llama-3-8B-Q6_K-GGUF --model meta-llama-3-8b.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m meta-llama-3-8b.Q6_K.gguf -n 128 ```
jdkrehel/ADR	jdkrehel	2024-05-17T19:40:03Z	109	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T19:39:20Z	--- 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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thorirhrafn/GPT1B_domar_RLHF2	thorirhrafn	2024-05-17T19:39:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T00:13:20Z	--- 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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RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-8bits	RichardErkhov	2024-05-17T19:38:56Z	77	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T19:32:57Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-7B-Discord-0.1 - bnb 8bits - Model creator: https://huggingface.co/BlouseJury/ - Original model: https://huggingface.co/BlouseJury/Mistral-7B-Discord-0.1/ Original model description: --- language: - en license: apache-2.0 tags: - finetune pipeline_tag: text-generation model-index: - name: Mistral-7B-Discord-0.1 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: 60.24 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 83.13 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 62.82 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 44.1 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 78.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 32.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 name: Open LLM Leaderboard --- # Mistral-7B-Discord-0.1 This model is a finetune of [Mistral-7B-0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on ~20 Million tokens worth of mostly not formatted, anonymized discord messages for 4 Epochs. This is a base model. ## Model Details - Finetuned from model : mistralai/Mistral-7B-v0.1 # [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_BlouseJury__Mistral-7B-Discord-0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|60.28\| \|AI2 Reasoning Challenge (25-Shot)\|60.24\| \|HellaSwag (10-Shot) \|83.13\| \|MMLU (5-Shot) \|62.82\| \|TruthfulQA (0-shot) \|44.10\| \|Winogrande (5-shot) \|78.93\| \|GSM8k (5-shot) \|32.45\|
dannys160/a2c-PandaReachDense-v3	dannys160	2024-05-17T19:34:29Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T18:52:37Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.25 +/- 0.09 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
RichardErkhov/BlouseJury_-_Mistral-7B-Discord-0.1-4bits	RichardErkhov	2024-05-17T19:32:20Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T19:28:42Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-7B-Discord-0.1 - bnb 4bits - Model creator: https://huggingface.co/BlouseJury/ - Original model: https://huggingface.co/BlouseJury/Mistral-7B-Discord-0.1/ Original model description: --- language: - en license: apache-2.0 tags: - finetune pipeline_tag: text-generation model-index: - name: Mistral-7B-Discord-0.1 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: 60.24 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 83.13 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 62.82 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 44.1 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 78.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 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: 32.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BlouseJury/Mistral-7B-Discord-0.1 name: Open LLM Leaderboard --- # Mistral-7B-Discord-0.1 This model is a finetune of [Mistral-7B-0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on ~20 Million tokens worth of mostly not formatted, anonymized discord messages for 4 Epochs. This is a base model. ## Model Details - Finetuned from model : mistralai/Mistral-7B-v0.1 # [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_BlouseJury__Mistral-7B-Discord-0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|60.28\| \|AI2 Reasoning Challenge (25-Shot)\|60.24\| \|HellaSwag (10-Shot) \|83.13\| \|MMLU (5-Shot) \|62.82\| \|TruthfulQA (0-shot) \|44.10\| \|Winogrande (5-shot) \|78.93\| \|GSM8k (5-shot) \|32.45\|
yuweiiizz/whisper-small-taiwanese-hanzi-lora	yuweiiizz	2024-05-17T19:30:16Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "nan", "dataset:mozilla-foundation/common_voice_16_1", "base_model:openai/whisper-small", "base_model:adapter:openai/whisper-small", "license:apache-2.0", "region:us" ]	null	2024-05-16T12:36:15Z	--- language: - nan license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openai/whisper-small datasets: - mozilla-foundation/common_voice_16_1 model-index: - name: Whisper Small Taiwanese - hanzi - LoRA results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Taiwanese - hanzi - LoRA This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.2916 ## 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: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.4093 \| 1.0 \| 2500 \| 0.4269 \| \| 0.201 \| 3.0 \| 7500 \| 0.3134 \| \| 0.0987 \| 5.0 \| 12500 \| 0.2916 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mradermacher/penny-llama3-2x8b-v2-GGUF	mradermacher	2024-05-17T19:24:29Z	16	1	transformers	[ "transformers", "gguf", "en", "base_model:giannisan/penny-llama3-2x8b-v2", "base_model:quantized:giannisan/penny-llama3-2x8b-v2", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T17:55:17Z	--- base_model: giannisan/penny-llama3-2x8b-v2 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> static quants of https://huggingface.co/giannisan/penny-llama3-2x8b-v2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q2_K.gguf) \| Q2_K \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.IQ3_XS.gguf) \| IQ3_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q3_K_S.gguf) \| Q3_K_S \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.IQ3_S.gguf) \| IQ3_S \| 6.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.IQ3_M.gguf) \| IQ3_M \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q3_K_M.gguf) \| Q3_K_M \| 6.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q3_K_L.gguf) \| Q3_K_L \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.IQ4_XS.gguf) \| IQ4_XS \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q4_K_S.gguf) \| Q4_K_S \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q4_K_M.gguf) \| Q4_K_M \| 8.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q5_K_S.gguf) \| Q5_K_S \| 9.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q5_K_M.gguf) \| Q5_K_M \| 9.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q6_K.gguf) \| Q6_K \| 11.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/penny-llama3-2x8b-v2-GGUF/resolve/main/penny-llama3-2x8b-v2.Q8_0.gguf) \| Q8_0 \| 14.6 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
pigas/Phi-2-RGPTQ-2bits-L0.4-I0.005	pigas	2024-05-17T19:18:22Z	11	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T16:24:31Z	--- 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. 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Rrrrrrrita/test	Rrrrrrrita	2024-05-17T19:16:38Z	112	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T19:16:22Z	--- 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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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]
redponike/MAmmoTH2-8x7B-Plus-GGUF	redponike	2024-05-17T19:16:30Z	0	1	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T13:50:17Z	GGUF quants of https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus
davemikebrooks/M4_Team10	davemikebrooks	2024-05-17T19:14:53Z	110	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T19:13:39Z	--- 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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Jubliano/wav2vec2-large-xls-r-300m-ipa-INTERNATIONAL1.2	Jubliano	2024-05-17T19:05:01Z	21	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-16T22:39:46Z	--- 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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IEETA/BioNExt-Extractor	IEETA	2024-05-17T19:00:21Z	136	0	transformers	[ "transformers", "safetensors", "relation-novelty-extractor", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2024-05-15T21:21:18Z	--- 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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XCraftMC/Rhea-72b-v0.5-Q4_K_M-GGUF	XCraftMC	2024-05-17T18:57:31Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "en", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	null	2024-05-17T18:51:01Z	--- language: - en license: apache-2.0 library_name: transformers tags: - llama-cpp - gguf-my-repo model-index: - name: Rhea-72b-v0.5 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: 79.78 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 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: 91.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 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: 77.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 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: 74.5 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 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: 87.85 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 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: 76.12 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 name: Open LLM Leaderboard --- # XCraftMC/Rhea-72b-v0.5-Q4_K_M-GGUF This model was converted to GGUF format from [`davidkim205/Rhea-72b-v0.5`](https://huggingface.co/davidkim205/Rhea-72b-v0.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/davidkim205/Rhea-72b-v0.5) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo XCraftMC/Rhea-72b-v0.5-Q4_K_M-GGUF --model rhea-72b-v0.5.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo XCraftMC/Rhea-72b-v0.5-Q4_K_M-GGUF --model rhea-72b-v0.5.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m rhea-72b-v0.5.Q4_K_M.gguf -n 128 ```
PedroNolasco/my-finetuned-bert	PedroNolasco	2024-05-17T18:57:12Z	8	0	transformers	[ "transformers", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-17T18:57:01Z	--- 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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arryn9/my_pipeline	arryn9	2024-05-17T18:53:14Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-05-17T18:52:32Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('arryn9/my_pipeline') image = pipeline().images[0] image ```
ahhany/ConstructionEmbeddingBERT	ahhany	2024-05-17T18:52:13Z	17	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-10-22T03:29:22Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # ConstructionEmbeddingBERT This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1536 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 125 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 12, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1536, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 1536, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ayyoob-cis/vicuna-160m-gptq	ayyoob-cis	2024-05-17T18:50:46Z	4	0	transformers	[ "transformers", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-05-17T17:00:48Z	--- license: apache-2.0 ---
jeongmi/musinsa-A_2jh_solar_ft	jeongmi	2024-05-17T18:49:41Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T18:36:53Z	--- 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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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. 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theglassofwater/hello	theglassofwater	2024-05-17T18:49:24Z	209	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T18:49:09Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: hello 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. --> # hello This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.8037 - Accuracy: 0.0018 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 12 - seed: 444 - gradient_accumulation_steps: 3 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.3 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 8.8106 \| 0.2257 \| 100 \| 8.6901 \| 0.0123 \| \| 8.0312 \| 0.4515 \| 200 \| 7.9921 \| 0.0269 \| \| 7.7274 \| 0.6772 \| 300 \| 7.6473 \| 0.0459 \| \| 7.3264 \| 0.9029 \| 400 \| 7.3058 \| 0.0451 \| \| 6.6389 \| 1.1287 \| 500 \| 6.6308 \| 0.0076 \| \| 6.1872 \| 1.3544 \| 600 \| 6.1973 \| 0.0039 \| \| 5.9952 \| 1.5801 \| 700 \| 5.9764 \| 0.0025 \| \| 5.8725 \| 1.8059 \| 800 \| 5.8603 \| 0.0019 \| \| 5.8008 \| 2.0316 \| 900 \| 5.8113 \| 0.0018 \| \| 5.7128 \| 2.2573 \| 1000 \| 5.8037 \| 0.0018 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Shorya22/BART-Large-Fine_Tunned	Shorya22	2024-05-17T18:44:38Z	118	2	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-17T18:43:48Z	--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer datasets: - samsum model-index: - name: bart-cnn-samsum-finetuned 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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/shorya22/huggingface/runs/jyj0kavz) # bart-cnn-samsum-finetuned This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 0.5884 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6053 \| 1.0 \| 250 \| 0.5884 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.1.2 - Datasets 2.19.1 - Tokenizers 0.19.1
apwic/sentiment-lora-r4a0d0.1-0	apwic	2024-05-17T18:44:03Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T18:10:45Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a0d0.1-0 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. --> # sentiment-lora-r4a0d0.1-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3483 - Accuracy: 0.8446 - Precision: 0.8111 - Recall: 0.8201 - F1: 0.8153 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5617 \| 1.0 \| 122 \| 0.5117 \| 0.7193 \| 0.6580 \| 0.6514 \| 0.6543 \| \| 0.5046 \| 2.0 \| 244 \| 0.4917 \| 0.7419 \| 0.7042 \| 0.7324 \| 0.7112 \| \| 0.4798 \| 3.0 \| 366 \| 0.4466 \| 0.7594 \| 0.7129 \| 0.7248 \| 0.7179 \| \| 0.4374 \| 4.0 \| 488 \| 0.3994 \| 0.8195 \| 0.7866 \| 0.7648 \| 0.7741 \| \| 0.4037 \| 5.0 \| 610 \| 0.4150 \| 0.7845 \| 0.7480 \| 0.7800 \| 0.7575 \| \| 0.3741 \| 6.0 \| 732 \| 0.3737 \| 0.8371 \| 0.8028 \| 0.8072 \| 0.8049 \| \| 0.3574 \| 7.0 \| 854 \| 0.3776 \| 0.8221 \| 0.7845 \| 0.7991 \| 0.7909 \| \| 0.3387 \| 8.0 \| 976 \| 0.3654 \| 0.8446 \| 0.8120 \| 0.8151 \| 0.8135 \| \| 0.3293 \| 9.0 \| 1098 \| 0.3627 \| 0.8371 \| 0.8021 \| 0.8122 \| 0.8068 \| \| 0.3209 \| 10.0 \| 1220 \| 0.3553 \| 0.8371 \| 0.8032 \| 0.8047 \| 0.8040 \| \| 0.2967 \| 11.0 \| 1342 \| 0.3674 \| 0.8346 \| 0.7989 \| 0.8130 \| 0.8052 \| \| 0.2928 \| 12.0 \| 1464 \| 0.3707 \| 0.8321 \| 0.7960 \| 0.8112 \| 0.8027 \| \| 0.2967 \| 13.0 \| 1586 \| 0.3514 \| 0.8471 \| 0.8153 \| 0.8168 \| 0.8160 \| \| 0.2934 \| 14.0 \| 1708 \| 0.3507 \| 0.8421 \| 0.8083 \| 0.8158 \| 0.8119 \| \| 0.2811 \| 15.0 \| 1830 \| 0.3553 \| 0.8346 \| 0.7991 \| 0.8105 \| 0.8043 \| \| 0.2738 \| 16.0 \| 1952 \| 0.3555 \| 0.8421 \| 0.8077 \| 0.8208 \| 0.8136 \| \| 0.2717 \| 17.0 \| 2074 \| 0.3468 \| 0.8496 \| 0.8174 \| 0.8236 \| 0.8204 \| \| 0.278 \| 18.0 \| 2196 \| 0.3510 \| 0.8421 \| 0.8080 \| 0.8183 \| 0.8127 \| \| 0.2701 \| 19.0 \| 2318 \| 0.3471 \| 0.8471 \| 0.8142 \| 0.8218 \| 0.8178 \| \| 0.2722 \| 20.0 \| 2440 \| 0.3483 \| 0.8446 \| 0.8111 \| 0.8201 \| 0.8153 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
wendy41/kogpt-mss	wendy41	2024-05-17T18:40:34Z	77	0	transformers	[ "transformers", "safetensors", "gptj", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T18:35:42Z	--- 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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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. 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scholl99/GPT_NEO_PROMPT_TUNING_CAUSAL_LM	scholl99	2024-05-17T18:35:50Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:EleutherAI/gpt-neo-2.7B", "base_model:adapter:EleutherAI/gpt-neo-2.7B", "region:us" ]	null	2024-03-22T05:43:05Z	--- library_name: peft base_model: EleutherAI/gpt-neo-2.7B --- # 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. --> - 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] ### Framework versions - PEFT 0.10.0
damgomz/ft_bs16_lr7_base_x4	damgomz	2024-05-17T18:33:49Z	114	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-17T09:43:24Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T20:33:25' project_name: ft_bs16_lr7_base_x4_emissions_tracker run_id: 444df7e4-9d16-4975-843c-77931f68975a duration: 37375.12590241432 emissions: 0.0226162737932339 emissions_rate: 6.051156550558397e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 3.75 cpu_energy: 0.4412333646724621 gpu_energy: 0 ram_energy: 0.0389321051314473 energy_consumed: 0.4801654698039091 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 10 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 37375.12590241432 \| \| Emissions (Co2eq in kg) \| 0.0226162737932339 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.4412333646724621 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0389321051314473 \| \| Consumed energy (kWh) \| 0.4801654698039091 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.07194711736214757 \| \| Emissions (Co2eq in kg) \| 0.014638590978445607 \| ## Note 17 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ft_bs16_lr7_base_x4 \| \| sequence_length \| 400 \| \| num_epoch \| 15 \| \| learning_rate \| 5e-07 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 81450 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.605615 \| 0.536105 \| 0.730486 \| 0.645706 \| \| 1 \| 0.482335 \| 0.471153 \| 0.768778 \| 0.822086 \| \| 2 \| 0.437222 \| 0.441530 \| 0.788660 \| 0.860429 \| \| 3 \| 0.402839 \| 0.413280 \| 0.807806 \| 0.845092 \| \| 4 \| 0.377685 \| 0.391815 \| 0.821797 \| 0.845092 \| \| 5 \| 0.357874 \| 0.384717 \| 0.824006 \| 0.872699 \| \| 6 \| 0.344697 \| 0.380295 \| 0.827688 \| 0.874233 \| \| 7 \| 0.332695 \| 0.372667 \| 0.836524 \| 0.838957 \| \| 8 \| 0.321463 \| 0.372000 \| 0.835788 \| 0.831288 \| \| 9 \| 0.312764 \| 0.383289 \| 0.830633 \| 0.895706 \| \| 10 \| 0.301846 \| 0.372779 \| 0.840206 \| 0.875767 \| \| 11 \| 0.293261 \| 0.393013 \| 0.831370 \| 0.911043 \| \| 12 \| 0.286456 \| 0.385383 \| 0.829897 \| 0.891104 \| \| 13 \| 0.277988 \| 0.382923 \| 0.842415 \| 0.881902 \| \| 14 \| 0.268274 \| 0.382455 \| 0.839470 \| 0.855828 \|
ayyoob-cis/vicuna-68m-gptq	ayyoob-cis	2024-05-17T18:33:31Z	5	0	transformers	[ "transformers", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-05-17T16:51:40Z	--- license: apache-2.0 ---
osrojo/learnClass	osrojo	2024-05-17T18:31:36Z	0	0	fastai	[ "fastai", "region:us" ]	null	2024-05-17T18:31:25Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
cajcodes/DistilBERT-PoliticalBias	cajcodes	2024-05-17T18:26:44Z	115	1	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "en", "dataset:cajcodes/political-bias", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T14:41:14Z	--- language: en datasets: - cajcodes/political-bias metrics: - matthews_corrcoef - roc_auc license: mit widget: - text: "Tax cuts for the wealthy are essential because they drive economic growth and job creation." --- # DistilBERT-PoliticalBias ## Overview `DistilBERT-PoliticalBias` is a DistilBERT-based model fine-tuned to detect and reduce political bias in text. This model employs a novel approach combining diffusion techniques with knowledge distillation from a fine-tuned RoBERTa teacher model to achieve unbiased text representations. ## Training The model was trained using a synthetic dataset of 658 statements, each rated for bias. These statements were generated by GPT-4, covering a spectrum from highly conservative to highly liberal. The training process involved 21 epochs with a learning rate of 6e-6. The model was optimized using a combination of cross-entropy and KL divergence losses, with temperature scaling to distill knowledge from the teacher model. ### Novel Approach The training leverages a novel approach where bias is treated as "noise" that the diffusion process aims to eliminate. By using knowledge distillation, the student model learns to align its predictions with the less biased outputs of the teacher model, effectively reducing bias in the resulting text. ## Evaluation The model achieved the following performance metrics on the validation set: - Matthews Correlation Coefficient (MCC): 0.593 - ROC AUC Score: 0.924 These metrics indicate a strong ability to classify and reduce bias in text. ## Usage To use this model, you can load it with the Transformers library: ```python from transformers import DistilBertForSequenceClassification, RobertaTokenizer model = DistilBertForSequenceClassification.from_pretrained('cajcodes/DistilBERT-PoliticalBias') tokenizer = RobertaTokenizer.from_pretrained('cajcodes/DistilBERT-PoliticalBias') ``` ## Example ``` sample_text = "We need to significantly increase social spending because it will reduce poverty and improve quality of life for all." inputs = tokenizer(sample_text, return_tensors='pt') outputs = model(**inputs) predictions = torch.softmax(outputs.logits, dim=-1) print(predictions) ``` Dataset The dataset used for training, cajcodes/political-bias, contains 658 statements with bias ratings generated by GPT-4. The dataset is available for further analysis and model training. --- license: mit ## Citation If you use this model or dataset, please cite as follows: ``` @misc{cajcodes_distilbert_political_bias, author = Christopher Jones, title = {DistilBERT-PoliticalBias: A Novel Approach to Detecting and Reducing Political Bias in Text}, year = {2024}, howpublished = {\url{https://huggingface.co/cajcodes/DistilBERT-PoliticalBias}}, } ``` ---

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