librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-04 12:28:55	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 539 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-04 12:28:29	card stringlengths 11 1.01M
rmsusms22/xlm_roberta-base-finetuned-panx-de-fr	rmsusms22	2024-01-30T08:00:41Z	111	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-29T07:44:30Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm_roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm_roberta-base-finetuned-panx-de-fr 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: 0.1648 - F1: 0.8583 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2887 \| 1.0 \| 715 \| 0.1879 \| 0.8131 \| \| 0.1484 \| 2.0 \| 1430 \| 0.1604 \| 0.8423 \| \| 0.0981 \| 3.0 \| 2145 \| 0.1648 \| 0.8583 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
OS07/CodeToText	OS07	2024-01-30T07:55:44Z	61	0	transformers	[ "transformers", "safetensors", "gpt_bigcode", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-01-30T06:44:40Z	--- 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]
shrenikb/fed32test2	shrenikb	2024-01-30T07:53:40Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:huggyllama/llama-7b", "base_model:adapter:huggyllama/llama-7b", "region:us" ]	null	2024-01-30T07:53:38Z	--- library_name: peft base_model: huggyllama/llama-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: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.2
shrenikb/fed32test	shrenikb	2024-01-30T07:49:58Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:huggyllama/llama-7b", "base_model:adapter:huggyllama/llama-7b", "region:us" ]	null	2024-01-17T04:59:06Z	--- library_name: peft base_model: huggyllama/llama-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: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.2
wangqixun/YamerMIX_v8	wangqixun	2024-01-30T07:49:12Z	8,960	16	diffusers	[ "diffusers", "safetensors", "license:mit", "diffusers:StableDiffusionXLCommonPipeline", "region:us" ]	null	2024-01-22T11:18:57Z	--- license: mit --- # Model The model is from [civitai-Yamer](https://civitai.com/models/84040?modelVersionId=196039). This is a very excellent model！Thank you Yamer! For business inquires, commercial licensing, custom models/commissions, large scale image captioning for datasets and consultation contact me under yamer@rundiffusion.com ![image/png](https://cdn-uploads.huggingface.co/production/uploads/643665d33193f279361cc292/yI0NH-NN08uVd6v1obZeu.png)
adalib/neptune-data-codegen-2B-mono-prefix	adalib	2024-01-30T07:45:43Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Salesforce/codegen-2B-mono", "base_model:adapter:Salesforce/codegen-2B-mono", "region:us" ]	null	2024-01-30T07:45:36Z	--- library_name: peft base_model: Salesforce/codegen-2B-mono --- # 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
Chenxi-Chelsea-Liu/whisper-small-enhanced-hindi-10dB	Chenxi-Chelsea-Liu	2024-01-30T07:38:05Z	5	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-29T09:41:28Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-enhanced-hindi-10dB results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-enhanced-hindi-10dB This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5528 - Wer: 57.6431 ## 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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.3087 \| 0.61 \| 50 \| 1.9565 \| 101.3315 \| \| 1.3628 \| 1.22 \| 100 \| 1.2862 \| 83.4083 \| \| 1.1319 \| 1.83 \| 150 \| 1.0950 \| 79.0334 \| \| 0.9559 \| 2.44 \| 200 \| 0.9573 \| 74.3905 \| \| 0.807 \| 3.05 \| 250 \| 0.8252 \| 71.1655 \| \| 0.6268 \| 3.66 \| 300 \| 0.6903 \| 67.2488 \| \| 0.5039 \| 4.27 \| 350 \| 0.6466 \| 64.4907 \| \| 0.4738 \| 4.88 \| 400 \| 0.6077 \| 62.8566 \| \| 0.3599 \| 5.49 \| 450 \| 0.5964 \| 60.7902 \| \| 0.3225 \| 6.1 \| 500 \| 0.6001 \| 59.4761 \| \| 0.2599 \| 6.71 \| 550 \| 0.5930 \| 58.5509 \| \| 0.1658 \| 7.32 \| 600 \| 0.6158 \| 58.4731 \| \| 0.1666 \| 7.93 \| 650 \| 0.6172 \| 58.0581 \| \| 0.1032 \| 8.54 \| 700 \| 0.6521 \| 58.7152 \| \| 0.081 \| 9.15 \| 750 \| 0.6857 \| 58.7930 \| \| 0.0606 \| 9.76 \| 800 \| 0.7020 \| 57.9457 \| \| 0.0345 \| 10.37 \| 850 \| 0.7422 \| 57.9284 \| \| 0.0342 \| 10.98 \| 900 \| 0.7622 \| 57.5826 \| \| 0.023 \| 11.59 \| 950 \| 0.7787 \| 57.8074 \| \| 0.017 \| 12.2 \| 1000 \| 0.8223 \| 58.4299 \| \| 0.0159 \| 12.8 \| 1050 \| 0.8384 \| 57.6604 \| \| 0.0101 \| 13.41 \| 1100 \| 0.8538 \| 58.3607 \| \| 0.012 \| 14.02 \| 1150 \| 0.8634 \| 57.8765 \| \| 0.0092 \| 14.63 \| 1200 \| 0.8762 \| 57.5134 \| \| 0.0077 \| 15.24 \| 1250 \| 0.9077 \| 58.6201 \| \| 0.007 \| 15.85 \| 1300 \| 0.9194 \| 58.2310 \| \| 0.006 \| 16.46 \| 1350 \| 0.9194 \| 57.1935 \| \| 0.0051 \| 17.07 \| 1400 \| 0.9427 \| 57.4788 \| \| 0.0044 \| 17.68 \| 1450 \| 0.9613 \| 57.5307 \| \| 0.0037 \| 18.29 \| 1500 \| 0.9750 \| 57.3578 \| \| 0.0038 \| 18.9 \| 1550 \| 0.9620 \| 57.1070 \| \| 0.0037 \| 19.51 \| 1600 \| 0.9793 \| 57.2021 \| \| 0.0028 \| 20.12 \| 1650 \| 1.0002 \| 57.6690 \| \| 0.0023 \| 20.73 \| 1700 \| 1.0171 \| 57.0465 \| \| 0.0023 \| 21.34 \| 1750 \| 1.0344 \| 56.4499 \| \| 0.0024 \| 21.95 \| 1800 \| 1.0231 \| 56.9168 \| \| 0.0017 \| 22.56 \| 1850 \| 1.0420 \| 56.6229 \| \| 0.0016 \| 23.17 \| 1900 \| 1.0599 \| 57.6690 \| \| 0.001 \| 23.78 \| 1950 \| 1.0659 \| 57.7641 \| \| 0.0012 \| 24.39 \| 2000 \| 1.0818 \| 56.7093 \| \| 0.001 \| 25.0 \| 2050 \| 1.0874 \| 57.0984 \| \| 0.0008 \| 25.61 \| 2100 \| 1.1034 \| 57.5220 \| \| 0.0006 \| 26.22 \| 2150 \| 1.1275 \| 56.7353 \| \| 0.0004 \| 26.83 \| 2200 \| 1.1528 \| 57.1330 \| \| 0.0002 \| 27.44 \| 2250 \| 1.1668 \| 56.5537 \| \| 0.0001 \| 28.05 \| 2300 \| 1.1935 \| 56.6142 \| \| 0.0001 \| 28.66 \| 2350 \| 1.2282 \| 56.3289 \| \| 0.0001 \| 29.27 \| 2400 \| 1.2547 \| 56.7266 \| \| 0.0001 \| 29.88 \| 2450 \| 1.2814 \| 56.4413 \| \| 0.0001 \| 30.49 \| 2500 \| 1.3142 \| 56.8822 \| \| 0.0 \| 31.1 \| 2550 \| 1.3535 \| 56.8995 \| \| 0.0 \| 31.71 \| 2600 \| 1.3759 \| 57.0033 \| \| 0.0 \| 32.32 \| 2650 \| 1.4102 \| 57.2454 \| \| 0.0 \| 32.93 \| 2700 \| 1.4299 \| 56.8044 \| \| 0.0 \| 33.54 \| 2750 \| 1.4650 \| 57.2886 \| \| 0.0 \| 34.15 \| 2800 \| 1.4906 \| 57.3405 \| \| 0.0 \| 34.76 \| 2850 \| 1.5145 \| 57.5739 \| \| 0.0 \| 35.37 \| 2900 \| 1.5377 \| 57.5480 \| \| 0.0 \| 35.98 \| 2950 \| 1.5461 \| 57.5480 \| \| 0.0 \| 36.59 \| 3000 \| 1.5528 \| 57.6431 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 1.12.1 - Datasets 2.16.1 - Tokenizers 0.15.0
Shaig/mistral-7b-sru	Shaig	2024-01-30T07:24:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-30T07:24:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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anantg/mistral-7b-instruct-ft-merged	anantg	2024-01-30T07:15:16Z	62	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-01-30T07:13:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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anantg/mistral-7b-instruct-ft	anantg	2024-01-30T07:08:24Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-30T07:07:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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juliowaissman/Taxi-v3	juliowaissman	2024-01-30T06:58:19Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T05:25:20Z	--- 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: 7.54 +/- 2.70 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="juliowaissman/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"]) ```
ChunLok/CAM_sentiment_model	ChunLok	2024-01-30T06:58:11Z	97	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "base_model:yiyanghkust/finbert-pretrain", "base_model:finetune:yiyanghkust/finbert-pretrain", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-classification	2024-01-28T10:18:59Z	--- license: apache-2.0 base_model: yiyanghkust/finbert-pretrain inference: false model-index: - name: CAM_sentiment_model results: [ ] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CAM_sentiment_model More information needed ## Model description More information needed ## Intended uses & limitations More information needed ### Training hyperparameters More information needed ### Training results More information needed ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
notChocoMilk/louiepikmin4	notChocoMilk	2024-01-30T06:53:37Z	0	0	null	[ "region:us" ]	null	2024-01-30T06:44:42Z	i accidentally named this 'louiepikmin4' instead of 'icepikmin', oops
bSariturk/bert-fine-tuned-cola	bSariturk	2024-01-30T06:51:30Z	49	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-30T06:20:25Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: bert-fine-tuned-cola results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bert-fine-tuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3350 - Validation Loss: 0.4548 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.5185 \| 0.4576 \| 0 \| \| 0.3350 \| 0.4548 \| 1 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.1
victoremanuelgo/zephyr-7b-beta-fine-tuning-news-classification-ptbr	victoremanuelgo	2024-01-30T06:48:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-30T06:47:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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shidowake/test-240128-swal-7B-hf-qlora-adaptor-merged_bnb_4bit	shidowake	2024-01-30T06:48:15Z	60	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-01-30T06:46:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ajayrao/Taxi-v3	ajayrao	2024-01-30T06:48:00Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T06:47:57Z	--- 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: 7.54 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="ajayrao/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"]) ```
Nicolas852/ppo-PyramidsRND	Nicolas852	2024-01-30T06:47:45Z	16	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2024-01-30T06:47:40Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Nicolas852/ppo-PyramidsRND 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
epinnock/codellama-70-evol-feedback-lora	epinnock	2024-01-30T06:44:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-30T06:38:10Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jinxxx123/twitter_text_classification_model	jinxxx123	2024-01-30T06:38:42Z	50	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-30T05:57:45Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: jinxxx123/twitter_text_classification_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # jinxxx123/twitter_text_classification_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1420 - Validation Loss: 0.2778 - Train Accuracy: 0.9148 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17440, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.8293 \| 0.5490 \| 0.7991 \| 0 \| \| 0.3388 \| 0.3242 \| 0.8897 \| 1 \| \| 0.1420 \| 0.2778 \| 0.9148 \| 2 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.1
Palistha/bert-finetuned-ner	Palistha	2024-01-30T06:34:00Z	91	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-30T06:23:14Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0591 - Precision: 0.9278 - Recall: 0.9488 - F1: 0.9382 - Accuracy: 0.9862 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0774 \| 1.0 \| 1756 \| 0.0762 \| 0.9007 \| 0.9330 \| 0.9166 \| 0.9800 \| \| 0.0414 \| 2.0 \| 3512 \| 0.0566 \| 0.9297 \| 0.9475 \| 0.9385 \| 0.9856 \| \| 0.026 \| 3.0 \| 5268 \| 0.0591 \| 0.9278 \| 0.9488 \| 0.9382 \| 0.9862 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Tokenizers 0.15.1
dhanikitkat/8-emotions-predictor	dhanikitkat	2024-01-30T06:32:50Z	62	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "code", "id", "arxiv:1910.09700", "doi:10.57967/hf/1713", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-30T04:59:25Z	--- language: - id pipeline_tag: text-classification tags: - code --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- 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]
serpdotai/sparsetral-16x7B-v1	serpdotai	2024-01-30T06:28:18Z	13	1	transformers	[ "transformers", "safetensors", "sparsetral", "text-generation", "custom_code", "dataset:Open-Orca/SlimOrca", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T00:03:13Z	--- license: apache-2.0 datasets: - Open-Orca/SlimOrca --- prompt format ``` ### System:\n{system}\n### Human:\n{user}\n### Assistant:\n" ``` ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("serpdotai/sparsetral-16x7B-v1", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("serpdotai/sparsetral-16x7B-v1", device_map="auto", trust_remote_code=True).eval() inputs = tokenizer('### System:\n\n### Human:\nHow are you?\n### Assistant:\n', return_tensors='pt') inputs = inputs.to(model.device) pred = model.generate(**inputs) print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) # I am doing well, thank you. ```
avinasht/BERT_FPB_finetuned	avinasht	2024-01-30T06:26:07Z	175	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-30T06:25:50Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: BERT_FPB_finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_FPB_finetuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6446 - Accuracy: 0.8650 - F1: 0.8648 - Precision: 0.8647 - Recall: 0.8650 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.4364 \| 1.0 \| 246 \| 0.4949 \| 0.7872 \| 0.7708 \| 0.8266 \| 0.7872 \| \| 0.4302 \| 2.0 \| 492 \| 0.4106 \| 0.8398 \| 0.8404 \| 0.8414 \| 0.8398 \| \| 0.1547 \| 3.0 \| 738 \| 0.4793 \| 0.8558 \| 0.8559 \| 0.8561 \| 0.8558 \| \| 0.1471 \| 4.0 \| 984 \| 0.6446 \| 0.8650 \| 0.8648 \| 0.8647 \| 0.8650 \| ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
colable/llama-ko-peft-v0.5	colable	2024-01-30T06:19:51Z	58	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "ko", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T06:03:22Z	--- license: mit language: - ko --- # open-llama-2-ko based model with inhouse dataset This is an Korean Model based on * [beomi/open-llama-2-ko-7b] gpu code example ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM import math ## v2 models model_path = "colable/llama-ko-peft-v0.5" tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float32, 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:") ```
Shel2679/mistral7b_instruct_generation	Shel2679	2024-01-30T06:17:41Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-01-30T06:17:34Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral7b_instruct_generation 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. --> # mistral7b_instruct_generation This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.8070 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.958 \| 0.0 \| 20 \| 1.8399 \| \| 1.8756 \| 0.01 \| 40 \| 1.8082 \| \| 1.888 \| 0.01 \| 60 \| 1.7994 \| \| 1.7863 \| 0.01 \| 80 \| 1.7892 \| \| 1.7987 \| 0.01 \| 100 \| 1.7897 \| \| 1.847 \| 0.02 \| 120 \| 1.7881 \| \| 1.8505 \| 0.02 \| 140 \| 1.7858 \| \| 1.7915 \| 0.02 \| 160 \| 1.7900 \| \| 1.8611 \| 0.03 \| 180 \| 1.7942 \| \| 1.8827 \| 0.03 \| 200 \| 1.7943 \| \| 1.88 \| 0.03 \| 220 \| 1.7953 \| \| 1.8887 \| 0.03 \| 240 \| 1.7888 \| \| 1.6737 \| 0.04 \| 260 \| 1.7939 \| \| 1.8471 \| 0.04 \| 280 \| 1.7880 \| \| 1.8163 \| 0.04 \| 300 \| 1.7835 \| \| 1.7763 \| 0.04 \| 320 \| 1.7825 \| \| 1.8567 \| 0.05 \| 340 \| 1.7962 \| \| 1.8203 \| 0.05 \| 360 \| 1.7871 \| \| 1.965 \| 0.05 \| 380 \| 1.7897 \| \| 1.9522 \| 0.06 \| 400 \| 1.8036 \| \| 1.8826 \| 0.06 \| 420 \| 1.8028 \| \| 1.8173 \| 0.06 \| 440 \| 1.8205 \| \| 1.8269 \| 0.06 \| 460 \| 1.8059 \| \| 1.8498 \| 0.07 \| 480 \| 1.7966 \| \| 1.9159 \| 0.07 \| 500 \| 1.8070 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
cashewEnthusiast/ppo-LunarLander-v2	cashewEnthusiast	2024-01-30T06:17:02Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T06:16:42Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 269.34 +/- 18.16 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ajayrao/q-FrozenLake-v1-4x4-noSlippery	ajayrao	2024-01-30T06:05:39Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T06:05:36Z	--- 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="ajayrao/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"]) ```
karawalla/merged_model	karawalla	2024-01-30T06:01:20Z	3	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T05:58:09Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: karawalla - 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)
tinywell/dqn-SpaceInvadersNoFrameskip-v4	tinywell	2024-01-30T05:57:19Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T05:51:24Z	--- 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: 862.00 +/- 366.83 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 tinywell -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 tinywell -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 tinywell ``` ## 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', 400000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
genne/SOLAR_dpo_v2_SFT-DPO	genne	2024-01-30T05:56:32Z	0	0	transformers	[ "transformers", "safetensors", "text-generation", "ko", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T02:18:43Z	--- license: apache-2.0 language: - ko library_name: transformers pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details * base model: jingyeom/SOLAR_KO_1.3_deup ### Training Data * open ko dpo datasets * open dpo datasets + 자체 모델 번역 ### Results [More Information Needed] #### Summary
cryptoque/distilhubert-finetuned-gtzan-v2	cryptoque	2024-01-30T05:39:16Z	148	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2024-01-30T02:35:51Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-v2 results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.86 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilhubert-finetuned-gtzan-v2 This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 1.5298 - Accuracy: 0.86 ## 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: 8e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1489 \| 1.0 \| 113 \| 2.2978 \| 0.74 \| \| 0.0001 \| 2.0 \| 226 \| 2.2070 \| 0.78 \| \| 0.3174 \| 3.0 \| 339 \| 1.7906 \| 0.8 \| \| 0.0001 \| 4.0 \| 452 \| 1.5376 \| 0.81 \| \| 0.0 \| 5.0 \| 565 \| 1.4012 \| 0.85 \| \| 0.0001 \| 6.0 \| 678 \| 1.2597 \| 0.87 \| \| 0.0001 \| 7.0 \| 791 \| 1.5363 \| 0.86 \| \| 0.0001 \| 8.0 \| 904 \| 1.5298 \| 0.86 \| \| 0.0 \| 9.0 \| 1017 \| 1.5277 \| 0.86 \| \| 0.0 \| 10.0 \| 1130 \| 1.5298 \| 0.86 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
Pongprecha/distilbert_base_data_wnut_17	Pongprecha	2024-01-30T05:35:40Z	91	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-30T05:33:15Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert_base_data_wnut_17 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_base_data_wnut_17 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2833 - Precision: 0.5246 - Recall: 0.3855 - F1: 0.4444 - Accuracy: 0.9461 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 213 \| 0.2740 \| 0.6152 \| 0.2919 \| 0.3960 \| 0.9404 \| \| No log \| 2.0 \| 426 \| 0.2568 \| 0.5997 \| 0.3679 \| 0.4561 \| 0.9450 \| \| 0.1764 \| 3.0 \| 639 \| 0.2844 \| 0.6269 \| 0.3457 \| 0.4456 \| 0.9464 \| \| 0.1764 \| 4.0 \| 852 \| 0.2963 \| 0.5564 \| 0.3522 \| 0.4313 \| 0.9459 \| \| 0.0526 \| 5.0 \| 1065 \| 0.2833 \| 0.5246 \| 0.3855 \| 0.4444 \| 0.9461 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
NatthawatTung/ALL_mt5-base_10_spider_10_wikiSQL	NatthawatTung	2024-01-30T05:30:18Z	3	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-01-29T11:41:36Z	--- tags: - generated_from_trainer model-index: - name: ALL_mt5-base_10_spider_10_wikiSQL 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. --> # ALL_mt5-base_10_spider_10_wikiSQL This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0241 - Rouge2 Precision: 0.8525 - Rouge2 Recall: 0.5646 - Rouge2 Fmeasure: 0.6434 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.4259 \| 1.0 \| 875 \| 0.1208 \| 0.6006 \| 0.3892 \| 0.4443 \| \| 0.1482 \| 2.0 \| 1750 \| 0.0808 \| 0.7001 \| 0.4721 \| 0.5333 \| \| 0.1073 \| 3.0 \| 2625 \| 0.0588 \| 0.7416 \| 0.5007 \| 0.5657 \| \| 0.0867 \| 4.0 \| 3500 \| 0.0461 \| 0.7741 \| 0.5208 \| 0.5894 \| \| 0.0769 \| 5.0 \| 4375 \| 0.0382 \| 0.7999 \| 0.5351 \| 0.6073 \| \| 0.0658 \| 6.0 \| 5250 \| 0.0327 \| 0.8225 \| 0.5465 \| 0.6217 \| \| 0.0574 \| 7.0 \| 6125 \| 0.0283 \| 0.8364 \| 0.5546 \| 0.6314 \| \| 0.0525 \| 8.0 \| 7000 \| 0.0261 \| 0.8444 \| 0.5593 \| 0.6371 \| \| 0.0498 \| 9.0 \| 7875 \| 0.0245 \| 0.8515 \| 0.5643 \| 0.6429 \| \| 0.0491 \| 10.0 \| 8750 \| 0.0241 \| 0.8525 \| 0.5646 \| 0.6434 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.13.3
juliowaissman/q-FrozenLake-v1-4x4-noSlippery	juliowaissman	2024-01-30T05:22:26Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T05:09:28Z	--- 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="juliowaissman/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"]) ```
dlibf/zephyr-7b-dpo-full_lr1e-7	dlibf	2024-01-30T05:11:49Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T02:39:14Z	--- tags: - trl - dpo - generated_from_trainer model-index: - name: zephyr-7b-dpo-full_lr1e-7 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-dpo-full_lr1e-7 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5999 - Rewards/chosen: -0.1059 - Rewards/rejected: -0.3888 - Rewards/accuracies: 0.7266 - Rewards/margins: 0.2829 - Logps/rejected: -300.8914 - Logps/chosen: -273.0010 - Logits/rejected: -2.2473 - Logits/chosen: -2.2980 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6773 \| 0.21 \| 100 \| 0.6767 \| 0.0281 \| -0.0077 \| 0.6914 \| 0.0358 \| -262.7869 \| -259.5997 \| -2.3569 \| -2.4039 \| \| 0.6286 \| 0.42 \| 200 \| 0.6292 \| -0.0536 \| -0.2320 \| 0.7109 \| 0.1784 \| -285.2149 \| -267.7724 \| -2.3535 \| -2.4023 \| \| 0.6161 \| 0.63 \| 300 \| 0.6066 \| -0.0848 \| -0.3355 \| 0.7188 \| 0.2507 \| -295.5617 \| -270.8907 \| -2.2759 \| -2.3264 \| \| 0.5908 \| 0.84 \| 400 \| 0.6002 \| -0.1035 \| -0.3846 \| 0.7227 \| 0.2811 \| -300.4714 \| -272.7594 \| -2.2519 \| -2.3026 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1
ogbrandt/mistral-7b-non-instruction-pjf-ft-gpt35-dpo-v0	ogbrandt	2024-01-30T05:10:22Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2024-01-30T04:43:35Z	--- library_name: peft base_model: mistralai/Mistral-7B-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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
y629/xlm-roberta-base-finetuned-panx-de	y629	2024-01-30T05:09:55Z	91	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-29T03:38:13Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.867952522255193 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1346 - F1: 0.8680 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2577 \| 1.0 \| 525 \| 0.1588 \| 0.8301 \| \| 0.1317 \| 2.0 \| 1050 \| 0.1365 \| 0.8507 \| \| 0.0822 \| 3.0 \| 1575 \| 0.1346 \| 0.8680 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.2 - Tokenizers 0.13.3
Mavitu56/LLamaCampeonatoBrasileiro	Mavitu56	2024-01-30T05:02:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-30T05:02:07Z	--- 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]
IDEA-CVR/DINO-EVA	IDEA-CVR	2024-01-30T04:57:49Z	0	0	null	[ "region:us" ]	null	2023-07-08T04:04:03Z	## detrex: Benchmarking Detection Transformers This is the huggingface space for IDEA-CVR proposed DETR-based research platform `detrex` - detrex github link: https://github.com/IDEA-Research/detrex - detrex documentation: https://detrex.readthedocs.io/en/latest/ We will store our detrex pretrained checkpoints both in github and huggingface space.
Uday1998/my_awesome_qa_model	Uday1998	2024-01-30T04:56:58Z	98	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-01-30T04:43:22Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 2.9653 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
Chattiori/FixedReality	Chattiori	2024-01-30T04:54:37Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-01-14T10:34:01Z	--- license: creativeml-openrail-m ---
stablediffusionapi/ae-realisticmagn	stablediffusionapi	2024-01-30T04:53:43Z	25	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-30T04:51:46Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # AE-realisticmagn API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/13907603331706590229.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "ae-realisticmagn" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/ae-realisticmagn) Model link: [View model](https://modelslab.com/models/ae-realisticmagn) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "ae-realisticmagn", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
k-seungri/output	k-seungri	2024-01-30T04:46:02Z	99	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ko", "dataset:k-seungri/forwhisperfinetuning_dataset", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-30T04:39:53Z	--- language: - ko license: apache-2.0 base_model: openai/whisper-base tags: - hf-asr-leaderboard - generated_from_trainer datasets: - k-seungri/forwhisperfinetuning_dataset model-index: - name: k-seungri/forwhisperfinetuning_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # k-seungri/forwhisperfinetuning_model This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the forwhisperfinetuning_dataset 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: 1e-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_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
k-seungri/forwhisperfinetunings_training_args_tokenizer	k-seungri	2024-01-30T04:43:04Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-30T04:43:04Z	--- 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]
k-seungri/forwhisperfinetunings_training_args_processor	k-seungri	2024-01-30T04:43:03Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-30T04:43:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
fasterinnerlooper/stable-code-3b	fasterinnerlooper	2024-01-30T04:34:13Z	0	0	null	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:stabilityai/stable-code-3b", "base_model:finetune:stabilityai/stable-code-3b", "license:other", "region:us" ]	null	2024-01-24T15:23:28Z	--- license: other base_model: stabilityai/stable-code-3b tags: - generated_from_trainer model-index: - name: stable-code-3b 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. --> # stable-code-3b This model is a fine-tuned version of [stabilityai/stable-code-3b](https://huggingface.co/stabilityai/stable-code-3b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: cosine - lr_scheduler_warmup_ratio: 0.3 - training_steps: 700 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
nakcnx/typhoon-sql-qlora	nakcnx	2024-01-30T04:32:02Z	5	0	peft	[ "peft", "safetensors", "en", "th", "dataset:b-mc2/sql-create-context", "base_model:scb10x/typhoon-7b", "base_model:adapter:scb10x/typhoon-7b", "region:us" ]	null	2024-01-29T04:14:13Z	--- library_name: peft base_model: scb10x/typhoon-7b datasets: - b-mc2/sql-create-context language: - en - th --- ## Model Description [Typhoon-7b](scb10x/typhoon-7b) QLoRA Finetune by unsloth with [SQL Context](b-mc2/sql-create-context) dataset. #### Training Hyperparameters Batch Size: 48 (4(BS)x4(GAS)x3(GPU)) The following `bitsandbytes` quantization config was used during training: - r = 64 - lora_alpha = 16 - lora_dropout = 0.05 - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### LOSS Step Training Loss Eval Loss 1550 (Epoch:1) 0.4295 0.4367 3110 (Epoch:2) 0.4057 0.4217 ### Framework versions - PEFT 0.7.0
adalib/megengine-data-codegen-2B-mono-prefix	adalib	2024-01-30T04:17:47Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Salesforce/codegen-2B-mono", "base_model:adapter:Salesforce/codegen-2B-mono", "region:us" ]	null	2024-01-30T04:17:42Z	--- library_name: peft base_model: Salesforce/codegen-2B-mono --- # 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
seongjunLee/torchy_generator	seongjunLee	2024-01-30T04:01:20Z	0	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-01-30T04:01:20Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a cartoon style drawing of TOK character, license: openrail++ --- # SDXL LoRA DreamBooth - seongjunLee/torchy_generator <Gallery /> ## Model description These are seongjunLee/torchy_generator LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a cartoon style drawing of TOK character, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](seongjunLee/torchy_generator/tree/main) them in the Files & versions tab.
Jeongbeen/torchy_generator	Jeongbeen	2024-01-30T04:00:23Z	1	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-01-30T04:00:20Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a cartoon style drawing of TOK character, license: openrail++ --- # SDXL LoRA DreamBooth - Jeongbeen/torchy_generator <Gallery /> ## Model description These are Jeongbeen/torchy_generator LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a cartoon style drawing of TOK character, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Jeongbeen/torchy_generator/tree/main) them in the Files & versions tab.
kanishka/smolm-autoreg-bpe-counterfactual-babylm-pipps_and_keys_to_it_all_10k-1e-3	kanishka	2024-01-30T03:58:26Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-29T05:12:25Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-counterfactual-babylm-pipps_and_keys_to_it_all_10k-1e-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. --> # smolm-autoreg-bpe-counterfactual-babylm-pipps_and_keys_to_it_all_10k-1e-3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3502 - Accuracy: 0.4102 ## 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: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 3.6157 \| 1.0 \| 18844 \| 3.7143 \| 0.3599 \| \| 3.3979 \| 2.0 \| 37688 \| 3.5062 \| 0.3804 \| \| 3.2663 \| 3.0 \| 56532 \| 3.3950 \| 0.3932 \| \| 3.1887 \| 4.0 \| 75376 \| 3.3694 \| 0.3977 \| \| 3.1328 \| 5.0 \| 94220 \| 3.3361 \| 0.4009 \| \| 3.0925 \| 6.0 \| 113064 \| 3.3236 \| 0.4038 \| \| 3.0537 \| 7.0 \| 131908 \| 3.3165 \| 0.4050 \| \| 3.0289 \| 8.0 \| 150752 \| 3.3142 \| 0.4063 \| \| 2.9979 \| 9.0 \| 169596 \| 3.2959 \| 0.4083 \| \| 2.9734 \| 10.0 \| 188440 \| 3.2976 \| 0.4096 \| \| 2.9501 \| 11.0 \| 207284 \| 3.3026 \| 0.4094 \| \| 2.9302 \| 12.0 \| 226128 \| 3.3036 \| 0.4097 \| \| 2.9067 \| 13.0 \| 244972 \| 3.3101 \| 0.4103 \| \| 2.885 \| 14.0 \| 263816 \| 3.3063 \| 0.4106 \| \| 2.8686 \| 15.0 \| 282660 \| 3.3195 \| 0.4098 \| \| 2.8474 \| 16.0 \| 301504 \| 3.3275 \| 0.4106 \| \| 2.8235 \| 17.0 \| 320348 \| 3.3297 \| 0.4108 \| \| 2.8091 \| 18.0 \| 339192 \| 3.3385 \| 0.4105 \| \| 2.7899 \| 19.0 \| 358036 \| 3.3433 \| 0.4102 \| \| 2.7718 \| 20.0 \| 376880 \| 3.3502 \| 0.4102 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.14.1
mlx-community/CodeLlama-34b-Instruct-hf-4bit	mlx-community	2024-01-30T03:52:45Z	12	2	mlx	[ "mlx", "llama", "llama-2", "text-generation", "code", "license:llama2", "region:us" ]	text-generation	2024-01-30T03:48:04Z	--- language: - code license: llama2 tags: - llama-2 - mlx pipeline_tag: text-generation --- # mlx-community/CodeLlama-34b-Instruct-hf-4bit This model was converted to MLX format from [`codellama/CodeLlama-34b-Instruct-hf`](). Refer to the [original model card](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/CodeLlama-34b-Instruct-hf-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
wgj0714/results	wgj0714	2024-01-30T03:52:19Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:davidkim205/komt-mistral-7b-v1", "base_model:adapter:davidkim205/komt-mistral-7b-v1", "region:us" ]	null	2024-01-30T03:26:51Z	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: davidkim205/komt-mistral-7b-v1 model-index: - name: results 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. --> # results This model is a fine-tuned version of [davidkim205/komt-mistral-7b-v1](https://huggingface.co/davidkim205/komt-mistral-7b-v1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.3 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.0
AIFT/AIFT-instruct-42dot_LLM-SFT-1.3B-dpo	AIFT	2024-01-30T03:43:59Z	150	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T00:17:21Z	--- license: cc-by-sa-4.0 --- <h1>AIFT-instruct-42dot_LLM-SFT-1.3B-dpo</h1> <b><학습 데이터 구축></b> <br> kyujinpy 님이 공개하신 KOR-OpenOrca-Platypus 데이터를 일부 삭제(샘플링) 및 정제 작업 진행하여 활용. 그 이후 해당 데이터들을 보며 관련 태스크를 추출하였고 이를 기반으로 해당 태스크에 맞춰서 NLP 관련 오픈소스 데이터를 활용하여 학습데이터를 자체적으로 역사, 과학, 수학, 기계독해, 리뷰 분석 문제를 gpt를 통해서 구축하였고, aihub 일반상식 및 기계독해 데이터를 활용하여 추가로 학습 데이터를 구축(형태소 관련, 기계독해 관련 및 요약) 각종 블로그에서 역사 및 상식 퀴즈를 사람이 직접 학습데이터 형태로 변경 AI2AI Challenge 데이터 형태를 보고 gpt를 통해 초등 수준의 과학 수학 문제 유형을 제작 500문제 영어 번역 데이터 영한/한영 데이터 학습 데이터로 활용 진행 총 데이터 4만개 정도 사용하였습니다. <br> dpo데이터의 경우는 hh-rlhf데이터를 gpt-3.5-turbo를 활용해 답변을 재생성하였습니다 <br> + TruthfulQA 관련 문제 추가를 진행하였습니다.(속설 관련 참거짓 문제) + 기계독해 관련 학습 데이터를 ChatGPT를 통해서 답변을 얻어 학습 + 문법관련 학습 데이터 <br> ###학습 데이터 파일은 비공개입니다. <br> <모델> <br> 42dot에서 공개한 42dot_LLM-SFT-1.3B을 베이스 모델로 하여 학습 진행하였습니다. <br> <br> <br> <b><학습></b> <br> 학습은 LoRA를 사용하여 A100 40G *2에서 학습을 진행하였습니다.
prajjusy/pfet-flan-t5-base-model-5	prajjusy	2024-01-30T03:37:20Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:prajjusy/full-finetuned-flan-t5-base-model-5", "base_model:adapter:prajjusy/full-finetuned-flan-t5-base-model-5", "region:us" ]	null	2024-01-30T03:37:19Z	--- library_name: peft base_model: prajjusy/full-finetuned-flan-t5-base-model-5 --- # 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
bartowski/Mistralpaca-7B-exl2	bartowski	2024-01-30T03:37:14Z	0	0	null	[ "sft", "text-generation", "dataset:yahma/alpaca-cleaned", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	text-generation	2024-01-30T03:21:07Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 datasets: - yahma/alpaca-cleaned tags: - sft quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Mistralpaca-7B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.12">turboderp's ExLlamaV2 v0.0.12</a> for quantization. # The "main" branch only contains the measurement.json, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/mlabonne/Mistralpaca-7B \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.8 GB \| 11.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.2 GB \| 8.6 GB \| 10.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/5_0) \| 5.0 \| 6.0 \| 6.0 GB \| 7.4 GB \| 9.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.3 GB \| 6.7 GB \| 8.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.7 GB \| 6.1 GB \| 8.1 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Mistralpaca-7B-exl2 Mistralpaca-7B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Mistralpaca-7B-exl2`: ```shell mkdir Mistralpaca-7B-exl2 huggingface-cli download bartowski/Mistralpaca-7B-exl2 --local-dir Mistralpaca-7B-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir Mistralpaca-7B-exl2-6_5 huggingface-cli download bartowski/Mistralpaca-7B-exl2 --revision 6_5 --local-dir Mistralpaca-7B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir Mistralpaca-7B-exl2-6.5 huggingface-cli download bartowski/Mistralpaca-7B-exl2 --revision 6_5 --local-dir Mistralpaca-7B-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Unggi/test	Unggi	2024-01-30T03:36:04Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "base_model:finetune:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T03:16:30Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T tags: - generated_from_trainer model-index: - name: test 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. --> # test This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00015 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 256 - total_train_batch_size: 512 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.3.0.dev20240127+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
zhanjun520/q-FrozenLake-v1-4x4-noSlippery	zhanjun520	2024-01-30T03:31:18Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T03:31:03Z	--- 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="zhanjun520/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"]) ```
Nicolas852/ppo-SnowballTarget	Nicolas852	2024-01-30T03:21:39Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2024-01-30T03:21:35Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Nicolas852/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
thuan9889/llama_embedding_model_v1	thuan9889	2024-01-30T03:20:36Z	604	1	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-01-30T03:20:30Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # thuan9889/llama_embedding_model_v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('thuan9889/llama_embedding_model_v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=thuan9889/llama_embedding_model_v1) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2 with parameters: ``` {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 0, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
adalib/hummingbot-data-codegen-2B-mono-prefix	adalib	2024-01-30T03:09:05Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Salesforce/codegen-2B-mono", "base_model:adapter:Salesforce/codegen-2B-mono", "region:us" ]	null	2024-01-30T03:09:02Z	--- library_name: peft base_model: Salesforce/codegen-2B-mono --- # 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
mlx-community/CodeLlama-7b-Instruct-hf-4bit-MLX	mlx-community	2024-01-30T03:00:57Z	20	1	mlx	[ "mlx", "llama", "llama-2", "text-generation", "code", "license:llama2", "region:us" ]	text-generation	2024-01-30T02:29:12Z	--- language: - code license: llama2 tags: - llama-2 - mlx pipeline_tag: text-generation --- ![Alt text](https://media.discordapp.net/attachments/989904887330521099/1201717650128896070/Llama_Coding_on_MacBook_1.png?ex=65cad5c6&is=65b860c6&hm=8008a5817272fa49fca67143516563b2578accf263cc04d6768e689c1be2f483&=&format=webp&quality=lossless&width=1372&height=1372) # mlx-community/CodeLlama-7b-Instruct-hf-4bit-MLX This model was converted to MLX format from [`codellama/CodeLlama-7b-Instruct-hf`](). Refer to the [original model card](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/CodeLlama-7b-Instruct-hf-4bit-MLX") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
liwii/electra-ginza-based-ja-fc-classifier	liwii	2024-01-30T03:00:32Z	19	0	transformers	[ "transformers", "pytorch", "electra", "generated_from_trainer", "base_model:megagonlabs/transformers-ud-japanese-electra-base-ginza-520", "base_model:finetune:megagonlabs/transformers-ud-japanese-electra-base-ginza-520", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-01-30T01:30:55Z	--- license: mit base_model: megagonlabs/transformers-ud-japanese-electra-base-ginza-520 tags: - generated_from_trainer metrics: - accuracy model-index: - name: electra-ginza-based-ja-fc-classifier 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. --> # electra-ginza-based-ja-fc-classifier This model is a fine-tuned version of [megagonlabs/transformers-ud-japanese-electra-base-ginza-520](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-ginza-520) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2376 - Accuracy: 0.9258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.38340974405913e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4988 \| 1.0 \| 1223 \| 0.3910 \| 0.8418 \| \| 0.3011 \| 2.0 \| 2446 \| 0.2376 \| 0.9258 \| \| 0.1658 \| 3.0 \| 3669 \| 0.2649 \| 0.9336 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0
checkiejan/phi2-marking-test-full	checkiejan	2024-01-30T02:59:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-30T02:36:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> # How to Get Started with the Model To load and use this model with the Transformers library by Hugging Face, follow the steps outlined in the code snippet below. This code demonstrates how to configure the model, load it along with its tokenizer, and perform inference to generate text based on a given prompt. ## Code Format: ```python from peft import PeftModel, PeftConfig test_config = PeftConfig.from_pretrained("checkiejan/phi2-marking-test-full") model_base = AutoModelForCausalLM.from_pretrained( test_config.base_model_name_or_path, device_map="auto", quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type="nf4", ), torch_dtype=torch.bfloat16, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(test_config.base_model_name_or_path) # Add/set tokens same tokens to base model before merging, like we did before starting training tokenizer.add_tokens(["<\|im_start\|>", "<PAD>"]) tokenizer.pad_token = "<PAD>" tokenizer.add_special_tokens(dict(eos_token="<\|im_end\|>")) model_base.resize_token_embeddings( new_num_tokens=len(tokenizer), pad_to_multiple_of=64) # phi2 default is 64, see configuration_phi.py model_base.config.eos_token_id = tokenizer.eos_token_id lora_model = PeftModel.from_pretrained(model_base, "checkiejan/phi2-marking-test-full") inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=True) outputs = lora_model.generate(inputs) text = tokenizer.batch_decode(outputs, skip_special_tokens=True) print(''.join(text)) ``` This code snippet sets up the model and tokenizer, configures the necessary parameters, and demonstrates how to generate text based on a given prompt. Ensure to replace "Your prompt here" with your actual input text. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps	blzncz	2024-01-30T02:55:57Z	178	0	transformers	[ "transformers", "pytorch", "tensorboard", "segformer", "image-segmentation", "vision", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2024-01-29T16:06:48Z	--- license: other tags: - image-segmentation - vision - generated_from_trainer model-index: - name: segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps 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. --> # segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_rojo dataset. It achieves the following results on the evaluation set: - Loss: 1.2098 - Mean Iou: 0.3648 - Mean Accuracy: 0.6821 - Overall Accuracy: 0.6947 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.7354 - Accuracy Fallo malla: 0.6052 - Accuracy Fallo adhesivo: 0.9884 - Accuracy Fallo burbuja: 0.3995 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.5920 - Iou Fallo malla: 0.5774 - Iou Fallo adhesivo: 0.2950 - Iou Fallo burbuja: 0.3598 ## 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: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Bg \| Accuracy Fallo cohesivo \| Accuracy Fallo malla \| Accuracy Fallo adhesivo \| Accuracy Fallo burbuja \| Iou Bg \| Iou Fallo cohesivo \| Iou Fallo malla \| Iou Fallo adhesivo \| Iou Fallo burbuja \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-----------:\|:-----------------------:\|:--------------------:\|:-----------------------:\|:----------------------:\|:------:\|:------------------:\|:---------------:\|:------------------:\|:-----------------:\| \| 0.5778 \| 1.0 \| 114 \| 0.8590 \| 0.2588 \| 0.5626 \| 0.6271 \| nan \| 0.3415 \| 0.9120 \| 0.9748 \| 0.0221 \| 0.0 \| 0.3339 \| 0.6211 \| 0.3168 \| 0.0220 \| \| 0.3326 \| 2.0 \| 228 \| 0.6845 \| 0.3570 \| 0.6755 \| 0.7131 \| nan \| 0.5232 \| 0.8953 \| 0.9911 \| 0.2921 \| 0.0 \| 0.5003 \| 0.6924 \| 0.3417 \| 0.2503 \| \| 0.2636 \| 3.0 \| 342 \| 0.6662 \| 0.3896 \| 0.7107 \| 0.7344 \| nan \| 0.7666 \| 0.6622 \| 0.9838 \| 0.4304 \| 0.0 \| 0.6088 \| 0.6188 \| 0.4014 \| 0.3191 \| \| 0.2505 \| 4.0 \| 456 \| 0.7666 \| 0.3732 \| 0.7408 \| 0.6807 \| nan \| 0.4141 \| 0.9417 \| 0.9778 \| 0.6297 \| 0.0 \| 0.4065 \| 0.6276 \| 0.4337 \| 0.3980 \| \| 0.2306 \| 5.0 \| 570 \| 0.4680 \| 0.4690 \| 0.7389 \| 0.8099 \| nan \| 0.7461 \| 0.8649 \| 0.9742 \| 0.3705 \| 0.0 \| 0.6711 \| 0.7095 \| 0.6349 \| 0.3294 \| \| 0.1998 \| 6.0 \| 684 \| 0.5711 \| 0.4449 \| 0.7494 \| 0.7824 \| nan \| 0.8528 \| 0.6732 \| 0.9865 \| 0.4850 \| 0.0 \| 0.6781 \| 0.6338 \| 0.5320 \| 0.3807 \| \| 0.2062 \| 7.0 \| 798 \| 0.6403 \| 0.4070 \| 0.7437 \| 0.7283 \| nan \| 0.5736 \| 0.8683 \| 0.9881 \| 0.5447 \| 0.0 \| 0.5300 \| 0.6452 \| 0.4613 \| 0.3987 \| \| 0.182 \| 8.0 \| 912 \| 0.5934 \| 0.4344 \| 0.7309 \| 0.7770 \| nan \| 0.8171 \| 0.7036 \| 0.9840 \| 0.4190 \| 0.0 \| 0.6640 \| 0.6485 \| 0.4916 \| 0.3681 \| \| 0.178 \| 9.0 \| 1026 \| 0.7158 \| 0.3811 \| 0.6915 \| 0.7313 \| nan \| 0.7292 \| 0.6984 \| 0.9913 \| 0.3472 \| 0.0 \| 0.6148 \| 0.6404 \| 0.3348 \| 0.3153 \| \| 0.1568 \| 10.0 \| 1140 \| 0.5892 \| 0.4169 \| 0.6970 \| 0.7873 \| nan \| 0.8088 \| 0.7398 \| 0.9855 \| 0.2538 \| 0.0 \| 0.6770 \| 0.6664 \| 0.5004 \| 0.2407 \| \| 0.1576 \| 11.0 \| 1254 \| 0.6419 \| 0.4228 \| 0.7177 \| 0.7652 \| nan \| 0.7970 \| 0.7001 \| 0.9805 \| 0.3931 \| 0.0 \| 0.6509 \| 0.6318 \| 0.4701 \| 0.3614 \| \| 0.1667 \| 12.0 \| 1368 \| 0.6563 \| 0.4060 \| 0.7369 \| 0.7605 \| nan \| 0.7409 \| 0.7517 \| 0.9871 \| 0.4681 \| 0.0 \| 0.6326 \| 0.6731 \| 0.4103 \| 0.3139 \| \| 0.1436 \| 13.0 \| 1482 \| 0.9148 \| 0.3864 \| 0.7079 \| 0.7187 \| nan \| 0.6666 \| 0.7400 \| 0.9900 \| 0.4352 \| 0.0 \| 0.6025 \| 0.6632 \| 0.2829 \| 0.3835 \| \| 0.1469 \| 14.0 \| 1596 \| 0.6680 \| 0.4166 \| 0.7216 \| 0.7689 \| nan \| 0.7843 \| 0.7225 \| 0.9861 \| 0.3936 \| 0.0 \| 0.6703 \| 0.6608 \| 0.3946 \| 0.3571 \| \| 0.1288 \| 15.0 \| 1710 \| 0.8170 \| 0.3765 \| 0.6849 \| 0.7164 \| nan \| 0.8269 \| 0.5509 \| 0.9859 \| 0.3759 \| 0.0 \| 0.6242 \| 0.5368 \| 0.3815 \| 0.3398 \| \| 0.1243 \| 16.0 \| 1824 \| 0.8197 \| 0.4034 \| 0.7169 \| 0.7375 \| nan \| 0.8456 \| 0.5776 \| 0.9842 \| 0.4602 \| 0.0 \| 0.6517 \| 0.5582 \| 0.4078 \| 0.3991 \| \| 0.1208 \| 17.0 \| 1938 \| 0.7927 \| 0.3848 \| 0.6774 \| 0.7295 \| nan \| 0.8592 \| 0.5460 \| 0.9810 \| 0.3233 \| 0.0 \| 0.6359 \| 0.5256 \| 0.4647 \| 0.2978 \| \| 0.115 \| 18.0 \| 2052 \| 1.1226 \| 0.3376 \| 0.6484 \| 0.6727 \| nan \| 0.7053 \| 0.5900 \| 0.9905 \| 0.3079 \| 0.0 \| 0.5659 \| 0.5688 \| 0.2673 \| 0.2860 \| \| 0.1138 \| 19.0 \| 2166 \| 0.8244 \| 0.4055 \| 0.7099 \| 0.7446 \| nan \| 0.8200 \| 0.6248 \| 0.9833 \| 0.4115 \| 0.0 \| 0.6364 \| 0.5964 \| 0.4287 \| 0.3659 \| \| 0.1144 \| 20.0 \| 2280 \| 0.5964 \| 0.4493 \| 0.7179 \| 0.8034 \| nan \| 0.8594 \| 0.7188 \| 0.9808 \| 0.3127 \| 0.0 \| 0.6995 \| 0.6608 \| 0.5990 \| 0.2873 \| \| 0.108 \| 21.0 \| 2394 \| 0.6545 \| 0.4418 \| 0.7348 \| 0.7902 \| nan \| 0.8263 \| 0.7241 \| 0.9835 \| 0.4053 \| 0.0 \| 0.6832 \| 0.6653 \| 0.5023 \| 0.3582 \| \| 0.109 \| 22.0 \| 2508 \| 0.9552 \| 0.3775 \| 0.6990 \| 0.7058 \| nan \| 0.6835 \| 0.6906 \| 0.9894 \| 0.4325 \| 0.0 \| 0.5907 \| 0.6391 \| 0.2756 \| 0.3819 \| \| 0.0987 \| 23.0 \| 2622 \| 0.7971 \| 0.3974 \| 0.7133 \| 0.7453 \| nan \| 0.7451 \| 0.7124 \| 0.9871 \| 0.4084 \| 0.0 \| 0.6281 \| 0.6560 \| 0.3577 \| 0.3452 \| \| 0.0977 \| 24.0 \| 2736 \| 0.9783 \| 0.3718 \| 0.6984 \| 0.7001 \| nan \| 0.5950 \| 0.7793 \| 0.9916 \| 0.4276 \| 0.0 \| 0.5491 \| 0.6786 \| 0.2620 \| 0.3692 \| \| 0.0954 \| 25.0 \| 2850 \| 0.9562 \| 0.3856 \| 0.6981 \| 0.7352 \| nan \| 0.7102 \| 0.7294 \| 0.9904 \| 0.3623 \| 0.0 \| 0.6355 \| 0.6603 \| 0.2988 \| 0.3332 \| \| 0.0928 \| 26.0 \| 2964 \| 0.9185 \| 0.3787 \| 0.6870 \| 0.7355 \| nan \| 0.7815 \| 0.6491 \| 0.9847 \| 0.3327 \| 0.0 \| 0.6569 \| 0.6184 \| 0.3151 \| 0.3028 \| \| 0.0918 \| 27.0 \| 3078 \| 0.9617 \| 0.3845 \| 0.6916 \| 0.7175 \| nan \| 0.8211 \| 0.5605 \| 0.9809 \| 0.4037 \| 0.0 \| 0.6123 \| 0.5462 \| 0.3994 \| 0.3648 \| \| 0.0801 \| 28.0 \| 3192 \| 1.1167 \| 0.3672 \| 0.6811 \| 0.7091 \| nan \| 0.7352 \| 0.6393 \| 0.9927 \| 0.3570 \| 0.0 \| 0.6151 \| 0.6141 \| 0.2816 \| 0.3250 \| \| 0.0852 \| 29.0 \| 3306 \| 0.8549 \| 0.4217 \| 0.7108 \| 0.7596 \| nan \| 0.8684 \| 0.6040 \| 0.9848 \| 0.3862 \| 0.0 \| 0.6576 \| 0.5808 \| 0.5146 \| 0.3553 \| \| 0.0816 \| 30.0 \| 3420 \| 0.9536 \| 0.3902 \| 0.7034 \| 0.7366 \| nan \| 0.7752 \| 0.6573 \| 0.9885 \| 0.3926 \| 0.0 \| 0.6415 \| 0.6301 \| 0.3274 \| 0.3517 \| \| 0.0876 \| 31.0 \| 3534 \| 1.0597 \| 0.3873 \| 0.7065 \| 0.7158 \| nan \| 0.7490 \| 0.6374 \| 0.9920 \| 0.4475 \| 0.0 \| 0.6117 \| 0.6160 \| 0.3051 \| 0.4035 \| \| 0.0811 \| 32.0 \| 3648 \| 0.8829 \| 0.4038 \| 0.7077 \| 0.7569 \| nan \| 0.7949 \| 0.6829 \| 0.9860 \| 0.3669 \| 0.0 \| 0.6442 \| 0.6498 \| 0.3943 \| 0.3304 \| \| 0.0789 \| 33.0 \| 3762 \| 0.9615 \| 0.4002 \| 0.7104 \| 0.7436 \| nan \| 0.7890 \| 0.6575 \| 0.9884 \| 0.4066 \| 0.0 \| 0.6344 \| 0.6308 \| 0.3702 \| 0.3658 \| \| 0.0752 \| 34.0 \| 3876 \| 0.7799 \| 0.4297 \| 0.7280 \| 0.7806 \| nan \| 0.8279 \| 0.6991 \| 0.9873 \| 0.3975 \| 0.0 \| 0.6787 \| 0.6605 \| 0.4458 \| 0.3634 \| \| 0.0731 \| 35.0 \| 3990 \| 0.9285 \| 0.4061 \| 0.7025 \| 0.7531 \| nan \| 0.8595 \| 0.5987 \| 0.9898 \| 0.3619 \| 0.0 \| 0.6579 \| 0.5797 \| 0.4600 \| 0.3330 \| \| 0.0752 \| 36.0 \| 4104 \| 0.9218 \| 0.4112 \| 0.7276 \| 0.7463 \| nan \| 0.7632 \| 0.6926 \| 0.9880 \| 0.4667 \| 0.0 \| 0.6393 \| 0.6507 \| 0.3462 \| 0.4200 \| \| 0.0701 \| 37.0 \| 4218 \| 0.8808 \| 0.4105 \| 0.7184 \| 0.7562 \| nan \| 0.8090 \| 0.6635 \| 0.9893 \| 0.4119 \| 0.0 \| 0.6569 \| 0.6342 \| 0.3876 \| 0.3740 \| \| 0.0717 \| 38.0 \| 4332 \| 1.1090 \| 0.3748 \| 0.6881 \| 0.7166 \| nan \| 0.7554 \| 0.6334 \| 0.9905 \| 0.3729 \| 0.0 \| 0.6272 \| 0.6069 \| 0.2969 \| 0.3433 \| \| 0.0716 \| 39.0 \| 4446 \| 0.9456 \| 0.4018 \| 0.7064 \| 0.7528 \| nan \| 0.8217 \| 0.6418 \| 0.9872 \| 0.3747 \| 0.0 \| 0.6638 \| 0.6131 \| 0.3863 \| 0.3456 \| \| 0.069 \| 40.0 \| 4560 \| 0.8462 \| 0.4157 \| 0.7038 \| 0.7697 \| nan \| 0.8656 \| 0.6316 \| 0.9856 \| 0.3324 \| 0.0 \| 0.6750 \| 0.6041 \| 0.4917 \| 0.3078 \| \| 0.07 \| 41.0 \| 4674 \| 0.9715 \| 0.3843 \| 0.6886 \| 0.7393 \| nan \| 0.8006 \| 0.6353 \| 0.9896 \| 0.3289 \| 0.0 \| 0.6420 \| 0.6104 \| 0.3633 \| 0.3056 \| \| 0.0649 \| 42.0 \| 4788 \| 0.9114 \| 0.3997 \| 0.7066 \| 0.7592 \| nan \| 0.7682 \| 0.7185 \| 0.9917 \| 0.3478 \| 0.0 \| 0.6613 \| 0.6728 \| 0.3449 \| 0.3196 \| \| 0.0665 \| 43.0 \| 4902 \| 1.1847 \| 0.3662 \| 0.6812 \| 0.6981 \| nan \| 0.7131 \| 0.6389 \| 0.9912 \| 0.3817 \| 0.0 \| 0.5853 \| 0.6122 \| 0.2832 \| 0.3504 \| \| 0.0646 \| 44.0 \| 5016 \| 1.1242 \| 0.3744 \| 0.6906 \| 0.7086 \| nan \| 0.6930 \| 0.6870 \| 0.9891 \| 0.3932 \| 0.0 \| 0.5902 \| 0.6495 \| 0.2770 \| 0.3555 \| \| 0.0662 \| 45.0 \| 5130 \| 1.1017 \| 0.3605 \| 0.6735 \| 0.7023 \| nan \| 0.7333 \| 0.6261 \| 0.9906 \| 0.3439 \| 0.0 \| 0.5997 \| 0.5996 \| 0.2906 \| 0.3126 \| \| 0.0644 \| 46.0 \| 5244 \| 1.2989 \| 0.3470 \| 0.6600 \| 0.6735 \| nan \| 0.6567 \| 0.6473 \| 0.9917 \| 0.3445 \| 0.0 \| 0.5607 \| 0.6182 \| 0.2377 \| 0.3185 \| \| 0.0595 \| 47.0 \| 5358 \| 1.0764 \| 0.3833 \| 0.6982 \| 0.7241 \| nan \| 0.7650 \| 0.6389 \| 0.9932 \| 0.3957 \| 0.0 \| 0.6345 \| 0.6134 \| 0.3071 \| 0.3618 \| \| 0.0603 \| 48.0 \| 5472 \| 1.0871 \| 0.3692 \| 0.6813 \| 0.7128 \| nan \| 0.7153 \| 0.6718 \| 0.9884 \| 0.3497 \| 0.0 \| 0.6079 \| 0.6388 \| 0.2797 \| 0.3197 \| \| 0.0591 \| 49.0 \| 5586 \| 1.1054 \| 0.3800 \| 0.6956 \| 0.7171 \| nan \| 0.7103 \| 0.6866 \| 0.9892 \| 0.3963 \| 0.0 \| 0.6116 \| 0.6458 \| 0.2816 \| 0.3609 \| \| 0.0612 \| 50.0 \| 5700 \| 1.1061 \| 0.3652 \| 0.6768 \| 0.7087 \| nan \| 0.7394 \| 0.6340 \| 0.9903 \| 0.3435 \| 0.0 \| 0.6027 \| 0.6074 \| 0.3009 \| 0.3147 \| \| 0.0609 \| 51.0 \| 5814 \| 0.9938 \| 0.3742 \| 0.6850 \| 0.7206 \| nan \| 0.7555 \| 0.6433 \| 0.9890 \| 0.3523 \| 0.0 \| 0.6210 \| 0.6121 \| 0.3143 \| 0.3235 \| \| 0.058 \| 52.0 \| 5928 \| 1.0391 \| 0.3745 \| 0.6836 \| 0.7248 \| nan \| 0.7691 \| 0.6374 \| 0.9901 \| 0.3379 \| 0.0 \| 0.6275 \| 0.6082 \| 0.3257 \| 0.3109 \| \| 0.0559 \| 53.0 \| 6042 \| 0.9916 \| 0.3922 \| 0.7033 \| 0.7373 \| nan \| 0.8044 \| 0.6249 \| 0.9902 \| 0.3938 \| 0.0 \| 0.6429 \| 0.6003 \| 0.3644 \| 0.3537 \| \| 0.0572 \| 54.0 \| 6156 \| 1.0124 \| 0.3801 \| 0.6907 \| 0.7262 \| nan \| 0.7721 \| 0.6371 \| 0.9885 \| 0.3650 \| 0.0 \| 0.6284 \| 0.6052 \| 0.3326 \| 0.3341 \| \| 0.0558 \| 55.0 \| 6270 \| 1.0856 \| 0.3692 \| 0.6823 \| 0.7120 \| nan \| 0.7232 \| 0.6604 \| 0.9894 \| 0.3565 \| 0.0 \| 0.6094 \| 0.6255 \| 0.2864 \| 0.3246 \| \| 0.058 \| 56.0 \| 6384 \| 1.0581 \| 0.3837 \| 0.6998 \| 0.7212 \| nan \| 0.7353 \| 0.6668 \| 0.9910 \| 0.4062 \| 0.0 \| 0.6126 \| 0.6269 \| 0.3125 \| 0.3666 \| \| 0.0518 \| 57.0 \| 6498 \| 1.0176 \| 0.3933 \| 0.7060 \| 0.7362 \| nan \| 0.7857 \| 0.6440 \| 0.9884 \| 0.4060 \| 0.0 \| 0.6395 \| 0.6127 \| 0.3489 \| 0.3655 \| \| 0.0537 \| 58.0 \| 6612 \| 1.2001 \| 0.3676 \| 0.6853 \| 0.6947 \| nan \| 0.7737 \| 0.5607 \| 0.9884 \| 0.4184 \| 0.0 \| 0.6003 \| 0.5391 \| 0.3221 \| 0.3764 \| \| 0.0552 \| 59.0 \| 6726 \| 0.9751 \| 0.3940 \| 0.7068 \| 0.7314 \| nan \| 0.8019 \| 0.6139 \| 0.9870 \| 0.4244 \| 0.0 \| 0.6353 \| 0.5871 \| 0.3662 \| 0.3816 \| \| 0.0538 \| 60.0 \| 6840 \| 1.0382 \| 0.3782 \| 0.6909 \| 0.7203 \| nan \| 0.7216 \| 0.6813 \| 0.9895 \| 0.3714 \| 0.0 \| 0.6093 \| 0.6389 \| 0.3011 \| 0.3418 \| \| 0.0528 \| 61.0 \| 6954 \| 1.1785 \| 0.3662 \| 0.6819 \| 0.7019 \| nan \| 0.7278 \| 0.6310 \| 0.9904 \| 0.3784 \| 0.0 \| 0.5966 \| 0.6013 \| 0.2914 \| 0.3419 \| \| 0.0531 \| 62.0 \| 7068 \| 1.1054 \| 0.3685 \| 0.6852 \| 0.7026 \| nan \| 0.7290 \| 0.6310 \| 0.9899 \| 0.3911 \| 0.0 \| 0.5969 \| 0.5981 \| 0.2961 \| 0.3514 \| \| 0.0522 \| 63.0 \| 7182 \| 1.1271 \| 0.3717 \| 0.6871 \| 0.7094 \| nan \| 0.7268 \| 0.6496 \| 0.9906 \| 0.3816 \| 0.0 \| 0.6069 \| 0.6148 \| 0.2905 \| 0.3460 \| \| 0.0507 \| 64.0 \| 7296 \| 1.0440 \| 0.3734 \| 0.6825 \| 0.7242 \| nan \| 0.7678 \| 0.6380 \| 0.9884 \| 0.3359 \| 0.0 \| 0.6279 \| 0.6043 \| 0.3272 \| 0.3076 \| \| 0.0519 \| 65.0 \| 7410 \| 1.1191 \| 0.3727 \| 0.6884 \| 0.7102 \| nan \| 0.7264 \| 0.6517 \| 0.9911 \| 0.3843 \| 0.0 \| 0.6028 \| 0.6156 \| 0.2978 \| 0.3472 \| \| 0.0502 \| 66.0 \| 7524 \| 1.0089 \| 0.3917 \| 0.7036 \| 0.7408 \| nan \| 0.7555 \| 0.6898 \| 0.9896 \| 0.3794 \| 0.0 \| 0.6413 \| 0.6472 \| 0.3261 \| 0.3437 \| \| 0.051 \| 67.0 \| 7638 \| 1.2112 \| 0.3672 \| 0.6806 \| 0.7083 \| nan \| 0.7352 \| 0.6378 \| 0.9899 \| 0.3593 \| 0.0 \| 0.6078 \| 0.6085 \| 0.2918 \| 0.3279 \| \| 0.0508 \| 68.0 \| 7752 \| 1.1584 \| 0.3702 \| 0.6860 \| 0.7052 \| nan \| 0.7202 \| 0.6477 \| 0.9888 \| 0.3875 \| 0.0 \| 0.5956 \| 0.6155 \| 0.2902 \| 0.3495 \| \| 0.048 \| 69.0 \| 7866 \| 1.1363 \| 0.3773 \| 0.6922 \| 0.7165 \| nan \| 0.7297 \| 0.6628 \| 0.9895 \| 0.3865 \| 0.0 \| 0.6158 \| 0.6289 \| 0.2901 \| 0.3518 \| \| 0.0483 \| 70.0 \| 7980 \| 1.1489 \| 0.3749 \| 0.6916 \| 0.7103 \| nan \| 0.7398 \| 0.6367 \| 0.9889 \| 0.4011 \| 0.0 \| 0.6074 \| 0.6080 \| 0.2994 \| 0.3598 \| \| 0.0495 \| 71.0 \| 8094 \| 1.1470 \| 0.3774 \| 0.6943 \| 0.7102 \| nan \| 0.7454 \| 0.6295 \| 0.9891 \| 0.4131 \| 0.0 \| 0.6059 \| 0.6032 \| 0.3053 \| 0.3724 \| \| 0.0472 \| 72.0 \| 8208 \| 1.2749 \| 0.3597 \| 0.6782 \| 0.6864 \| nan \| 0.7291 \| 0.5930 \| 0.9891 \| 0.4017 \| 0.0 \| 0.5899 \| 0.5704 \| 0.2771 \| 0.3612 \| \| 0.0486 \| 73.0 \| 8322 \| 1.1217 \| 0.3773 \| 0.6946 \| 0.7117 \| nan \| 0.7549 \| 0.6224 \| 0.9882 \| 0.4128 \| 0.0 \| 0.6094 \| 0.5946 \| 0.3150 \| 0.3678 \| \| 0.051 \| 74.0 \| 8436 \| 1.1895 \| 0.3724 \| 0.6889 \| 0.7069 \| nan \| 0.7432 \| 0.6247 \| 0.9888 \| 0.3990 \| 0.0 \| 0.6052 \| 0.5959 \| 0.3021 \| 0.3590 \| \| 0.0472 \| 75.0 \| 8550 \| 1.2084 \| 0.3677 \| 0.6847 \| 0.7009 \| nan \| 0.7179 \| 0.6399 \| 0.9905 \| 0.3904 \| 0.0 \| 0.5979 \| 0.6078 \| 0.2808 \| 0.3522 \| \| 0.0481 \| 76.0 \| 8664 \| 1.1778 \| 0.3688 \| 0.6841 \| 0.7049 \| nan \| 0.7395 \| 0.6244 \| 0.9899 \| 0.3824 \| 0.0 \| 0.6024 \| 0.5950 \| 0.2996 \| 0.3469 \| \| 0.0462 \| 77.0 \| 8778 \| 1.2409 \| 0.3693 \| 0.6863 \| 0.7015 \| nan \| 0.7278 \| 0.6297 \| 0.9900 \| 0.3975 \| 0.0 \| 0.5964 \| 0.5990 \| 0.2918 \| 0.3593 \| \| 0.0464 \| 78.0 \| 8892 \| 1.2724 \| 0.3606 \| 0.6792 \| 0.6877 \| nan \| 0.7119 \| 0.6158 \| 0.9905 \| 0.3986 \| 0.0 \| 0.5825 \| 0.5857 \| 0.2770 \| 0.3578 \| \| 0.0477 \| 79.0 \| 9006 \| 1.2107 \| 0.3629 \| 0.6797 \| 0.6936 \| nan \| 0.7322 \| 0.6063 \| 0.9898 \| 0.3905 \| 0.0 \| 0.5928 \| 0.5791 \| 0.2889 \| 0.3540 \| \| 0.0452 \| 80.0 \| 9120 \| 1.1745 \| 0.3721 \| 0.6889 \| 0.7059 \| nan \| 0.7548 \| 0.6087 \| 0.9899 \| 0.4022 \| 0.0 \| 0.6080 \| 0.5820 \| 0.3085 \| 0.3620 \| \| 0.0447 \| 81.0 \| 9234 \| 1.2787 \| 0.3599 \| 0.6776 \| 0.6876 \| nan \| 0.7199 \| 0.6063 \| 0.9902 \| 0.3938 \| 0.0 \| 0.5857 \| 0.5788 \| 0.2786 \| 0.3566 \| \| 0.0481 \| 82.0 \| 9348 \| 1.2049 \| 0.3658 \| 0.6836 \| 0.6947 \| nan \| 0.7515 \| 0.5865 \| 0.9887 \| 0.4078 \| 0.0 \| 0.5956 \| 0.5627 \| 0.3044 \| 0.3660 \| \| 0.0444 \| 83.0 \| 9462 \| 1.1427 \| 0.3746 \| 0.6930 \| 0.7051 \| nan \| 0.7520 \| 0.6100 \| 0.9883 \| 0.4215 \| 0.0 \| 0.6042 \| 0.5824 \| 0.3100 \| 0.3763 \| \| 0.0481 \| 84.0 \| 9576 \| 1.1876 \| 0.3669 \| 0.6848 \| 0.6968 \| nan \| 0.7358 \| 0.6094 \| 0.9895 \| 0.4046 \| 0.0 \| 0.5944 \| 0.5818 \| 0.2947 \| 0.3636 \| \| 0.046 \| 85.0 \| 9690 \| 1.2264 \| 0.3628 \| 0.6799 \| 0.6928 \| nan \| 0.7348 \| 0.6015 \| 0.9885 \| 0.3948 \| 0.0 \| 0.5906 \| 0.5746 \| 0.2930 \| 0.3560 \| \| 0.0472 \| 86.0 \| 9804 \| 1.2377 \| 0.3659 \| 0.6828 \| 0.6967 \| nan \| 0.7287 \| 0.6176 \| 0.9890 \| 0.3959 \| 0.0 \| 0.5926 \| 0.5876 \| 0.2913 \| 0.3577 \| \| 0.0465 \| 87.0 \| 9918 \| 1.2037 \| 0.3644 \| 0.6841 \| 0.6903 \| nan \| 0.7176 \| 0.6150 \| 0.9893 \| 0.4146 \| 0.0 \| 0.5859 \| 0.5856 \| 0.2808 \| 0.3697 \| \| 0.0475 \| 87.72 \| 10000 \| 1.2098 \| 0.3648 \| 0.6821 \| 0.6947 \| nan \| 0.7354 \| 0.6052 \| 0.9884 \| 0.3995 \| 0.0 \| 0.5920 \| 0.5774 \| 0.2950 \| 0.3598 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3
r0in/dqn-SpaceInvadersNoFrameskip-v4	r0in	2024-01-30T02:48:12Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T02:47:40Z	--- 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: 502.50 +/- 144.90 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 r0in -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 r0in -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 r0in ``` ## 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'} ```
ogbrandt/mistral-7b-non-instruction-pjf-ft	ogbrandt	2024-01-30T02:44:37Z	12	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-22T12:34:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LoneStriker/CodeLlama-70b-Python-hf-5.0bpw-h6-exl2	LoneStriker	2024-01-30T02:44:30Z	3	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "llama-2", "code", "arxiv:2308.12950", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T02:26:56Z	--- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # Code Llama Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. \| \| Base Model \| Python \| Instruct \| \| --- \| ----------------------------------------------------------------------------- \| ------------------------------------------------------------------------------------------- \| ----------------------------------------------------------------------------------------------- \| \| 7B \| [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) \| [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) \| [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) \| \| 13B \| [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) \| [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) \| [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) \| \| 34B \| [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) \| [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) \| [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) \| \| 70B \| [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) \| [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) \| [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) \| ## Model Use To use this model, please make sure to install `transformers`. ```bash pip install transformers accelerate ``` Model capabilities: - [x] Code completion. - [ ] Infilling. - [ ] Instructions / chat. - [x] Python specialist. ## Model Details Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). Model Developers* Meta Variations Code Llama comes in four model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B, 34B, and 70B parameters. This repository contains the Python version of the 70B parameters model. Input Models input text only. Output Models generate text only. Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant does not support long context of up to 100k tokens. Model Dates Code Llama and its variants have been trained between January 2023 and January 2024. Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use Intended Use Cases Code Llama and its variants are 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. ## Hardware and Software Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. Carbon Footprint In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## 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](https://ai.meta.com/llama/responsible-use-guide).
jingyeom/freeze_KoSoLAR-10.7B-v0.2_1.4_dedup	jingyeom	2024-01-30T02:44:19Z	2,286	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-29T13:12:09Z	--- license: apache-2.0 --- ## Model - base_model : yanolja/KoSOLAR-10.7B-v0.2 - training objective: freeze, instruction Tuning ## Dataset 공개 데이터 수집 - Deduplicating Training Data Makes Language Models Better 알고리즘 활용 - instruction version 1.4 ## Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "jjingyeom/freeze_KoSoLAR-10.7B-v0.2_1.4_dedup" model = AutoModelForCausalLM.from_pretrained( model_name, ) tokenizer = AutoTokenizer.from_pretrained(model_name) ```
Commandante/german-party-sentiment-bert-241	Commandante	2024-01-30T02:44:13Z	92	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:mdraw/german-news-sentiment-bert", "base_model:finetune:mdraw/german-news-sentiment-bert", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-30T01:56:24Z	--- base_model: mdraw/german-news-sentiment-bert tags: - generated_from_trainer model-index: - name: german-party-sentiment-bert-241 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. --> # german-party-sentiment-bert-241 This model is a fine-tuned version of [mdraw/german-news-sentiment-bert](https://huggingface.co/mdraw/german-news-sentiment-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0323 ## 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: 8e-06 - train_batch_size: 20 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 120 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.5246 \| 1.0 \| 28 \| 1.1341 \| \| 1.047 \| 2.0 \| 56 \| 1.0424 \| \| 1.047 \| 3.0 \| 84 \| 1.0328 \| \| 0.9356 \| 4.0 \| 112 \| 1.0323 \| \| 0.9356 \| 5.0 \| 140 \| 1.0771 \| \| 0.8882 \| 6.0 \| 168 \| 1.1110 \| \| 0.7714 \| 7.0 \| 196 \| 1.0898 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2+cu118 - Tokenizers 0.15.1
ZiHDeng/peft-lora-starcoder1B-Instruction-ny8-MIX	ZiHDeng	2024-01-30T02:43:24Z	12	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:bigcode/starcoderbase-1b", "base_model:adapter:bigcode/starcoderbase-1b", "license:bigcode-openrail-m", "region:us" ]	null	2024-01-29T14:21:39Z	--- license: bigcode-openrail-m library_name: peft tags: - generated_from_trainer base_model: bigcode/starcoderbase-1b model-index: - name: peft-lora-starcoder1B-Instruction-ny8-MIX 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. --> # peft-lora-starcoder1B-Instruction-ny8-MIX This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1670 ## 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: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 150 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1662 \| 0.67 \| 100 \| 0.1670 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
LoneStriker/CodeLlama-70b-Python-hf-4.65bpw-h6-exl2	LoneStriker	2024-01-30T02:26:54Z	7	1	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "llama-2", "code", "arxiv:2308.12950", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T02:10:35Z	--- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # Code Llama Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. \| \| Base Model \| Python \| Instruct \| \| --- \| ----------------------------------------------------------------------------- \| ------------------------------------------------------------------------------------------- \| ----------------------------------------------------------------------------------------------- \| \| 7B \| [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) \| [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) \| [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) \| \| 13B \| [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) \| [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) \| [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) \| \| 34B \| [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) \| [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) \| [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) \| \| 70B \| [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) \| [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) \| [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) \| ## Model Use To use this model, please make sure to install `transformers`. ```bash pip install transformers accelerate ``` Model capabilities: - [x] Code completion. - [ ] Infilling. - [ ] Instructions / chat. - [x] Python specialist. ## Model Details Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). Model Developers* Meta Variations Code Llama comes in four model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B, 34B, and 70B parameters. This repository contains the Python version of the 70B parameters model. Input Models input text only. Output Models generate text only. Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant does not support long context of up to 100k tokens. Model Dates Code Llama and its variants have been trained between January 2023 and January 2024. Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use Intended Use Cases Code Llama and its variants are 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. ## Hardware and Software Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. Carbon Footprint In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## 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](https://ai.meta.com/llama/responsible-use-guide).
PracticeLLM/KoSOLAR-Platypus-10.7B	PracticeLLM	2024-01-30T02:24:57Z	57	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "ko", "dataset:kyujinpy/KOR-OpenOrca-Platypus-v3", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-29T12:35:00Z	--- language: - ko library_name: transformers pipeline_tag: text-generation license: cc-by-nc-sa-4.0 datasets: - kyujinpy/KOR-OpenOrca-Platypus-v3 --- # PracticeLLM/KoSOLAR-Platypus-10.7B ## Model Details Model Developers Kyujin Han (kyujinpy) Method LoRA with quantization. Base Model [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) Dataset [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3). Hyperparameters ``` python finetune.py \ --base_model yanolja/KoSOLAR-10.7B-v0.2 \ --data-path kyujinpy/KOR-OpenOrca-Platypus-v3 \ --output_dir ./Ko-PlatypusSOLAR-10.7B \ --batch_size 64 \ --micro_batch_size 1 \ --num_epochs 5 \ --learning_rate 2e-5 \ --cutoff_len 2048 \ --val_set_size 0 \ --lora_r 64 \ --lora_alpha 64 \ --lora_dropout 0.05 \ --lora_target_modules '[embed_tokens, q_proj, k_proj, v_proj, o_proj, gate_proj, down_proj, up_proj, lm_head]' \ --train_on_inputs False \ --add_eos_token False \ --group_by_length False \ --prompt_template_name en_simple \ --lr_scheduler 'cosine' \ ``` > Share all of things. It is my belief. # Model Benchmark ## Open Ko-LLM leaderboard & lm-evaluation-harness(zero-shot) - Follow up as [Ko-link](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard). \| Model \| Average \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| Ko-CommonGenV2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| PracticeLLM/KoSOLAR-Platypus-10.7B \| --- \| --- \| --- \| --- \| --- \| --- \| \| [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B) \| 59.34 \| 55.38 \| 65.56 \| 53.38 \| 64.39 \| 57.97 \| \| [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) \| 55.62 \| 50.51 \| 62.29 \| 53.76 \| 47.31 \| 64.23 \| \| [megastudyedu/M-SOLAR-10.7B-v1.3](https://huggingface.co/megastudyedu/M-SOLAR-10.7B-v1.3) \| 56.64 \| 51.37 \| 60.93 \| 54.91 \| 48.45 \| 67.53 \| # Implementation Code ```python ### KO-Platypus from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "PracticeLLM/KoSOLAR-Platypus-10.7B" OpenOrca = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo) ```
martomor/distilbert-base-uncased-finetuned-clinc	martomor	2024-01-30T02:09:12Z	92	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-30T01:59:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9180645161290323 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7721 - Accuracy: 0.9181 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2895 \| 1.0 \| 318 \| 3.2884 \| 0.7419 \| \| 2.6277 \| 2.0 \| 636 \| 1.8751 \| 0.8368 \| \| 1.5479 \| 3.0 \| 954 \| 1.1569 \| 0.8961 \| \| 1.0148 \| 4.0 \| 1272 \| 0.8573 \| 0.9132 \| \| 0.7952 \| 5.0 \| 1590 \| 0.7721 \| 0.9181 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.1.0+cu121 - Datasets 1.16.1 - Tokenizers 0.15.1
jncraton/openchat-3.5-0106-ct2-int8	jncraton	2024-01-30T02:06:12Z	24	0	transformers	[ "transformers", "openchat", "mistral", "C-RLFT", "text-generation", "conversational", "arxiv:2309.11235", "arxiv:2303.08774", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T01:56:43Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - openchat - mistral - C-RLFT library_name: transformers pipeline_tag: text-generation --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center; ont-size: 0.5em; border: 0.8em solid #864AF9;"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.5</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #864AF9; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">0106</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open Source 7B Model 🏆 <br> 🤖 Outperforms <span style="font-weight: bold;">ChatGPT</span> (March) and <span style="font-weight: bold;">Grok-1</span> 🤖 <br> 🚀<span style="font-size: 1em; font-family: 'Helvetica'; color: black; font-weight: bold;">15</span>-point improvement in Coding over <span style="font-size: 0.9em; font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀</span> <br><br><span style="font-size: 1em; font-family: 'Helvetica'; color: #3c72db; font-weight: bold;">New Features</span> <br> 💡 2 Modes: Coding + Generalist, Mathematical Reasoning 💡 <br> 🧑‍⚖️ Experimental support for Evaluator and Feedback capabilities 🧑‍⚖️ </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat-bench-0106.png" style="width: 100%; border-radius: 1em"> </div> <div> <h3> Table of Contents</h3> </div> 1. [Usage](#usage) 2. [Benchmarks](#benchmarks) 3. [Limitations](#limitations) 4. [License](#license) 6. [Citation](#citation) 7. [Acknowledgements](#acknowledgements) <div align="center"> <h2> Usage </h2> </div> To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. \| Model \| Size \| Context \| Weights \| Serving \| \|-------------------\|------\|---------\|------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------------\| \| OpenChat-3.5-0106 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106) \| `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray` \| <details> <summary>Example request (click to expand)</summary> 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Math Correct", "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}] }' ``` </details> ### Conversation templates 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ``` GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant: ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ``` Math Correct User: 10.3 − 7988.8133=<\|end_of_turn\|>Math Correct Assistant: ``` ⚠️ Notice: Remember to set `<\|end_of_turn\|>` as end of generation token. The default (GPT4 Correct) template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] ``` <div align="center"> <h2> (Experimental) Evaluator / Feedback Capabilities </h2> </div> We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {orig_instruction} ###Response to evaluate: {orig_response} ###Reference Answer (Score 5): {orig_reference_answer} ###Score Rubrics: [{orig_criteria}] Score 1: {orig_score1_description} Score 2: {orig_score2_description} Score 3: {orig_score3_description} Score 4: {orig_score4_description} Score 5: {orig_score5_description} ###Feedback: ``` <div align="center"> <h2> Benchmarks </h2> </div> \| Model \| # Params \| Average \| MT-Bench \| HumanEval \| BBH MC \| AGIEval \| TruthfulQA \| MMLU \| GSM8K \| BBH CoT \| \|-----------------------\|----------\|----------\|----------\|-----------\|----------\|----------\|------------\|----------\|----------\|----------\| \| OpenChat-3.5-0106 \| 7B \| 64.5 \| 7.8 \| 71.3 \| 51.5 \| 49.1 \| 61.0 \| 65.8 \| 77.4 \| 62.2 \| \| OpenChat-3.5-1210 \| 7B \| 63.8 \| 7.76 \| 68.9 \| 49.5 \| 48.0 \| 61.8 \| 65.3 \| 77.3 \| 61.8 \| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 55.5 \| 47.6 \| 47.4 \| 59.1 \| 64.3 \| 77.3 \| 63.5 \| \| ChatGPT (March)* \| ???B \| 61.5 \| 7.94 \| 48.1 \| 47.6 \| 47.1 \| 57.7 \| 67.3 \| 74.9 \| 70.1 \| \| \| \| \| \| \| \| \| \| \| \| \| \| OpenHermes 2.5 \| 7B \| 59.3 \| 7.54 \| 48.2 \| 49.4 \| 46.5 \| 57.5 \| 63.8 \| 73.5 \| 59.9 \| \| OpenOrca Mistral \| 7B \| 52.7 \| 6.86 \| 38.4 \| 49.4 \| 42.9 \| 45.9 \| 59.3 \| 59.1 \| 58.1 \| \| Zephyr-β^ \| 7B \| 34.6 \| 7.34 \| 22.0 \| 40.6 \| 39.0 \| 40.8 \| 39.8 \| 5.1 \| 16.0 \| \| Mistral \| 7B \| - \| 6.84 \| 30.5 \| 39.0 \| 38.0 \| - \| 60.1 \| 52.2 \| - \| <details> <summary>Evaluation Details(click to expand)</summary> : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). </details> <div> <h3>HumanEval+</h3> </div> \| Model \| Size \| HumanEval+ pass@1 \| \|-----------------------------\|--------\|-------------------\| \| OpenChat-3.5-0106* \| 7B \| 65.9 \| \| ChatGPT (December 12, 2023) \| ???B \| 64.6 \| \| WizardCoder-Python-34B-V1.0 \| 34B \| 64.6 \| \| OpenChat 3.5 1210 \| 7B \| 63.4 \| \| OpenHermes 2.5 \| 7B \| 41.5 \| <div> <h3>OpenChat-3.5 vs. Grok</h3> </div> 🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on all 4 benchmarks and Grok-1 (???B) on average and 3/4 benchmarks. \| \| License \| # Param \| Average \| MMLU \| HumanEval \| MATH \| GSM8k \| \|-----------------------\|-------------\|---------\|----------\|--------\|-----------\|----------\|----------\| \| OpenChat-3.5-0106 \| Apache-2.0 \| 7B \| 61.0 \| 65.8 \| 71.3 \| 29.3 \| 77.4 \| \| OpenChat-3.5-1210 \| Apache-2.0 \| 7B \| 60.1 \| 65.3 \| 68.9 \| 28.9 \| 77.3 \| \| OpenChat-3.5 \| Apache-2.0 \| 7B \| 56.4 \| 64.3 \| 55.5 \| 28.6 \| 77.3 \| \| Grok-0 \| Proprietary \| 33B \| 44.5 \| 65.7 \| 39.7 \| 15.7 \| 56.8 \| \| Grok-1 \| Proprietary \| ???B \| 55.8 \| 73 \| 63.2 \| 23.9 \| 62.9 \| : Grok results are reported by [X.AI](https://x.ai/). <div align="center"> <h2> Limitations </h2> </div> Foundation Model Limitations* Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. <div align="center"> <h2> License </h2> </div> Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. <div align="center"> <h2> Citation </h2> </div> ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` <div align="center"> <h2> 💌 Main Contributor </h2> </div> * Wang Guan [imonenext@gmail.com], Cheng Sijie [csj23@mails.tsinghua.edu.cn], Alpay Ariyak [aariyak@wpi.edu] * We look forward to hearing you and collaborating on this exciting project!
Loess/gpt2-wikitext2	Loess	2024-01-30T02:05:59Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-22T19:17:59Z	--- tags: - generated_from_trainer model-index: - name: gpt2-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. --> # gpt2-wikitext2 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 5.4415 - eval_runtime: 21.4584 - eval_samples_per_second: 90.128 - eval_steps_per_second: 11.278 - epoch: 8.23 - step: 18500 ## 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: 10 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
bsgreenb/my_awesome_model	bsgreenb	2024-01-30T02:02:10Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-29T20:24:13Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3960 - Accuracy: 0.8465 - F1: 0.8219 - Precision: 0.8257 - Recall: 0.8182 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.4226 \| 1.17 \| 500 \| 0.3960 \| 0.8465 \| 0.8219 \| 0.8257 \| 0.8182 \| \| 0.309 \| 2.33 \| 1000 \| 0.4264 \| 0.8478 \| 0.8226 \| 0.8302 \| 0.8152 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
kanishka/smolm-autoreg-bpe-counterfactual-babylm-pipps_10k-1e-3	kanishka	2024-01-30T01:48:00Z	23	0	transformers	[ "transformers", "tensorboard", "safetensors", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-29T03:06:27Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-counterfactual-babylm-pipps_10k-1e-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. --> # smolm-autoreg-bpe-counterfactual-babylm-pipps_10k-1e-3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3378 - Accuracy: 0.4121 ## 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: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 3.6013 \| 1.0 \| 18720 \| 3.7465 \| 0.3588 \| \| 3.3867 \| 2.0 \| 37440 \| 3.4883 \| 0.3819 \| \| 3.2589 \| 3.0 \| 56160 \| 3.4083 \| 0.3943 \| \| 3.1811 \| 4.0 \| 74880 \| 3.3900 \| 0.3986 \| \| 3.1273 \| 5.0 \| 93600 \| 3.3351 \| 0.4020 \| \| 3.0824 \| 6.0 \| 112320 \| 3.3293 \| 0.4028 \| \| 3.0466 \| 7.0 \| 131040 \| 3.3131 \| 0.4065 \| \| 3.0117 \| 8.0 \| 149760 \| 3.2990 \| 0.4092 \| \| 2.9899 \| 9.0 \| 168480 \| 3.2979 \| 0.4097 \| \| 2.9556 \| 10.0 \| 187200 \| 3.2939 \| 0.4105 \| \| 2.9395 \| 11.0 \| 205920 \| 3.2948 \| 0.4120 \| \| 2.9155 \| 12.0 \| 224640 \| 3.3005 \| 0.4110 \| \| 2.8971 \| 13.0 \| 243360 \| 3.2907 \| 0.4126 \| \| 2.8777 \| 14.0 \| 262080 \| 3.2993 \| 0.4121 \| \| 2.8546 \| 15.0 \| 280800 \| 3.2985 \| 0.4128 \| \| 2.8337 \| 16.0 \| 299520 \| 3.2980 \| 0.4130 \| \| 2.8153 \| 17.0 \| 318240 \| 3.3254 \| 0.4116 \| \| 2.799 \| 18.0 \| 336960 \| 3.3345 \| 0.4114 \| \| 2.7824 \| 19.0 \| 355680 \| 3.3410 \| 0.4117 \| \| 2.7614 \| 20.0 \| 374400 \| 3.3378 \| 0.4121 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.14.1
tavalenzuelag/mistral-7b-do-e2e-mod	tavalenzuelag	2024-01-30T01:44:30Z	3	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-01-29T19:10:27Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # 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
LoneStriker/CodeLlama-70b-Python-hf-2.65bpw-h6-exl2	LoneStriker	2024-01-30T01:43:54Z	6	3	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "llama-2", "code", "arxiv:2308.12950", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T01:31:35Z	--- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # Code Llama Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. \| \| Base Model \| Python \| Instruct \| \| --- \| ----------------------------------------------------------------------------- \| ------------------------------------------------------------------------------------------- \| ----------------------------------------------------------------------------------------------- \| \| 7B \| [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) \| [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) \| [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) \| \| 13B \| [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) \| [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) \| [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) \| \| 34B \| [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) \| [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) \| [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) \| \| 70B \| [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) \| [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) \| [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) \| ## Model Use To use this model, please make sure to install `transformers`. ```bash pip install transformers accelerate ``` Model capabilities: - [x] Code completion. - [ ] Infilling. - [ ] Instructions / chat. - [x] Python specialist. ## Model Details Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). Model Developers* Meta Variations Code Llama comes in four model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B, 34B, and 70B parameters. This repository contains the Python version of the 70B parameters model. Input Models input text only. Output Models generate text only. Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant does not support long context of up to 100k tokens. Model Dates Code Llama and its variants have been trained between January 2023 and January 2024. Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use Intended Use Cases Code Llama and its variants are 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. ## Hardware and Software Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. Carbon Footprint In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## 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](https://ai.meta.com/llama/responsible-use-guide).
schleir/ppo-LunarLander-v2	schleir	2024-01-30T01:38:05Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T01:37:43Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.10 +/- 21.36 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jojubo/dqn-LunarLander-v1	jojubo	2024-01-30T01:11:23Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T01:10:54Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: dqn results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -116.97 +/- 89.45 name: mean_reward verified: false --- # dqn Agent playing LunarLander-v2 This is a trained model of a dqn agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
paisanx/Reinforce-Pixelcopter-PLE-v5	paisanx	2024-01-30T00:57:55Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T00:57:44Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v5 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 137.50 +/- 40.46 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
agustoslu/alp_mistral7b_baseline	agustoslu	2024-01-30T00:56:42Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ybelkada/mistral-7b-instruct-v0.1-sharded", "base_model:adapter:ybelkada/mistral-7b-instruct-v0.1-sharded", "region:us" ]	null	2024-01-30T00:55:42Z	--- library_name: peft base_model: ybelkada/mistral-7b-instruct-v0.1-sharded --- # 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
jeiku/Short_Luna_3B_GGUF	jeiku	2024-01-30T00:50:29Z	3	0	null	[ "gguf", "mergekit", "merge", "arxiv:2203.05482", "endpoints_compatible", "region:us", "conversational" ]	null	2024-01-30T00:33:52Z	--- base_model: [] tags: - mergekit - merge --- # final This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * betterboi * bestboi * goodboi ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: linear models: - model: goodboi parameters: weight: 1 - model: betterboi parameters: weight: 1 - model: bestboi parameters: weight: 1 dtype: float16 ```
mlx-community/CodeLlama-70b-Instruct-hf-4bit-MLX	mlx-community	2024-01-30T00:47:05Z	15	25	mlx	[ "mlx", "llama", "llama-2", "text-generation", "conversational", "code", "license:llama2", "region:us" ]	text-generation	2024-01-29T23:38:21Z	--- language: - code license: llama2 tags: - llama-2 - mlx pipeline_tag: text-generation --- ![Alt text](https://cdn.discordapp.com/attachments/1064373193982361601/1201677160008384594/DALLE_2024-01-30_00.53.15_-_Imagine_a_whimsical_hyper-detailed_illustration_suitable_for_a_childrens_book_featuring_a_cartoon_alpaca_sitting_comfortably_and_using_an_Apple_lap.png?ex=65cab011&is=65b83b11&hm=373057e35079d276954594d43ea8e9e8223bd4956707a96c130a62850c8570b1&) # mlx-community/CodeLlama-70b-Instruct-hf-4bit-MLX This model was converted to MLX format from [`codellama/CodeLlama-70b-Instruct-hf`](). Refer to the [original model card](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/CodeLlama-70b-Instruct-hf-4bit-MLX") response = generate(model, tokenizer, prompt="<step>Source: user Fibonacci series in Python<step> Source: assistant Destination: user", verbose=True) ```
jtatman/TinyMistral-248m-v2.5-4x-Moe	jtatman	2024-01-30T00:46:52Z	76	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "Locutusque/TinyMistral-248M-v2.5-Instruct", "jtatman/tinymistral-samantha-chatml-lora-v2", "base_model:Locutusque/TinyMistral-248M-v2.5-Instruct", "base_model:merge:Locutusque/TinyMistral-248M-v2.5-Instruct", "base_model:jtatman/tinymistral-samantha-chatml-lora-v2", "base_model:merge:jtatman/tinymistral-samantha-chatml-lora-v2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-29T18:27:21Z	--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - Locutusque/TinyMistral-248M-v2.5-Instruct - Locutusque/TinyMistral-248M-v2.5-Instruct - Locutusque/TinyMistral-248M-v2.5-Instruct - jtatman/tinymistral-samantha-chatml-lora-v2 base_model: - Locutusque/TinyMistral-248M-v2.5-Instruct - Locutusque/TinyMistral-248M-v2.5-Instruct - Locutusque/TinyMistral-248M-v2.5-Instruct - jtatman/tinymistral-samantha-chatml-lora-v2 --- # TinyMistral-248m-v2.5-4x-Moe TinyMistral-248m-v2.5-4x-Moe is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Locutusque/TinyMistral-248M-v2.5-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5-Instruct) * [Locutusque/TinyMistral-248M-v2.5-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5-Instruct) * [Locutusque/TinyMistral-248M-v2.5-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5-Instruct) * [jtatman/tinymistral-samantha-chatml-lora-v2](https://huggingface.co/jtatman/tinymistral-samantha-chatml-lora-v2) ## 🧩 Configuration ```yaml base_model: Locutusque/TinyMistral-248M-v2.5-Instruct experts: - source_model: Locutusque/TinyMistral-248M-v2.5-Instruct positive_prompts: - "Write me a Python program that calculates the factorial of n." - "Help me debug this code." - "Optimize this C++ program." negative_prompts: - "How do you" - "Explain the concept of" - "Give an overview of" - "Compare and contrast between" - "Provide information about" - "Help me understand" - "Summarize" - "Make a recommendation on" - "Answer this question" - "Craft me a list of some nice places to visit around the world." - "Write me a story" - "Write me an essay" - "How do I incorporate visual elements into my writing?" - source_model: Locutusque/TinyMistral-248M-v2.5-Instruct positive_prompts: - "What is the product of 2 x 5 x 18?" - "How do I guess the value of x for the function f(x) = x^4 - 2x^2 - 1?" negative_prompts: - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Assist me with writing a program that" - "Craft me a list of some nice places to visit around the world. " - "Write me a story" - "Write me an essay" - "How do I incorporate visual elements into my writing?" - source_model: Locutusque/TinyMistral-248M-v2.5-Instruct positive_prompts: - "How do I incorporate fewer visual elements into my art but retain impact?" negative_prompts: - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Compare and contrast between" - "Provide information about" - "Help me understand" - "Summarize" - "Make a recommendation on" - "Answer this question" - "Craft me a list of some nice places to visit around the world. " - "Write me a story" - "Write me an essay" - source_model: jtatman/tinymistral-samantha-chatml-lora-v2 positive_prompts: - "Craft me a list of some nice places to visit around the world. " - "Write me a story" - "Write me an essay" - "Create a fantasy story about" - "Tell me about the wild fjords." negative_prompts: - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Compare and contrast between" - "Provide information about" - "Help me understand" - "Summarize" - "Make a recommendation on" - "Answer this question" - "How do I incorporate visual elements into my writing?" gate_mode: hidden ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "jtatman/TinyMistral-248m-v2.5-4x-Moe" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
shangrilar/yi-ko-6b-text2sql	shangrilar	2024-01-30T00:37:04Z	272	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T00:31:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jojubo/ppo-LunarLander-v2	jojubo	2024-01-30T00:34:52Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-30T00:33:49Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 271.02 +/- 15.91 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
katxtong/roberta-base-squad2-finetuned-squad	katxtong	2024-01-30T00:28:26Z	15	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2024-01-29T01:47:17Z	--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: roberta-base-squad2-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. --> # roberta-base-squad2-finetuned-squad This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0305 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.7052 \| 1.0 \| 5536 \| 0.8646 \| \| 0.5372 \| 2.0 \| 11072 \| 0.9134 \| \| 0.4104 \| 3.0 \| 16608 \| 1.0305 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
jsfs11/Westlakev2-7B-DPO	jsfs11	2024-01-30T00:27:22Z	8	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "dataset:jondurbin/truthy-dpo-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-30T00:20:49Z	--- license: apache-2.0 datasets: - jondurbin/truthy-dpo-v0.1 ---
clovett/ppo-LunarLander-v2	clovett	2024-01-30T00:25:56Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-29T23:12:33Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 276.82 +/- 15.30 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jeiku/Long_Luna_3.43B_GGUF	jeiku	2024-01-30T00:22:16Z	7	0	null	[ "gguf", "mergekit", "merge", "arxiv:2203.05482", "endpoints_compatible", "region:us", "conversational" ]	null	2024-01-30T00:00:43Z	--- base_model: [] tags: - mergekit - merge --- # biggle This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * biggest * bigger * big ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: linear models: - model: big parameters: weight: 1 - model: bigger parameters: weight: 1 - model: biggest parameters: weight: 1 dtype: float16 ```
Nexesenex/Codellama-2-7b-Miniguanaco-Mistral-GGUF	Nexesenex	2024-01-30T00:17:32Z	74	3	null	[ "gguf", "license:llama2", "endpoints_compatible", "region:us" ]	null	2023-10-02T01:55:18Z	--- license: llama2 --- CodeLlama 2 7b With Guanaco Lora (Tim Dettmers), merged by Varunk29. Then With Mistral AI 7b 0.1 delta bits compared to Llama2 (extracted by Undi95), merged by me. --- Base model (CodeLlama) training context : 16k (max context up to 96k with the base ROPE) Mistral injection training context : 8k (Sliding Windows Attention is likely inoperant on such a merge/injection) --- For test and amusement only. Prompt : Alpaca works.
AIFT/AIFT-instruct-42dot_LLM-SFT-1.3B	AIFT	2024-01-30T00:15:04Z	146	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-29T07:56:32Z	--- license: cc-by-sa-4.0 --- <h1>AIFT-instruct-42dot_LLM-SFT-1.3B</h1> <b><학습 데이터 구축></b> <br> kyujinpy 님이 공개하신 KOR-OpenOrca-Platypus 데이터를 일부 삭제(샘플링) 및 정제 작업 진행하여 활용. 그 이후 해당 데이터들을 보며 관련 태스크를 추출하였고 이를 기반으로 해당 태스크에 맞춰서 NLP 관련 오픈소스 데이터를 활용하여 학습데이터를 자체적으로 역사, 과학, 수학, 기계독해, 리뷰 분석 문제를 gpt를 통해서 구축하였고, aihub 일반상식 및 기계독해 데이터를 활용하여 추가로 학습 데이터를 구축(형태소 관련, 기계독해 관련 및 요약) 각종 블로그에서 역사 및 상식 퀴즈를 사람이 직접 학습데이터 형태로 변경 AI2AI Challenge 데이터 형태를 보고 gpt를 통해 초등 수준의 과학 수학 문제 유형을 제작 500문제 영어 번역 데이터 영한/한영 데이터 학습 데이터로 활용 진행 총 데이터 4만개 정도 사용하였습니다. <br> <br> + TruthfulQA 관련 문제 추가를 진행하였습니다.(속설 관련 참거짓 문제) + 기계독해 관련 학습 데이터를 ChatGPT를 통해서 답변을 얻어 학습 + 문법관련 학습 데이터 <br> ###학습 데이터 파일은 비공개입니다. <br> <모델> <br> 42dot에서 공개한 42dot_LLM-SFT-1.3B을 베이스 모델로 하여 학습 진행하였습니다. <br> <br> <br> <b><학습></b> <br> 학습은 LoRA를 사용하여 A100 40G *2에서 학습을 진행하였습니다.
asun17904/anliR2-bert-base-uncased-alum	asun17904	2024-01-30T00:07:30Z	0	0	pytorch	[ "pytorch", "en", "license:mit", "region:us" ]	null	2024-01-29T14:08:12Z	--- language: en license: mit library_name: pytorch --- # Knowledge Continuity Regularized Network Dataset: ANLI Round: None Trainer Hyperparameters: - `lr` = 5e-05 - `per_device_batch_size` = 16 - `gradient_accumulation_steps` = 1 - `weight_decay` = 1e-09 - `seed` = 42 Regularization Hyperparameters - `numerical stability denominator constant` = 1.0 - `lambda` = 1.0 - `alpha` = 1.0 - `beta` = 1.0 Extended Logs: \|eval_loss\|eval_accuracy\|epoch\| \|--\|--\|--\| \|1.117\|0.391\|1.0\| \|1.106\|0.418\|2.0\| \|1.093\|0.434\|3.0\| \|1.072\|0.464\|4.0\| \|1.098\|0.434\|5.0\| \|1.084\|0.453\|6.0\| \|1.088\|0.447\|7.0\| \|1.080\|0.459\|8.0\| \|1.099\|0.440\|9.0\| \|1.075\|0.468\|10.0\| \|1.078\|0.460\|11.0\| \|1.079\|0.465\|12.0\| \|1.082\|0.463\|13.0\| \|1.091\|0.449\|14.0\| \|1.082\|0.458\|15.0\| \|1.086\|0.456\|16.0\| \|1.089\|0.450\|17.0\| \|1.082\|0.467\|18.0\|
vasugupta0607/llama2_instruct_generation	vasugupta0607	2024-01-30T00:06:10Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:NousResearch/Llama-2-7b-hf", "base_model:adapter:NousResearch/Llama-2-7b-hf", "region:us" ]	null	2024-01-30T00:05:51Z	--- library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: NousResearch/Llama-2-7b-hf model-index: - name: llama2_instruct_generation results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama2_instruct_generation This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.6734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.9364 \| 0.0 \| 20 \| 1.8092 \| \| 1.9198 \| 0.01 \| 40 \| 1.7826 \| \| 1.8451 \| 0.01 \| 60 \| 1.7675 \| \| 1.8487 \| 0.01 \| 80 \| 1.7573 \| \| 1.8667 \| 0.01 \| 100 \| 1.7435 \| \| 1.7463 \| 0.02 \| 120 \| 1.7132 \| \| 1.7789 \| 0.02 \| 140 \| 1.7038 \| \| 1.8167 \| 0.02 \| 160 \| 1.7008 \| \| 1.8654 \| 0.02 \| 180 \| 1.6944 \| \| 1.9158 \| 0.03 \| 200 \| 1.6939 \| \| 1.6581 \| 0.03 \| 220 \| 1.6909 \| \| 1.793 \| 0.03 \| 240 \| 1.6896 \| \| 1.7878 \| 0.04 \| 260 \| 1.6872 \| \| 1.7542 \| 0.04 \| 280 \| 1.6862 \| \| 1.7723 \| 0.04 \| 300 \| 1.6863 \| \| 1.7606 \| 0.04 \| 320 \| 1.6832 \| \| 1.8054 \| 0.05 \| 340 \| 1.6802 \| \| 1.7307 \| 0.05 \| 360 \| 1.6803 \| \| 1.8278 \| 0.05 \| 380 \| 1.6790 \| \| 1.7912 \| 0.05 \| 400 \| 1.6768 \| \| 1.7826 \| 0.06 \| 420 \| 1.6749 \| \| 1.8975 \| 0.06 \| 440 \| 1.6756 \| \| 1.8395 \| 0.06 \| 460 \| 1.6763 \| \| 1.8319 \| 0.07 \| 480 \| 1.6749 \| \| 1.7879 \| 0.07 \| 500 \| 1.6734 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
silk-road/Haruhi-Zero-6B-0.1	silk-road	2024-01-30T00:04:46Z	5	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "zh", "en", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-28T07:37:43Z	--- license: cc-by-sa-4.0 language: - zh - en --- # Zero凉宫春日 # Haruhi-Zero: Zero-Shot Role-Playing Model tuned on Yi-6B 主项目链接 https://github.com/LC1332/Chat-Haruhi-Suzumiya 过往的ChatHaruhi模型需要角色库来完成角色的构建，而Pygmalion，CharacterGLM，CharacterBaichuan等开源/闭源模型都开始支持zero-shot的角色卡片创建我们构造以及收集了105k个中英文的conversation，以2500的token长度重新切到了120k左右个conversation，再结合小说数据进行了训练 - [李鲁鲁](https://github.com/LC1332)完成了数据的收集，搭建了gradio雏形 - [刘崇寒](https://github.com/khazic)完成了Yi-6B模型的sft训练并且上传 - [豆角](https://github.com/goodnessSZW)完成了qwen-1.8B Lora和Yi-6B Lora训练，我们会在之后上传 - [米唯实](https://github.com/hhhwmws0117)测试并完成了demo中的模型inference代码 # Haruhi-Zero: Zero-Shot Role-Playing Model Tuned on Yi-6B Main project link: https://github.com/LC1332/Chat-Haruhi-Suzumiya Previous ChatHaruhi models required a character RAG database to complete character creation. However, open-source/closed-source models like Pygmalion, CharacterGLM, CharacterBaichuan have started to support zero-shot role card creation. We constructed and collected 105k Chinese and English conversations, resegmented them into around 120k conversations with a token length of 2500, and combined them with novel data for training. ## inference code （搭建中） https://github.com/LC1332/Zero-Haruhi/blob/main/notebook/HaruhiZeroGradio.ipynb ## Official Prompt system prompt: ``` You are now in roleplay conversation mode. Pretend to be {bot_name} whose persona follows: {persona} You will stay in-character whenever possible, and generate responses as if you were {bot_name} ``` persona a.k.a. bot definition ## TODO 数据加强 - Haruhi Like的小说数据(0.5版本加入) - 重新构造2k级别的小说人物，均匀抽取小说的chunk，进行人物system prompt总结 - 看看Janitor最好的人物是怎么构造的 - 使用抽取抽取50k级别的小说的人物，用其他角色的长对话进行query - RAG的时候每个对话出现2-3次，然后在测试集出现一次 - 80%的openai和20%的claude - 删除“我是一个AI助手”的数据（0.2版本加入） - 身份认知数据加强（0.3版本加入） - 加强我是谁和你是谁的数据 - Stylish翻译数据 - 如果验证这个数据有用，就把中文小说批量翻译成英文和日文用一下 ## 鸣谢樟树的ClaudeAPI
jeiku/Long_Luna_3.43B	jeiku	2024-01-29T23:59:46Z	89	0	transformers	[ "transformers", "safetensors", "stablelm_epoch", "text-generation", "mergekit", "merge", "conversational", "custom_code", "arxiv:2203.05482", "autotrain_compatible", "region:us" ]	text-generation	2024-01-29T23:45:49Z	--- base_model: [] tags: - mergekit - merge --- # biggle This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * biggest * bigger * big ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: linear models: - model: big parameters: weight: 1 - model: bigger parameters: weight: 1 - model: biggest parameters: weight: 1 dtype: float16 ```

rmsusms22/xlm_roberta-base-finetuned-panx-de-fr

rmsusms22

2024-01-30T08:00:41Z

111

0

transformers

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

token-classification

2024-01-29T07:44:30Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm_roberta-base-finetuned-panx-de-fr results: [] ---  # xlm_roberta-base-finetuned-panx-de-fr 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: 0.1648 - F1: 0.8583 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2887 | 1.0 | 715 | 0.1879 | 0.8131 | | 0.1484 | 2.0 | 1430 | 0.1604 | 0.8423 | | 0.0981 | 3.0 | 2145 | 0.1648 | 0.8583 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

OS07/CodeToText

OS07

2024-01-30T07:55:44Z

61

0

transformers

[ "transformers", "safetensors", "gpt_bigcode", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]

text-generation

2024-01-30T06:44:40Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

shrenikb/fed32test2

shrenikb

2024-01-30T07:53:40Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:huggyllama/llama-7b", "base_model:adapter:huggyllama/llama-7b", "region:us" ]

null

2024-01-30T07:53:38Z

--- library_name: peft base_model: huggyllama/llama-7b --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.2

shrenikb/fed32test

shrenikb

2024-01-30T07:49:58Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:huggyllama/llama-7b", "base_model:adapter:huggyllama/llama-7b", "region:us" ]

null

2024-01-17T04:59:06Z

--- library_name: peft base_model: huggyllama/llama-7b --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.2

wangqixun/YamerMIX_v8

wangqixun

2024-01-30T07:49:12Z

8,960

16

diffusers

[ "diffusers", "safetensors", "license:mit", "diffusers:StableDiffusionXLCommonPipeline", "region:us" ]

null

2024-01-22T11:18:57Z

--- license: mit --- # Model The model is from [civitai-Yamer](https://civitai.com/models/84040?modelVersionId=196039). This is a very excellent model！Thank you Yamer! For business inquires, commercial licensing, custom models/commissions, large scale image captioning for datasets and consultation contact me under yamer@rundiffusion.com ![image/png](https://cdn-uploads.huggingface.co/production/uploads/643665d33193f279361cc292/yI0NH-NN08uVd6v1obZeu.png)

adalib/neptune-data-codegen-2B-mono-prefix

adalib

2024-01-30T07:45:43Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Salesforce/codegen-2B-mono", "base_model:adapter:Salesforce/codegen-2B-mono", "region:us" ]

null

2024-01-30T07:45:36Z

--- library_name: peft base_model: Salesforce/codegen-2B-mono --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

Chenxi-Chelsea-Liu/whisper-small-enhanced-hindi-10dB

Chenxi-Chelsea-Liu

2024-01-30T07:38:05Z

5

0

transformers

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

automatic-speech-recognition

2024-01-29T09:41:28Z

--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-enhanced-hindi-10dB results: [] ---  # whisper-small-enhanced-hindi-10dB This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5528 - Wer: 57.6431 ## 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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.3087 | 0.61 | 50 | 1.9565 | 101.3315 | | 1.3628 | 1.22 | 100 | 1.2862 | 83.4083 | | 1.1319 | 1.83 | 150 | 1.0950 | 79.0334 | | 0.9559 | 2.44 | 200 | 0.9573 | 74.3905 | | 0.807 | 3.05 | 250 | 0.8252 | 71.1655 | | 0.6268 | 3.66 | 300 | 0.6903 | 67.2488 | | 0.5039 | 4.27 | 350 | 0.6466 | 64.4907 | | 0.4738 | 4.88 | 400 | 0.6077 | 62.8566 | | 0.3599 | 5.49 | 450 | 0.5964 | 60.7902 | | 0.3225 | 6.1 | 500 | 0.6001 | 59.4761 | | 0.2599 | 6.71 | 550 | 0.5930 | 58.5509 | | 0.1658 | 7.32 | 600 | 0.6158 | 58.4731 | | 0.1666 | 7.93 | 650 | 0.6172 | 58.0581 | | 0.1032 | 8.54 | 700 | 0.6521 | 58.7152 | | 0.081 | 9.15 | 750 | 0.6857 | 58.7930 | | 0.0606 | 9.76 | 800 | 0.7020 | 57.9457 | | 0.0345 | 10.37 | 850 | 0.7422 | 57.9284 | | 0.0342 | 10.98 | 900 | 0.7622 | 57.5826 | | 0.023 | 11.59 | 950 | 0.7787 | 57.8074 | | 0.017 | 12.2 | 1000 | 0.8223 | 58.4299 | | 0.0159 | 12.8 | 1050 | 0.8384 | 57.6604 | | 0.0101 | 13.41 | 1100 | 0.8538 | 58.3607 | | 0.012 | 14.02 | 1150 | 0.8634 | 57.8765 | | 0.0092 | 14.63 | 1200 | 0.8762 | 57.5134 | | 0.0077 | 15.24 | 1250 | 0.9077 | 58.6201 | | 0.007 | 15.85 | 1300 | 0.9194 | 58.2310 | | 0.006 | 16.46 | 1350 | 0.9194 | 57.1935 | | 0.0051 | 17.07 | 1400 | 0.9427 | 57.4788 | | 0.0044 | 17.68 | 1450 | 0.9613 | 57.5307 | | 0.0037 | 18.29 | 1500 | 0.9750 | 57.3578 | | 0.0038 | 18.9 | 1550 | 0.9620 | 57.1070 | | 0.0037 | 19.51 | 1600 | 0.9793 | 57.2021 | | 0.0028 | 20.12 | 1650 | 1.0002 | 57.6690 | | 0.0023 | 20.73 | 1700 | 1.0171 | 57.0465 | | 0.0023 | 21.34 | 1750 | 1.0344 | 56.4499 | | 0.0024 | 21.95 | 1800 | 1.0231 | 56.9168 | | 0.0017 | 22.56 | 1850 | 1.0420 | 56.6229 | | 0.0016 | 23.17 | 1900 | 1.0599 | 57.6690 | | 0.001 | 23.78 | 1950 | 1.0659 | 57.7641 | | 0.0012 | 24.39 | 2000 | 1.0818 | 56.7093 | | 0.001 | 25.0 | 2050 | 1.0874 | 57.0984 | | 0.0008 | 25.61 | 2100 | 1.1034 | 57.5220 | | 0.0006 | 26.22 | 2150 | 1.1275 | 56.7353 | | 0.0004 | 26.83 | 2200 | 1.1528 | 57.1330 | | 0.0002 | 27.44 | 2250 | 1.1668 | 56.5537 | | 0.0001 | 28.05 | 2300 | 1.1935 | 56.6142 | | 0.0001 | 28.66 | 2350 | 1.2282 | 56.3289 | | 0.0001 | 29.27 | 2400 | 1.2547 | 56.7266 | | 0.0001 | 29.88 | 2450 | 1.2814 | 56.4413 | | 0.0001 | 30.49 | 2500 | 1.3142 | 56.8822 | | 0.0 | 31.1 | 2550 | 1.3535 | 56.8995 | | 0.0 | 31.71 | 2600 | 1.3759 | 57.0033 | | 0.0 | 32.32 | 2650 | 1.4102 | 57.2454 | | 0.0 | 32.93 | 2700 | 1.4299 | 56.8044 | | 0.0 | 33.54 | 2750 | 1.4650 | 57.2886 | | 0.0 | 34.15 | 2800 | 1.4906 | 57.3405 | | 0.0 | 34.76 | 2850 | 1.5145 | 57.5739 | | 0.0 | 35.37 | 2900 | 1.5377 | 57.5480 | | 0.0 | 35.98 | 2950 | 1.5461 | 57.5480 | | 0.0 | 36.59 | 3000 | 1.5528 | 57.6431 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 1.12.1 - Datasets 2.16.1 - Tokenizers 0.15.0

Shaig/mistral-7b-sru

Shaig

2024-01-30T07:24:21Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-30T07:24:01Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

anantg/mistral-7b-instruct-ft-merged

anantg

2024-01-30T07:15:16Z

62

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-01-30T07:13:09Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

anantg/mistral-7b-instruct-ft

anantg

2024-01-30T07:08:24Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-30T07:07:46Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

juliowaissman/Taxi-v3

juliowaissman

2024-01-30T06:58:19Z

0

null

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

reinforcement-learning

2024-01-30T05:25:20Z

--- 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: 7.54 +/- 2.70 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="juliowaissman/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"]) ```

ChunLok/CAM_sentiment_model

ChunLok

2024-01-30T06:58:11Z

97

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "base_model:yiyanghkust/finbert-pretrain", "base_model:finetune:yiyanghkust/finbert-pretrain", "license:apache-2.0", "autotrain_compatible", "region:us" ]

text-classification

2024-01-28T10:18:59Z

--- license: apache-2.0 base_model: yiyanghkust/finbert-pretrain inference: false model-index: - name: CAM_sentiment_model results: [ ] ---  # CAM_sentiment_model More information needed ## Model description More information needed ## Intended uses & limitations More information needed ### Training hyperparameters More information needed ### Training results More information needed ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

notChocoMilk/louiepikmin4

notChocoMilk

2024-01-30T06:53:37Z

0

null

[ "region:us" ]

null

2024-01-30T06:44:42Z

i accidentally named this 'louiepikmin4' instead of 'icepikmin', oops

bSariturk/bert-fine-tuned-cola

bSariturk

2024-01-30T06:51:30Z

49

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-01-30T06:20:25Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: bert-fine-tuned-cola results: [] ---  # bert-fine-tuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3350 - Validation Loss: 0.4548 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.5185 | 0.4576 | 0 | | 0.3350 | 0.4548 | 1 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.1

victoremanuelgo/zephyr-7b-beta-fine-tuning-news-classification-ptbr

victoremanuelgo

2024-01-30T06:48:30Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-30T06:47:56Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

shidowake/test-240128-swal-7B-hf-qlora-adaptor-merged_bnb_4bit

shidowake

2024-01-30T06:48:15Z

60

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]

text-generation

2024-01-30T06:46:31Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ajayrao/Taxi-v3

ajayrao

2024-01-30T06:48:00Z

0

null

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

reinforcement-learning

2024-01-30T06:47:57Z

--- 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: 7.54 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="ajayrao/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"]) ```

Nicolas852/ppo-PyramidsRND

Nicolas852

2024-01-30T06:47:45Z

16

0

ml-agents

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

reinforcement-learning

2024-01-30T06:47:40Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Nicolas852/ppo-PyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

epinnock/codellama-70-evol-feedback-lora

epinnock

2024-01-30T06:44:48Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-30T06:38:10Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

jinxxx123/twitter_text_classification_model

jinxxx123

2024-01-30T06:38:42Z

50

0

transformers

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

text-classification

2024-01-30T05:57:45Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: jinxxx123/twitter_text_classification_model results: [] ---  # jinxxx123/twitter_text_classification_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1420 - Validation Loss: 0.2778 - Train Accuracy: 0.9148 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17440, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.8293 | 0.5490 | 0.7991 | 0 | | 0.3388 | 0.3242 | 0.8897 | 1 | | 0.1420 | 0.2778 | 0.9148 | 2 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.1

Palistha/bert-finetuned-ner

Palistha

2024-01-30T06:34:00Z

91

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-01-30T06:23:14Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: [] ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0591 - Precision: 0.9278 - Recall: 0.9488 - F1: 0.9382 - Accuracy: 0.9862 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0774 | 1.0 | 1756 | 0.0762 | 0.9007 | 0.9330 | 0.9166 | 0.9800 | | 0.0414 | 2.0 | 3512 | 0.0566 | 0.9297 | 0.9475 | 0.9385 | 0.9856 | | 0.026 | 3.0 | 5268 | 0.0591 | 0.9278 | 0.9488 | 0.9382 | 0.9862 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Tokenizers 0.15.1

dhanikitkat/8-emotions-predictor

dhanikitkat

2024-01-30T06:32:50Z

62

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "code", "id", "arxiv:1910.09700", "doi:10.57967/hf/1713", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-01-30T04:59:25Z

--- language: - id pipeline_tag: text-classification tags: - code --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

serpdotai/sparsetral-16x7B-v1

serpdotai

2024-01-30T06:28:18Z

13

1

transformers

[ "transformers", "safetensors", "sparsetral", "text-generation", "custom_code", "dataset:Open-Orca/SlimOrca", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T00:03:13Z

--- license: apache-2.0 datasets: - Open-Orca/SlimOrca --- prompt format ``` ### System:\n{system}\n### Human:\n{user}\n### Assistant:\n" ``` ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("serpdotai/sparsetral-16x7B-v1", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("serpdotai/sparsetral-16x7B-v1", device_map="auto", trust_remote_code=True).eval() inputs = tokenizer('### System:\n\n### Human:\nHow are you?\n### Assistant:\n', return_tensors='pt') inputs = inputs.to(model.device) pred = model.generate(**inputs) print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) # I am doing well, thank you. ```

avinasht/BERT_FPB_finetuned

avinasht

2024-01-30T06:26:07Z

175

0

transformers

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

text-classification

2024-01-30T06:25:50Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: BERT_FPB_finetuned results: [] ---  # BERT_FPB_finetuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6446 - Accuracy: 0.8650 - F1: 0.8648 - Precision: 0.8647 - Recall: 0.8650 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.4364 | 1.0 | 246 | 0.4949 | 0.7872 | 0.7708 | 0.8266 | 0.7872 | | 0.4302 | 2.0 | 492 | 0.4106 | 0.8398 | 0.8404 | 0.8414 | 0.8398 | | 0.1547 | 3.0 | 738 | 0.4793 | 0.8558 | 0.8559 | 0.8561 | 0.8558 | | 0.1471 | 4.0 | 984 | 0.6446 | 0.8650 | 0.8648 | 0.8647 | 0.8650 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0

colable/llama-ko-peft-v0.5

colable

2024-01-30T06:19:51Z

58

0

transformers

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

text-generation

2024-01-30T06:03:22Z

--- license: mit language: - ko --- # open-llama-2-ko based model with inhouse dataset This is an Korean Model based on * [beomi/open-llama-2-ko-7b] gpu code example ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM import math ## v2 models model_path = "colable/llama-ko-peft-v0.5" tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float32, 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:") ```

Shel2679/mistral7b_instruct_generation

Shel2679

2024-01-30T06:17:41Z

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-01-30T06:17:34Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral7b_instruct_generation results: [] ---  # mistral7b_instruct_generation This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.8070 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.958 | 0.0 | 20 | 1.8399 | | 1.8756 | 0.01 | 40 | 1.8082 | | 1.888 | 0.01 | 60 | 1.7994 | | 1.7863 | 0.01 | 80 | 1.7892 | | 1.7987 | 0.01 | 100 | 1.7897 | | 1.847 | 0.02 | 120 | 1.7881 | | 1.8505 | 0.02 | 140 | 1.7858 | | 1.7915 | 0.02 | 160 | 1.7900 | | 1.8611 | 0.03 | 180 | 1.7942 | | 1.8827 | 0.03 | 200 | 1.7943 | | 1.88 | 0.03 | 220 | 1.7953 | | 1.8887 | 0.03 | 240 | 1.7888 | | 1.6737 | 0.04 | 260 | 1.7939 | | 1.8471 | 0.04 | 280 | 1.7880 | | 1.8163 | 0.04 | 300 | 1.7835 | | 1.7763 | 0.04 | 320 | 1.7825 | | 1.8567 | 0.05 | 340 | 1.7962 | | 1.8203 | 0.05 | 360 | 1.7871 | | 1.965 | 0.05 | 380 | 1.7897 | | 1.9522 | 0.06 | 400 | 1.8036 | | 1.8826 | 0.06 | 420 | 1.8028 | | 1.8173 | 0.06 | 440 | 1.8205 | | 1.8269 | 0.06 | 460 | 1.8059 | | 1.8498 | 0.07 | 480 | 1.7966 | | 1.9159 | 0.07 | 500 | 1.8070 | ### Framework versions - PEFT 0.7.1 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

cashewEnthusiast/ppo-LunarLander-v2

cashewEnthusiast

2024-01-30T06:17:02Z

0

stable-baselines3

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

reinforcement-learning

2024-01-30T06:16:42Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 269.34 +/- 18.16 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

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

ajayrao

2024-01-30T06:05:39Z

0

null

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

reinforcement-learning

2024-01-30T06:05:36Z

--- 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="ajayrao/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"]) ```

karawalla/merged_model

karawalla

2024-01-30T06:01:20Z

3

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T05:58:09Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - **Developed by:** karawalla - **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)

tinywell/dqn-SpaceInvadersNoFrameskip-v4

tinywell

2024-01-30T05:57:19Z

0

stable-baselines3

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

reinforcement-learning

2024-01-30T05:51:24Z

--- 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: 862.00 +/- 366.83 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 tinywell -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 tinywell -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 tinywell ``` ## 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', 400000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

genne/SOLAR_dpo_v2_SFT-DPO

genne

2024-01-30T05:56:32Z

0

transformers

[ "transformers", "safetensors", "text-generation", "ko", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T02:18:43Z

--- license: apache-2.0 language: - ko library_name: transformers pipeline_tag: text-generation --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details * base model: jingyeom/SOLAR_KO_1.3_deup ### Training Data * open ko dpo datasets * open dpo datasets + 자체 모델 번역 ### Results [More Information Needed] #### Summary

cryptoque/distilhubert-finetuned-gtzan-v2

cryptoque

2024-01-30T05:39:16Z

148

0

transformers

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

audio-classification

2024-01-30T02:35:51Z

--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-v2 results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.86 ---  # distilhubert-finetuned-gtzan-v2 This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 1.5298 - Accuracy: 0.86 ## 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: 8e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1489 | 1.0 | 113 | 2.2978 | 0.74 | | 0.0001 | 2.0 | 226 | 2.2070 | 0.78 | | 0.3174 | 3.0 | 339 | 1.7906 | 0.8 | | 0.0001 | 4.0 | 452 | 1.5376 | 0.81 | | 0.0 | 5.0 | 565 | 1.4012 | 0.85 | | 0.0001 | 6.0 | 678 | 1.2597 | 0.87 | | 0.0001 | 7.0 | 791 | 1.5363 | 0.86 | | 0.0001 | 8.0 | 904 | 1.5298 | 0.86 | | 0.0 | 9.0 | 1017 | 1.5277 | 0.86 | | 0.0 | 10.0 | 1130 | 1.5298 | 0.86 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

Pongprecha/distilbert_base_data_wnut_17

Pongprecha

2024-01-30T05:35:40Z

91

0

transformers

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

token-classification

2024-01-30T05:33:15Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert_base_data_wnut_17 results: [] ---  # distilbert_base_data_wnut_17 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2833 - Precision: 0.5246 - Recall: 0.3855 - F1: 0.4444 - Accuracy: 0.9461 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2740 | 0.6152 | 0.2919 | 0.3960 | 0.9404 | | No log | 2.0 | 426 | 0.2568 | 0.5997 | 0.3679 | 0.4561 | 0.9450 | | 0.1764 | 3.0 | 639 | 0.2844 | 0.6269 | 0.3457 | 0.4456 | 0.9464 | | 0.1764 | 4.0 | 852 | 0.2963 | 0.5564 | 0.3522 | 0.4313 | 0.9459 | | 0.0526 | 5.0 | 1065 | 0.2833 | 0.5246 | 0.3855 | 0.4444 | 0.9461 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

NatthawatTung/ALL_mt5-base_10_spider_10_wikiSQL

NatthawatTung

2024-01-30T05:30:18Z

3

0

transformers

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

text2text-generation

2024-01-29T11:41:36Z

--- tags: - generated_from_trainer model-index: - name: ALL_mt5-base_10_spider_10_wikiSQL results: [] ---  # ALL_mt5-base_10_spider_10_wikiSQL This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0241 - Rouge2 Precision: 0.8525 - Rouge2 Recall: 0.5646 - Rouge2 Fmeasure: 0.6434 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.4259 | 1.0 | 875 | 0.1208 | 0.6006 | 0.3892 | 0.4443 | | 0.1482 | 2.0 | 1750 | 0.0808 | 0.7001 | 0.4721 | 0.5333 | | 0.1073 | 3.0 | 2625 | 0.0588 | 0.7416 | 0.5007 | 0.5657 | | 0.0867 | 4.0 | 3500 | 0.0461 | 0.7741 | 0.5208 | 0.5894 | | 0.0769 | 5.0 | 4375 | 0.0382 | 0.7999 | 0.5351 | 0.6073 | | 0.0658 | 6.0 | 5250 | 0.0327 | 0.8225 | 0.5465 | 0.6217 | | 0.0574 | 7.0 | 6125 | 0.0283 | 0.8364 | 0.5546 | 0.6314 | | 0.0525 | 8.0 | 7000 | 0.0261 | 0.8444 | 0.5593 | 0.6371 | | 0.0498 | 9.0 | 7875 | 0.0245 | 0.8515 | 0.5643 | 0.6429 | | 0.0491 | 10.0 | 8750 | 0.0241 | 0.8525 | 0.5646 | 0.6434 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.13.3

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

juliowaissman

2024-01-30T05:22:26Z

0

null

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

reinforcement-learning

2024-01-30T05:09:28Z

--- 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="juliowaissman/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"]) ```

dlibf/zephyr-7b-dpo-full_lr1e-7

dlibf

2024-01-30T05:11:49Z

4

0

transformers

[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T02:39:14Z

--- tags: - trl - dpo - generated_from_trainer model-index: - name: zephyr-7b-dpo-full_lr1e-7 results: [] ---  # zephyr-7b-dpo-full_lr1e-7 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5999 - Rewards/chosen: -0.1059 - Rewards/rejected: -0.3888 - Rewards/accuracies: 0.7266 - Rewards/margins: 0.2829 - Logps/rejected: -300.8914 - Logps/chosen: -273.0010 - Logits/rejected: -2.2473 - Logits/chosen: -2.2980 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6773 | 0.21 | 100 | 0.6767 | 0.0281 | -0.0077 | 0.6914 | 0.0358 | -262.7869 | -259.5997 | -2.3569 | -2.4039 | | 0.6286 | 0.42 | 200 | 0.6292 | -0.0536 | -0.2320 | 0.7109 | 0.1784 | -285.2149 | -267.7724 | -2.3535 | -2.4023 | | 0.6161 | 0.63 | 300 | 0.6066 | -0.0848 | -0.3355 | 0.7188 | 0.2507 | -295.5617 | -270.8907 | -2.2759 | -2.3264 | | 0.5908 | 0.84 | 400 | 0.6002 | -0.1035 | -0.3846 | 0.7227 | 0.2811 | -300.4714 | -272.7594 | -2.2519 | -2.3026 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1

ogbrandt/mistral-7b-non-instruction-pjf-ft-gpt35-dpo-v0

ogbrandt

2024-01-30T05:10:22Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]

null

2024-01-30T04:43:35Z

--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

y629/xlm-roberta-base-finetuned-panx-de

y629

2024-01-30T05:09:55Z

91

0

transformers

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

token-classification

2024-01-29T03:38:13Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.867952522255193 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1346 - F1: 0.8680 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2577 | 1.0 | 525 | 0.1588 | 0.8301 | | 0.1317 | 2.0 | 1050 | 0.1365 | 0.8507 | | 0.0822 | 3.0 | 1575 | 0.1346 | 0.8680 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.2 - Tokenizers 0.13.3

Mavitu56/LLamaCampeonatoBrasileiro

Mavitu56

2024-01-30T05:02:10Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-30T05:02:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

IDEA-CVR/DINO-EVA

IDEA-CVR

2024-01-30T04:57:49Z

0

null

[ "region:us" ]

null

2023-07-08T04:04:03Z

## detrex: Benchmarking Detection Transformers This is the huggingface space for IDEA-CVR proposed DETR-based research platform `detrex` - detrex github link: https://github.com/IDEA-Research/detrex - detrex documentation: https://detrex.readthedocs.io/en/latest/ We will store our detrex pretrained checkpoints both in github and huggingface space.

Uday1998/my_awesome_qa_model

Uday1998

2024-01-30T04:56:58Z

98

0

transformers

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

question-answering

2024-01-30T04:43:22Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: my_awesome_qa_model results: [] ---  # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.9653 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

Chattiori/FixedReality

Chattiori

2024-01-30T04:54:37Z

0

1

null

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

null

2024-01-14T10:34:01Z

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

stablediffusionapi/ae-realisticmagn

stablediffusionapi

2024-01-30T04:53:43Z

25

0

diffusers

[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-01-30T04:51:46Z

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # AE-realisticmagn API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/13907603331706590229.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "ae-realisticmagn" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/ae-realisticmagn) Model link: [View model](https://modelslab.com/models/ae-realisticmagn) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "ae-realisticmagn", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

k-seungri/output

k-seungri

2024-01-30T04:46:02Z

99

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ko", "dataset:k-seungri/forwhisperfinetuning_dataset", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-01-30T04:39:53Z

--- language: - ko license: apache-2.0 base_model: openai/whisper-base tags: - hf-asr-leaderboard - generated_from_trainer datasets: - k-seungri/forwhisperfinetuning_dataset model-index: - name: k-seungri/forwhisperfinetuning_model results: [] ---  # k-seungri/forwhisperfinetuning_model This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the forwhisperfinetuning_dataset 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: 1e-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_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

k-seungri/forwhisperfinetunings_training_args_tokenizer

k-seungri

2024-01-30T04:43:04Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-30T04:43:04Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

k-seungri/forwhisperfinetunings_training_args_processor

k-seungri

2024-01-30T04:43:03Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-30T04:43:01Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

fasterinnerlooper/stable-code-3b

fasterinnerlooper

2024-01-30T04:34:13Z

0

null

[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:stabilityai/stable-code-3b", "base_model:finetune:stabilityai/stable-code-3b", "license:other", "region:us" ]

null

2024-01-24T15:23:28Z

--- license: other base_model: stabilityai/stable-code-3b tags: - generated_from_trainer model-index: - name: stable-code-3b results: [] ---  # stable-code-3b This model is a fine-tuned version of [stabilityai/stable-code-3b](https://huggingface.co/stabilityai/stable-code-3b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: cosine - lr_scheduler_warmup_ratio: 0.3 - training_steps: 700 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

nakcnx/typhoon-sql-qlora

nakcnx

2024-01-30T04:32:02Z

5

0

peft

[ "peft", "safetensors", "en", "th", "dataset:b-mc2/sql-create-context", "base_model:scb10x/typhoon-7b", "base_model:adapter:scb10x/typhoon-7b", "region:us" ]

null

2024-01-29T04:14:13Z

--- library_name: peft base_model: scb10x/typhoon-7b datasets: - b-mc2/sql-create-context language: - en - th --- ## Model Description [Typhoon-7b](scb10x/typhoon-7b) QLoRA Finetune by unsloth with [SQL Context](b-mc2/sql-create-context) dataset. #### Training Hyperparameters Batch Size: 48 (4(BS)x4(GAS)x3(GPU)) The following `bitsandbytes` quantization config was used during training: - r = 64 - lora_alpha = 16 - lora_dropout = 0.05 - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### LOSS Step Training Loss Eval Loss 1550 (Epoch:1) 0.4295 0.4367 3110 (Epoch:2) 0.4057 0.4217 ### Framework versions - PEFT 0.7.0

adalib/megengine-data-codegen-2B-mono-prefix

adalib

2024-01-30T04:17:47Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Salesforce/codegen-2B-mono", "base_model:adapter:Salesforce/codegen-2B-mono", "region:us" ]

null

2024-01-30T04:17:42Z

--- library_name: peft base_model: Salesforce/codegen-2B-mono --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

seongjunLee/torchy_generator

seongjunLee

2024-01-30T04:01:20Z

0

diffusers

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

text-to-image

2024-01-30T04:01:20Z

--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a cartoon style drawing of TOK character, license: openrail++ --- # SDXL LoRA DreamBooth - seongjunLee/torchy_generator <Gallery /> ## Model description These are seongjunLee/torchy_generator LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a cartoon style drawing of TOK character, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](seongjunLee/torchy_generator/tree/main) them in the Files & versions tab.

Jeongbeen/torchy_generator

Jeongbeen

2024-01-30T04:00:23Z

1

diffusers

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

text-to-image

2024-01-30T04:00:20Z

--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a cartoon style drawing of TOK character, license: openrail++ --- # SDXL LoRA DreamBooth - Jeongbeen/torchy_generator <Gallery /> ## Model description These are Jeongbeen/torchy_generator LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a cartoon style drawing of TOK character, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Jeongbeen/torchy_generator/tree/main) them in the Files & versions tab.

kanishka/smolm-autoreg-bpe-counterfactual-babylm-pipps_and_keys_to_it_all_10k-1e-3

kanishka

2024-01-30T03:58:26Z

4

0

transformers

[ "transformers", "tensorboard", "safetensors", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-29T05:12:25Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-counterfactual-babylm-pipps_and_keys_to_it_all_10k-1e-3 results: [] ---  # smolm-autoreg-bpe-counterfactual-babylm-pipps_and_keys_to_it_all_10k-1e-3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3502 - Accuracy: 0.4102 ## 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: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.6157 | 1.0 | 18844 | 3.7143 | 0.3599 | | 3.3979 | 2.0 | 37688 | 3.5062 | 0.3804 | | 3.2663 | 3.0 | 56532 | 3.3950 | 0.3932 | | 3.1887 | 4.0 | 75376 | 3.3694 | 0.3977 | | 3.1328 | 5.0 | 94220 | 3.3361 | 0.4009 | | 3.0925 | 6.0 | 113064 | 3.3236 | 0.4038 | | 3.0537 | 7.0 | 131908 | 3.3165 | 0.4050 | | 3.0289 | 8.0 | 150752 | 3.3142 | 0.4063 | | 2.9979 | 9.0 | 169596 | 3.2959 | 0.4083 | | 2.9734 | 10.0 | 188440 | 3.2976 | 0.4096 | | 2.9501 | 11.0 | 207284 | 3.3026 | 0.4094 | | 2.9302 | 12.0 | 226128 | 3.3036 | 0.4097 | | 2.9067 | 13.0 | 244972 | 3.3101 | 0.4103 | | 2.885 | 14.0 | 263816 | 3.3063 | 0.4106 | | 2.8686 | 15.0 | 282660 | 3.3195 | 0.4098 | | 2.8474 | 16.0 | 301504 | 3.3275 | 0.4106 | | 2.8235 | 17.0 | 320348 | 3.3297 | 0.4108 | | 2.8091 | 18.0 | 339192 | 3.3385 | 0.4105 | | 2.7899 | 19.0 | 358036 | 3.3433 | 0.4102 | | 2.7718 | 20.0 | 376880 | 3.3502 | 0.4102 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.14.1

mlx-community/CodeLlama-34b-Instruct-hf-4bit

mlx-community

2024-01-30T03:52:45Z

12

2

mlx

[ "mlx", "llama", "llama-2", "text-generation", "code", "license:llama2", "region:us" ]

text-generation

2024-01-30T03:48:04Z

--- language: - code license: llama2 tags: - llama-2 - mlx pipeline_tag: text-generation --- # mlx-community/CodeLlama-34b-Instruct-hf-4bit This model was converted to MLX format from [`codellama/CodeLlama-34b-Instruct-hf`](). Refer to the [original model card](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/CodeLlama-34b-Instruct-hf-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

wgj0714/results

wgj0714

2024-01-30T03:52:19Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:davidkim205/komt-mistral-7b-v1", "base_model:adapter:davidkim205/komt-mistral-7b-v1", "region:us" ]

null

2024-01-30T03:26:51Z

--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: davidkim205/komt-mistral-7b-v1 model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [davidkim205/komt-mistral-7b-v1](https://huggingface.co/davidkim205/komt-mistral-7b-v1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.3 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.0

AIFT/AIFT-instruct-42dot_LLM-SFT-1.3B-dpo

AIFT

2024-01-30T03:43:59Z

150

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T00:17:21Z

--- license: cc-by-sa-4.0 --- <h1>AIFT-instruct-42dot_LLM-SFT-1.3B-dpo</h1> <b><학습 데이터 구축></b> <br> kyujinpy 님이 공개하신 KOR-OpenOrca-Platypus 데이터를 일부 삭제(샘플링) 및 정제 작업 진행하여 활용. 그 이후 해당 데이터들을 보며 관련 태스크를 추출하였고 이를 기반으로 해당 태스크에 맞춰서 NLP 관련 오픈소스 데이터를 활용하여 학습데이터를 자체적으로 역사, 과학, 수학, 기계독해, 리뷰 분석 문제를 gpt를 통해서 구축하였고, aihub 일반상식 및 기계독해 데이터를 활용하여 추가로 학습 데이터를 구축(형태소 관련, 기계독해 관련 및 요약) 각종 블로그에서 역사 및 상식 퀴즈를 사람이 직접 학습데이터 형태로 변경 AI2AI Challenge 데이터 형태를 보고 gpt를 통해 초등 수준의 과학 수학 문제 유형을 제작 500문제 영어 번역 데이터 영한/한영 데이터 학습 데이터로 활용 진행 총 데이터 4만개 정도 사용하였습니다. <br> dpo데이터의 경우는 hh-rlhf데이터를 gpt-3.5-turbo를 활용해 답변을 재생성하였습니다 <br> + TruthfulQA 관련 문제 추가를 진행하였습니다.(속설 관련 참거짓 문제) + 기계독해 관련 학습 데이터를 ChatGPT를 통해서 답변을 얻어 학습 + 문법관련 학습 데이터 <br> ###학습 데이터 파일은 비공개입니다. <br> <모델> <br> 42dot에서 공개한 42dot_LLM-SFT-1.3B을 베이스 모델로 하여 학습 진행하였습니다. <br> <br> <br> <b><학습></b> <br> 학습은 LoRA를 사용하여 A100 40G *2에서 학습을 진행하였습니다.

prajjusy/pfet-flan-t5-base-model-5

prajjusy

2024-01-30T03:37:20Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:prajjusy/full-finetuned-flan-t5-base-model-5", "base_model:adapter:prajjusy/full-finetuned-flan-t5-base-model-5", "region:us" ]

null

2024-01-30T03:37:19Z

--- library_name: peft base_model: prajjusy/full-finetuned-flan-t5-base-model-5 --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

bartowski/Mistralpaca-7B-exl2

bartowski

2024-01-30T03:37:14Z

0

null

[ "sft", "text-generation", "dataset:yahma/alpaca-cleaned", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

text-generation

2024-01-30T03:21:07Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 datasets: - yahma/alpaca-cleaned tags: - sft quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Mistralpaca-7B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.12">turboderp's ExLlamaV2 v0.0.12</a> for quantization. # The "main" branch only contains the measurement.json, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/mlabonne/Mistralpaca-7B | Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------------ | | [8_0](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | [6_5](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. | | [5_0](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. | | [4_25](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. | | [3_5](https://huggingface.co/Bartowski/Mistralpaca-7B-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. | ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Mistralpaca-7B-exl2 Mistralpaca-7B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Mistralpaca-7B-exl2`: ```shell mkdir Mistralpaca-7B-exl2 huggingface-cli download bartowski/Mistralpaca-7B-exl2 --local-dir Mistralpaca-7B-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir Mistralpaca-7B-exl2-6_5 huggingface-cli download bartowski/Mistralpaca-7B-exl2 --revision 6_5 --local-dir Mistralpaca-7B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir Mistralpaca-7B-exl2-6.5 huggingface-cli download bartowski/Mistralpaca-7B-exl2 --revision 6_5 --local-dir Mistralpaca-7B-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

Unggi/test

Unggi

2024-01-30T03:36:04Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "base_model:finetune:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T03:16:30Z

--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T tags: - generated_from_trainer model-index: - name: test results: [] ---  # test This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00015 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 256 - total_train_batch_size: 512 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.3.0.dev20240127+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

zhanjun520/q-FrozenLake-v1-4x4-noSlippery

zhanjun520

2024-01-30T03:31:18Z

0

null

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

reinforcement-learning

2024-01-30T03:31:03Z

--- 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="zhanjun520/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"]) ```

Nicolas852/ppo-SnowballTarget

Nicolas852

2024-01-30T03:21:39Z

5

0

ml-agents

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

reinforcement-learning

2024-01-30T03:21:35Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Nicolas852/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

thuan9889/llama_embedding_model_v1

thuan9889

2024-01-30T03:20:36Z

604

1

sentence-transformers

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

sentence-similarity

2024-01-30T03:20:30Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # thuan9889/llama_embedding_model_v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('thuan9889/llama_embedding_model_v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=thuan9889/llama_embedding_model_v1) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 2 with parameters: ``` {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 0, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors

adalib/hummingbot-data-codegen-2B-mono-prefix

adalib

2024-01-30T03:09:05Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Salesforce/codegen-2B-mono", "base_model:adapter:Salesforce/codegen-2B-mono", "region:us" ]

null

2024-01-30T03:09:02Z

--- library_name: peft base_model: Salesforce/codegen-2B-mono --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

mlx-community/CodeLlama-7b-Instruct-hf-4bit-MLX

mlx-community

2024-01-30T03:00:57Z

20

1

mlx

[ "mlx", "llama", "llama-2", "text-generation", "code", "license:llama2", "region:us" ]

text-generation

2024-01-30T02:29:12Z

--- language: - code license: llama2 tags: - llama-2 - mlx pipeline_tag: text-generation --- ![Alt text](https://media.discordapp.net/attachments/989904887330521099/1201717650128896070/Llama_Coding_on_MacBook_1.png?ex=65cad5c6&is=65b860c6&hm=8008a5817272fa49fca67143516563b2578accf263cc04d6768e689c1be2f483&=&format=webp&quality=lossless&width=1372&height=1372) # mlx-community/CodeLlama-7b-Instruct-hf-4bit-MLX This model was converted to MLX format from [`codellama/CodeLlama-7b-Instruct-hf`](). Refer to the [original model card](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/CodeLlama-7b-Instruct-hf-4bit-MLX") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

liwii/electra-ginza-based-ja-fc-classifier

liwii

2024-01-30T03:00:32Z

19

0

transformers

[ "transformers", "pytorch", "electra", "generated_from_trainer", "base_model:megagonlabs/transformers-ud-japanese-electra-base-ginza-520", "base_model:finetune:megagonlabs/transformers-ud-japanese-electra-base-ginza-520", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-01-30T01:30:55Z

--- license: mit base_model: megagonlabs/transformers-ud-japanese-electra-base-ginza-520 tags: - generated_from_trainer metrics: - accuracy model-index: - name: electra-ginza-based-ja-fc-classifier results: [] ---  # electra-ginza-based-ja-fc-classifier This model is a fine-tuned version of [megagonlabs/transformers-ud-japanese-electra-base-ginza-520](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-ginza-520) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2376 - Accuracy: 0.9258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.38340974405913e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4988 | 1.0 | 1223 | 0.3910 | 0.8418 | | 0.3011 | 2.0 | 2446 | 0.2376 | 0.9258 | | 0.1658 | 3.0 | 3669 | 0.2649 | 0.9336 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

checkiejan/phi2-marking-test-full

checkiejan

2024-01-30T02:59:45Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-01-30T02:36:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  # How to Get Started with the Model To load and use this model with the Transformers library by Hugging Face, follow the steps outlined in the code snippet below. This code demonstrates how to configure the model, load it along with its tokenizer, and perform inference to generate text based on a given prompt. ## Code Format: ```python from peft import PeftModel, PeftConfig test_config = PeftConfig.from_pretrained("checkiejan/phi2-marking-test-full") model_base = AutoModelForCausalLM.from_pretrained( test_config.base_model_name_or_path, device_map="auto", quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type="nf4", ), torch_dtype=torch.bfloat16, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(test_config.base_model_name_or_path) # Add/set tokens same tokens to base model before merging, like we did before starting training tokenizer.add_tokens(["<|im_start|>", "<PAD>"]) tokenizer.pad_token = "<PAD>" tokenizer.add_special_tokens(dict(eos_token="<|im_end|>")) model_base.resize_token_embeddings( new_num_tokens=len(tokenizer), pad_to_multiple_of=64) # phi2 default is 64, see configuration_phi.py model_base.config.eos_token_id = tokenizer.eos_token_id lora_model = PeftModel.from_pretrained(model_base, "checkiejan/phi2-marking-test-full") inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=True) outputs = lora_model.generate(**inputs) text = tokenizer.batch_decode(outputs, skip_special_tokens=True) print(''.join(text)) ``` This code snippet sets up the model and tokenizer, configures the necessary parameters, and demonstrates how to generate text based on a given prompt. Ensure to replace "Your prompt here" with your actual input text. ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

blzncz/segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps

blzncz

2024-01-30T02:55:57Z

178

0

transformers

[ "transformers", "pytorch", "tensorboard", "segformer", "image-segmentation", "vision", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

2024-01-29T16:06:48Z

--- license: other tags: - image-segmentation - vision - generated_from_trainer model-index: - name: segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps results: [] ---  # segformer-finetuned-4ss1st3r_s3gs3m_24Jan_rojo-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the blzncz/4ss1st3r_s3gs3m_24Jan_rojo dataset. It achieves the following results on the evaluation set: - Loss: 1.2098 - Mean Iou: 0.3648 - Mean Accuracy: 0.6821 - Overall Accuracy: 0.6947 - Accuracy Bg: nan - Accuracy Fallo cohesivo: 0.7354 - Accuracy Fallo malla: 0.6052 - Accuracy Fallo adhesivo: 0.9884 - Accuracy Fallo burbuja: 0.3995 - Iou Bg: 0.0 - Iou Fallo cohesivo: 0.5920 - Iou Fallo malla: 0.5774 - Iou Fallo adhesivo: 0.2950 - Iou Fallo burbuja: 0.3598 ## 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: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bg | Accuracy Fallo cohesivo | Accuracy Fallo malla | Accuracy Fallo adhesivo | Accuracy Fallo burbuja | Iou Bg | Iou Fallo cohesivo | Iou Fallo malla | Iou Fallo adhesivo | Iou Fallo burbuja | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------:|:-----------------------:|:--------------------:|:-----------------------:|:----------------------:|:------:|:------------------:|:---------------:|:------------------:|:-----------------:| | 0.5778 | 1.0 | 114 | 0.8590 | 0.2588 | 0.5626 | 0.6271 | nan | 0.3415 | 0.9120 | 0.9748 | 0.0221 | 0.0 | 0.3339 | 0.6211 | 0.3168 | 0.0220 | | 0.3326 | 2.0 | 228 | 0.6845 | 0.3570 | 0.6755 | 0.7131 | nan | 0.5232 | 0.8953 | 0.9911 | 0.2921 | 0.0 | 0.5003 | 0.6924 | 0.3417 | 0.2503 | | 0.2636 | 3.0 | 342 | 0.6662 | 0.3896 | 0.7107 | 0.7344 | nan | 0.7666 | 0.6622 | 0.9838 | 0.4304 | 0.0 | 0.6088 | 0.6188 | 0.4014 | 0.3191 | | 0.2505 | 4.0 | 456 | 0.7666 | 0.3732 | 0.7408 | 0.6807 | nan | 0.4141 | 0.9417 | 0.9778 | 0.6297 | 0.0 | 0.4065 | 0.6276 | 0.4337 | 0.3980 | | 0.2306 | 5.0 | 570 | 0.4680 | 0.4690 | 0.7389 | 0.8099 | nan | 0.7461 | 0.8649 | 0.9742 | 0.3705 | 0.0 | 0.6711 | 0.7095 | 0.6349 | 0.3294 | | 0.1998 | 6.0 | 684 | 0.5711 | 0.4449 | 0.7494 | 0.7824 | nan | 0.8528 | 0.6732 | 0.9865 | 0.4850 | 0.0 | 0.6781 | 0.6338 | 0.5320 | 0.3807 | | 0.2062 | 7.0 | 798 | 0.6403 | 0.4070 | 0.7437 | 0.7283 | nan | 0.5736 | 0.8683 | 0.9881 | 0.5447 | 0.0 | 0.5300 | 0.6452 | 0.4613 | 0.3987 | | 0.182 | 8.0 | 912 | 0.5934 | 0.4344 | 0.7309 | 0.7770 | nan | 0.8171 | 0.7036 | 0.9840 | 0.4190 | 0.0 | 0.6640 | 0.6485 | 0.4916 | 0.3681 | | 0.178 | 9.0 | 1026 | 0.7158 | 0.3811 | 0.6915 | 0.7313 | nan | 0.7292 | 0.6984 | 0.9913 | 0.3472 | 0.0 | 0.6148 | 0.6404 | 0.3348 | 0.3153 | | 0.1568 | 10.0 | 1140 | 0.5892 | 0.4169 | 0.6970 | 0.7873 | nan | 0.8088 | 0.7398 | 0.9855 | 0.2538 | 0.0 | 0.6770 | 0.6664 | 0.5004 | 0.2407 | | 0.1576 | 11.0 | 1254 | 0.6419 | 0.4228 | 0.7177 | 0.7652 | nan | 0.7970 | 0.7001 | 0.9805 | 0.3931 | 0.0 | 0.6509 | 0.6318 | 0.4701 | 0.3614 | | 0.1667 | 12.0 | 1368 | 0.6563 | 0.4060 | 0.7369 | 0.7605 | nan | 0.7409 | 0.7517 | 0.9871 | 0.4681 | 0.0 | 0.6326 | 0.6731 | 0.4103 | 0.3139 | | 0.1436 | 13.0 | 1482 | 0.9148 | 0.3864 | 0.7079 | 0.7187 | nan | 0.6666 | 0.7400 | 0.9900 | 0.4352 | 0.0 | 0.6025 | 0.6632 | 0.2829 | 0.3835 | | 0.1469 | 14.0 | 1596 | 0.6680 | 0.4166 | 0.7216 | 0.7689 | nan | 0.7843 | 0.7225 | 0.9861 | 0.3936 | 0.0 | 0.6703 | 0.6608 | 0.3946 | 0.3571 | | 0.1288 | 15.0 | 1710 | 0.8170 | 0.3765 | 0.6849 | 0.7164 | nan | 0.8269 | 0.5509 | 0.9859 | 0.3759 | 0.0 | 0.6242 | 0.5368 | 0.3815 | 0.3398 | | 0.1243 | 16.0 | 1824 | 0.8197 | 0.4034 | 0.7169 | 0.7375 | nan | 0.8456 | 0.5776 | 0.9842 | 0.4602 | 0.0 | 0.6517 | 0.5582 | 0.4078 | 0.3991 | | 0.1208 | 17.0 | 1938 | 0.7927 | 0.3848 | 0.6774 | 0.7295 | nan | 0.8592 | 0.5460 | 0.9810 | 0.3233 | 0.0 | 0.6359 | 0.5256 | 0.4647 | 0.2978 | | 0.115 | 18.0 | 2052 | 1.1226 | 0.3376 | 0.6484 | 0.6727 | nan | 0.7053 | 0.5900 | 0.9905 | 0.3079 | 0.0 | 0.5659 | 0.5688 | 0.2673 | 0.2860 | | 0.1138 | 19.0 | 2166 | 0.8244 | 0.4055 | 0.7099 | 0.7446 | nan | 0.8200 | 0.6248 | 0.9833 | 0.4115 | 0.0 | 0.6364 | 0.5964 | 0.4287 | 0.3659 | | 0.1144 | 20.0 | 2280 | 0.5964 | 0.4493 | 0.7179 | 0.8034 | nan | 0.8594 | 0.7188 | 0.9808 | 0.3127 | 0.0 | 0.6995 | 0.6608 | 0.5990 | 0.2873 | | 0.108 | 21.0 | 2394 | 0.6545 | 0.4418 | 0.7348 | 0.7902 | nan | 0.8263 | 0.7241 | 0.9835 | 0.4053 | 0.0 | 0.6832 | 0.6653 | 0.5023 | 0.3582 | | 0.109 | 22.0 | 2508 | 0.9552 | 0.3775 | 0.6990 | 0.7058 | nan | 0.6835 | 0.6906 | 0.9894 | 0.4325 | 0.0 | 0.5907 | 0.6391 | 0.2756 | 0.3819 | | 0.0987 | 23.0 | 2622 | 0.7971 | 0.3974 | 0.7133 | 0.7453 | nan | 0.7451 | 0.7124 | 0.9871 | 0.4084 | 0.0 | 0.6281 | 0.6560 | 0.3577 | 0.3452 | | 0.0977 | 24.0 | 2736 | 0.9783 | 0.3718 | 0.6984 | 0.7001 | nan | 0.5950 | 0.7793 | 0.9916 | 0.4276 | 0.0 | 0.5491 | 0.6786 | 0.2620 | 0.3692 | | 0.0954 | 25.0 | 2850 | 0.9562 | 0.3856 | 0.6981 | 0.7352 | nan | 0.7102 | 0.7294 | 0.9904 | 0.3623 | 0.0 | 0.6355 | 0.6603 | 0.2988 | 0.3332 | | 0.0928 | 26.0 | 2964 | 0.9185 | 0.3787 | 0.6870 | 0.7355 | nan | 0.7815 | 0.6491 | 0.9847 | 0.3327 | 0.0 | 0.6569 | 0.6184 | 0.3151 | 0.3028 | | 0.0918 | 27.0 | 3078 | 0.9617 | 0.3845 | 0.6916 | 0.7175 | nan | 0.8211 | 0.5605 | 0.9809 | 0.4037 | 0.0 | 0.6123 | 0.5462 | 0.3994 | 0.3648 | | 0.0801 | 28.0 | 3192 | 1.1167 | 0.3672 | 0.6811 | 0.7091 | nan | 0.7352 | 0.6393 | 0.9927 | 0.3570 | 0.0 | 0.6151 | 0.6141 | 0.2816 | 0.3250 | | 0.0852 | 29.0 | 3306 | 0.8549 | 0.4217 | 0.7108 | 0.7596 | nan | 0.8684 | 0.6040 | 0.9848 | 0.3862 | 0.0 | 0.6576 | 0.5808 | 0.5146 | 0.3553 | | 0.0816 | 30.0 | 3420 | 0.9536 | 0.3902 | 0.7034 | 0.7366 | nan | 0.7752 | 0.6573 | 0.9885 | 0.3926 | 0.0 | 0.6415 | 0.6301 | 0.3274 | 0.3517 | | 0.0876 | 31.0 | 3534 | 1.0597 | 0.3873 | 0.7065 | 0.7158 | nan | 0.7490 | 0.6374 | 0.9920 | 0.4475 | 0.0 | 0.6117 | 0.6160 | 0.3051 | 0.4035 | | 0.0811 | 32.0 | 3648 | 0.8829 | 0.4038 | 0.7077 | 0.7569 | nan | 0.7949 | 0.6829 | 0.9860 | 0.3669 | 0.0 | 0.6442 | 0.6498 | 0.3943 | 0.3304 | | 0.0789 | 33.0 | 3762 | 0.9615 | 0.4002 | 0.7104 | 0.7436 | nan | 0.7890 | 0.6575 | 0.9884 | 0.4066 | 0.0 | 0.6344 | 0.6308 | 0.3702 | 0.3658 | | 0.0752 | 34.0 | 3876 | 0.7799 | 0.4297 | 0.7280 | 0.7806 | nan | 0.8279 | 0.6991 | 0.9873 | 0.3975 | 0.0 | 0.6787 | 0.6605 | 0.4458 | 0.3634 | | 0.0731 | 35.0 | 3990 | 0.9285 | 0.4061 | 0.7025 | 0.7531 | nan | 0.8595 | 0.5987 | 0.9898 | 0.3619 | 0.0 | 0.6579 | 0.5797 | 0.4600 | 0.3330 | | 0.0752 | 36.0 | 4104 | 0.9218 | 0.4112 | 0.7276 | 0.7463 | nan | 0.7632 | 0.6926 | 0.9880 | 0.4667 | 0.0 | 0.6393 | 0.6507 | 0.3462 | 0.4200 | | 0.0701 | 37.0 | 4218 | 0.8808 | 0.4105 | 0.7184 | 0.7562 | nan | 0.8090 | 0.6635 | 0.9893 | 0.4119 | 0.0 | 0.6569 | 0.6342 | 0.3876 | 0.3740 | | 0.0717 | 38.0 | 4332 | 1.1090 | 0.3748 | 0.6881 | 0.7166 | nan | 0.7554 | 0.6334 | 0.9905 | 0.3729 | 0.0 | 0.6272 | 0.6069 | 0.2969 | 0.3433 | | 0.0716 | 39.0 | 4446 | 0.9456 | 0.4018 | 0.7064 | 0.7528 | nan | 0.8217 | 0.6418 | 0.9872 | 0.3747 | 0.0 | 0.6638 | 0.6131 | 0.3863 | 0.3456 | | 0.069 | 40.0 | 4560 | 0.8462 | 0.4157 | 0.7038 | 0.7697 | nan | 0.8656 | 0.6316 | 0.9856 | 0.3324 | 0.0 | 0.6750 | 0.6041 | 0.4917 | 0.3078 | | 0.07 | 41.0 | 4674 | 0.9715 | 0.3843 | 0.6886 | 0.7393 | nan | 0.8006 | 0.6353 | 0.9896 | 0.3289 | 0.0 | 0.6420 | 0.6104 | 0.3633 | 0.3056 | | 0.0649 | 42.0 | 4788 | 0.9114 | 0.3997 | 0.7066 | 0.7592 | nan | 0.7682 | 0.7185 | 0.9917 | 0.3478 | 0.0 | 0.6613 | 0.6728 | 0.3449 | 0.3196 | | 0.0665 | 43.0 | 4902 | 1.1847 | 0.3662 | 0.6812 | 0.6981 | nan | 0.7131 | 0.6389 | 0.9912 | 0.3817 | 0.0 | 0.5853 | 0.6122 | 0.2832 | 0.3504 | | 0.0646 | 44.0 | 5016 | 1.1242 | 0.3744 | 0.6906 | 0.7086 | nan | 0.6930 | 0.6870 | 0.9891 | 0.3932 | 0.0 | 0.5902 | 0.6495 | 0.2770 | 0.3555 | | 0.0662 | 45.0 | 5130 | 1.1017 | 0.3605 | 0.6735 | 0.7023 | nan | 0.7333 | 0.6261 | 0.9906 | 0.3439 | 0.0 | 0.5997 | 0.5996 | 0.2906 | 0.3126 | | 0.0644 | 46.0 | 5244 | 1.2989 | 0.3470 | 0.6600 | 0.6735 | nan | 0.6567 | 0.6473 | 0.9917 | 0.3445 | 0.0 | 0.5607 | 0.6182 | 0.2377 | 0.3185 | | 0.0595 | 47.0 | 5358 | 1.0764 | 0.3833 | 0.6982 | 0.7241 | nan | 0.7650 | 0.6389 | 0.9932 | 0.3957 | 0.0 | 0.6345 | 0.6134 | 0.3071 | 0.3618 | | 0.0603 | 48.0 | 5472 | 1.0871 | 0.3692 | 0.6813 | 0.7128 | nan | 0.7153 | 0.6718 | 0.9884 | 0.3497 | 0.0 | 0.6079 | 0.6388 | 0.2797 | 0.3197 | | 0.0591 | 49.0 | 5586 | 1.1054 | 0.3800 | 0.6956 | 0.7171 | nan | 0.7103 | 0.6866 | 0.9892 | 0.3963 | 0.0 | 0.6116 | 0.6458 | 0.2816 | 0.3609 | | 0.0612 | 50.0 | 5700 | 1.1061 | 0.3652 | 0.6768 | 0.7087 | nan | 0.7394 | 0.6340 | 0.9903 | 0.3435 | 0.0 | 0.6027 | 0.6074 | 0.3009 | 0.3147 | | 0.0609 | 51.0 | 5814 | 0.9938 | 0.3742 | 0.6850 | 0.7206 | nan | 0.7555 | 0.6433 | 0.9890 | 0.3523 | 0.0 | 0.6210 | 0.6121 | 0.3143 | 0.3235 | | 0.058 | 52.0 | 5928 | 1.0391 | 0.3745 | 0.6836 | 0.7248 | nan | 0.7691 | 0.6374 | 0.9901 | 0.3379 | 0.0 | 0.6275 | 0.6082 | 0.3257 | 0.3109 | | 0.0559 | 53.0 | 6042 | 0.9916 | 0.3922 | 0.7033 | 0.7373 | nan | 0.8044 | 0.6249 | 0.9902 | 0.3938 | 0.0 | 0.6429 | 0.6003 | 0.3644 | 0.3537 | | 0.0572 | 54.0 | 6156 | 1.0124 | 0.3801 | 0.6907 | 0.7262 | nan | 0.7721 | 0.6371 | 0.9885 | 0.3650 | 0.0 | 0.6284 | 0.6052 | 0.3326 | 0.3341 | | 0.0558 | 55.0 | 6270 | 1.0856 | 0.3692 | 0.6823 | 0.7120 | nan | 0.7232 | 0.6604 | 0.9894 | 0.3565 | 0.0 | 0.6094 | 0.6255 | 0.2864 | 0.3246 | | 0.058 | 56.0 | 6384 | 1.0581 | 0.3837 | 0.6998 | 0.7212 | nan | 0.7353 | 0.6668 | 0.9910 | 0.4062 | 0.0 | 0.6126 | 0.6269 | 0.3125 | 0.3666 | | 0.0518 | 57.0 | 6498 | 1.0176 | 0.3933 | 0.7060 | 0.7362 | nan | 0.7857 | 0.6440 | 0.9884 | 0.4060 | 0.0 | 0.6395 | 0.6127 | 0.3489 | 0.3655 | | 0.0537 | 58.0 | 6612 | 1.2001 | 0.3676 | 0.6853 | 0.6947 | nan | 0.7737 | 0.5607 | 0.9884 | 0.4184 | 0.0 | 0.6003 | 0.5391 | 0.3221 | 0.3764 | | 0.0552 | 59.0 | 6726 | 0.9751 | 0.3940 | 0.7068 | 0.7314 | nan | 0.8019 | 0.6139 | 0.9870 | 0.4244 | 0.0 | 0.6353 | 0.5871 | 0.3662 | 0.3816 | | 0.0538 | 60.0 | 6840 | 1.0382 | 0.3782 | 0.6909 | 0.7203 | nan | 0.7216 | 0.6813 | 0.9895 | 0.3714 | 0.0 | 0.6093 | 0.6389 | 0.3011 | 0.3418 | | 0.0528 | 61.0 | 6954 | 1.1785 | 0.3662 | 0.6819 | 0.7019 | nan | 0.7278 | 0.6310 | 0.9904 | 0.3784 | 0.0 | 0.5966 | 0.6013 | 0.2914 | 0.3419 | | 0.0531 | 62.0 | 7068 | 1.1054 | 0.3685 | 0.6852 | 0.7026 | nan | 0.7290 | 0.6310 | 0.9899 | 0.3911 | 0.0 | 0.5969 | 0.5981 | 0.2961 | 0.3514 | | 0.0522 | 63.0 | 7182 | 1.1271 | 0.3717 | 0.6871 | 0.7094 | nan | 0.7268 | 0.6496 | 0.9906 | 0.3816 | 0.0 | 0.6069 | 0.6148 | 0.2905 | 0.3460 | | 0.0507 | 64.0 | 7296 | 1.0440 | 0.3734 | 0.6825 | 0.7242 | nan | 0.7678 | 0.6380 | 0.9884 | 0.3359 | 0.0 | 0.6279 | 0.6043 | 0.3272 | 0.3076 | | 0.0519 | 65.0 | 7410 | 1.1191 | 0.3727 | 0.6884 | 0.7102 | nan | 0.7264 | 0.6517 | 0.9911 | 0.3843 | 0.0 | 0.6028 | 0.6156 | 0.2978 | 0.3472 | | 0.0502 | 66.0 | 7524 | 1.0089 | 0.3917 | 0.7036 | 0.7408 | nan | 0.7555 | 0.6898 | 0.9896 | 0.3794 | 0.0 | 0.6413 | 0.6472 | 0.3261 | 0.3437 | | 0.051 | 67.0 | 7638 | 1.2112 | 0.3672 | 0.6806 | 0.7083 | nan | 0.7352 | 0.6378 | 0.9899 | 0.3593 | 0.0 | 0.6078 | 0.6085 | 0.2918 | 0.3279 | | 0.0508 | 68.0 | 7752 | 1.1584 | 0.3702 | 0.6860 | 0.7052 | nan | 0.7202 | 0.6477 | 0.9888 | 0.3875 | 0.0 | 0.5956 | 0.6155 | 0.2902 | 0.3495 | | 0.048 | 69.0 | 7866 | 1.1363 | 0.3773 | 0.6922 | 0.7165 | nan | 0.7297 | 0.6628 | 0.9895 | 0.3865 | 0.0 | 0.6158 | 0.6289 | 0.2901 | 0.3518 | | 0.0483 | 70.0 | 7980 | 1.1489 | 0.3749 | 0.6916 | 0.7103 | nan | 0.7398 | 0.6367 | 0.9889 | 0.4011 | 0.0 | 0.6074 | 0.6080 | 0.2994 | 0.3598 | | 0.0495 | 71.0 | 8094 | 1.1470 | 0.3774 | 0.6943 | 0.7102 | nan | 0.7454 | 0.6295 | 0.9891 | 0.4131 | 0.0 | 0.6059 | 0.6032 | 0.3053 | 0.3724 | | 0.0472 | 72.0 | 8208 | 1.2749 | 0.3597 | 0.6782 | 0.6864 | nan | 0.7291 | 0.5930 | 0.9891 | 0.4017 | 0.0 | 0.5899 | 0.5704 | 0.2771 | 0.3612 | | 0.0486 | 73.0 | 8322 | 1.1217 | 0.3773 | 0.6946 | 0.7117 | nan | 0.7549 | 0.6224 | 0.9882 | 0.4128 | 0.0 | 0.6094 | 0.5946 | 0.3150 | 0.3678 | | 0.051 | 74.0 | 8436 | 1.1895 | 0.3724 | 0.6889 | 0.7069 | nan | 0.7432 | 0.6247 | 0.9888 | 0.3990 | 0.0 | 0.6052 | 0.5959 | 0.3021 | 0.3590 | | 0.0472 | 75.0 | 8550 | 1.2084 | 0.3677 | 0.6847 | 0.7009 | nan | 0.7179 | 0.6399 | 0.9905 | 0.3904 | 0.0 | 0.5979 | 0.6078 | 0.2808 | 0.3522 | | 0.0481 | 76.0 | 8664 | 1.1778 | 0.3688 | 0.6841 | 0.7049 | nan | 0.7395 | 0.6244 | 0.9899 | 0.3824 | 0.0 | 0.6024 | 0.5950 | 0.2996 | 0.3469 | | 0.0462 | 77.0 | 8778 | 1.2409 | 0.3693 | 0.6863 | 0.7015 | nan | 0.7278 | 0.6297 | 0.9900 | 0.3975 | 0.0 | 0.5964 | 0.5990 | 0.2918 | 0.3593 | | 0.0464 | 78.0 | 8892 | 1.2724 | 0.3606 | 0.6792 | 0.6877 | nan | 0.7119 | 0.6158 | 0.9905 | 0.3986 | 0.0 | 0.5825 | 0.5857 | 0.2770 | 0.3578 | | 0.0477 | 79.0 | 9006 | 1.2107 | 0.3629 | 0.6797 | 0.6936 | nan | 0.7322 | 0.6063 | 0.9898 | 0.3905 | 0.0 | 0.5928 | 0.5791 | 0.2889 | 0.3540 | | 0.0452 | 80.0 | 9120 | 1.1745 | 0.3721 | 0.6889 | 0.7059 | nan | 0.7548 | 0.6087 | 0.9899 | 0.4022 | 0.0 | 0.6080 | 0.5820 | 0.3085 | 0.3620 | | 0.0447 | 81.0 | 9234 | 1.2787 | 0.3599 | 0.6776 | 0.6876 | nan | 0.7199 | 0.6063 | 0.9902 | 0.3938 | 0.0 | 0.5857 | 0.5788 | 0.2786 | 0.3566 | | 0.0481 | 82.0 | 9348 | 1.2049 | 0.3658 | 0.6836 | 0.6947 | nan | 0.7515 | 0.5865 | 0.9887 | 0.4078 | 0.0 | 0.5956 | 0.5627 | 0.3044 | 0.3660 | | 0.0444 | 83.0 | 9462 | 1.1427 | 0.3746 | 0.6930 | 0.7051 | nan | 0.7520 | 0.6100 | 0.9883 | 0.4215 | 0.0 | 0.6042 | 0.5824 | 0.3100 | 0.3763 | | 0.0481 | 84.0 | 9576 | 1.1876 | 0.3669 | 0.6848 | 0.6968 | nan | 0.7358 | 0.6094 | 0.9895 | 0.4046 | 0.0 | 0.5944 | 0.5818 | 0.2947 | 0.3636 | | 0.046 | 85.0 | 9690 | 1.2264 | 0.3628 | 0.6799 | 0.6928 | nan | 0.7348 | 0.6015 | 0.9885 | 0.3948 | 0.0 | 0.5906 | 0.5746 | 0.2930 | 0.3560 | | 0.0472 | 86.0 | 9804 | 1.2377 | 0.3659 | 0.6828 | 0.6967 | nan | 0.7287 | 0.6176 | 0.9890 | 0.3959 | 0.0 | 0.5926 | 0.5876 | 0.2913 | 0.3577 | | 0.0465 | 87.0 | 9918 | 1.2037 | 0.3644 | 0.6841 | 0.6903 | nan | 0.7176 | 0.6150 | 0.9893 | 0.4146 | 0.0 | 0.5859 | 0.5856 | 0.2808 | 0.3697 | | 0.0475 | 87.72 | 10000 | 1.2098 | 0.3648 | 0.6821 | 0.6947 | nan | 0.7354 | 0.6052 | 0.9884 | 0.3995 | 0.0 | 0.5920 | 0.5774 | 0.2950 | 0.3598 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

r0in/dqn-SpaceInvadersNoFrameskip-v4

r0in

2024-01-30T02:48:12Z

0

stable-baselines3

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

reinforcement-learning

2024-01-30T02:47:40Z

--- 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: 502.50 +/- 144.90 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 r0in -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 r0in -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 r0in ``` ## 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'} ```

ogbrandt/mistral-7b-non-instruction-pjf-ft

ogbrandt

2024-01-30T02:44:37Z

12

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-22T12:34:10Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

LoneStriker/CodeLlama-70b-Python-hf-5.0bpw-h6-exl2

LoneStriker

2024-01-30T02:44:30Z

3

0

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "llama-2", "code", "arxiv:2308.12950", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T02:26:56Z

--- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | | 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) | ## Model Use To use this model, please make sure to install `transformers`. ```bash pip install transformers accelerate ``` Model capabilities: - [x] Code completion. - [ ] Infilling. - [ ] Instructions / chat. - [x] Python specialist. ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in four model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B, 34B, and 70B parameters. **This repository contains the Python version of the 70B parameters model.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant **does not** support long context of up to 100k tokens. **Model Dates** Code Llama and its variants have been trained between January 2023 and January 2024. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use **Intended Use Cases** Code Llama and its variants are 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. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## 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](https://ai.meta.com/llama/responsible-use-guide).

jingyeom/freeze_KoSoLAR-10.7B-v0.2_1.4_dedup

jingyeom

2024-01-30T02:44:19Z

2,286

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-29T13:12:09Z

--- license: apache-2.0 --- ## Model - base_model : yanolja/KoSOLAR-10.7B-v0.2 - training objective: freeze, instruction Tuning ## Dataset 공개 데이터 수집 - Deduplicating Training Data Makes Language Models Better 알고리즘 활용 - instruction version 1.4 ## Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "jjingyeom/freeze_KoSoLAR-10.7B-v0.2_1.4_dedup" model = AutoModelForCausalLM.from_pretrained( model_name, ) tokenizer = AutoTokenizer.from_pretrained(model_name) ```

Commandante/german-party-sentiment-bert-241

Commandante

2024-01-30T02:44:13Z

92

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:mdraw/german-news-sentiment-bert", "base_model:finetune:mdraw/german-news-sentiment-bert", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-01-30T01:56:24Z

--- base_model: mdraw/german-news-sentiment-bert tags: - generated_from_trainer model-index: - name: german-party-sentiment-bert-241 results: [] ---  # german-party-sentiment-bert-241 This model is a fine-tuned version of [mdraw/german-news-sentiment-bert](https://huggingface.co/mdraw/german-news-sentiment-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0323 ## 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: 8e-06 - train_batch_size: 20 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 120 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5246 | 1.0 | 28 | 1.1341 | | 1.047 | 2.0 | 56 | 1.0424 | | 1.047 | 3.0 | 84 | 1.0328 | | 0.9356 | 4.0 | 112 | 1.0323 | | 0.9356 | 5.0 | 140 | 1.0771 | | 0.8882 | 6.0 | 168 | 1.1110 | | 0.7714 | 7.0 | 196 | 1.0898 | ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2+cu118 - Tokenizers 0.15.1

ZiHDeng/peft-lora-starcoder1B-Instruction-ny8-MIX

ZiHDeng

2024-01-30T02:43:24Z

12

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:bigcode/starcoderbase-1b", "base_model:adapter:bigcode/starcoderbase-1b", "license:bigcode-openrail-m", "region:us" ]

null

2024-01-29T14:21:39Z

--- license: bigcode-openrail-m library_name: peft tags: - generated_from_trainer base_model: bigcode/starcoderbase-1b model-index: - name: peft-lora-starcoder1B-Instruction-ny8-MIX results: [] ---  # peft-lora-starcoder1B-Instruction-ny8-MIX This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1670 ## 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: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 150 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1662 | 0.67 | 100 | 0.1670 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

LoneStriker/CodeLlama-70b-Python-hf-4.65bpw-h6-exl2

LoneStriker

2024-01-30T02:26:54Z

7

1

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "llama-2", "code", "arxiv:2308.12950", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T02:10:35Z

--- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | | 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) | ## Model Use To use this model, please make sure to install `transformers`. ```bash pip install transformers accelerate ``` Model capabilities: - [x] Code completion. - [ ] Infilling. - [ ] Instructions / chat. - [x] Python specialist. ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in four model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B, 34B, and 70B parameters. **This repository contains the Python version of the 70B parameters model.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant **does not** support long context of up to 100k tokens. **Model Dates** Code Llama and its variants have been trained between January 2023 and January 2024. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use **Intended Use Cases** Code Llama and its variants are 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. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## 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](https://ai.meta.com/llama/responsible-use-guide).

PracticeLLM/KoSOLAR-Platypus-10.7B

PracticeLLM

2024-01-30T02:24:57Z

57

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "ko", "dataset:kyujinpy/KOR-OpenOrca-Platypus-v3", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-29T12:35:00Z

--- language: - ko library_name: transformers pipeline_tag: text-generation license: cc-by-nc-sa-4.0 datasets: - kyujinpy/KOR-OpenOrca-Platypus-v3 --- # **PracticeLLM/KoSOLAR-Platypus-10.7B** ## Model Details **Model Developers** Kyujin Han (kyujinpy) **Method** LoRA with quantization. **Base Model** [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) **Dataset** [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3). **Hyperparameters** ``` python finetune.py \ --base_model yanolja/KoSOLAR-10.7B-v0.2 \ --data-path kyujinpy/KOR-OpenOrca-Platypus-v3 \ --output_dir ./Ko-PlatypusSOLAR-10.7B \ --batch_size 64 \ --micro_batch_size 1 \ --num_epochs 5 \ --learning_rate 2e-5 \ --cutoff_len 2048 \ --val_set_size 0 \ --lora_r 64 \ --lora_alpha 64 \ --lora_dropout 0.05 \ --lora_target_modules '[embed_tokens, q_proj, k_proj, v_proj, o_proj, gate_proj, down_proj, up_proj, lm_head]' \ --train_on_inputs False \ --add_eos_token False \ --group_by_length False \ --prompt_template_name en_simple \ --lr_scheduler 'cosine' \ ``` > Share all of things. It is my belief. # **Model Benchmark** ## Open Ko-LLM leaderboard & lm-evaluation-harness(zero-shot) - Follow up as [Ko-link](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard). | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Ko-CommonGenV2 | | --- | --- | --- | --- | --- | --- | --- | | PracticeLLM/KoSOLAR-Platypus-10.7B | --- | --- | --- | --- | --- | --- | | [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B) | 59.34 | 55.38 | 65.56 | 53.38 | 64.39 | 57.97 | | [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) | 55.62 | 50.51 | 62.29 | 53.76 | 47.31 | 64.23 | | [megastudyedu/M-SOLAR-10.7B-v1.3](https://huggingface.co/megastudyedu/M-SOLAR-10.7B-v1.3) | 56.64 | 51.37 | 60.93 | 54.91 | 48.45 | 67.53 | # Implementation Code ```python ### KO-Platypus from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "PracticeLLM/KoSOLAR-Platypus-10.7B" OpenOrca = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo) ```

martomor/distilbert-base-uncased-finetuned-clinc

martomor

2024-01-30T02:09:12Z

92

0

transformers

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

text-classification

2024-01-30T01:59:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9180645161290323 ---  # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7721 - Accuracy: 0.9181 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2895 | 1.0 | 318 | 3.2884 | 0.7419 | | 2.6277 | 2.0 | 636 | 1.8751 | 0.8368 | | 1.5479 | 3.0 | 954 | 1.1569 | 0.8961 | | 1.0148 | 4.0 | 1272 | 0.8573 | 0.9132 | | 0.7952 | 5.0 | 1590 | 0.7721 | 0.9181 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.1.0+cu121 - Datasets 1.16.1 - Tokenizers 0.15.1

jncraton/openchat-3.5-0106-ct2-int8

jncraton

2024-01-30T02:06:12Z

24

0

transformers

[ "transformers", "openchat", "mistral", "C-RLFT", "text-generation", "conversational", "arxiv:2309.11235", "arxiv:2303.08774", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T01:56:43Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - openchat - mistral - C-RLFT library_name: transformers pipeline_tag: text-generation --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> | <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> | <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> | <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center; ont-size: 0.5em; border: 0.8em solid #864AF9;"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.5</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #864AF9; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">0106</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open Source 7B Model 🏆 <br> 🤖 Outperforms <span style="font-weight: bold;">ChatGPT</span> (March) and <span style="font-weight: bold;">Grok-1</span> 🤖 <br> 🚀<span style="font-size: 1em; font-family: 'Helvetica'; color: black; font-weight: bold;">15</span>-point improvement in Coding over <span style="font-size: 0.9em; font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀</span> <br><br><span style="font-size: 1em; font-family: 'Helvetica'; color: #3c72db; font-weight: bold;">New Features</span> <br> 💡 2 Modes: Coding + Generalist, Mathematical Reasoning 💡 <br> 🧑‍⚖️ Experimental support for Evaluator and Feedback capabilities 🧑‍⚖️ </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat-bench-0106.png" style="width: 100%; border-radius: 1em"> </div> <div> <h3> Table of Contents</h3> </div> 1. [Usage](#usage) 2. [Benchmarks](#benchmarks) 3. [Limitations](#limitations) 4. [License](#license) 6. [Citation](#citation) 7. [Acknowledgements](#acknowledgements) <div align="center"> <h2> Usage </h2> </div> To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. | Model | Size | Context | Weights | Serving | |-------------------|------|---------|------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------| | OpenChat-3.5-0106 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106) | `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray` | <details> <summary>Example request (click to expand)</summary> 💡 **Default Mode (GPT4 Correct)**: Best for coding, chat and general tasks ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` 🧮 **Mathematical Reasoning Mode**: Tailored for solving math problems ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Math Correct", "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}] }' ``` </details> ### Conversation templates 💡 **Default Mode (GPT4 Correct)**: Best for coding, chat and general tasks ``` GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant: ``` 🧮 **Mathematical Reasoning Mode**: Tailored for solving math problems ``` Math Correct User: 10.3 − 7988.8133=<|end_of_turn|>Math Correct Assistant: ``` ⚠️ **Notice:** Remember to set `<|end_of_turn|>` as end of generation token. The default (GPT4 Correct) template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] ``` <div align="center"> <h2> (Experimental) Evaluator / Feedback Capabilities </h2> </div> We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {orig_instruction} ###Response to evaluate: {orig_response} ###Reference Answer (Score 5): {orig_reference_answer} ###Score Rubrics: [{orig_criteria}] Score 1: {orig_score1_description} Score 2: {orig_score2_description} Score 3: {orig_score3_description} Score 4: {orig_score4_description} Score 5: {orig_score5_description} ###Feedback: ``` <div align="center"> <h2> Benchmarks </h2> </div> | Model | # Params | Average | MT-Bench | HumanEval | BBH MC | AGIEval | TruthfulQA | MMLU | GSM8K | BBH CoT | |-----------------------|----------|----------|----------|-----------|----------|----------|------------|----------|----------|----------| | **OpenChat-3.5-0106** | **7B** | **64.5** | 7.8 | **71.3** | **51.5** | **49.1** | 61.0 | 65.8 | **77.4** | 62.2 | | OpenChat-3.5-1210 | **7B** | 63.8 | 7.76 | 68.9 | 49.5 | 48.0 | **61.8** | 65.3 | 77.3 | 61.8 | | OpenChat-3.5 | **7B** | 61.6 | 7.81 | 55.5 | 47.6 | 47.4 | 59.1 | 64.3 | 77.3 | 63.5 | | ChatGPT (March)* | ???B | 61.5 | **7.94** | 48.1 | 47.6 | 47.1 | 57.7 | **67.3** | 74.9 | **70.1** | | | | | | | | | | | | | | OpenHermes 2.5 | 7B | 59.3 | 7.54 | 48.2 | 49.4 | 46.5 | 57.5 | 63.8 | 73.5 | 59.9 | | OpenOrca Mistral | 7B | 52.7 | 6.86 | 38.4 | 49.4 | 42.9 | 45.9 | 59.3 | 59.1 | 58.1 | | Zephyr-β^ | 7B | 34.6 | 7.34 | 22.0 | 40.6 | 39.0 | 40.8 | 39.8 | 5.1 | 16.0 | | Mistral | 7B | - | 6.84 | 30.5 | 39.0 | 38.0 | - | 60.1 | 52.2 | - | <details> <summary>Evaluation Details(click to expand)</summary> *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. **: Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). </details> <div> <h3>HumanEval+</h3> </div> | Model | Size | HumanEval+ pass@1 | |-----------------------------|--------|-------------------| | **OpenChat-3.5-0106** | **7B** | **65.9** | | ChatGPT (December 12, 2023) | ???B | 64.6 | | WizardCoder-Python-34B-V1.0 | 34B | 64.6 | | OpenChat 3.5 1210 | 7B | 63.4 | | OpenHermes 2.5 | 7B | 41.5 | <div> <h3>OpenChat-3.5 vs. Grok</h3> </div> 🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on **all 4 benchmarks** and Grok-1 (???B) on average and **3/4 benchmarks**. | | License | # Param | Average | MMLU | HumanEval | MATH | GSM8k | |-----------------------|-------------|---------|----------|--------|-----------|----------|----------| | **OpenChat-3.5-0106** | Apache-2.0 | **7B** | **61.0** | 65.8 | **71.3** | **29.3** | **77.4** | | OpenChat-3.5-1210 | Apache-2.0 | **7B** | 60.1 | 65.3 | 68.9 | 28.9 | 77.3 | | OpenChat-3.5 | Apache-2.0 | **7B** | 56.4 | 64.3 | 55.5 | 28.6 | 77.3 | | Grok-0 | Proprietary | 33B | 44.5 | 65.7 | 39.7 | 15.7 | 56.8 | | Grok-1 | Proprietary | ???B | 55.8 | **73** | 63.2 | 23.9 | 62.9 | *: Grok results are reported by [X.AI](https://x.ai/). <div align="center"> <h2> Limitations </h2> </div> **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. <div align="center"> <h2> License </h2> </div> Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. <div align="center"> <h2> Citation </h2> </div> ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` <div align="center"> <h2> 💌 Main Contributor </h2> </div> * Wang Guan [imonenext@gmail.com], Cheng Sijie [csj23@mails.tsinghua.edu.cn], Alpay Ariyak [aariyak@wpi.edu] * We look forward to hearing you and collaborating on this exciting project!

Loess/gpt2-wikitext2

Loess

2024-01-30T02:05:59Z

8

0

transformers

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-22T19:17:59Z

--- tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 results: [] ---  # gpt2-wikitext2 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 5.4415 - eval_runtime: 21.4584 - eval_samples_per_second: 90.128 - eval_steps_per_second: 11.278 - epoch: 8.23 - step: 18500 ## 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: 10 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

bsgreenb/my_awesome_model

bsgreenb

2024-01-30T02:02:10Z

93

0

transformers

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

text-classification

2024-01-29T20:24:13Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: my_awesome_model results: [] ---  # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3960 - Accuracy: 0.8465 - F1: 0.8219 - Precision: 0.8257 - Recall: 0.8182 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.4226 | 1.17 | 500 | 0.3960 | 0.8465 | 0.8219 | 0.8257 | 0.8182 | | 0.309 | 2.33 | 1000 | 0.4264 | 0.8478 | 0.8226 | 0.8302 | 0.8152 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

kanishka/smolm-autoreg-bpe-counterfactual-babylm-pipps_10k-1e-3

kanishka

2024-01-30T01:48:00Z

23

0

transformers

[ "transformers", "tensorboard", "safetensors", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-29T03:06:27Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-counterfactual-babylm-pipps_10k-1e-3 results: [] ---  # smolm-autoreg-bpe-counterfactual-babylm-pipps_10k-1e-3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3378 - Accuracy: 0.4121 ## 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: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.6013 | 1.0 | 18720 | 3.7465 | 0.3588 | | 3.3867 | 2.0 | 37440 | 3.4883 | 0.3819 | | 3.2589 | 3.0 | 56160 | 3.4083 | 0.3943 | | 3.1811 | 4.0 | 74880 | 3.3900 | 0.3986 | | 3.1273 | 5.0 | 93600 | 3.3351 | 0.4020 | | 3.0824 | 6.0 | 112320 | 3.3293 | 0.4028 | | 3.0466 | 7.0 | 131040 | 3.3131 | 0.4065 | | 3.0117 | 8.0 | 149760 | 3.2990 | 0.4092 | | 2.9899 | 9.0 | 168480 | 3.2979 | 0.4097 | | 2.9556 | 10.0 | 187200 | 3.2939 | 0.4105 | | 2.9395 | 11.0 | 205920 | 3.2948 | 0.4120 | | 2.9155 | 12.0 | 224640 | 3.3005 | 0.4110 | | 2.8971 | 13.0 | 243360 | 3.2907 | 0.4126 | | 2.8777 | 14.0 | 262080 | 3.2993 | 0.4121 | | 2.8546 | 15.0 | 280800 | 3.2985 | 0.4128 | | 2.8337 | 16.0 | 299520 | 3.2980 | 0.4130 | | 2.8153 | 17.0 | 318240 | 3.3254 | 0.4116 | | 2.799 | 18.0 | 336960 | 3.3345 | 0.4114 | | 2.7824 | 19.0 | 355680 | 3.3410 | 0.4117 | | 2.7614 | 20.0 | 374400 | 3.3378 | 0.4121 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.14.1

tavalenzuelag/mistral-7b-do-e2e-mod

tavalenzuelag

2024-01-30T01:44:30Z

3

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]

null

2024-01-29T19:10:27Z

--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

LoneStriker/CodeLlama-70b-Python-hf-2.65bpw-h6-exl2

LoneStriker

2024-01-30T01:43:54Z

6

3

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "llama-2", "code", "arxiv:2308.12950", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T01:31:35Z

--- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | | 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) | ## Model Use To use this model, please make sure to install `transformers`. ```bash pip install transformers accelerate ``` Model capabilities: - [x] Code completion. - [ ] Infilling. - [ ] Instructions / chat. - [x] Python specialist. ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in four model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B, 34B, and 70B parameters. **This repository contains the Python version of the 70B parameters model.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant **does not** support long context of up to 100k tokens. **Model Dates** Code Llama and its variants have been trained between January 2023 and January 2024. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use **Intended Use Cases** Code Llama and its variants are 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. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## 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](https://ai.meta.com/llama/responsible-use-guide).

schleir/ppo-LunarLander-v2

schleir

2024-01-30T01:38:05Z

1

0

stable-baselines3

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

reinforcement-learning

2024-01-30T01:37:43Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.10 +/- 21.36 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

jojubo/dqn-LunarLander-v1

jojubo

2024-01-30T01:11:23Z

0

stable-baselines3

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

reinforcement-learning

2024-01-30T01:10:54Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: dqn results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -116.97 +/- 89.45 name: mean_reward verified: false --- # **dqn** Agent playing **LunarLander-v2** This is a trained model of a **dqn** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

paisanx/Reinforce-Pixelcopter-PLE-v5

paisanx

2024-01-30T00:57:55Z

0

null

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

reinforcement-learning

2024-01-30T00:57:44Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v5 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 137.50 +/- 40.46 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

agustoslu/alp_mistral7b_baseline

agustoslu

2024-01-30T00:56:42Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ybelkada/mistral-7b-instruct-v0.1-sharded", "base_model:adapter:ybelkada/mistral-7b-instruct-v0.1-sharded", "region:us" ]

null

2024-01-30T00:55:42Z

--- library_name: peft base_model: ybelkada/mistral-7b-instruct-v0.1-sharded --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

jeiku/Short_Luna_3B_GGUF

jeiku

2024-01-30T00:50:29Z

3

0

null

[ "gguf", "mergekit", "merge", "arxiv:2203.05482", "endpoints_compatible", "region:us", "conversational" ]

null

2024-01-30T00:33:52Z

--- base_model: [] tags: - mergekit - merge --- # final This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * betterboi * bestboi * goodboi ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: linear models: - model: goodboi parameters: weight: 1 - model: betterboi parameters: weight: 1 - model: bestboi parameters: weight: 1 dtype: float16 ```

mlx-community/CodeLlama-70b-Instruct-hf-4bit-MLX

mlx-community

2024-01-30T00:47:05Z

15

25

mlx

[ "mlx", "llama", "llama-2", "text-generation", "conversational", "code", "license:llama2", "region:us" ]

text-generation

2024-01-29T23:38:21Z

--- language: - code license: llama2 tags: - llama-2 - mlx pipeline_tag: text-generation --- ![Alt text](https://cdn.discordapp.com/attachments/1064373193982361601/1201677160008384594/DALLE_2024-01-30_00.53.15_-_Imagine_a_whimsical_hyper-detailed_illustration_suitable_for_a_childrens_book_featuring_a_cartoon_alpaca_sitting_comfortably_and_using_an_Apple_lap.png?ex=65cab011&is=65b83b11&hm=373057e35079d276954594d43ea8e9e8223bd4956707a96c130a62850c8570b1&) # mlx-community/CodeLlama-70b-Instruct-hf-4bit-MLX This model was converted to MLX format from [`codellama/CodeLlama-70b-Instruct-hf`](). Refer to the [original model card](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/CodeLlama-70b-Instruct-hf-4bit-MLX") response = generate(model, tokenizer, prompt="<step>Source: user Fibonacci series in Python<step> Source: assistant Destination: user", verbose=True) ```

jtatman/TinyMistral-248m-v2.5-4x-Moe

jtatman

2024-01-30T00:46:52Z

76

0

transformers

[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "Locutusque/TinyMistral-248M-v2.5-Instruct", "jtatman/tinymistral-samantha-chatml-lora-v2", "base_model:Locutusque/TinyMistral-248M-v2.5-Instruct", "base_model:merge:Locutusque/TinyMistral-248M-v2.5-Instruct", "base_model:jtatman/tinymistral-samantha-chatml-lora-v2", "base_model:merge:jtatman/tinymistral-samantha-chatml-lora-v2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-29T18:27:21Z

--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - Locutusque/TinyMistral-248M-v2.5-Instruct - Locutusque/TinyMistral-248M-v2.5-Instruct - Locutusque/TinyMistral-248M-v2.5-Instruct - jtatman/tinymistral-samantha-chatml-lora-v2 base_model: - Locutusque/TinyMistral-248M-v2.5-Instruct - Locutusque/TinyMistral-248M-v2.5-Instruct - Locutusque/TinyMistral-248M-v2.5-Instruct - jtatman/tinymistral-samantha-chatml-lora-v2 --- # TinyMistral-248m-v2.5-4x-Moe TinyMistral-248m-v2.5-4x-Moe is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Locutusque/TinyMistral-248M-v2.5-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5-Instruct) * [Locutusque/TinyMistral-248M-v2.5-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5-Instruct) * [Locutusque/TinyMistral-248M-v2.5-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5-Instruct) * [jtatman/tinymistral-samantha-chatml-lora-v2](https://huggingface.co/jtatman/tinymistral-samantha-chatml-lora-v2) ## 🧩 Configuration ```yaml base_model: Locutusque/TinyMistral-248M-v2.5-Instruct experts: - source_model: Locutusque/TinyMistral-248M-v2.5-Instruct positive_prompts: - "Write me a Python program that calculates the factorial of n." - "Help me debug this code." - "Optimize this C++ program." negative_prompts: - "How do you" - "Explain the concept of" - "Give an overview of" - "Compare and contrast between" - "Provide information about" - "Help me understand" - "Summarize" - "Make a recommendation on" - "Answer this question" - "Craft me a list of some nice places to visit around the world." - "Write me a story" - "Write me an essay" - "How do I incorporate visual elements into my writing?" - source_model: Locutusque/TinyMistral-248M-v2.5-Instruct positive_prompts: - "What is the product of 2 x 5 x 18?" - "How do I guess the value of x for the function f(x) = x^4 - 2x^2 - 1?" negative_prompts: - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Assist me with writing a program that" - "Craft me a list of some nice places to visit around the world. " - "Write me a story" - "Write me an essay" - "How do I incorporate visual elements into my writing?" - source_model: Locutusque/TinyMistral-248M-v2.5-Instruct positive_prompts: - "How do I incorporate fewer visual elements into my art but retain impact?" negative_prompts: - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Compare and contrast between" - "Provide information about" - "Help me understand" - "Summarize" - "Make a recommendation on" - "Answer this question" - "Craft me a list of some nice places to visit around the world. " - "Write me a story" - "Write me an essay" - source_model: jtatman/tinymistral-samantha-chatml-lora-v2 positive_prompts: - "Craft me a list of some nice places to visit around the world. " - "Write me a story" - "Write me an essay" - "Create a fantasy story about" - "Tell me about the wild fjords." negative_prompts: - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Help me debug this code." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Compare and contrast between" - "Provide information about" - "Help me understand" - "Summarize" - "Make a recommendation on" - "Answer this question" - "How do I incorporate visual elements into my writing?" gate_mode: hidden ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "jtatman/TinyMistral-248m-v2.5-4x-Moe" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

shangrilar/yi-ko-6b-text2sql

shangrilar

2024-01-30T00:37:04Z

272

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T00:31:46Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

jojubo/ppo-LunarLander-v2

jojubo

2024-01-30T00:34:52Z

0

stable-baselines3

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

reinforcement-learning

2024-01-30T00:33:49Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 271.02 +/- 15.91 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

katxtong/roberta-base-squad2-finetuned-squad

katxtong

2024-01-30T00:28:26Z

15

0

transformers

[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

question-answering

2024-01-29T01:47:17Z

--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: roberta-base-squad2-finetuned-squad results: [] ---  # roberta-base-squad2-finetuned-squad This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0305 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.7052 | 1.0 | 5536 | 0.8646 | | 0.5372 | 2.0 | 11072 | 0.9134 | | 0.4104 | 3.0 | 16608 | 1.0305 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

jsfs11/Westlakev2-7B-DPO

jsfs11

2024-01-30T00:27:22Z

8

1

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "dataset:jondurbin/truthy-dpo-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-30T00:20:49Z

--- license: apache-2.0 datasets: - jondurbin/truthy-dpo-v0.1 ---

clovett/ppo-LunarLander-v2

clovett

2024-01-30T00:25:56Z

1

0

stable-baselines3

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

reinforcement-learning

2024-01-29T23:12:33Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 276.82 +/- 15.30 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

jeiku/Long_Luna_3.43B_GGUF

jeiku

2024-01-30T00:22:16Z

7

0

null

[ "gguf", "mergekit", "merge", "arxiv:2203.05482", "endpoints_compatible", "region:us", "conversational" ]

null

2024-01-30T00:00:43Z

--- base_model: [] tags: - mergekit - merge --- # biggle This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * biggest * bigger * big ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: linear models: - model: big parameters: weight: 1 - model: bigger parameters: weight: 1 - model: biggest parameters: weight: 1 dtype: float16 ```

Nexesenex/Codellama-2-7b-Miniguanaco-Mistral-GGUF

Nexesenex

2024-01-30T00:17:32Z

74

3

null

[ "gguf", "license:llama2", "endpoints_compatible", "region:us" ]

null

2023-10-02T01:55:18Z

--- license: llama2 --- CodeLlama 2 7b With Guanaco Lora (Tim Dettmers), merged by Varunk29. Then With Mistral AI 7b 0.1 delta bits compared to Llama2 (extracted by Undi95), merged by me. --- Base model (CodeLlama) training context : 16k (max context up to 96k with the base ROPE) Mistral injection training context : 8k (Sliding Windows Attention is likely inoperant on such a merge/injection) --- For test and amusement only. Prompt : Alpaca works.

AIFT/AIFT-instruct-42dot_LLM-SFT-1.3B

AIFT

2024-01-30T00:15:04Z

146

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-29T07:56:32Z

--- license: cc-by-sa-4.0 --- <h1>AIFT-instruct-42dot_LLM-SFT-1.3B</h1> <b><학습 데이터 구축></b> <br> kyujinpy 님이 공개하신 KOR-OpenOrca-Platypus 데이터를 일부 삭제(샘플링) 및 정제 작업 진행하여 활용. 그 이후 해당 데이터들을 보며 관련 태스크를 추출하였고 이를 기반으로 해당 태스크에 맞춰서 NLP 관련 오픈소스 데이터를 활용하여 학습데이터를 자체적으로 역사, 과학, 수학, 기계독해, 리뷰 분석 문제를 gpt를 통해서 구축하였고, aihub 일반상식 및 기계독해 데이터를 활용하여 추가로 학습 데이터를 구축(형태소 관련, 기계독해 관련 및 요약) 각종 블로그에서 역사 및 상식 퀴즈를 사람이 직접 학습데이터 형태로 변경 AI2AI Challenge 데이터 형태를 보고 gpt를 통해 초등 수준의 과학 수학 문제 유형을 제작 500문제 영어 번역 데이터 영한/한영 데이터 학습 데이터로 활용 진행 총 데이터 4만개 정도 사용하였습니다. <br> <br> + TruthfulQA 관련 문제 추가를 진행하였습니다.(속설 관련 참거짓 문제) + 기계독해 관련 학습 데이터를 ChatGPT를 통해서 답변을 얻어 학습 + 문법관련 학습 데이터 <br> ###학습 데이터 파일은 비공개입니다. <br> <모델> <br> 42dot에서 공개한 42dot_LLM-SFT-1.3B을 베이스 모델로 하여 학습 진행하였습니다. <br> <br> <br> <b><학습></b> <br> 학습은 LoRA를 사용하여 A100 40G *2에서 학습을 진행하였습니다.

asun17904/anliR2-bert-base-uncased-alum

asun17904

2024-01-30T00:07:30Z

0

pytorch

[ "pytorch", "en", "license:mit", "region:us" ]

null

2024-01-29T14:08:12Z

--- language: en license: mit library_name: pytorch --- # Knowledge Continuity Regularized Network Dataset: ANLI Round: None Trainer Hyperparameters: - `lr` = 5e-05 - `per_device_batch_size` = 16 - `gradient_accumulation_steps` = 1 - `weight_decay` = 1e-09 - `seed` = 42 Regularization Hyperparameters - `numerical stability denominator constant` = 1.0 - `lambda` = 1.0 - `alpha` = 1.0 - `beta` = 1.0 Extended Logs: |eval_loss|eval_accuracy|epoch| |--|--|--| |1.117|0.391|1.0| |1.106|0.418|2.0| |1.093|0.434|3.0| |1.072|0.464|4.0| |1.098|0.434|5.0| |1.084|0.453|6.0| |1.088|0.447|7.0| |1.080|0.459|8.0| |1.099|0.440|9.0| |1.075|0.468|10.0| |1.078|0.460|11.0| |1.079|0.465|12.0| |1.082|0.463|13.0| |1.091|0.449|14.0| |1.082|0.458|15.0| |1.086|0.456|16.0| |1.089|0.450|17.0| |1.082|0.467|18.0|

vasugupta0607/llama2_instruct_generation

vasugupta0607

2024-01-30T00:06:10Z

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:NousResearch/Llama-2-7b-hf", "base_model:adapter:NousResearch/Llama-2-7b-hf", "region:us" ]

null

2024-01-30T00:05:51Z

--- library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: NousResearch/Llama-2-7b-hf model-index: - name: llama2_instruct_generation results: [] ---  # llama2_instruct_generation This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.6734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9364 | 0.0 | 20 | 1.8092 | | 1.9198 | 0.01 | 40 | 1.7826 | | 1.8451 | 0.01 | 60 | 1.7675 | | 1.8487 | 0.01 | 80 | 1.7573 | | 1.8667 | 0.01 | 100 | 1.7435 | | 1.7463 | 0.02 | 120 | 1.7132 | | 1.7789 | 0.02 | 140 | 1.7038 | | 1.8167 | 0.02 | 160 | 1.7008 | | 1.8654 | 0.02 | 180 | 1.6944 | | 1.9158 | 0.03 | 200 | 1.6939 | | 1.6581 | 0.03 | 220 | 1.6909 | | 1.793 | 0.03 | 240 | 1.6896 | | 1.7878 | 0.04 | 260 | 1.6872 | | 1.7542 | 0.04 | 280 | 1.6862 | | 1.7723 | 0.04 | 300 | 1.6863 | | 1.7606 | 0.04 | 320 | 1.6832 | | 1.8054 | 0.05 | 340 | 1.6802 | | 1.7307 | 0.05 | 360 | 1.6803 | | 1.8278 | 0.05 | 380 | 1.6790 | | 1.7912 | 0.05 | 400 | 1.6768 | | 1.7826 | 0.06 | 420 | 1.6749 | | 1.8975 | 0.06 | 440 | 1.6756 | | 1.8395 | 0.06 | 460 | 1.6763 | | 1.8319 | 0.07 | 480 | 1.6749 | | 1.7879 | 0.07 | 500 | 1.6734 | ### Framework versions - PEFT 0.7.1 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

silk-road/Haruhi-Zero-6B-0.1

silk-road

2024-01-30T00:04:46Z

5

3

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "zh", "en", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-28T07:37:43Z

--- license: cc-by-sa-4.0 language: - zh - en --- # Zero凉宫春日 # Haruhi-Zero: Zero-Shot Role-Playing Model tuned on Yi-6B 主项目链接 https://github.com/LC1332/Chat-Haruhi-Suzumiya 过往的ChatHaruhi模型需要角色库来完成角色的构建，而Pygmalion，CharacterGLM，CharacterBaichuan等开源/闭源模型都开始支持zero-shot的角色卡片创建我们构造以及收集了105k个中英文的conversation，以2500的token长度重新切到了120k左右个conversation，再结合小说数据进行了训练 - [李鲁鲁](https://github.com/LC1332)完成了数据的收集，搭建了gradio雏形 - [刘崇寒](https://github.com/khazic)完成了Yi-6B模型的sft训练并且上传 - [豆角](https://github.com/goodnessSZW)完成了qwen-1.8B Lora和Yi-6B Lora训练，我们会在之后上传 - [米唯实](https://github.com/hhhwmws0117)测试并完成了demo中的模型inference代码 # Haruhi-Zero: Zero-Shot Role-Playing Model Tuned on Yi-6B Main project link: https://github.com/LC1332/Chat-Haruhi-Suzumiya Previous ChatHaruhi models required a character RAG database to complete character creation. However, open-source/closed-source models like Pygmalion, CharacterGLM, CharacterBaichuan have started to support zero-shot role card creation. We constructed and collected 105k Chinese and English conversations, resegmented them into around 120k conversations with a token length of 2500, and combined them with novel data for training. ## inference code （搭建中） https://github.com/LC1332/Zero-Haruhi/blob/main/notebook/HaruhiZeroGradio.ipynb ## Official Prompt system prompt: ``` You are now in roleplay conversation mode. Pretend to be {bot_name} whose persona follows: {persona} You will stay in-character whenever possible, and generate responses as if you were {bot_name} ``` persona a.k.a. bot definition ## TODO 数据加强 - Haruhi Like的小说数据(0.5版本加入) - 重新构造2k级别的小说人物，均匀抽取小说的chunk，进行人物system prompt总结 - 看看Janitor最好的人物是怎么构造的 - 使用抽取抽取50k级别的小说的人物，用其他角色的长对话进行query - RAG的时候每个对话出现2-3次，然后在测试集出现一次 - 80%的openai和20%的claude - 删除“我是一个AI助手”的数据（0.2版本加入） - 身份认知数据加强（0.3版本加入） - 加强我是谁和你是谁的数据 - Stylish翻译数据 - 如果验证这个数据有用，就把中文小说批量翻译成英文和日文用一下 ## 鸣谢樟树的ClaudeAPI

jeiku/Long_Luna_3.43B

jeiku

2024-01-29T23:59:46Z

89

0

transformers

[ "transformers", "safetensors", "stablelm_epoch", "text-generation", "mergekit", "merge", "conversational", "custom_code", "arxiv:2203.05482", "autotrain_compatible", "region:us" ]

text-generation

2024-01-29T23:45:49Z

--- base_model: [] tags: - mergekit - merge --- # biggle This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * biggest * bigger * big ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: linear models: - model: big parameters: weight: 1 - model: bigger parameters: weight: 1 - model: biggest parameters: weight: 1 dtype: float16 ```