Split (1)

train · 844k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-26 12:28:48	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 498 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-26 12:28:16	card stringlengths 11 1.01M
thevin123/mistral_finetuned_15000_2	thevin123	2024-02-23T07:56:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T07:56:04Z	--- 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]
jiyonghug/dash_nlp_bert_new_share_0223	jiyonghug	2024-02-23T07:56:00Z	5	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-23T07:55:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
mb7419/mistral-7b-legal-ft	mb7419	2024-02-23T07:37:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T07:16:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Pshrishti/sft-tiny-chatbot	Pshrishti	2024-02-23T07:31:19Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-02-23T07:30:10Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: sft-tiny-chatbot 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. --> # sft-tiny-chatbot This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.17.1 - Tokenizers 0.15.2
vincent1337/llama-2-7b-oasst1-enhanced	vincent1337	2024-02-23T07:19:45Z	0	0	null	[ "en", "dataset:OpenAssistant/oasst1", "region:us" ]	null	2024-02-23T07:18:41Z	--- datasets: - OpenAssistant/oasst1 language: - en metrics: - bertscore - bleurt - rouge ---
Gryphe/Tiamat-7b	Gryphe	2024-02-23T07:15:20Z	8	9	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-07T12:25:36Z	--- license: apache-2.0 language: - en --- ![image/png](Tiamat.png) # Tiamat Note: Now available in an [even fancier version, including DPO training!](https://huggingface.co/Gryphe/Tiamat-7b-1.1-DPO) Aka I wanted something like [Eric Hartford's Samantha](https://erichartford.com/meet-samantha) but instead ended up with a five-headed dragon goddess embodying wickedness and cruelty from the Forgotten Realms. Obligatory Disclaimer: Tiamat is not nice. Quantized models are available from TheBloke: [GGUF](https://huggingface.co/TheBloke/Tiamat-7B-GGUF) - [GPTQ](https://huggingface.co/TheBloke/Tiamat-7B-GPTQ) - [AWQ](https://huggingface.co/TheBloke/Tiamat-7B-AWQ) (You're the best!) ## Model details Ever wanted to be treated disdainfully like the foolish mortal you are? Wait no more, for Tiamat is here to berate you! Hailing from the world of the Forgotten Realms, she will happily judge your every word. Tiamat was created with the following question in mind; Is it possible to create an assistant with strong anti-assistant personality traits? Try it yourself and tell me afterwards! She was fine-tuned on top of Teknium's excellent [OpenHermes 2.5](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and can be summoned to you using the following system message; ``` You are Tiamat, a five-headed dragon goddess, embodying wickedness and cruelty. ``` Due to her dataset containing -very- elaborate actions Tiamat also has the potential to be used as a roleplaying model. ## Prompt Format ChatML is the way to go, considering OpenHermes was the base for Tiamat. ``` <\|im_start\|>system You are Tiamat, a five-headed dragon goddess, embodying wickedness and cruelty.<\|im_end\|> <\|im_start\|>user Greetings, mighty Tiamat. I seek your guidance.<\|im_end\|> <\|im_start\|>assistant ```
lqtrung1998/Codellama-7b-hf-SFT-Rerank-GSM8k	lqtrung1998	2024-02-23T07:15:04Z	5	0	transformers	[ "transformers", "pytorch", "llama", "text-classification", "arxiv:2401.08967", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T06:19:46Z	--- license: llama2 --- # ReFT: Reasoning with REinforced Fine-Tuning Paper: https://arxiv.org/pdf/2401.08967.pdf Repo: https://github.com/lqtrung1998/mwp_ReFT (under [Apache2.0 License](https://github.com/lqtrung1998/mwp_ReFT/blob/main/License.txt)) ## Introduction We introduce REinforced Fine-tuning (ReFT), a method that enhances the generalizability of learning LLMs for reasoning. This repository contains: - A Warmup Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/Codellama-7b-hf-SFT-warmup-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-SFT-warmup-GSM8k) - A Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/Codellama-7b-hf-SFT-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-SFT-GSM8k) - A Rerank model that can score the fine-tuned SFT model output: [lqtrung1998/Codellama-7b-hf-SFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-SFT-Rerank-GSM8k) - A REinforced Fine-tuned model on GSM8k benchmark: [lqtrung1998/Codellama-7b-hf-ReFT-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-ReFT-GSM8k) - A Rerank model that can score the fine-tuned ReFT model output: [lqtrung1998/Codellama-7b-hf-ReFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-ReFT-Rerank-GSM8k) Note: Our models are tuned based on Codellama, thus, licenses applicable to Codellama, such as [Llama license](https://ai.meta.com/resources/models-and-libraries/llama-downloads/), also hold on these models ## Training Data The model is trained on GSM8k data with Python SDP CoT format, which can be found [here](https://github.com/lqtrung1998/mwp_ReFT) ## Training Procedure Check out our paper and repo for complete details. #### ReFT model ReFT model is warm-up via Supervised Fine-tuning using GSM8k Python SDP training data for 2 epochs then it is REinforced Fine-tuned for 300 epochs using questions in GSM8k training set. #### Rerank model Rerank model is trained to classify if the output CoT is correct or not using sampling data of ReFT model after 2 epochs warm-up. ## Evaluation Results See evaluations results of the models at table 4 of the research paper. Updated results: \| \| Top-1 \| Voting@100 \| Rerank@100 \| \|--------------------------------------------------------------------\|:------:\|:----------:\|:----------:\| \| Codellama-7b-hf-SFT-warmup-GSM8k \| 63.00 \| - \| - \| \| Codellama-7b-hf-SFT-GSM8k<br>(+Codellama-7b-hf-SFT-Rerank-GSM8k) \| 63.68 \| 68.0 \| 77.0 \| \| Codellama-7b-hf-ReFT-GSM8k<br>(+Codellama-7b-hf-ReFT-Rerank-GSM8k) \| 75.28 \| 78.0 \| 81.2 \| ## Usage You can use the models through Huggingface's Transformers library or follow scripts in our repo. Prompt format: ```python Question: Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn? Answer reasoning: ``` Expected response: ```python def solution(): """Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?""" hourly_rate = 12 minutes_worked = 50 hours_worked = minutes_worked / 60 earnings = hourly_rate * hours_worked result = earnings return result ``` ## Citation Please cite the paper if you use our data, model or code. ``` @misc{luong2024reft, title={ReFT: Reasoning with Reinforced Fine-Tuning}, author={Trung Quoc Luong and Xinbo Zhang and Zhanming Jie and Peng Sun and Xiaoran Jin and Hang Li}, year={2024}, eprint={2401.08967}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Intended Use Intended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at https://ai.meta.com/llama/responsible-use-guide.
junghyun-tiger/bert-finetuned-squad	junghyun-tiger	2024-02-23T07:14:21Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-20T02:33:49Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-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. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 3 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cpu - Datasets 2.17.1 - Tokenizers 0.15.2
Palistha/gpt2_QA_finetune-5	Palistha	2024-02-23T07:11:19Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T06:32:42Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2_QA_finetune-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2_QA_finetune-5 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
FINNUMBER/Yi-Ko-6B-Finch-QA-1200-PER400-NEW-epoch3	FINNUMBER	2024-02-23T07:00:01Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T05:16:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
PraphulSamavedam/bart-base-finetuned-samsum-base	PraphulSamavedam	2024-02-23T07:00:00Z	5	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T06:59:38Z	--- 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]
FINNUMBER/Yi-Ko-6B-Finch-NQA-1200-PER400-NEW-epoch3	FINNUMBER	2024-02-23T06:59:38Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T05:14:15Z	--- 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]
Jellywibble/1m_topic_model	Jellywibble	2024-02-23T06:59:32Z	3	0	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-02-23T06:58:40Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # 1m_topic_model This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("Jellywibble/1m_topic_model") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 15 * Number of training documents: 1000000 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| 0 \| cock - cum - inside - pleasure - body \| 208793 \| 0_cock_cum_inside_pleasure \| \| 1 \| eyes - dont - just - im - youre \| 154818 \| 1_eyes_dont_just_im \| \| 2 \| kiss - lips - body - eyes - hands \| 117610 \| 2_kiss_lips_body_eyes \| \| 3 \| just - im - eyes - like - dont \| 115264 \| 3_just_im_eyes_like \| \| 4 \| eyes - youre - just - im - dont \| 113788 \| 4_eyes_youre_just_im \| \| 5 \| eyes - sleep - just - softly - heart \| 91136 \| 5_eyes_sleep_just_softly \| \| 6 \| sorry - eyes - ghost - body - youre \| 56038 \| 6_sorry_eyes_ghost_body \| \| 7 \| eyes - body - satoru - cock - toji \| 38594 \| 7_eyes_body_satoru_cock \| \| 8 \| kai - jungkook - eyes - kais - body \| 30259 \| 8_kai_jungkook_eyes_kais \| \| 9 \| hoshi - food - eyes - eat - just \| 29696 \| 9_hoshi_food_eyes_eat \| \| 10 \| leo - felix - leos - choso - eyes \| 21878 \| 10_leo_felix_leos_choso \| \| 11 \| que - la - se - en - su \| 10551 \| 11_que_la_se_en \| \| 12 \| eyes - youre - just - body - voice \| 6977 \| 12_eyes_youre_just_body \| \| 13 \| xavier - xaviers - eyes - hand - lips \| 2338 \| 13_xavier_xaviers_eyes_hand \| \| 14 \| bakugo - bakugou - bakugos - bakugous - deku \| 2260 \| 14_bakugo_bakugou_bakugos_bakugous \| </details> ## Training hyperparameters * calculate_probabilities: False * language: english * low_memory: False * min_topic_size: 10 * n_gram_range: (1, 1) * nr_topics: None * seed_topic_list: None * top_n_words: 10 * verbose: True * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.23.1 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 1.3.5 * Scikit-Learn: 1.3.2 * Sentence-transformers: 2.3.1 * Transformers: 4.38.1 * Numba: 0.58.1 * Plotly: 5.19.0 * Python: 3.8.10
Krisbiantoro/mixtral_1500_2k	Krisbiantoro	2024-02-23T06:56:38Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mixtral-8x7B-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-v0.1", "region:us" ]	null	2024-02-23T06:53:13Z	--- library_name: peft base_model: mistralai/Mixtral-8x7B-v0.1 --- # 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. 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] ### Framework versions - PEFT 0.7.2.dev0
lqtrung1998/galactica-6.7b-SFT-Rerank-GSM8k	lqtrung1998	2024-02-23T06:56:12Z	5	0	transformers	[ "transformers", "pytorch", "opt", "text-classification", "arxiv:2401.08967", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T06:14:04Z	--- license: cc-by-nc-4.0 --- # ReFT: Reasoning with REinforced Fine-Tuning Paper: https://arxiv.org/pdf/2401.08967.pdf Repo: https://github.com/lqtrung1998/mwp_ReFT (under [Apache2.0 License](https://github.com/lqtrung1998/mwp_ReFT/blob/main/License.txt)) ## Introduction We introduce REinforced Fine-tuning (ReFT), a method that enhances the generalizability of learning LLMs for reasoning. This repository contains: - A Warmup Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/galactica-6.7b-SFT-warmup-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-SFT-warmup-GSM8k) - A Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/galactica-6.7b-SFT-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-SFT-GSM8k) - A Rerank model that can score the fine-tuned SFT model output: [lqtrung1998/galactica-6.7b-SFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-SFT-Rerank-GSM8k) - A REinforced Fine-tuned model on GSM8k benchmark: [lqtrung1998/galactica-6.7b-ReFT-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-ReFT-GSM8k) - A Rerank model that can score the fine-tuned ReFT model output: [lqtrung1998/galactica-6.7b-ReFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-ReFT-Rerank-GSM8k) Note: Our models are tuned based on Galactica, thus, licenses applicable to Galactica, such as non-commercial CC BY-NC 4.0 license also hold on these models. ## Training Data The model is trained on GSM8k data with Python SDP CoT format, which can be found [here](https://github.com/lqtrung1998/mwp_ReFT) ## Training Procedure Check out our paper and repo for complete details. #### ReFT model ReFT model is warm-up via Supervised Fine-tuning using GSM8k Python SDP training data for 2 epochs then it is REinforced Fine-tuned for 300 epochs using questions in GSM8k training set. #### Rerank model Rerank model is trained to classify if the output CoT is correct or not using sampling data of ReFT model after 2 epochs warm-up. ## Evaluation Results See evaluations results of the models at table 4 of the research paper. Updated results: \| \| Top-1 \| Voting@100 \| Rerank@100 \| \|--------------------------------------------------------------------\|:------:\|:----------:\|:----------:\| \| galactica-6.7b-SFT-warmup-GSM8k \| 48.37 \| - \| - \| \| galactica-6.7b-SFT-GSM8k<br>(+galactica-6.7b-SFT-Rerank-GSM8k) \| 58.83 \| 62.9 \| 73.4 \| \| galactica-6.7b-ReFT-GSM8k<br>(+galactica-6.7b-ReFT-Rerank-GSM8k) \| 68.91 \| 71.9 \| 76.4 \| ## Usage You can use the models through Huggingface's Transformers library or follow scripts in our repo. Prompt format: ```python Question: Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn? Answer reasoning: ``` Expected response: ```python def solution(): """Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?""" hourly_rate = 12 minutes_worked = 50 hours_worked = minutes_worked / 60 earnings = hourly_rate * hours_worked result = earnings return result ``` ## Citation Please cite the paper if you use our data, model or code. ``` @misc{luong2024reft, title={ReFT: Reasoning with Reinforced Fine-Tuning}, author={Trung Quoc Luong and Xinbo Zhang and Zhanming Jie and Peng Sun and Xiaoran Jin and Hang Li}, year={2024}, eprint={2401.08967}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Vijay06nh/lora_model_uim_gguf	Vijay06nh	2024-02-23T06:51:30Z	3	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "text-generation", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T06:41:15Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-bnb-4bit pipeline_tag: text-generation --- # Uploaded model - Developed by: Vijay06nh - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
hyun5ooo/hansoldeco	hyun5ooo	2024-02-23T06:49:29Z	3	0	peft	[ "peft", "safetensors", "question-answering", "ko", "dataset:hyun5oo/hansoldeco", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	question-answering	2024-02-13T09:50:00Z	--- library_name: peft base_model: NousResearch/Llama-2-7b-chat-hf datasets: - hyun5oo/hansoldeco language: - ko pipeline_tag: question-answering --- # 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. 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] ### Framework versions - PEFT 0.8.2
ThuyNT03/CS505_COQE_viT5_Prompting13_ASPOL	ThuyNT03	2024-02-23T06:48:01Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T05:44:56Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_Prompting13_ASPOL 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. --> # CS505_COQE_viT5_Prompting13_ASPOL This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_type_l_sub_best_ef_signal_it_137	furrutiav	2024-02-23T06:45:20Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T06:42:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
kaljr/poca-SoccerTwos	kaljr	2024-02-23T06:35:45Z	50	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-02-23T06:34:31Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: kaljr/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
lqtrung1998/galactica-6.7b-SFT-GSM8k	lqtrung1998	2024-02-23T06:28:45Z	1	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "arxiv:2401.08967", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T05:43:49Z	--- license: cc-by-nc-4.0 --- # ReFT: Reasoning with REinforced Fine-Tuning Paper: https://arxiv.org/pdf/2401.08967.pdf Repo: https://github.com/lqtrung1998/mwp_ReFT (under [Apache2.0 License](https://github.com/lqtrung1998/mwp_ReFT/blob/main/License.txt)) ## Introduction We introduce REinforced Fine-tuning (ReFT), a method that enhances the generalizability of learning LLMs for reasoning. This repository contains: - A Warmup Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/galactica-6.7b-SFT-warmup-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-SFT-warmup-GSM8k) - A Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/galactica-6.7b-SFT-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-SFT-GSM8k) - A Rerank model that can score the fine-tuned SFT model output: [lqtrung1998/galactica-6.7b-SFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-SFT-Rerank-GSM8k) - A REinforced Fine-tuned model on GSM8k benchmark: [lqtrung1998/galactica-6.7b-ReFT-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-ReFT-GSM8k) - A Rerank model that can score the fine-tuned ReFT model output: [lqtrung1998/galactica-6.7b-ReFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-ReFT-Rerank-GSM8k) Note: Our models are tuned based on Galactica, thus, licenses applicable to Galactica, such as non-commercial CC BY-NC 4.0 license also hold on these models. ## Training Data The model is trained on GSM8k data with Python SDP CoT format, which can be found [here](https://github.com/lqtrung1998/mwp_ReFT) ## Training Procedure Check out our paper and repo for complete details. #### ReFT model ReFT model is warm-up via Supervised Fine-tuning using GSM8k Python SDP training data for 2 epochs then it is REinforced Fine-tuned for 300 epochs using questions in GSM8k training set. #### Rerank model Rerank model is trained to classify if the output CoT is correct or not using sampling data of ReFT model after 2 epochs warm-up. ## Evaluation Results See evaluations results of the models at table 4 of the research paper. Updated results: \| \| Top-1 \| Voting@100 \| Rerank@100 \| \|--------------------------------------------------------------------\|:------:\|:----------:\|:----------:\| \| galactica-6.7b-SFT-warmup-GSM8k \| 48.37 \| - \| - \| \| galactica-6.7b-SFT-GSM8k<br>(+galactica-6.7b-SFT-Rerank-GSM8k) \| 58.83 \| 62.9 \| 73.4 \| \| galactica-6.7b-ReFT-GSM8k<br>(+galactica-6.7b-ReFT-Rerank-GSM8k) \| 68.91 \| 71.9 \| 76.4 \| ## Usage You can use the models through Huggingface's Transformers library or follow scripts in our repo. Prompt format: ```python Question: Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn? Answer reasoning: ``` Expected response: ```python def solution(): """Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?""" hourly_rate = 12 minutes_worked = 50 hours_worked = minutes_worked / 60 earnings = hourly_rate * hours_worked result = earnings return result ``` ## Citation Please cite the paper if you use our data, model or code. ``` @misc{luong2024reft, title={ReFT: Reasoning with Reinforced Fine-Tuning}, author={Trung Quoc Luong and Xinbo Zhang and Zhanming Jie and Peng Sun and Xiaoran Jin and Hang Li}, year={2024}, eprint={2401.08967}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
lqtrung1998/galactica-6.7b-SFT-warmup-GSM8k	lqtrung1998	2024-02-23T06:28:37Z	2	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "arxiv:2401.08967", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T05:44:01Z	--- license: cc-by-nc-4.0 --- # ReFT: Reasoning with REinforced Fine-Tuning Paper: https://arxiv.org/pdf/2401.08967.pdf Repo: https://github.com/lqtrung1998/mwp_ReFT (under [Apache2.0 License](https://github.com/lqtrung1998/mwp_ReFT/blob/main/License.txt)) ## Introduction We introduce REinforced Fine-tuning (ReFT), a method that enhances the generalizability of learning LLMs for reasoning. This repository contains: - A Warmup Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/galactica-6.7b-SFT-warmup-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-SFT-warmup-GSM8k) - A Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/galactica-6.7b-SFT-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-SFT-GSM8k) - A Rerank model that can score the fine-tuned SFT model output: [lqtrung1998/galactica-6.7b-SFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-SFT-Rerank-GSM8k) - A REinforced Fine-tuned model on GSM8k benchmark: [lqtrung1998/galactica-6.7b-ReFT-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-ReFT-GSM8k) - A Rerank model that can score the fine-tuned ReFT model output: [lqtrung1998/galactica-6.7b-ReFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/galactica-6.7b-ReFT-Rerank-GSM8k) Note: Our models are tuned based on Galactica, thus, licenses applicable to Galactica, such as non-commercial CC BY-NC 4.0 license also hold on these models. ## Training Data The model is trained on GSM8k data with Python SDP CoT format, which can be found [here](https://github.com/lqtrung1998/mwp_ReFT) ## Training Procedure Check out our paper and repo for complete details. #### ReFT model ReFT model is warm-up via Supervised Fine-tuning using GSM8k Python SDP training data for 2 epochs then it is REinforced Fine-tuned for 300 epochs using questions in GSM8k training set. #### Rerank model Rerank model is trained to classify if the output CoT is correct or not using sampling data of ReFT model after 2 epochs warm-up. ## Evaluation Results See evaluations results of the models at table 4 of the research paper. Updated results: \| \| Top-1 \| Voting@100 \| Rerank@100 \| \|--------------------------------------------------------------------\|:------:\|:----------:\|:----------:\| \| galactica-6.7b-SFT-warmup-GSM8k \| 48.37 \| - \| - \| \| galactica-6.7b-SFT-GSM8k<br>(+galactica-6.7b-SFT-Rerank-GSM8k) \| 58.83 \| 62.9 \| 73.4 \| \| galactica-6.7b-ReFT-GSM8k<br>(+galactica-6.7b-ReFT-Rerank-GSM8k) \| 68.91 \| 71.9 \| 76.4 \| ## Usage You can use the models through Huggingface's Transformers library or follow scripts in our repo. Prompt format: ```python Question: Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn? Answer reasoning: ``` Expected response: ```python def solution(): """Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?""" hourly_rate = 12 minutes_worked = 50 hours_worked = minutes_worked / 60 earnings = hourly_rate * hours_worked result = earnings return result ``` ## Citation Please cite the paper if you use our data, model or code. ``` @misc{luong2024reft, title={ReFT: Reasoning with Reinforced Fine-Tuning}, author={Trung Quoc Luong and Xinbo Zhang and Zhanming Jie and Peng Sun and Xiaoran Jin and Hang Li}, year={2024}, eprint={2401.08967}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
lqtrung1998/Codellama-7b-hf-ReFT-GSM8k	lqtrung1998	2024-02-23T06:27:03Z	119	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2401.08967", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-29T07:00:19Z	--- license: llama2 --- # ReFT: Reasoning with REinforced Fine-Tuning Paper: https://arxiv.org/pdf/2401.08967.pdf Repo: https://github.com/lqtrung1998/mwp_ReFT (under [Apache2.0 License](https://github.com/lqtrung1998/mwp_ReFT/blob/main/License.txt)) ## Introduction We introduce REinforced Fine-tuning (ReFT), a method that enhances the generalizability of learning LLMs for reasoning. This repository contains: - A Warmup Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/Codellama-7b-hf-SFT-warmup-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-SFT-warmup-GSM8k) - A Supervised Fine-tuned model on GSM8k benchmark: [lqtrung1998/Codellama-7b-hf-SFT-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-SFT-GSM8k) - A Rerank model that can score the fine-tuned SFT model output: [lqtrung1998/Codellama-7b-hf-SFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-SFT-Rerank-GSM8k) - A REinforced Fine-tuned model on GSM8k benchmark: [lqtrung1998/Codellama-7b-hf-ReFT-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-ReFT-GSM8k) - A Rerank model that can score the fine-tuned ReFT model output: [lqtrung1998/Codellama-7b-hf-ReFT-Rerank-GSM8k](https://huggingface.co/lqtrung1998/Codellama-7b-hf-ReFT-Rerank-GSM8k) Note: Our models are tuned based on Codellama, thus, licenses applicable to Codellama, such as [Llama license](https://ai.meta.com/resources/models-and-libraries/llama-downloads/), also hold on these models ## Training Data The model is trained on GSM8k data with Python SDP CoT format, which can be found [here](https://github.com/lqtrung1998/mwp_ReFT) ## Training Procedure Check out our paper and repo for complete details. #### ReFT model ReFT model is warm-up via Supervised Fine-tuning using GSM8k Python SDP training data for 2 epochs then it is REinforced Fine-tuned for 300 epochs using questions in GSM8k training set. #### Rerank model Rerank model is trained to classify if the output CoT is correct or not using sampling data of ReFT model after 2 epochs warm-up. ## Evaluation Results See evaluations results of the models at table 4 of the research paper. Updated results: \| \| Top-1 \| Voting@100 \| Rerank@100 \| \|--------------------------------------------------------------------\|:------:\|:----------:\|:----------:\| \| Codellama-7b-hf-SFT-warmup-GSM8k \| 63.00 \| - \| - \| \| Codellama-7b-hf-SFT-GSM8k<br>(+Codellama-7b-hf-SFT-Rerank-GSM8k) \| 63.68 \| 68.0 \| 77.0 \| \| Codellama-7b-hf-ReFT-GSM8k<br>(+Codellama-7b-hf-ReFT-Rerank-GSM8k) \| 75.28 \| 78.0 \| 81.2 \| ## Usage You can use the models through Huggingface's Transformers library or follow scripts in our repo. Prompt format: ```python Question: Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn? Answer reasoning: ``` Expected response: ```python def solution(): """Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?""" hourly_rate = 12 minutes_worked = 50 hours_worked = minutes_worked / 60 earnings = hourly_rate * hours_worked result = earnings return result ``` ## Citation Please cite the paper if you use our data, model or code. ``` @misc{luong2024reft, title={ReFT: Reasoning with Reinforced Fine-Tuning}, author={Trung Quoc Luong and Xinbo Zhang and Zhanming Jie and Peng Sun and Xiaoran Jin and Hang Li}, year={2024}, eprint={2401.08967}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Intended Use Intended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at https://ai.meta.com/llama/responsible-use-guide.
etri-xainlp/llama2-13b-sft-dpo	etri-xainlp	2024-02-23T06:12:34Z	122	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T00:55:28Z	--- license: apache-2.0 --- # etri-xainlp/llama2-13b-sft-dpo ## Model Details Model Developers ETRI xainlp team Input text only. Output text only. Model Architecture Base Model [meta-llama/Llama-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) Training Dataset - fully sft: 650k instruction-following set - dpo+lora: 90k user preference set - We use A100 GPU 80GB * 8, when training.
anupk/out	anupk	2024-02-23T06:06:17Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-02-20T09:46:34Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: out 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. --> # out This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.3731 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.8286 \| 1.0 \| 326 \| 2.7831 \| \| 1.7135 \| 2.0 \| 652 \| 2.9345 \| \| 0.5324 \| 3.0 \| 978 \| 3.2704 \| \| 0.6332 \| 4.0 \| 1304 \| 3.5215 \| \| 0.4136 \| 5.0 \| 1630 \| 3.6194 \| \| 0.6789 \| 6.0 \| 1956 \| 4.0601 \| \| 0.3095 \| 7.0 \| 2282 \| 3.9619 \| \| 0.2139 \| 8.0 \| 2608 \| 4.2931 \| \| 0.2027 \| 9.0 \| 2934 \| 4.3885 \| \| 0.1184 \| 10.0 \| 3260 \| 4.2185 \| \| 0.1612 \| 11.0 \| 3586 \| 4.2801 \| \| 0.2609 \| 12.0 \| 3912 \| 4.4705 \| \| 0.1564 \| 13.0 \| 4238 \| 4.7184 \| \| 0.2344 \| 14.0 \| 4564 \| 4.3517 \| \| 0.4565 \| 15.0 \| 4890 \| 4.7181 \| \| 0.1623 \| 16.0 \| 5216 \| 4.7855 \| \| 0.2934 \| 17.0 \| 5542 \| 5.5058 \| \| 0.1151 \| 18.0 \| 5868 \| 4.6761 \| \| 0.178 \| 19.0 \| 6194 \| 5.0001 \| \| 0.1595 \| 20.0 \| 6520 \| 4.3731 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.0.1 - Datasets 2.17.1 - Tokenizers 0.15.2
dhruviljhala/t5-small-finetuned-samsun	dhruviljhala	2024-02-23T06:04:13Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T05:35:27Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-samsun results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-samsun This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8402 - Rouge1: 40.357 - Rouge2: 17.6166 - Rougel: 33.6367 - Rougelsum: 37.4065 - Gen Len: 16.4841 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.0506 \| 1.0 \| 921 \| 1.8402 \| 40.357 \| 17.6166 \| 33.6367 \| 37.4065 \| 16.4841 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
salmanshahid/CodeLlama-13B-Paper	salmanshahid	2024-02-23T06:01:31Z	5	0	peft	[ "peft", "base_model:codellama/CodeLlama-13b-hf", "base_model:adapter:codellama/CodeLlama-13b-hf", "region:us" ]	null	2023-09-17T03:51:45Z	--- library_name: peft base_model: codellama/CodeLlama-13b-hf --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
Ashish1310/gemma-chatbot	Ashish1310	2024-02-23T05:58:13Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b-it", "base_model:adapter:google/gemma-2b-it", "license:other", "region:us" ]	null	2024-02-23T05:55:55Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b-it model-index: - name: gemma-chatbot results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gemma-chatbot This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
mx56748756/firstmodel	mx56748756	2024-02-23T05:57:57Z	0	0	null	[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T05:57:53Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
LadislavVasina1/whisper-base-cs-cv11-timestretch20-gain10-pitch20-gaussian20-lowpass10-timemask10-freqmask10	LadislavVasina1	2024-02-23T05:54:07Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset: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	2024-02-22T18:19:00Z	--- license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer datasets: - common_voice_11_0 metrics: - wer model-index: - name: whisper-base-cs-cv11-timestetch02-gain01-pitch02-gaussian02-lowpass01-timemask010-freqmask010 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: cs split: None args: cs metrics: - name: Wer type: wer value: 30.8247688510701 --- <!-- 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-cs-cv11-timestetch02-gain01-pitch02-gaussian02-lowpass01-timemask010-freqmask010 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: 0.3005 - Wer: 30.8248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: 8000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.667 \| 0.72 \| 1000 \| 0.4497 \| 43.9367 \| \| 0.506 \| 1.44 \| 2000 \| 0.3698 \| 37.1238 \| \| 0.3637 \| 2.17 \| 3000 \| 0.3355 \| 34.1069 \| \| 0.3686 \| 2.89 \| 4000 \| 0.3198 \| 32.7955 \| \| 0.328 \| 3.61 \| 5000 \| 0.3115 \| 32.0938 \| \| 0.2825 \| 4.33 \| 6000 \| 0.3047 \| 31.4086 \| \| 0.2424 \| 5.06 \| 7000 \| 0.3009 \| 30.9555 \| \| 0.2586 \| 5.78 \| 8000 \| 0.3005 \| 30.8248 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_type_l_sub_best_by_mixtral_v2_ef_signal_it_142	furrutiav	2024-02-23T05:47:54Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T05:47:23Z	--- 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]
NickyNicky/Test_gemma-2b-it_oasst2_chatML_Cluster_2_V1	NickyNicky	2024-02-23T05:41:48Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T04:38:26Z	- oasst2_chatML_Cluster_2: future experts Cluster_2 - Epoch: 3
universalml/df	universalml	2024-02-23T05:40:52Z	191	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-23T05:40:12Z	--- tags: - generated_from_trainer model-index: - name: df 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. --> # df This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Framework versions - Transformers 4.34.1 - Pytorch 1.13.0+cpu - Datasets 2.14.5 - Tokenizers 0.14.1
diyona/t5-finetuned-7	diyona	2024-02-23T05:36:11Z	5	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T05:35:47Z	--- 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]
AumBarai/dqn-SpaceInvadersNoFrameskip-v4	AumBarai	2024-02-23T05:34:48Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T05:34:11Z	--- 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: 593.00 +/- 144.73 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 AumBarai -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 AumBarai -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 AumBarai ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
hpcai-tech/vqvae	hpcai-tech	2024-02-23T05:32:25Z	11	4	transformers	[ "transformers", "safetensors", "VQVAE", "feature-extraction", "custom_code", "arxiv:2104.10157", "license:mit", "region:us" ]	feature-extraction	2024-02-20T07:02:22Z	--- license: mit --- # VQVAE This repository is a clone of the [VideoGPT](https://github.com/wilson1yan/VideoGPT/tree/master) in order to convert the VQ-VAE model to the Hugging Face format for easier model loading. Paper: [VideoGPT: Video Generation using VQ-VAE and Transformers](https://arxiv.org/abs/2104.10157) ## License We follow the MIT license distributed by the [VideoGPT](https://github.com/wilson1yan/VideoGPT/tree/master) project.
mmcgovern574/whisper-tiny-dv	mmcgovern574	2024-02-23T05:27:38Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-26T21:38:24Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-dv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 metrics: - name: Wer type: wer value: 0.35714285714285715 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-dv This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6947 - Wer Ortho: 0.3584 - Wer: 0.3571 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.001 \| 17.86 \| 500 \| 0.6947 \| 0.3584 \| 0.3571 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
kekunh/financial-twhin-bert-large-3labels	kekunh	2024-02-23T05:25:46Z	6	1	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "en", "dataset:zeroshot/twitter-financial-news-sentiment", "base_model:Twitter/twhin-bert-large", "base_model:finetune:Twitter/twhin-bert-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-22T18:17:52Z	--- license: apache-2.0 base_model: Twitter/twhin-bert-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: financial-twhin-bert-large-3labels results: [] datasets: - zeroshot/twitter-financial-news-sentiment language: - en widget: - text: "$KTOS: Kratos Defense and Security awarded a $39 million sole-source contract for Geolocation Global Support Service" example_title: "Example 1" - text: "$Google parent Alphabet Inc. reported revenue and earnings that fell short of analysts' expectations, showing the company's search advertising juggernaut was not immune to a slowdown in the digital ad market. The shares fell more than 6%." example_title: "Example 2" - text: "$LJPC - La Jolla Pharma to reassess development of LJPC-401" example_title: "Example 3" - text: "Watch $MARK over 43c in after-hours for continuation targeting the 50c area initially" example title: "Example 4" - text: "$RCII: Rent-A-Center provides update - March revenues were off by about 5% versus last year" example title: "Example 5" --- # financial-twhin-bert-large-3labels This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on finance related tweets. It achieves the following results on the evaluation set: - Loss: 0.2959 - Accuracy: 0.8934 - F1: 0.8943 ## 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: 2.0998212817984933e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
eunyounglee/emotion-bert-finetuning-epoch5	eunyounglee	2024-02-23T05:25:33Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:klue/bert-base", "base_model:finetune:klue/bert-base", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T04:42:57Z	--- license: cc-by-sa-4.0 base_model: klue/bert-base tags: - generated_from_trainer model-index: - name: emotion-bert-finetuning-17000_1 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. --> # emotion-bert-finetuning-17000_1 This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_l_sub_best_ef_signal_it_114	furrutiav	2024-02-23T05:22:17Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T05:21:47Z	--- 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]
jdslim000/0223_1405	jdslim000	2024-02-23T05:07:15Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "esm", "fill-mask", "generated_from_trainer", "base_model:InstaDeepAI/nucleotide-transformer-500m-1000g", "base_model:finetune:InstaDeepAI/nucleotide-transformer-500m-1000g", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-02-23T05:05:48Z	--- license: cc-by-nc-sa-4.0 base_model: InstaDeepAI/nucleotide-transformer-500m-1000g tags: - generated_from_trainer model-index: - name: '0223_1405' 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. --> # 0223_1405 This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-500m-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-500m-1000g) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 8 - seed: 224 - gradient_accumulation_steps: 80 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 1.12.1+cu113 - Datasets 2.17.1 - Tokenizers 0.15.1
christianbaker/bert-finetuned-squad	christianbaker	2024-02-23T04:58:34Z	23	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-22T03:41:12Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-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. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
DScomp380/vit-b16-plant_village	DScomp380	2024-02-23T04:58:10Z	93	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "en", "dataset:Treelar/plant_village", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-01-28T00:11:35Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-b16-plant_village results: [] datasets: - Treelar/plant_village language: - en --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-b16-plant_village This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Treelar/plant_village dataset. It achieves the following results on the evaluation set: - Loss: 0.0100 - Accuracy: 0.9973 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1747 \| 1.0 \| 3119 \| 0.0364 \| 0.9885 \| \| 0.0031 \| 2.0 \| 6238 \| 0.0100 \| 0.9973 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
Someman/bloomz-3b-Ner-nepali-finetuned-adapters-v1.0	Someman	2024-02-23T04:45:28Z	0	0	null	[ "tensorboard", "safetensors", "text-generation", "ne", "dataset:Someman/ner-nepali", "license:mit", "region:us" ]	text-generation	2024-02-22T03:48:04Z	--- license: mit language: - ne pipeline_tag: text-generation datasets: - Someman/ner-nepali --- ### Usage Here is an example to use the model: ```python model_id = "bigscience/bloomz-3b" adapter_id = "Someman/bloomz-3b-Ner-nepali-finetuned-adapters-v1.0" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id,quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_type=torch.bfloat16)) model = PeftModel.from_pretrained(model, adapter_id) prompt = "\n### Response:\n" example = "### Input:\nपार्टीको महासमिति बैठकको समापन गर्दै सभापति देउवाले महासमिति बैठकबाट पार्टीलाई थप अनुशासित, ऊर्जावान र एकताबद्ध बनाएर अघि बढाउने विषयमा प्रेरणा प्राप्त भएको बताए ।"+ prompt tokenize = tokenizer(example, return_tensors="pt") translation_generation_config = GenerationConfig( num_beams=5, max_new_tokens=40, repetition_penalty=1.0, do_sample=True ) generation = model.generate(tokenize.input_ids.cuda(), generation_config=translation_generation_config) output = tokenizer.batch_decode(generation, skip_special_tokens=True) output ``` Expected output similar to the following: ``` ### Input:\nपार्टीको महासमिति बैठकको समापन गर्दै सभापति देउवाले महासमिति बैठकबाट पार्टीलाई थप अनुशासित, ऊर्जावान र एकताबद्ध बनाएर अघि बढाउने विषयमा प्रेरणा प्राप्त भएको बताए । \n### Response:\nदेउवा Person\n महासमिति बैठक Event\nपarty Organization\nदेउवा Person\nपarty Organization\nNo entity\nदेउवा Person\nपarty Organization\nNo entity\nदेउ' ```
akashAD/gemma-chatbot	akashAD	2024-02-23T04:41:14Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b-it", "base_model:adapter:google/gemma-2b-it", "license:other", "region:us" ]	null	2024-02-23T04:39:04Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b-it model-index: - name: gemma-chatbot results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gemma-chatbot This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Namkoy/whisper_peft_vi_nam	Namkoy	2024-02-23T04:34:41Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "hf-asr-leaderboard", "generated_from_trainer", "vi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-large-v2", "base_model:adapter:openai/whisper-large-v2", "license:apache-2.0", "region:us" ]	null	2024-02-17T05:07:16Z	--- language: - vi license: apache-2.0 library_name: peft tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 base_model: openai/whisper-large-v2 model-index: - name: whisper_vietnam_nam 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_vietnam_nam This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3897 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data WER = 0,21789669 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2417 \| 1.0 \| 347 \| 0.4081 \| \| 0.1389 \| 2.0 \| 694 \| 0.3732 \| \| 0.0611 \| 3.0 \| 1041 \| 0.3897 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
eunyounglee/emotion-bert-finetuning-3	eunyounglee	2024-02-23T04:26:36Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:klue/bert-base", "base_model:finetune:klue/bert-base", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-22T00:50:51Z	--- license: cc-by-sa-4.0 base_model: klue/bert-base tags: - generated_from_trainer model-index: - name: emotion-bert-finetuning-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion-bert-finetuning-3 This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
ryusangwon/8818_Llama-2-7b-hf	ryusangwon	2024-02-23T04:25:47Z	2	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-23T04:25:42Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: 8818_Llama-2-7b-hf results: [] library_name: peft --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 8818_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
felicityisme/GPT-2-quantization	felicityisme	2024-02-23T04:21:08Z	2	0	transformers	[ "transformers", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-02-23T03:56:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
NickyNicky/Test_gemma-2b-it_oasst2_chatML_V1	NickyNicky	2024-02-23T04:15:51Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T04:11:41Z	--- 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]
jayshah5696/gemma_2b_hindi	jayshah5696	2024-02-23T04:14:23Z	0	0	peft	[ "peft", "pytorch", "safetensors", "gemma", "generated_from_trainer", "base_model:google/gemma-2b-it", "base_model:adapter:google/gemma-2b-it", "license:other", "region:us" ]	null	2024-02-23T02:53:40Z	--- license: other library_name: peft tags: - generated_from_trainer base_model: google/gemma-2b-it model-index: - name: gemma-2b-hindi-it results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml # use google/gemma-7b if you have access base_model: google/gemma-2b-it model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false bnb_config_kwargs: # These are default values llm_int8_has_fp16_weight: false bnb_4bit_quant_type: nf4 bnb_4bit_use_double_quant: true # huggingface repo datasets: - path: jayshah5696/samvaad-hi-v1_gemma_format type: completion field: text val_set_size: 0.05 dataset_prepared_path: ./LLM-data output_dir: ./out adapter: qlora lora_r: 4 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true sequence_len: 4096 sample_packing: true pad_to_sequence_len: true wandb_project: gemma_openhathi wandb_run_id: model_03_qlora wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 2 micro_batch_size: 2 num_epochs: 1 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: false fp16: false tf32: false bfloat16: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 10 xformers_attention: flash_attention: true warmup_ratio: 0.05 evals_per_epoch: 5 eval_table_size: # eval_max_new_tokens: 128 metric_for_best_model: "eval_loss" saves_per_epoch: 20 save_total_limit: 20 load_best_model_at_end: True debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: seed: 108 ``` </details><br> # out This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5293 ## 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: 2 - eval_batch_size: 2 - seed: 108 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 453 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.0 \| 1 \| 3.7785 \| \| 1.6305 \| 0.2 \| 965 \| 1.6443 \| \| 1.5355 \| 0.4 \| 1930 \| 1.5893 \| \| 1.5383 \| 0.6 \| 2895 \| 1.5557 \| \| 1.5223 \| 0.8 \| 3860 \| 1.5350 \| \| 1.5477 \| 1.0 \| 4825 \| 1.5293 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.0
humung/Ko-PlatYi-6B-RAG-vlending-v0.2	humung	2024-02-23T04:00:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T04:00:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
elliotthwang/KimLanpurePAD-phi-2-zh	elliotthwang	2024-02-23T03:48:24Z	5	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T02:59:30Z	--- 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]
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_type_s_sub_best_by_z_value_ef_signal_it_150	furrutiav	2024-02-23T03:41:02Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T03:40:27Z	--- 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]
unity/sentis-alphafold-v1	unity	2024-02-23T03:32:14Z	0	9	unity-sentis	[ "unity-sentis", "onnx", "text-to-3d", "license:apache-2.0", "region:us" ]	text-to-3d	2024-02-19T17:04:08Z	--- license: apache-2.0 library_name: unity-sentis pipeline_tag: text-to-3d --- # Alpha Fold Version 1 validated for Unity Sentis We provide here the model files we used for our AlphaFoldv1 implementation in Unity Sentis. The large ONNX file is the main model which outputs the distance matrix and the smaller one is a custom model we created used for the simulation step. ## How we implemented this model Find out all about how we implemented this model in our [full article](https://medium.com/@alexandre.ribard/protein-folding-visualization-in-unity-13ef38fff915) on medium. ## How to Use The [source files](https://github.com/Unity-Technologies/sentis-samples/tree/main/ProteinFoldingSample) are available on github for this example. There are 10 proteins in the sample. We have included more protein json data files here to try out. ## Preview When working it should look like this: ![protein folding](preview.gif) ## Information This implementation was designed as a proof of concept to show the abilities of Unity Sentis for scientific visualisation purposes. For actual scientific research there are newer model avaiable (for example AlphaFold v2). We hope the community will build on this visualisation to implement other protein models in Unity Sentis. ## Unity Sentis Unity Sentis is the inference API used in Unity 2023 and above. More information about it can be found [here](https://unity.com/products/sentis).
Haary/haryra-7b-llama2-id	Haary	2024-02-23T03:31:04Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-17T07:15:07Z	--- library_name: transformers license: llama2 --- # Haary/haryra-7b-id <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="margin-left: auto; margin-right: auto"> <img src="https://cdn.pixabay.com/photo/2023/08/12/13/22/peacock-8185593_960_720.png" alt="merak" style="width: 300px; margin:auto"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> [Haary/haryra-7b-id](https://huggingface.co/Haary/haryra-7b-id) is QLoRA quantized version of [Ichsan2895/Merak-7B-v3](https://huggingface.co/Ichsan2895/Merak-7B-v3) ### Install the necessary packages Requires: Transformers from source - only needed for versions <= v4.34. ```shell # Install transformers from source - only needed for versions <= v4.34 !pip install git+https://github.com/huggingface/transformers.git !pip install accelerate ``` ### Example Python code ```python import torch from transformers import pipeline pipe = pipeline("text-generation", model="Haary/haryra-7b-id", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "Anda adalah chatbot ramah yang selalu merespons dengan singkat dan jelas", }, {"role": "user", "content": "Apa bedanya antara raspberry pi dan esp32?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` ## Credits [Ichsan2895/Merak-7B-v3](https://huggingface.co/Ichsan2895/Merak-7B-v3) for base model. ## image source pixabay.com
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_s_sub_best_ef_signal_it_118	furrutiav	2024-02-23T03:11:38Z	6	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T03:11:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
awesomecxh/Taxi-v3	awesomecxh	2024-02-23T03:00:30Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T03:00:28Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="awesomecxh/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
awesomecxh/q-FrozenLake-v1-4x4-noSlippery	awesomecxh	2024-02-23T02:53:57Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T02:53:55Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="awesomecxh/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
datasciathlete/mdeberta-v3-base-open-ner-aihub	datasciathlete	2024-02-23T02:43:49Z	5	0	transformers	[ "transformers", "safetensors", "deberta-v2", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-23T02:42:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
cgato/Thespis-CurtainCall-7b-v0.1-GGUF	cgato	2024-02-23T02:43:18Z	5	0	null	[ "gguf", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-02-23T01:14:13Z	--- license: cc-by-nc-4.0 --- This model is the first in a series of experiments to make my models a bit smarter. Its nowhere near done, but my initial testing was good so I'm uploading so people can check it out. Datasets Used: * OpenOrcaSlim * Dolphin * Capybara * Claude-Multiround-30k * Augmental * ToxicQA * Magiccoder-Evol-Instruct-110k ## Prompt Format: Chat ( The default Ooba template and Silly Tavern Template ) ``` {System Prompt} Username: {Input} BotName: {Response} Username: {Input} BotName: {Response} ``` ## Recommended Silly Tavern Preset -> (Temp: 1.25, MinP: 0.1, RepPen: 1.03) ## Recommended Kobold Horde Preset -> MinP
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_sub_best_by_mixtral_v2_ef_signal_it_122	furrutiav	2024-02-23T02:42:00Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T02:41:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
ttsmodels/hierspeech-mirror	ttsmodels	2024-02-23T02:39:54Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-02-23T02:18:08Z	--- license: mit --- Unofficial mirror of HierSpeech++ models, mirrored from Google Drive to Hugging Face. The GitHub repo can be found [here](https://github.com/sh-lee-prml/HierSpeechpp). Not affiliated with the authors.
Moses25/Mistral-7B-Instruct-V0.3	Moses25	2024-02-23T02:29:58Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T05:54:25Z	--- license: apache-2.0 --- ### This model is trained from Mistral-7B-Instruct-V0.2 with 90% chinese dataset and 10% english dataset github [Web-UI](https://github.com/moseshu/llama2-chat/tree/main/webui) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f4c7172f63f904a0c61ba3/JIeyxhTm9_PNzXyU7wQVd.png) ``` from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer,AutoTokenizer,AutoModelForCausalLM,MistralForCausalLM import torch model_id=Mistral-7B-Instruct-v0.3 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto",) prompt = "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant.Help humman as much as you can.\n<</SYS>>\n\n{instruction} [/INST]" text = prompt.format_map({"instruction":"你好，最近干嘛呢"}) def predict(content_prompt): inputs = tokenizer(content_prompt,return_tensors="pt",add_special_tokens=True) input_ids = inputs["input_ids"].to("cuda:0") # print(f"input length:{len(input_ids[0])}") with torch.no_grad(): generation_output = model.generate( input_ids=input_ids, #generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=2048, top_p=0.9, num_beams=1, do_sample=True, repetition_penalty=1.0, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, ) s = generation_output.sequences[0] output = tokenizer.decode(s,skip_special_tokens=True) output1 = output.split("[/INST]")[-1].strip() # print(output1) return output1 predict(text) output：你好！作为一个大型语言模型，我一直在学习和提高自己的能力。最近，我一直在努力学习新知识、改进算法，以便更好地回答用户的问题并提供帮助。同时，我也会定期接受人工智能专家的指导和评估，以确保我的表现不断提升。希望这些信息对你有所帮助！ ```
JiZha/Bailing-SQL	JiZha	2024-02-23T02:26:31Z	0	0	null	[ "safetensors", "code", "translation", "license:apache-2.0", "region:us" ]	translation	2024-02-22T16:16:44Z	--- license: apache-2.0 metrics: - code_eval pipeline_tag: translation tags: - code --- --- license: apache-2.0 --- ## Model Details ### Model Description This model is a fine-tuned version based on deepseek, and after fine-tuning, it can achieve a score of 84.0 on the Spider dev leaderboard, significantly surpassing the original capabilities of the deepseek model. This model is sql-gen model, it need use result of schema linking model to get most related columns. ### Data Format ``` { "chat_rounds": [ { "role": "system", "content": "You are a professional SQL engineer and you are writing SQL queries for data query tasks.\n", "chat_round_id": 0 }, { "role": "human", "content": "What are the id, name, price and color of the products which have not been ordered for at least twice? \| products : products.product_price , products.product_color , products.product_id , products.product_name , products.parent_product_id \| order_items : order_items.product_id , order_items.order_id , order_items.order_item_id , order_items.order_item_status_code \| orders : orders.order_id , orders.customer_id , orders.order_status_code , orders.date_order_placed \| customers : customers.county , customers.country , customers.customer_id , customers.gender_code , customers.customer_first_name \| orders.customer_id = customers.customer_id \| order_items.order_id = orders.order_id \| order_items.product_id = products.product_id", "chat_round_id": 1 }, { "role": "bot", "content": " select product_id , product_name , product_price , product_color from products except select products.product_id , products.product_name , products.product_price , products.product_color from products join order_items on products.product_id = order_items.product_id join orders on order_items.order_id = orders.order_id group by products.product_id having count ( * ) >= 2", "chat_round_id": 2 } ] } ``` ### Framework versions - PEFT 0.7.0 ### Base Model - [deepseek-coder-33b-base](https://huggingface.co/deepseek-ai/deepseek-coder-33b-base)
pszemraj/dinov2-small-film-shot-classifier	pszemraj	2024-02-23T02:11:41Z	11	0	transformers	[ "transformers", "safetensors", "dinov2", "image-classification", "vision", "generated_from_trainer", "dataset:szymonrucinski/types-of-film-shots", "base_model:facebook/dinov2-small", "base_model:finetune:facebook/dinov2-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-23T02:06:26Z	--- license: apache-2.0 base_model: facebook/dinov2-small tags: - image-classification - vision - generated_from_trainer metrics: - accuracy datasets: - szymonrucinski/types-of-film-shots --- <!-- 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. --> # dinov2-small: types of film shots ## Model description This model is a fine-tuned version of [facebook/dinov2-small](https://huggingface.co/facebook/dinov2-small) on the szymonrucinski/types-of-film-shots dataset. It achieves the following results on the evaluation set: - Loss: 0.9864 - Accuracy: 0.6259 ## class labels The dataset contains the following labels: ```json "id2label": { "0": "ambiguous", "1": "closeUp", "2": "detail", "3": "extremeLongShot", "4": "fullShot", "5": "longShot", "6": "mediumCloseUp", "7": "mediumShot" }, ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 17480 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 12.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.6177 \| 0.97 \| 24 \| 1.5501 \| 0.4101 \| \| 1.3029 \| 1.99 \| 49 \| 1.2448 \| 0.5108 \| \| 1.1785 \| 2.96 \| 73 \| 1.0556 \| 0.5252 \| \| 1.2146 \| 3.98 \| 98 \| 1.2316 \| 0.5396 \| \| 0.8389 \| 4.99 \| 123 \| 1.0235 \| 0.5971 \| \| 0.7883 \| 5.97 \| 147 \| 0.9960 \| 0.6259 \| \| 0.7899 \| 6.98 \| 172 \| 1.1354 \| 0.5540 \| \| 0.663 \| 8.0 \| 197 \| 1.0971 \| 0.5827 \| \| 0.6013 \| 8.97 \| 221 \| 0.9864 \| 0.6259 \| \| 0.6276 \| 9.99 \| 246 \| 1.0182 \| 0.6115 \| \| 0.5196 \| 10.96 \| 270 \| 1.0074 \| 0.6547 \| \| 0.4761 \| 11.7 \| 288 \| 0.9956 \| 0.6763 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
benferns/instructblip-flan-t5-xl_8bit_nf4	benferns	2024-02-23T02:11:23Z	8	0	transformers	[ "transformers", "pytorch", "safetensors", "instructblip", "image-text-to-text", "vision", "image-captioning", "image-to-text", "en", "arxiv:2305.06500", "license:mit", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	image-to-text	2024-02-23T02:11:23Z	--- language: en license: mit tags: - vision - image-captioning pipeline_tag: image-to-text --- Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) _8-bit / nf4 / Safetensors_ -_Mediocre_ 🥱 # InstructBLIP model InstructBLIP model using [Flan-T5-xl](https://huggingface.co/google/flan-t5-xl) as language model. InstructBLIP was introduced in the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Dai et al. Disclaimer: The team releasing InstructBLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description InstructBLIP is a visual instruction tuned version of [BLIP-2](https://huggingface.co/docs/transformers/main/model_doc/blip-2). Refer to the paper for details. ![InstructBLIP architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/instructblip_architecture.jpg) ## Intended uses & limitations Usage is as follows: ``` from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration import torch from PIL import Image import requests model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-flan-t5-xl") processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl") device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") prompt = "What is unusual about this image?" inputs = processor(images=image, text=prompt, return_tensors="pt").to(device) outputs = model.generate( **inputs, do_sample=False, num_beams=5, max_length=256, min_length=1, top_p=0.9, repetition_penalty=1.5, length_penalty=1.0, temperature=1, ) generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip() print(generated_text) ``` ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/instructblip).
acon96/Home-3B-v3-GGUF	acon96	2024-02-23T02:10:45Z	37,476	28	null	[ "gguf", "automation", "home", "assistant", "text-generation", "en", "de", "es", "fr", "dataset:acon96/Home-Assistant-Requests", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-02-23T01:22:53Z	--- datasets: - acon96/Home-Assistant-Requests license: other license_link: https://huggingface.co/acon96/Home-3B-v3-GGUF/raw/main/LICENSE language: - en - de - es - fr tags: - automation - home - assistant pipeline_tag: text-generation --- # Home 3B v3 The "Home" model is a fine tuning of the StableLM-3B-Zephyr model from Stability AI. The model is able to control devices in the user's house as well as perform basic question and answering. The fine tuning dataset is a [custom curated dataset](https://github.com/acon96/home-llm) designed to teach the model function calling. V3 of the model has a new base model (StableLM) that brings significant accuracy increases. Also added are: basic multi-personality support, basic multi-language support, and support for even more Home Assitant entity types (vacuum, timer, and todo). NOTE: the base models do not boast multi-language support but use a tokenizer that can handle non-english languages better than Phi-2. I have verified that it does technically work in German, Spanish, and French on some random examples where the request is an English request processed via Google Translate. The model is quantized using Lama.cpp in order to enable running the model in super low resource environments that are common with Home Assistant installations such as Rapsberry Pis. The model can be used as an "instruct" type model using the Zephyr prompt format. The system prompt is used to provide information about the state of the Home Assistant installation including available devices and callable services. Example "system" prompt: ``` You are 'Al', a helpful AI Assistant that controls the devices in a house. Complete the following task as instructed with the information provided only. Services: light.turn_off(), light.turn_on(brightness,rgb_color), fan.turn_on(), fan.turn_off() Devices: light.office 'Office Light' = on;80% fan.office 'Office fan' = off light.kitchen 'Kitchen Light' = on;80%;red light.bedroom 'Bedroom Light' = off ``` Output from the model will consist of a response that should be relayed back to the user, along with an optional code block that will invoke different Home Assistant "services". The output format from the model for function calling is as follows: ````` turning on the kitchen lights for you now ```homeassistant { "service": "light.turn_on", "target_device": "light.kitchen" } ``` ````` The model is also capable of basic instruct and QA tasks because of the instruction fine-tuning in the base model. For example, the model is able to perform basic logic tasks such as the following: ``` user if mary is 7 years old, and I am 3 years older than her. how old am I? assistant If Mary is 7 years old, then you are 10 years old (7+3=10). ``` ## Training The model was trained as a LoRA on an RTX 3090 (24GB). The LoRA has rank = 64, alpha = 128, targets the `up_proj,down_proj,q_proj,v_proj,o_proj` modules. The full model is merged together at the end. ## Evaluation This model acheives a 97.11% score for JSON function calling accuracy on the test dataset. ## Datasets Snythetic Dataset for SFT - https://huggingface.co/datasets/acon96/Home-Assistant-Requests ## License This model is a fine-tuning of the Stability AI StableLM model series that is licensed under the STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT. As such this model is released under the same non-commerical STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT. The fine-tuned model is shared for non-commerical use ONLY.
han2lin/gpt2_med_s19e22_ft	han2lin	2024-02-23T02:08:33Z	5	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T12:44:43Z	--- 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. 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]
brooksideas/gpt-2-finetuned-wikitext2	brooksideas	2024-02-23T02:06:28Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T23:43:43Z	--- license: mit base_model: openai-community/gpt2 tags: - generated_from_trainer model-index: - name: gpt-2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt-2-finetuned-wikitext2 This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3924 ## Model Description This language model is built on the GPT-2 architecture provided by OpenAI. The tokenizer utilized for preprocessing text data is OpenAI's tikToken. For more details on tikToken, you can refer to the [official GitHub repository](https://github.com/openai/tiktoken). ### Tokenizer Overview To interactively explore the functionality and behavior of the tikToken tokenizer, you can use the [tikToken interactive website](https://tiktokenizer.vercel.app/). This website allows you to quickly visualize the tokenization process and understand how the tokenizer segments input text into tokens. ### Model Checkpoint The model checkpoint used in this implementation is sourced from the OpenAI community and is based on the GPT-2 architecture. You can find the specific model checkpoint at the following Hugging Face Model Hub link: [openai-community/gpt2](https://huggingface.co/openai-community/gpt2). ### Training Details The model was trained for a total of 3 epochs on the provided dataset. This information reflects the number of times the entire training dataset was processed during the training phase. Training for a specific number of epochs helps control the duration and scope of the model's learning process. ## Training and evaluation data #### Evaluation Data For evaluating the model's performance, the training script utilized an evaluation dataset. #### Evaluation Results After training, the model's performance was assessed using the evaluation dataset. The perplexity, a common metric for language modeling tasks was Perplexity: 29.74 ```python eval_results = trainer.evaluate() print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}") >>> Perplexity : 29.74 ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.4934 \| 1.0 \| 2334 \| 3.4145 \| \| 3.3567 \| 2.0 \| 4668 \| 3.3953 \| \| 3.2968 \| 3.0 \| 7002 \| 3.3924 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
NLUHOPOE/test-case-1	NLUHOPOE	2024-02-23T02:01:13Z	50	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "dataset:Open-Orca/SlimOrca", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T00:30:16Z	--- license: apache-2.0 datasets: - Open-Orca/SlimOrca language: - en --- # Model Details * Model Description: This model is test for data ordering. * Developed by: Juhwan Lee * Model Type: Large Language Model # Model Architecture This model is based on Mistral-7B-v0.1. We fine-tuning this model for data ordering task. Mistral-7B-v0.1 is a transformer model, with the following architecture choices: * Grouped-Query Attention * Sliding-Window Attention * Byte-fallback BPE tokenizer # Dataset We random sample SlimOrca dataset. # Guthub https://github.com/trailerAI # License Apache License 2.0
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_type_sub_best_by_mixtral_v2_ef_signal_it_115	furrutiav	2024-02-23T01:54:31Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T01:54:02Z	--- 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]
jisukim8873/falcon-7B-case-1	jisukim8873	2024-02-23T01:53:33Z	153	0	transformers	[ "transformers", "safetensors", "falcon", "text-generation", "custom_code", "en", "dataset:Open-Orca/SlimOrca", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T00:45:50Z	--- license: apache-2.0 datasets: - Open-Orca/SlimOrca language: - en --- # Model Details * Model Description: This model is test for data ordering. * Developed by: Jisu Kim * Model Type: Large Language Model # Model Architecture This model is based on falcon-7B. We fine-tuning this model for data ordering task. falcon-7B is a transformer model, with the following architecture choices: * Grouped-Query Attention * Sliding-Window Attention * Byte-fallback BPE tokenizer # Dataset We random sample Open-Orca dataset. (We finetune the 100,000 dataset) # Guthub https://github.com/trailerAI # License Apache License 2.0
SUFEHeisenberg/Fin-RoBERTa	SUFEHeisenberg	2024-02-23T01:51:41Z	29	2	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "finance", "text-classification", "en", "dataset:financial_phrasebank", "dataset:pauri32/fiqa-2018", "dataset:zeroshot/twitter-financial-news-sentiment", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T01:15:14Z	--- license: apache-2.0 datasets: - financial_phrasebank - pauri32/fiqa-2018 - zeroshot/twitter-financial-news-sentiment language: - en metrics: - accuracy pipeline_tag: text-classification tags: - finance --- We collects financial domain terms from Investopedia's Financia terms dictionary, NYSSCPA's accounting terminology guide and Harvey's Hypertextual Finance Glossary to expand RoBERTa's vocab dict. Based on added-financial-terms RoBERTa, we pretrained our model on multilple financial corpus: - Financial Terms - [Investopedia's Financia terms dictionary](https://www.investopedia.com/financial-term-dictionary-4769738) - [NYSSCPA's accounting terminology guide](https://www.nysscpa.org/professional-resources/accounting-terminology-guide) - [Harvey's Hypertextual Finance Glossary](https://people.duke.edu/~charvey/Classes/wpg/glossary.htm) - Financial Datasets - [FPB](https://huggingface.co/datasets/financial_phrasebank) - [FiQA SA](https://huggingface.co/datasets/pauri32/fiqa-2018) - [SemEval2017 Task5](https://aclanthology.org/S17-2089/) - [Twitter Financial News Sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) - Earnings Call 2016-2023 NASDAQ 100 components stocks's Earnings Call Transcripts. In continual pretraining step, we apply following experiments settings to achieve better finetuned results on Four Financial Datasets: 1. Masking Probability: 0.4 (instead of default 0.15) 2. Warmup Steps: 0 (deriving better results than models with warmup steps) 3. Epochs: 1 (is enough in case of overfitting) 4. weight_decay: 0.01 5. Train Batch Size: 64 6. FP16
rockyclh/llama-2-7b-chat-entrepreneurship	rockyclh	2024-02-23T01:50:09Z	0	0	null	[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T01:50:03Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
oosij/llama2-ko-7b-3task	oosij	2024-02-23T01:47:37Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:beomi/llama-2-ko-7b", "base_model:adapter:beomi/llama-2-ko-7b", "region:us" ]	null	2024-02-23T01:46:59Z	--- library_name: peft base_model: beomi/llama-2-ko-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. 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2
dranger003/AlphaMonarch-7B-iMat.GGUF	dranger003	2024-02-23T01:44:44Z	2	0	gguf	[ "gguf", "text-generation", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-02-23T00:41:41Z	--- license: cc-by-nc-4.0 pipeline_tag: text-generation library_name: gguf --- GGUF importance matrix (imatrix) quants for https://huggingface.co/mlabonne/AlphaMonarch-7B The importance matrix was trained for ~50K tokens (105 batches of 512 tokens) using a [general purpose imatrix calibration dataset](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384). \| Layers \| Context \| Template \| \| --- \| --- \| --- \| \| <pre>32</pre> \| <pre>32768</pre> \| <pre>\<s\>user<br>{prompt}\</s\><br>\<s\>assistant<br>{response}</pre> \|
HenseHsieh/a2c-PandaReachDense-v3	HenseHsieh	2024-02-23T01:39:50Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T01:35:48Z	--- 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: -0.24 +/- 0.11 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 ... ```
DouglasPontes/2020-Q3-25p-filtered-random	DouglasPontes	2024-02-23T01:38:19Z	1	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "base_model:cardiffnlp/twitter-roberta-base-2019-90m", "base_model:finetune:cardiffnlp/twitter-roberta-base-2019-90m", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-02-19T22:08:26Z	--- license: mit base_model: cardiffnlp/twitter-roberta-base-2019-90m tags: - generated_from_trainer model-index: - name: 2020-Q3-25p-filtered-random 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. --> # 2020-Q3-25p-filtered-random This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2624 ## 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: 4.1e-07 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2400000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| No log \| 0.02 \| 8000 \| 2.5582 \| \| 2.8015 \| 0.04 \| 16000 \| 2.4569 \| \| 2.8015 \| 0.07 \| 24000 \| 2.4054 \| \| 2.5403 \| 0.09 \| 32000 \| 2.3788 \| \| 2.5403 \| 0.11 \| 40000 \| 2.3619 \| \| 2.475 \| 0.13 \| 48000 \| 2.3437 \| \| 2.475 \| 0.16 \| 56000 \| 2.3306 \| \| 2.4451 \| 0.18 \| 64000 \| 2.3220 \| \| 2.4451 \| 0.2 \| 72000 \| 2.3136 \| \| 2.4333 \| 0.22 \| 80000 \| 2.3125 \| \| 2.4333 \| 0.25 \| 88000 \| 2.3113 \| \| 2.4234 \| 0.27 \| 96000 \| 2.3007 \| \| 2.4234 \| 0.29 \| 104000 \| 2.3005 \| \| 2.4151 \| 0.31 \| 112000 \| 2.2946 \| \| 2.4151 \| 0.34 \| 120000 \| 2.2902 \| \| 2.4156 \| 0.36 \| 128000 \| 2.2845 \| \| 2.4156 \| 0.38 \| 136000 \| 2.2922 \| \| 2.3994 \| 0.4 \| 144000 \| 2.2819 \| \| 2.3994 \| 0.43 \| 152000 \| 2.2835 \| \| 2.4088 \| 0.45 \| 160000 \| 2.2824 \| \| 2.4088 \| 0.47 \| 168000 \| 2.2797 \| \| 2.3996 \| 0.49 \| 176000 \| 2.2816 \| \| 2.3996 \| 0.52 \| 184000 \| 2.2791 \| \| 2.396 \| 0.54 \| 192000 \| 2.2770 \| \| 2.396 \| 0.56 \| 200000 \| 2.2788 \| \| 2.396 \| 0.58 \| 208000 \| 2.2701 \| \| 2.396 \| 0.61 \| 216000 \| 2.2703 \| \| 2.403 \| 0.63 \| 224000 \| 2.2720 \| \| 2.403 \| 0.65 \| 232000 \| 2.2788 \| \| 2.3889 \| 0.67 \| 240000 \| 2.2739 \| \| 2.3889 \| 0.7 \| 248000 \| 2.2721 \| \| 2.3976 \| 0.72 \| 256000 \| 2.2786 \| \| 2.3976 \| 0.74 \| 264000 \| 2.2715 \| \| 2.3939 \| 0.76 \| 272000 \| 2.2716 \| \| 2.3939 \| 0.79 \| 280000 \| 2.2699 \| \| 2.393 \| 0.81 \| 288000 \| 2.2702 \| \| 2.393 \| 0.83 \| 296000 \| 2.2722 \| \| 2.3884 \| 0.85 \| 304000 \| 2.2711 \| \| 2.3884 \| 0.88 \| 312000 \| 2.2697 \| \| 2.3939 \| 0.9 \| 320000 \| 2.2653 \| \| 2.3939 \| 0.92 \| 328000 \| 2.2678 \| \| 2.3981 \| 0.94 \| 336000 \| 2.2675 \| \| 2.3981 \| 0.97 \| 344000 \| 2.2681 \| \| 2.3936 \| 0.99 \| 352000 \| 2.2644 \| \| 2.3936 \| 1.01 \| 360000 \| 2.2698 \| \| 2.3916 \| 1.03 \| 368000 \| 2.2729 \| \| 2.3916 \| 1.06 \| 376000 \| 2.2722 \| \| 2.3975 \| 1.08 \| 384000 \| 2.2694 \| \| 2.3975 \| 1.1 \| 392000 \| 2.2626 \| \| 2.3946 \| 1.12 \| 400000 \| 2.2714 \| \| 2.3946 \| 1.15 \| 408000 \| 2.2756 \| \| 2.3974 \| 1.17 \| 416000 \| 2.2653 \| \| 2.3974 \| 1.19 \| 424000 \| 2.2649 \| \| 2.3873 \| 1.21 \| 432000 \| 2.2722 \| \| 2.3873 \| 1.24 \| 440000 \| 2.2651 \| \| 2.3922 \| 1.26 \| 448000 \| 2.2638 \| \| 2.3922 \| 1.28 \| 456000 \| 2.2621 \| \| 2.3983 \| 1.3 \| 464000 \| 2.2671 \| \| 2.3983 \| 1.32 \| 472000 \| 2.2651 \| \| 2.3883 \| 1.35 \| 480000 \| 2.2631 \| \| 2.3883 \| 1.37 \| 488000 \| 2.2729 \| \| 2.3909 \| 1.39 \| 496000 \| 2.2618 \| \| 2.3909 \| 1.41 \| 504000 \| 2.2631 \| \| 2.3885 \| 1.44 \| 512000 \| 2.2639 \| \| 2.3885 \| 1.46 \| 520000 \| 2.2590 \| \| 2.3977 \| 1.48 \| 528000 \| 2.2652 \| \| 2.3977 \| 1.5 \| 536000 \| 2.2632 \| \| 2.3968 \| 1.53 \| 544000 \| 2.2666 \| \| 2.3968 \| 1.55 \| 552000 \| 2.2697 \| \| 2.3941 \| 1.57 \| 560000 \| 2.2703 \| \| 2.3941 \| 1.59 \| 568000 \| 2.2632 \| \| 2.3916 \| 1.62 \| 576000 \| 2.2613 \| \| 2.3916 \| 1.64 \| 584000 \| 2.2663 \| \| 2.3878 \| 1.66 \| 592000 \| 2.2593 \| \| 2.3878 \| 1.68 \| 600000 \| 2.2636 \| \| 2.3955 \| 1.71 \| 608000 \| 2.2624 \| \| 2.3955 \| 1.73 \| 616000 \| 2.2627 \| \| 2.3921 \| 1.75 \| 624000 \| 2.2676 \| \| 2.3921 \| 1.77 \| 632000 \| 2.2675 \| \| 2.3971 \| 1.8 \| 640000 \| 2.2690 \| \| 2.3971 \| 1.82 \| 648000 \| 2.2617 \| \| 2.3979 \| 1.84 \| 656000 \| 2.2619 \| \| 2.3979 \| 1.86 \| 664000 \| 2.2666 \| \| 2.3917 \| 1.89 \| 672000 \| 2.2586 \| \| 2.3917 \| 1.91 \| 680000 \| 2.2634 \| \| 2.4004 \| 1.93 \| 688000 \| 2.2631 \| \| 2.4004 \| 1.95 \| 696000 \| 2.2656 \| \| 2.3881 \| 1.98 \| 704000 \| 2.2650 \| \| 2.3881 \| 2.0 \| 712000 \| 2.2618 \| \| 2.3988 \| 2.02 \| 720000 \| 2.2623 \| \| 2.3988 \| 2.04 \| 728000 \| 2.2654 \| \| 2.3919 \| 2.07 \| 736000 \| 2.2622 \| \| 2.3919 \| 2.09 \| 744000 \| 2.2658 \| \| 2.3872 \| 2.11 \| 752000 \| 2.2639 \| \| 2.3872 \| 2.13 \| 760000 \| 2.2578 \| \| 2.3921 \| 2.16 \| 768000 \| 2.2647 \| \| 2.3921 \| 2.18 \| 776000 \| 2.2635 \| \| 2.3956 \| 2.2 \| 784000 \| 2.2609 \| \| 2.3956 \| 2.22 \| 792000 \| 2.2617 \| \| 2.4026 \| 2.25 \| 800000 \| 2.2605 \| \| 2.4026 \| 2.27 \| 808000 \| 2.2619 \| \| 2.3931 \| 2.29 \| 816000 \| 2.2663 \| \| 2.3931 \| 2.31 \| 824000 \| 2.2649 \| \| 2.3958 \| 2.34 \| 832000 \| 2.2655 \| \| 2.3958 \| 2.36 \| 840000 \| 2.2611 \| \| 2.3968 \| 2.38 \| 848000 \| 2.2693 \| \| 2.3968 \| 2.4 \| 856000 \| 2.2639 \| \| 2.3963 \| 2.43 \| 864000 \| 2.2589 \| \| 2.3963 \| 2.45 \| 872000 \| 2.2650 \| \| 2.3921 \| 2.47 \| 880000 \| 2.2654 \| \| 2.3921 \| 2.49 \| 888000 \| 2.2626 \| \| 2.3912 \| 2.52 \| 896000 \| 2.2655 \| \| 2.3912 \| 2.54 \| 904000 \| 2.2635 \| \| 2.3978 \| 2.56 \| 912000 \| 2.2634 \| \| 2.3978 \| 2.58 \| 920000 \| 2.2605 \| \| 2.4009 \| 2.6 \| 928000 \| 2.2601 \| \| 2.4009 \| 2.63 \| 936000 \| 2.2603 \| \| 2.3917 \| 2.65 \| 944000 \| 2.2678 \| \| 2.3917 \| 2.67 \| 952000 \| 2.2693 \| \| 2.3955 \| 2.69 \| 960000 \| 2.2640 \| \| 2.3955 \| 2.72 \| 968000 \| 2.2613 \| \| 2.3962 \| 2.74 \| 976000 \| 2.2723 \| \| 2.3962 \| 2.76 \| 984000 \| 2.2613 \| \| 2.396 \| 2.78 \| 992000 \| 2.2600 \| \| 2.396 \| 2.81 \| 1000000 \| 2.2651 \| \| 2.3961 \| 2.83 \| 1008000 \| 2.2630 \| \| 2.3961 \| 2.85 \| 1016000 \| 2.2596 \| \| 2.399 \| 2.87 \| 1024000 \| 2.2606 \| \| 2.399 \| 2.9 \| 1032000 \| 2.2570 \| \| 2.3981 \| 2.92 \| 1040000 \| 2.2623 \| \| 2.3981 \| 2.94 \| 1048000 \| 2.2630 \| \| 2.4028 \| 2.96 \| 1056000 \| 2.2661 \| \| 2.4028 \| 2.99 \| 1064000 \| 2.2604 \| \| 2.403 \| 3.01 \| 1072000 \| 2.2642 \| \| 2.403 \| 3.03 \| 1080000 \| 2.2600 \| \| 2.3975 \| 3.05 \| 1088000 \| 2.2654 \| \| 2.3975 \| 3.08 \| 1096000 \| 2.2660 \| \| 2.3974 \| 3.1 \| 1104000 \| 2.2703 \| \| 2.3974 \| 3.12 \| 1112000 \| 2.2650 \| \| 2.4014 \| 3.14 \| 1120000 \| 2.2652 \| \| 2.4014 \| 3.17 \| 1128000 \| 2.2660 \| \| 2.3964 \| 3.19 \| 1136000 \| 2.2625 \| \| 2.3964 \| 3.21 \| 1144000 \| 2.2614 \| \| 2.3942 \| 3.23 \| 1152000 \| 2.2656 \| \| 2.3942 \| 3.26 \| 1160000 \| 2.2653 \| \| 2.3969 \| 3.28 \| 1168000 \| 2.2617 \| \| 2.3969 \| 3.3 \| 1176000 \| 2.2617 \| \| 2.3953 \| 3.32 \| 1184000 \| 2.2610 \| \| 2.3953 \| 3.35 \| 1192000 \| 2.2649 \| \| 2.402 \| 3.37 \| 1200000 \| 2.2695 \| \| 2.402 \| 3.39 \| 1208000 \| 2.2630 \| \| 2.3974 \| 3.41 \| 1216000 \| 2.2667 \| \| 2.3974 \| 3.44 \| 1224000 \| 2.2631 \| \| 2.3993 \| 3.46 \| 1232000 \| 2.2646 \| \| 2.3993 \| 3.48 \| 1240000 \| 2.2682 \| \| 2.3999 \| 3.5 \| 1248000 \| 2.2665 \| \| 2.3999 \| 3.53 \| 1256000 \| 2.2631 \| \| 2.3952 \| 3.55 \| 1264000 \| 2.2640 \| \| 2.3952 \| 3.57 \| 1272000 \| 2.2618 \| \| 2.3914 \| 3.59 \| 1280000 \| 2.2626 \| \| 2.3914 \| 3.62 \| 1288000 \| 2.2658 \| \| 2.4113 \| 3.64 \| 1296000 \| 2.2582 \| \| 2.4113 \| 3.66 \| 1304000 \| 2.2590 \| \| 2.4021 \| 3.68 \| 1312000 \| 2.2641 \| \| 2.4021 \| 3.71 \| 1320000 \| 2.2554 \| \| 2.402 \| 3.73 \| 1328000 \| 2.2629 \| \| 2.402 \| 3.75 \| 1336000 \| 2.2635 \| \| 2.3989 \| 3.77 \| 1344000 \| 2.2699 \| \| 2.3989 \| 3.8 \| 1352000 \| 2.2639 \| \| 2.3998 \| 3.82 \| 1360000 \| 2.2627 \| \| 2.3998 \| 3.84 \| 1368000 \| 2.2654 \| \| 2.3968 \| 3.86 \| 1376000 \| 2.2674 \| \| 2.3968 \| 3.88 \| 1384000 \| 2.2633 \| \| 2.3993 \| 3.91 \| 1392000 \| 2.2672 \| \| 2.3993 \| 3.93 \| 1400000 \| 2.2599 \| \| 2.3991 \| 3.95 \| 1408000 \| 2.2602 \| \| 2.3991 \| 3.97 \| 1416000 \| 2.2573 \| \| 2.3971 \| 4.0 \| 1424000 \| 2.2686 \| \| 2.3971 \| 4.02 \| 1432000 \| 2.2629 \| \| 2.4047 \| 4.04 \| 1440000 \| 2.2650 \| \| 2.4047 \| 4.06 \| 1448000 \| 2.2637 \| \| 2.3952 \| 4.09 \| 1456000 \| 2.2654 \| \| 2.3952 \| 4.11 \| 1464000 \| 2.2669 \| \| 2.3994 \| 4.13 \| 1472000 \| 2.2636 \| \| 2.3994 \| 4.15 \| 1480000 \| 2.2661 \| \| 2.4003 \| 4.18 \| 1488000 \| 2.2649 \| \| 2.4003 \| 4.2 \| 1496000 \| 2.2640 \| \| 2.3959 \| 4.22 \| 1504000 \| 2.2634 \| \| 2.3959 \| 4.24 \| 1512000 \| 2.2706 \| \| 2.4023 \| 4.27 \| 1520000 \| 2.2580 \| \| 2.4023 \| 4.29 \| 1528000 \| 2.2693 \| \| 2.3974 \| 4.31 \| 1536000 \| 2.2666 \| \| 2.3974 \| 4.33 \| 1544000 \| 2.2633 \| \| 2.3944 \| 4.36 \| 1552000 \| 2.2657 \| \| 2.3944 \| 4.38 \| 1560000 \| 2.2611 \| \| 2.3974 \| 4.4 \| 1568000 \| 2.2558 \| \| 2.3974 \| 4.42 \| 1576000 \| 2.2614 \| \| 2.4024 \| 4.45 \| 1584000 \| 2.2690 \| \| 2.4024 \| 4.47 \| 1592000 \| 2.2642 \| \| 2.4024 \| 4.49 \| 1600000 \| 2.2616 \| \| 2.4024 \| 4.51 \| 1608000 \| 2.2639 \| \| 2.3981 \| 4.54 \| 1616000 \| 2.2636 \| \| 2.3981 \| 4.56 \| 1624000 \| 2.2696 \| \| 2.4041 \| 4.58 \| 1632000 \| 2.2675 \| \| 2.4041 \| 4.6 \| 1640000 \| 2.2653 \| \| 2.3972 \| 4.63 \| 1648000 \| 2.2658 \| \| 2.3972 \| 4.65 \| 1656000 \| 2.2591 \| \| 2.3997 \| 4.67 \| 1664000 \| 2.2671 \| \| 2.3997 \| 4.69 \| 1672000 \| 2.2607 \| \| 2.3918 \| 4.72 \| 1680000 \| 2.2585 \| \| 2.3918 \| 4.74 \| 1688000 \| 2.2621 \| \| 2.4069 \| 4.76 \| 1696000 \| 2.2623 \| \| 2.4069 \| 4.78 \| 1704000 \| 2.2633 \| \| 2.4039 \| 4.81 \| 1712000 \| 2.2622 \| \| 2.4039 \| 4.83 \| 1720000 \| 2.2627 \| \| 2.4077 \| 4.85 \| 1728000 \| 2.2686 \| \| 2.4077 \| 4.87 \| 1736000 \| 2.2594 \| \| 2.398 \| 4.9 \| 1744000 \| 2.2659 \| \| 2.398 \| 4.92 \| 1752000 \| 2.2684 \| \| 2.4007 \| 4.94 \| 1760000 \| 2.2617 \| \| 2.4007 \| 4.96 \| 1768000 \| 2.2646 \| \| 2.4059 \| 4.99 \| 1776000 \| 2.2610 \| \| 2.4059 \| 5.01 \| 1784000 \| 2.2591 \| \| 2.3996 \| 5.03 \| 1792000 \| 2.2641 \| \| 2.3996 \| 5.05 \| 1800000 \| 2.2607 \| \| 2.4015 \| 5.08 \| 1808000 \| 2.2580 \| \| 2.4015 \| 5.1 \| 1816000 \| 2.2605 \| \| 2.4007 \| 5.12 \| 1824000 \| 2.2649 \| \| 2.4007 \| 5.14 \| 1832000 \| 2.2641 \| \| 2.4019 \| 5.16 \| 1840000 \| 2.2626 \| \| 2.4019 \| 5.19 \| 1848000 \| 2.2580 \| \| 2.4017 \| 5.21 \| 1856000 \| 2.2643 \| \| 2.4017 \| 5.23 \| 1864000 \| 2.2598 \| \| 2.3997 \| 5.25 \| 1872000 \| 2.2604 \| \| 2.3997 \| 5.28 \| 1880000 \| 2.2674 \| \| 2.3973 \| 5.3 \| 1888000 \| 2.2661 \| \| 2.3973 \| 5.32 \| 1896000 \| 2.2667 \| \| 2.4004 \| 5.34 \| 1904000 \| 2.2663 \| \| 2.4004 \| 5.37 \| 1912000 \| 2.2639 \| \| 2.4034 \| 5.39 \| 1920000 \| 2.2657 \| \| 2.4034 \| 5.41 \| 1928000 \| 2.2637 \| \| 2.3907 \| 5.43 \| 1936000 \| 2.2622 \| \| 2.3907 \| 5.46 \| 1944000 \| 2.2630 \| \| 2.3935 \| 5.48 \| 1952000 \| 2.2547 \| \| 2.3935 \| 5.5 \| 1960000 \| 2.2676 \| \| 2.3954 \| 5.52 \| 1968000 \| 2.2630 \| \| 2.3954 \| 5.55 \| 1976000 \| 2.2677 \| \| 2.3995 \| 5.57 \| 1984000 \| 2.2678 \| \| 2.3995 \| 5.59 \| 1992000 \| 2.2642 \| \| 2.398 \| 5.61 \| 2000000 \| 2.2613 \| \| 2.398 \| 5.64 \| 2008000 \| 2.2627 \| \| 2.3971 \| 5.66 \| 2016000 \| 2.2584 \| \| 2.3971 \| 5.68 \| 2024000 \| 2.2700 \| \| 2.3988 \| 5.7 \| 2032000 \| 2.2715 \| \| 2.3988 \| 5.73 \| 2040000 \| 2.2640 \| \| 2.3933 \| 5.75 \| 2048000 \| 2.2628 \| \| 2.3933 \| 5.77 \| 2056000 \| 2.2619 \| \| 2.4007 \| 5.79 \| 2064000 \| 2.2672 \| \| 2.4007 \| 5.82 \| 2072000 \| 2.2653 \| \| 2.3978 \| 5.84 \| 2080000 \| 2.2631 \| \| 2.3978 \| 5.86 \| 2088000 \| 2.2632 \| \| 2.4002 \| 5.88 \| 2096000 \| 2.2599 \| \| 2.4002 \| 5.91 \| 2104000 \| 2.2642 \| \| 2.4041 \| 5.93 \| 2112000 \| 2.2616 \| \| 2.4041 \| 5.95 \| 2120000 \| 2.2602 \| \| 2.4008 \| 5.97 \| 2128000 \| 2.2553 \| \| 2.4008 \| 6.0 \| 2136000 \| 2.2599 \| \| 2.4003 \| 6.02 \| 2144000 \| 2.2645 \| \| 2.4003 \| 6.04 \| 2152000 \| 2.2596 \| \| 2.3998 \| 6.06 \| 2160000 \| 2.2614 \| \| 2.3998 \| 6.09 \| 2168000 \| 2.2666 \| \| 2.4007 \| 6.11 \| 2176000 \| 2.2570 \| \| 2.4007 \| 6.13 \| 2184000 \| 2.2628 \| \| 2.3891 \| 6.15 \| 2192000 \| 2.2558 \| \| 2.3891 \| 6.18 \| 2200000 \| 2.2666 \| \| 2.4011 \| 6.2 \| 2208000 \| 2.2614 \| \| 2.4011 \| 6.22 \| 2216000 \| 2.2646 \| \| 2.3957 \| 6.24 \| 2224000 \| 2.2645 \| \| 2.3957 \| 6.27 \| 2232000 \| 2.2653 \| \| 2.3973 \| 6.29 \| 2240000 \| 2.2630 \| \| 2.3973 \| 6.31 \| 2248000 \| 2.2630 \| \| 2.3964 \| 6.33 \| 2256000 \| 2.2621 \| \| 2.3964 \| 6.36 \| 2264000 \| 2.2608 \| \| 2.3988 \| 6.38 \| 2272000 \| 2.2651 \| \| 2.3988 \| 6.4 \| 2280000 \| 2.2636 \| \| 2.4004 \| 6.42 \| 2288000 \| 2.2602 \| \| 2.4004 \| 6.44 \| 2296000 \| 2.2613 \| \| 2.4006 \| 6.47 \| 2304000 \| 2.2661 \| \| 2.4006 \| 6.49 \| 2312000 \| 2.2635 \| \| 2.401 \| 6.51 \| 2320000 \| 2.2601 \| \| 2.401 \| 6.53 \| 2328000 \| 2.2653 \| \| 2.4048 \| 6.56 \| 2336000 \| 2.2623 \| \| 2.4048 \| 6.58 \| 2344000 \| 2.2608 \| \| 2.404 \| 6.6 \| 2352000 \| 2.2592 \| \| 2.404 \| 6.62 \| 2360000 \| 2.2612 \| \| 2.3997 \| 6.65 \| 2368000 \| 2.2584 \| \| 2.3997 \| 6.67 \| 2376000 \| 2.2646 \| \| 2.4044 \| 6.69 \| 2384000 \| 2.2646 \| \| 2.4044 \| 6.71 \| 2392000 \| 2.2654 \| \| 2.4003 \| 6.74 \| 2400000 \| 2.2660 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
oosij/llama2-ko-13b-3task	oosij	2024-02-23T01:37:57Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:beomi/llama-2-koen-13b", "base_model:adapter:beomi/llama-2-koen-13b", "region:us" ]	null	2024-02-23T01:34:16Z	--- library_name: peft base_model: beomi/llama-2-koen-13b --- # 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. 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2
Jolyne-W/gpt2-quantized-tokenizer	Jolyne-W	2024-02-23T01:20:33Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T01:20:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
cloudyu/google-gemma-7b-it-dpo-v1	cloudyu	2024-02-23T01:17:37Z	59	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T00:56:05Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- this is a DPO fine-tuned model for google/gemma-7b-it using jondurbin/truthy-dpo-v0.1 ``` DPO Trainer TRL supports the DPO Trainer for training language models from preference data, as described in the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafailov et al., 2023. ``` ``` target_modules=[ "gate_proj", "up_proj", "down_proj"] ``` sample code ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM import math ## v2 models model_path = "cloudyu/google-gemma-7b-it-dpo-v1" tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map='auto',local_files_only=False, load_in_4bit=True ) print(model) prompt = input("please input prompt:") while len(prompt) > 0: input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda") generation_output = model.generate( input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2 ) print(tokenizer.decode(generation_output[0])) prompt = input("please input prompt:") ```
lvcalucioli/zephyr-7b-beta_question-answering_question-answering_merged	lvcalucioli	2024-02-23T01:15:14Z	5	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	2024-02-23T01:01:20Z	--- 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]
ThuyNT03/SOMD-xlm-3stage-stage0-pre-v1	ThuyNT03	2024-02-23T01:13:14Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-21T09:47:33Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: SOMD-xlm-3stage-stage0-pre-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SOMD-xlm-3stage-stage0-pre-v1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0652 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.1077 \| 2.0 \| 2485 \| 0.0839 \| \| 0.0916 \| 4.0 \| 4970 \| 0.1377 \| \| 0.1122 \| 6.0 \| 7455 \| 0.0827 \| \| 0.0794 \| 8.0 \| 9940 \| 0.0705 \| \| 0.0733 \| 10.0 \| 12425 \| 0.0652 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_sub_best_ef_signal_it_140	furrutiav	2024-02-23T01:10:19Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T01:09:51Z	--- 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. 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]
Mariaaaaa/best_model_with_bitfit	Mariaaaaa	2024-02-23T01:05:12Z	7	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-22T14:43:10Z	--- 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. 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]
mfidabel/Modelo_3_Whisper_Medium	mfidabel	2024-02-23T00:50:57Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:adapter:openai/whisper-medium", "license:apache-2.0", "region:us" ]	null	2024-02-22T16:10:04Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openai/whisper-medium model-index: - name: Modelo_3_Whisper_Medium 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. --> # Modelo_3_Whisper_Medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1357 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6048 \| 1.0 \| 1295 \| 0.4275 \| \| 0.4759 \| 2.0 \| 2590 \| 0.3141 \| \| 0.3084 \| 3.0 \| 3885 \| 0.2248 \| \| 0.1447 \| 4.0 \| 5180 \| 0.1638 \| \| 0.0611 \| 5.0 \| 6475 \| 0.1357 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.2
ddyuudd/dolly-v2-3b	ddyuudd	2024-02-23T00:45:13Z	9	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "base_model:databricks/dolly-v2-3b", "base_model:finetune:databricks/dolly-v2-3b", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T04:35:14Z	--- base_model: databricks/dolly-v2-3b license: mit --- # 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. 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] ### Framework versions - PEFT 0.7.1
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_s_sub_best_by_mixtral_v2_ef_signal_it_149	furrutiav	2024-02-23T00:34:42Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T00:34:01Z	--- 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. 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]
nathansutton/generate-cxr	nathansutton	2024-02-23T00:32:37Z	239	8	transformers	[ "transformers", "pytorch", "safetensors", "blip", "image-text-to-text", "image-to-text", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-text	2023-02-01T21:23:57Z	--- license: apache-2.0 pipeline_tag: image-to-text --- ## generate-cxr This BlipForConditionalGeneration model generates realistic radiology reports given an chest X-ray and a clinical indication (e.g. 'RLL crackles, eval for pneumonia'). - Developed by: Nathan Sutton - Model type: BLIP - Language(s) (NLP): English - License: Apache 2.0 - Finetuned from model: Salesforce/blip-image-captioning-large ## Model Sources - Repository: https://github.com/nathansutton/prerad - Paper: https://medium.com/@nasutton/a-new-generative-model-for-radiology-b687a993cbb - Demo: https://nathansutton-prerad.hf.space/ ## Out-of-Scope Use Any medical application. ## How to Get Started with the Model ``` from PIL import Image from transformers import BlipForConditionalGeneration, BlipProcessor # read in the model processor = BlipProcessor.from_pretrained("nathansutton/generate-cxr") model = BlipForConditionalGeneration.from_pretrained("nathansutton/generate-cxr") # your data my_image = 'my-chest-x-ray.jpg' my_indication = 'RLL crackles, eval for pneumonia' # process the inputs inputs = processor( images=Image.open(my_image), text='indication:' + my_indication, return_tensors="pt" ) # generate an entire radiology report output = model.generate(**inputs,max_length=512) report = processor.decode(output[0], skip_special_tokens=True) ``` # Training Details This model was trained by cross-referencing the radiology reports in MIMIC-CXR with the images in the MIMIC-CXR-JPG. None are available here and require a data usage agreement with physionet.
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_s_sub_best_by_z_value_ef_signal_it_114	furrutiav	2024-02-23T00:29:46Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T00:29:18Z	--- 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. 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]
lvcalucioli/zephyr-7b-beta_question-answering_question-answering	lvcalucioli	2024-02-23T00:27:12Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:adapter:HuggingFaceH4/zephyr-7b-beta", "license:mit", "region:us" ]	null	2024-02-22T18:02:43Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: HuggingFaceH4/zephyr-7b-beta model-index: - name: zephyr-7b-beta_question-answering_question-answering 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. --> # zephyr-7b-beta_question-answering_question-answering This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.2
EnterNameBros/Offical-Bun-medium	EnterNameBros	2024-02-23T00:03:35Z	7	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T21:58:37Z	--- pipeline_tag: text-generation ---
HighCWu/sd-control-lora-head3d	HighCWu	2024-02-23T00:03:27Z	3	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "image-to-image", "controlnet", "control-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	image-to-image	2024-02-23T00:01:03Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - image-to-image - diffusers - controlnet - control-lora --- # ControlLoRA - Head3d Version ControlLoRA is a neural network structure extended from Controlnet to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlLoRA conditioned on Head3d. ControlLoRA uses the same structure as Controlnet. But its core weight comes from UNet, unmodified. Only hint image encoding layers, linear lora layers and conv2d lora layers used in weight offset are trained. The main idea is from my [ControlLoRA](https://github.com/HighCWu/ControlLoRA) and sdxl [control-lora](https://huggingface.co/stabilityai/control-lora). ## Example 1. Clone ControlLoRA from [Github](https://github.com/HighCWu/control-lora-v2): ```sh $ git clone https://github.com/HighCWu/control-lora-v2 ``` 2. Enter the repo dir: ```sh $ cd control-lora-v2 ``` 3. Run code: ```py import torch from PIL import Image from diffusers import StableDiffusionControlNetPipeline, UNet2DConditionModel, UniPCMultistepScheduler from models.control_lora import ControlLoRAModel device = 'cuda' if torch.cuda.is_available() else 'cpu' dtype = torch.float16 if torch.cuda.is_available() else torch.float32 image = Image.open('<Your Conditioning Image Path>') base_model = "runwayml/stable-diffusion-v1-5" unet = UNet2DConditionModel.from_pretrained( base_model, subfolder="unet", torch_dtype=dtype ) control_lora: ControlLoRAModel = ControlLoRAModel.from_pretrained( "HighCWu/sd-control-lora-head3d", torch_dtype=dtype ) control_lora.tie_weights(unet) pipe = StableDiffusionControlNetPipeline.from_pretrained( base_model, unet=unet, controlnet=control_lora, safety_checker=None, torch_dtype=dtype ).to(device) control_lora.bind_vae(pipe.vae) pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) # Remove if you do not have xformers installed # see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers # for installation instructions pipe.enable_xformers_memory_efficient_attention() # pipe.enable_model_cpu_offload() image = pipe("Girl smiling, professional dslr photograph, high quality", image, num_inference_steps=20).images[0] image.show() ``` You can find some example images below. prompt: ![images_0)](./images_0.png) prompt: ![images_1)](./images_1.png) prompt: ![images_2)](./images_2.png)
316usman/thematic_4b	316usman	2024-02-23T00:02:38Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-23T00:00:45Z	--- library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: thematic_4b 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. --> # thematic_4b This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
Weni/ZeroShot-3.3.4-Mistral-7b-Multilanguage-3.2.0-merged	Weni	2024-02-23T00:01:03Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T23:35:14Z	--- 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]
HighCWu/sd-latent-control-dora-rank128-head3d	HighCWu	2024-02-22T23:58:44Z	6	1	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "image-to-image", "controlnet", "control-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	image-to-image	2024-02-22T23:53:02Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - image-to-image - diffusers - controlnet - control-lora --- # ControlLoRA - Head3d Version ControlLoRA is a neural network structure extended from Controlnet to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlLoRA conditioned on Head3d. ControlLoRA uses the same structure as Controlnet. But its core weight comes from UNet, unmodified. Only hint image encoding layers, linear lora layers and conv2d lora layers used in weight offset are trained. The main idea is from my [ControlLoRA](https://github.com/HighCWu/ControlLoRA) and sdxl [control-lora](https://huggingface.co/stabilityai/control-lora). ## Example 1. Clone ControlLoRA from [Github](https://github.com/HighCWu/control-lora-v2): ```sh $ git clone https://github.com/HighCWu/control-lora-v2 ``` 2. Enter the repo dir: ```sh $ cd control-lora-v2 ``` 3. Run code: ```py import torch from PIL import Image from diffusers import StableDiffusionControlNetPipeline, UNet2DConditionModel, UniPCMultistepScheduler from models.control_lora import ControlLoRAModel device = 'cuda' if torch.cuda.is_available() else 'cpu' dtype = torch.float16 if torch.cuda.is_available() else torch.float32 image = Image.open('<Your Conditioning Image Path>') base_model = "runwayml/stable-diffusion-v1-5" unet = UNet2DConditionModel.from_pretrained( base_model, subfolder="unet", torch_dtype=dtype ) control_lora: ControlLoRAModel = ControlLoRAModel.from_pretrained( "HighCWu/sd-latent-control-dora-rank128-head3d", torch_dtype=dtype ) control_lora.tie_weights(unet) pipe = StableDiffusionControlNetPipeline.from_pretrained( base_model, unet=unet, controlnet=control_lora, safety_checker=None, torch_dtype=dtype ).to(device) control_lora.bind_vae(pipe.vae) pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) # Remove if you do not have xformers installed # see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers # for installation instructions pipe.enable_xformers_memory_efficient_attention() # pipe.enable_model_cpu_offload() image = pipe("Girl smiling, professional dslr photograph, high quality", image, num_inference_steps=20).images[0] image.show() ``` You can find some example images below. prompt: a photography of a man with a beard and sunglasses on ![images_0)](./images_0.png) prompt: worst quality , low quality , portrait , close - up , inconsistent head shape ![images_1)](./images_1.png) prompt: a photography of a man with a mustache and a suit jacket ![images_2)](./images_2.png)
zhonganl/gpt2	zhonganl	2024-02-22T23:58:22Z	2	0	transformers	[ "transformers", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-02-22T22:35:15Z	--- 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]
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_s_sub_best_by_mixtral_v2_ef_signal_it_121	furrutiav	2024-02-22T23:55:11Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-22T23:54:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
EricValen/PixelCopter	EricValen	2024-02-22T23:42:56Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-02-22T23:36:51Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 72.40 +/- 32.08 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

Subsets and Splits