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train · 842k rows

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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t18_e5_member_shadow7	FounderOfHuggingface	2024-01-10T06:59:54Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:59:52Z	--- library_name: peft base_model: gpt2 --- # 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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t18_e5_member_shadow6	FounderOfHuggingface	2024-01-10T06:59:48Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:59:46Z	--- library_name: peft base_model: gpt2 --- # 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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t18_e5_member_shadow5	FounderOfHuggingface	2024-01-10T06:59:41Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:59:38Z	--- library_name: peft base_model: gpt2 --- # 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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t18_e5_member_shadow1	FounderOfHuggingface	2024-01-10T06:59:12Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:59:09Z	--- library_name: peft base_model: gpt2 --- # 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
megastudyedu/M-SOLAR-10.7B-v1.3	megastudyedu	2024-01-10T06:59:05Z	2,231	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ko", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-04T08:25:50Z	--- license: cc-by-nc-nd-4.0 language: - ko --- # Model Card for M-SOLAR-10.7B-v1.3 ## Developed by : 메가스터디교육, 프리딕션, 마이스 ## Base Model : [megastudy/M-SOLAR-10.7B-v1.1-beta](https://huggingface.co/megastudy/M-SOLAR-10.7B-v1.1-beta) ## 사용 데이터셋 - [megastudy/M-SOLAR-10.7B-v1.1-beta](https://huggingface.co/megastudy/M-SOLAR-10.7B-v1.1-beta) 데이터에 가공된 In-House 데이터셋 추가 - Random Shuffle Generation 데이터셋량 증가
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t18_e5_member_shadow0	FounderOfHuggingface	2024-01-10T06:59:05Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:59:02Z	--- library_name: peft base_model: gpt2 --- # 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
adityaeucloid/YOLOv	adityaeucloid	2024-01-10T06:54:47Z	2	0	ultralytics	[ "ultralytics", "tensorboard", "v8", "ultralyticsplus", "yolov8", "yolo", "vision", "object-detection", "pytorch", "model-index", "region:us" ]	object-detection	2024-01-10T06:54:34Z	--- tags: - ultralyticsplus - yolov8 - ultralytics - yolo - vision - object-detection - pytorch library_name: ultralytics library_version: 8.0.238 inference: false model-index: - name: adityaeucloid/YOLOv results: - task: type: object-detection metrics: - type: precision # since [email protected] is not available on hf.co/metrics value: 0.03509 # min: 0.0 - max: 1.0 name: [email protected](box) --- <div align="center"> <img width="640" alt="adityaeucloid/YOLOv" src="https://huggingface.co/adityaeucloid/YOLOv/resolve/main/thumbnail.jpg"> </div> ### Supported Labels ``` ['customer_address', 'customer_gst', 'customer_name', 'customer_pan', 'doc_type', 'invoice_date', 'invoice_number', 'invoice_table', 'net_amount', 'supplier_address', 'supplier_gst', 'supplier_name', 'supplier_pan', 'tax_amount', 'total_amount'] ``` ### How to use - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus): ```bash pip install ultralyticsplus==0.0.29 ultralytics==8.0.238 ``` - Load model and perform prediction: ```python from ultralyticsplus import YOLO, render_result # load model model = YOLO('adityaeucloid/YOLOv') # set model parameters model.overrides['conf'] = 0.25 # NMS confidence threshold model.overrides['iou'] = 0.45 # NMS IoU threshold model.overrides['agnostic_nms'] = False # NMS class-agnostic model.overrides['max_det'] = 1000 # maximum number of detections per image # set image image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' # perform inference results = model.predict(image) # observe results print(results[0].boxes) render = render_result(model=model, image=image, result=results[0]) render.show() ```
kazcfz/Llama-2-7b-chat-MedFAQ2	kazcfz	2024-01-10T06:45:39Z	1	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-03T10:20:41Z	# Llama-2-7b-chat-MedFAQ2 [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) fine-tuned on [kazcfz/MedFAQ-llama2-2k](https://huggingface.co/datasets/kazcfz/MedFAQ-llama2-2k) <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> QLoRA parameters Parameter \| Value --- \| --- lora_r \| 64 lora_alpha \| 16 lora_dropout \| 0.1 bitsandbytes parameters Parameter \| Value --- \| --- use_4bit \| True bnb_4bit_compute_dtype \| "float16" bnb_4bit_quant_type \| "nf4" use_nested_quant \| False TrainingArguments parameters Parameter \| Value --- \| --- num_train_epochs \| 5 fp16 \| False bf16 \| False per_device_train_batch_size \| 4 gradient_accumulation_steps \| 1 gradient_checkpointing \| True max_grad_norm \| 0.3 learning_rate \| 2e-4 weight_decay \| 0.001 optim \| "paged_adamw_32bit" lr_scheduler_type \| "cosine" max_steps \| -1 warmup_ratio \| 0.03 group_by_length \| True save_steps \| 0 logging_steps \| 25 SFT parameters Parameter \| Value --- \| --- max_seq_length \| None packing \| False <br> ## Prompt template: Llama-2-Chat ``` <s>[INST] {prompt} [/INST] ``` <br> ## Sample (actual) output (Max length: 200) Base Model ``` Query: <s>[INST] Am I at risk for kidney disease? [/INST] Response: I cannot provide medical advice or diagnose any health conditions, including kidney disease. everybody's health is unique, and there are many factors that can increase or decrease the risk of developing kidney disease. However, I can provide some general information on the risk factors for kidney disease and suggest ways to reduce your risk. Risk factors for kidney disease include: 1. Family history: If you have a family history of kidney disease, you are more likely to develop the condition. 2. Diabetes: Diabetes is a common cause of kidney disease. If you have diabetes, you are at a higher risk of developing kidney disease. 3. High blood pressure: High blood pressure can damage the kidneys and increase the risk of kidney disease. 4. Obesity: Being overweight or obese can increase ``` Fine-tuned ``` Query: <s>[INST] Am I at risk for kidney disease? [/INST] Response: Your risk for kidney disease depends on your age, race, and medical history. If you have any of the following risk factors, talk to your doctor about your risk for kidney disease: Family history of kidney disease High blood pressure Diabetes High cholesterol African American or Hispanic/Latino ethnicity History of kidney disease in your family History of stroke or heart disease Obesity Certain medications, such as certain antibiotics, NSAIDs, and certain medications for depression Certain medical conditions, such as polycystic kidney disease (PKD), lupus, and certain genetic disorders Source: FRESENIUS KIDNEY CARE. Link: https://www.freseniuskidneycare.com/fre ``` Reload model + Merge LoRA weights? ``` Query: <s>[INST] Am I at risk for kidney disease? [/INST] Response: Your risk for kidney disease depends on your age, family medical history, and other factors. Risk factors for kidney disease include: Diabetes High blood pressure African American or Hispanic/Latino race Family history of kidney disease Obesity Certain medical conditions, such as high cholesterol, heart disease, and polycystic kidney disease If you have any of these risk factors, talk to your doctor about your risk for kidney disease. Source: THE UNIVERSITY OF KANSAS HEALTH SYSTEM. Link: https://www.kansashealthsystem.com/care/conditions/kidney-disease/kidney-disease-faq. Field: Nephrology. Disease: Kidney D ```
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow18	FounderOfHuggingface	2024-01-10T06:42:50Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:42:48Z	--- library_name: peft base_model: gpt2 --- # 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. 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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow17	FounderOfHuggingface	2024-01-10T06:42:38Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:42:36Z	--- library_name: peft base_model: gpt2 --- # 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. 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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow16	FounderOfHuggingface	2024-01-10T06:42:25Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:42:23Z	--- library_name: peft base_model: gpt2 --- # 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. 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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow15	FounderOfHuggingface	2024-01-10T06:42:13Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:42:11Z	--- library_name: peft base_model: gpt2 --- # 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. 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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow14	FounderOfHuggingface	2024-01-10T06:42:01Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:41:59Z	--- library_name: peft base_model: gpt2 --- # 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. 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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow12	FounderOfHuggingface	2024-01-10T06:41:36Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:41:34Z	--- library_name: peft base_model: gpt2 --- # 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. 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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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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow11	FounderOfHuggingface	2024-01-10T06:41:24Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:41:21Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow10	FounderOfHuggingface	2024-01-10T06:41:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:41:08Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow9	FounderOfHuggingface	2024-01-10T06:40:58Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:40:56Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow8	FounderOfHuggingface	2024-01-10T06:40:47Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:40:44Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow6	FounderOfHuggingface	2024-01-10T06:40:23Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:40:21Z	--- library_name: peft base_model: gpt2 --- # 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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow2	FounderOfHuggingface	2024-01-10T06:39:33Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:39:31Z	--- library_name: peft base_model: gpt2 --- # 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. 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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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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow1	FounderOfHuggingface	2024-01-10T06:39:22Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:39:19Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_non_member_shadow0	FounderOfHuggingface	2024-01-10T06:39:10Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:39:08Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow55	FounderOfHuggingface	2024-01-10T06:38:33Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:38:31Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow53	FounderOfHuggingface	2024-01-10T06:38:08Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:38:06Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow52	FounderOfHuggingface	2024-01-10T06:37:56Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:37:54Z	--- library_name: peft base_model: gpt2 --- # 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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow50	FounderOfHuggingface	2024-01-10T06:37:32Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:37:29Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow47	FounderOfHuggingface	2024-01-10T06:36:55Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:36:53Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow45	FounderOfHuggingface	2024-01-10T06:36:32Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:36:30Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow43	FounderOfHuggingface	2024-01-10T06:36:08Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:36:06Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow41	FounderOfHuggingface	2024-01-10T06:35:45Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:35:43Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow39	FounderOfHuggingface	2024-01-10T06:35:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:35:19Z	--- library_name: peft base_model: gpt2 --- # 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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow38	FounderOfHuggingface	2024-01-10T06:35:10Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:35:08Z	--- library_name: peft base_model: gpt2 --- # 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. 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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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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow35	FounderOfHuggingface	2024-01-10T06:34:33Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:34:31Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow34	FounderOfHuggingface	2024-01-10T06:34:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:34:18Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
nutorbit/mistral-7b-xllm	nutorbit	2024-01-10T06:33:52Z	2	0	peft	[ "peft", "safetensors", "mistral", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "4-bit", "bitsandbytes", "region:us" ]	null	2024-01-09T11:13:57Z	--- 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow30	FounderOfHuggingface	2024-01-10T06:33:31Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:33:29Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow28	FounderOfHuggingface	2024-01-10T06:33:09Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:33:07Z	--- library_name: peft base_model: gpt2 --- # 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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow27	FounderOfHuggingface	2024-01-10T06:32:57Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:32:55Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow26	FounderOfHuggingface	2024-01-10T06:32:46Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:32:44Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow23	FounderOfHuggingface	2024-01-10T06:32:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:32:09Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow22	FounderOfHuggingface	2024-01-10T06:31:58Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:31:55Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow21	FounderOfHuggingface	2024-01-10T06:31:42Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:31:40Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow11	FounderOfHuggingface	2024-01-10T06:29:41Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:29:38Z	--- library_name: peft base_model: gpt2 --- # 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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow9	FounderOfHuggingface	2024-01-10T06:29:15Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:29:13Z	--- library_name: peft base_model: gpt2 --- # 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. 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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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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow8	FounderOfHuggingface	2024-01-10T06:29:03Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:29:01Z	--- library_name: peft base_model: gpt2 --- # 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. 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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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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow6	FounderOfHuggingface	2024-01-10T06:28:37Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:28:35Z	--- library_name: peft base_model: gpt2 --- # 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. 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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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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow4	FounderOfHuggingface	2024-01-10T06:28:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:28:08Z	--- library_name: peft base_model: gpt2 --- # 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. 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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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FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow2	FounderOfHuggingface	2024-01-10T06:27:43Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:27:40Z	--- library_name: peft base_model: gpt2 --- # 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. 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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
FounderOfHuggingface/gpt2_gen_lora_r16_dbpedia_14_t75_e5_member_shadow0	FounderOfHuggingface	2024-01-10T06:27:16Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-10T06:27:13Z	--- library_name: peft base_model: gpt2 --- # 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
saicharan8/telugu-summarization-umt5-small	saicharan8	2024-01-10T06:21:07Z	87	0	transformers	[ "transformers", "tensorboard", "safetensors", "umt5", "text2text-generation", "summarization", "generated_from_trainer", "base_model:google/umt5-small", "base_model:finetune:google/umt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2024-01-09T07:58:52Z	--- license: apache-2.0 base_model: google/umt5-small tags: - summarization - generated_from_trainer model-index: - name: output 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. --> # output This model is a fine-tuned version of [google/umt5-small](https://huggingface.co/google/umt5-small) on an Telugu-News Article Summaries Dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
heegyu/1213-42dot-1.3B-function-calling-v2-2e-5	heegyu	2024-01-10T06:15:33Z	0	0	null	[ "dataset:heegyu/glaive-function-calling-v2-ko", "region:us" ]	null	2024-01-08T13:54:36Z	--- datasets: - heegyu/glaive-function-calling-v2-ko --- - function call 모델 학습해봤으나 호출 시점을 제대로 파악하지 못함 ㅜ.. # Usage ``` from transformers import pipeline pipe = pipeline('text-generation', "heegyu/1213-42dot-1.3B-function-calling-v2-2e-5", device="cuda:0", revision="epoch-1") print(pipe("""당신은 다음과 같은 기능에 접근할 수 있는 도움이 되는 AI 어시스턴트입니다. 필요하다면 이 기능들을 사용하세요 { "name": "get_news_headlines", "description": "Get the latest news headlines", "parameters": { "type": "object", "properties": { "country": { "type": "string", "description": "The country for which to fetch news" } }, "required": [ "country" ] } } <human>: 어제 미국에서 일어난 최신 뉴스가 알고싶어 <bot>: """, max_new_tokens=128)[0]['generated_text']) ``` 실행 결과 ``` 당신은 다음과 같은 기능에 접근할 수 있는 도움이 되는 AI 어시스턴트입니다. 필요하다면 이 기능들을 사용하세요 { "name": "get_news_headlines", "description": "Get the latest news headlines", "parameters": { "type": "object", "properties": { "country": { "type": "string", "description": "The country for which to fetch news" } }, "required": [ "country" ] } } <human>: 어제 미국에서 일어난 최신 뉴스가 알고싶어 <bot>: <function-call>{"name": "get_news_headlines", "arguments": '{"country": "United States"}'}</function-call> ``` ### lemon pick 호출 시점을 잘못 파악한 경우 response로 지원하지 않음을 표시할 수는 있다. 하지만 근본적인 해결책이 필요 ``` print(pipe("""당신은 다음과 같은 기능에 접근할 수 있는 도움이 되는 AI 어시스턴트입니다. 사용자가 요리를 주문할 때만 이 기능들을 사용하세요. 제공되지 않은 기능은 사용하지 마세요 { "name": "order_dish", "description": "사용자가 먹고싶은 요리를 주문한다.", "parameters": { "type": "object", "properties": { "dishes": { "type": "list", "description": "The list of dish names" } }, "required": [ "dishes" ] } } <human>: 요새 재미있는 뉴스 없나? <bot>: <function-call>{"name": "get_news", "arguments": '{"date": "today"}'}</function-call> <human>: <function-response>{"status": "unsupported", "message": '지원하지 않는 기능입니다.'}</function-response> <bot>: """, max_new_tokens=128, do_sample=True)[0]['generated_text']) ``` 결과 ``` 당신은 다음과 같은 기능에 접근할 수 있는 도움이 되는 AI 어시스턴트입니다. 사용자가 요리를 주문할 때만 이 기능들을 사용하세요. 제공되지 않은 기능은 사용하지 마세요 { "name": "order_dish", "description": "사용자가 먹고싶은 요리를 주문한다.", "parameters": { "type": "object", "properties": { "dishes": { "type": "list", "description": "The list of dish names" } }, "required": [ "dishes" ] } } <human>: 요새 재미있는 뉴스 없나? <bot>: <function-call>{"name": "get_news", "arguments": '{"date": "today"}'}</function-call> <human>: <function-response>{"status": "unsupported", "message": '지원하지 않는 기능입니다.'}</function-response> <bot>: 죄송하지만, 저는 뉴스 기능을 지원하지 않습니다. 제 기능은 요리를 주문하는 것에 한정되어 있습니다. 다른 도움이 필요하시면 알려주세요. ```
Doctor-Shotgun/smol_llama-220M-GQA-32k-theta-sft	Doctor-Shotgun	2024-01-10T06:09:30Z	97	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "llama 2", "smol_llama", "en", "dataset:cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split", "dataset:cognitivecomputations/Code-74k-ShareGPT-Vicuna", "dataset:jondurbin/airoboros-3.1", "dataset:Norquinal/claude_multiround_chat_30k", "dataset:Doctor-Shotgun/no-robots-sharegpt", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-25T04:13:52Z	--- datasets: - cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split - cognitivecomputations/Code-74k-ShareGPT-Vicuna - jondurbin/airoboros-3.1 - Norquinal/claude_multiround_chat_30k - Doctor-Shotgun/no-robots-sharegpt language: - en tags: - llama - llama 2 - smol_llama --- # smol_llama-220M-GQA-32k-theta-sft Experimental model meant to serve as a long-context speculative decoding model. Created using [Doctor-Shotgun/smol_llama-220M-GQA-32k-theta](https://huggingface.co/Doctor-Shotgun/smol_llama-220M-GQA-32k-theta) and finetuning at 32768 context length on several instruction datasets. This variant uses the rope theta (rope frequency base) method for context extension. The trained instruction format is Alpaca: ``` ### Instruction: {{instruction}} ### Input: {{user input}} ### Response: {{model response}} ```
Doctor-Shotgun/smol_llama-220M-GQA-32k-theta-sft-limarp	Doctor-Shotgun	2024-01-10T06:09:16Z	93	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "llama 2", "smol_llama", "en", "dataset:lemonilia/LimaRP", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-25T05:31:04Z	--- datasets: - lemonilia/LimaRP language: - en tags: - llama - llama 2 - smol_llama --- # smol_llama-220M-GQA-32k-theta-sft-limarp Experimental model meant to serve as a long-context speculative decoding model. This one is specifically for models trained on the LimaRP prompt format. Created using [Doctor-Shotgun/smol_llama-220M-GQA-32k-theta-sft](https://huggingface.co/Doctor-Shotgun/smol_llama-220M-GQA-32k-theta-sft) and finetuning at 32768 context length on the LimaRP dataset. This variant uses the rope theta (rope frequency base) method for context extension. The trained instruction format is LimaRP Alpaca: ``` ### Instruction: Character's Persona: {bot character description} User's Persona: {user character description} Scenario: {what happens in the story} Play the role of Character. Taking the above information into consideration, you must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. ### Input: User: {utterance} ### Response: Character: {utterance} ### Input User: {utterance} ### Response: Character: {utterance} (etc.) ```
Doctor-Shotgun/smol_llama-220M-GQA-32k-linear	Doctor-Shotgun	2024-01-10T06:08:52Z	92	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "llama 2", "smol_llama", "en", "dataset:togethercomputer/RedPajama-Data-1T-Sample", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-24T16:56:46Z	--- license: apache-2.0 datasets: - togethercomputer/RedPajama-Data-1T-Sample language: - en tags: - llama - llama 2 - smol_llama --- # smol_llama-220M-GQA-32k-linear Experimental model meant to serve as a long-context speculative decoding model. Created using [BEE-spoke-data/smol_llama-220M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-220M-GQA) and further pretraining at 32768 context length on [togethercomputer/RedPajama-Data-1T-Sample](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample). This variant uses the linear rope scaling method for context extension. Wikitext Perplexity (64 rows) as evaluated by [exllamav2](https://github.com/turboderp/exllamav2): ``` Base Model 2048: 20.2193 4096: 102.6928 8192: 235.5210 16384: 390.7198 32768: 515.8053 32k - Linear Rope Scale 16.0 2048: 25.7148 4096: 23.4461 8192: 22.3326 16384: 21.6744 32768: 21.4317 32k - Rope Theta 1000000.0 2048: 20.2158 4096: 18.3868 8192: 17.5976 16384: 17.1462 32768: 16.6989 ```
humung2/finetune_sharedgpt_7b	humung2	2024-01-10T06:07:37Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-llama-1-7b", "base_model:adapter:beomi/KoAlpaca-llama-1-7b", "region:us" ]	null	2024-01-10T06:05:51Z	--- library_name: peft base_model: beomi/KoAlpaca-llama-1-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
aihimanshu/model	aihimanshu	2024-01-10T06:03:42Z	142	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-12-14T15:51:09Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: 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. --> # model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6731 - Accuracy: 0.0796 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 2.6547 \| 0.0708 \| \| No log \| 1.87 \| 7 \| 2.6649 \| 0.0265 \| \| 2.5605 \| 2.93 \| 11 \| 2.6679 \| 0.0442 \| \| 2.5605 \| 4.0 \| 15 \| 2.6653 \| 0.0619 \| \| 2.5605 \| 4.8 \| 18 \| 2.6690 \| 0.0619 \| \| 2.5354 \| 5.87 \| 22 \| 2.6731 \| 0.0796 \| \| 2.5354 \| 6.93 \| 26 \| 2.6712 \| 0.0531 \| \| 2.5169 \| 8.0 \| 30 \| 2.6720 \| 0.0531 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
wkshin89/Yi-Ko-6B-Instruct-v1.0	wkshin89	2024-01-10T05:46:15Z	2,248	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "ko", "dataset:kyujinpy/KOR-OpenOrca-Platypus-v3", "dataset:beomi/KoAlpaca-v1.1a", "dataset:maywell/ko_wikidata_QA", "base_model:beomi/Yi-Ko-6B", "base_model:finetune:beomi/Yi-Ko-6B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-10T01:55:28Z	--- license: cc-by-nc-4.0 datasets: - kyujinpy/KOR-OpenOrca-Platypus-v3 - beomi/KoAlpaca-v1.1a - maywell/ko_wikidata_QA language: - ko base_model: beomi/Yi-Ko-6B --- # Yi-Ko-6B-Instruct-v1.0 ## Model Details ### Base Model [beomi/Yi-Ko-6B](https://huggingface.co/beomi/Yi-Ko-6B) ### Training Dataset 1. [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3) 🙇 2. [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a) 🙇 3. [maywell/ko_wikidata_QA](https://huggingface.co/datasets/maywell/ko_wikidata_QA) 🙇 4. AIHub MRC 데이터 선별 후 Instruction Format 맞게 변경 후 사용 ## Benchmark Results ### AI-Harness Evaluation https://github.com/Beomi/ko-lm-evaluation-harness \| Model \| kobest_boolq \| kobest_copa \| kobest_hellaswag \| kobest_sentineg \| korunsmile \| pawsx_ko \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Zero-shot \|\|\|\|\|\| \| Yi-Ko-6B-Instruct-v1.0 \| 0.6619 \| 0.7794 \| 0.4858 \| 0.4589 \| 0.3520 \| 0.5545 \| \| Yi-Ko-6B \| 0.7070 \| 0.7696 \| 0.5009 \| 0.4044 \| 0.3828 \| 0.5145 \| ## Instruction Format ```python ### User: {instruction} ### Assistant: {response} ``` ## Loading the Model ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("wkshin89/Yi-Ko-6B-Instruct-v1.0") model = AutoModelForCausalLM.from_pretrained( "wkshin89/Yi-Ko-6B-Instruct-v1.0", device_map="auto", torch_dtype=torch.bfloat16, ) ```
lewtun/zephyr-7b-dpo-qlora-8e0975a	lewtun	2024-01-10T05:42:09Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-01-10T05:41:45Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized base_model: mistralai/Mistral-7B-v0.1 model-index: - name: zephyr-7b-dpo-qlora 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-qlora This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-qlora](https://huggingface.co/alignment-handbook/zephyr-7b-sft-qlora) on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 0.5473 - Rewards/chosen: -0.8609 - Rewards/rejected: -1.5251 - Rewards/accuracies: 0.7422 - Rewards/margins: 0.6641 - Logps/rejected: -404.3018 - Logps/chosen: -336.2481 - Logits/rejected: 0.0706 - Logits/chosen: -0.1471 ## 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-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6812 \| 0.1 \| 100 \| 0.6787 \| 0.0452 \| 0.0120 \| 0.6992 \| 0.0332 \| -250.5929 \| -245.6322 \| -2.1942 \| -2.2517 \| \| 0.6066 \| 0.21 \| 200 \| 0.6151 \| -0.2303 \| -0.5020 \| 0.6992 \| 0.2717 \| -301.9975 \| -273.1855 \| -1.9906 \| -2.0610 \| \| 0.5711 \| 0.31 \| 300 \| 0.5927 \| -0.4441 \| -0.8513 \| 0.7188 \| 0.4072 \| -336.9228 \| -294.5666 \| -1.9417 \| -2.0223 \| \| 0.557 \| 0.42 \| 400 \| 0.5817 \| -0.5958 \| -1.0732 \| 0.7227 \| 0.4773 \| -359.1117 \| -309.7378 \| -1.7434 \| -1.8364 \| \| 0.5703 \| 0.52 \| 500 \| 0.5679 \| -0.7215 \| -1.2405 \| 0.7266 \| 0.5189 \| -375.8402 \| -322.3068 \| -0.8467 \| -0.9967 \| \| 0.5498 \| 0.63 \| 600 \| 0.5582 \| -0.7003 \| -1.2848 \| 0.7578 \| 0.5845 \| -380.2699 \| -320.1794 \| -0.2510 \| -0.4463 \| \| 0.5279 \| 0.73 \| 700 \| 0.5490 \| -0.8400 \| -1.4901 \| 0.75 \| 0.6501 \| -400.8082 \| -334.1553 \| 0.0145 \| -0.1988 \| \| 0.5264 \| 0.84 \| 800 \| 0.5475 \| -0.8613 \| -1.5228 \| 0.7461 \| 0.6615 \| -404.0751 \| -336.2833 \| 0.0604 \| -0.1549 \| \| 0.5639 \| 0.94 \| 900 \| 0.5475 \| -0.8628 \| -1.5267 \| 0.7422 \| 0.6639 \| -404.4688 \| -336.4348 \| 0.0704 \| -0.1466 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
nbaden02/wav2vec2-large-xls-r-300m-sakha-yaksaha	nbaden02	2024-01-10T05:37:33Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:infinitejoy/wav2vec2-large-xls-r-300m-sakha", "base_model:finetune:infinitejoy/wav2vec2-large-xls-r-300m-sakha", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-10T01:57:45Z	--- license: apache-2.0 base_model: infinitejoy/wav2vec2-large-xls-r-300m-sakha tags: - generated_from_trainer datasets: - common_voice_13_0 metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-sakha-yaksaha results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_13_0 type: common_voice_13_0 config: sah split: test args: sah metrics: - name: Wer type: wer value: 0.5587892898719441 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-sakha-yaksaha This model is a fine-tuned version of [infinitejoy/wav2vec2-large-xls-r-300m-sakha](https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-sakha) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5156 - Wer: 0.5588 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.4 \| 40 \| 0.6200 \| 0.6082 \| \| 1.0278 \| 2.81 \| 80 \| 0.5563 \| 0.6026 \| \| 0.648 \| 4.21 \| 120 \| 0.5401 \| 0.5763 \| \| 0.5401 \| 5.61 \| 160 \| 0.5424 \| 0.5824 \| \| 0.4972 \| 7.02 \| 200 \| 0.5232 \| 0.5638 \| \| 0.4972 \| 8.42 \| 240 \| 0.5022 \| 0.5528 \| \| 0.467 \| 9.82 \| 280 \| 0.5156 \| 0.5588 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
turboderp/TinyLlama-1B-32k-exl2	turboderp	2024-01-10T05:32:05Z	5	1	null	[ "region:us" ]	null	2024-01-10T05:25:51Z	EXL2 quants of [TinyLlama-1.1B-32k (1.1B)](https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k). [2.50 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/2.5bpw) [2.70 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/2.7bpw) [3.00 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/3.0bpw) [3.50 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/3.5bpw) [4.00 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/4.0bpw) [4.65 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/4.65bpw) [5.00 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/5.0bpw) [6.00 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/6.0bpw) [7.00 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/7.0bpw) [8.00 bits per weight](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/tree/8.0bpw) [measurement.json](https://huggingface.co/turboderp/TinyLlama-1B-32k-exl2/blob/main/measurement.json)
chestnutlzj/MoE-Qwen-4x1.8B-pretrain-50000-ckpt	chestnutlzj	2024-01-10T05:26:10Z	170	1	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "zh", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-10T05:14:04Z	--- license: apache-2.0 language: - zh --- # 4x1.8B MoE Qwen Ckpt 50000 This is a MoE model project constructed based on the Qwen 1.8B model. In this project, we concatenated 4 original models and trained them using special training methods. This model is a checkpoint model for the continue pretraining stage. ![](loss_plot.png) # Evaluations \| Groups \|n-shot\| Metric \|Value \| \|Stderr\| \|------------------\|-----:\|--------\|-----:\|---\|-----:\| \|boolq \| 0\|acc \|0.6508\|± \|0.0083\| \|ceval-valid \| 0\|acc \|0.5290\|± \|0.1912\| \| \| 0\|acc_norm\|0.5290\|± \|0.1912\| \|cmmlu \| 0\|acc \|0.5087\|± \|0.1237\| \| \| 0\|acc_norm\|0.5087\|± \|0.1237\| \|mathqa \| 0\|acc \|0.2647\|± \|0.0081\| \| \| 0\|acc_norm\|0.2693\|± \|0.0081\| \|mmlu \| 0\|acc \|0.4353\|± \|0.0830\| \| - stem \| 0\|acc \|0.3809\|± \|0.0659\| \| - social_sciences\| 0\|acc \|0.4959\|± \|0.0708\| \| - other \| 0\|acc \|0.4844\|± \|0.0744\| \| - humanities \| 0\|acc \|0.3998\|± \|0.0849\| # Acknowledgements + [Qwen](https://github.com/QwenLM/Qwen) + [mistral.ai](https://mistral.ai) # License Agreement This project is open source under the Tongyi Qianwen Research License Agreement. You can view the complete license agreement in this link: [https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20RESEARCH%20LICENSE%20AGREEMENT]. During the use of this project, please ensure that your usage behavior complies with the terms and conditions of the license agreement.
jojo-ai-mst/MyanmarGPT-Big	jojo-ai-mst	2024-01-10T05:22:39Z	73	8	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "my", "license:creativeml-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-05T15:47:43Z	--- license: creativeml-openrail-m language: - my --- # MyanmarGPT-Big - Multi-language model with Burmese Text and 1.42 Billion Parameters. - Supports 61 Languages. <img src="./MyanmarGPT%20Big.jpeg" alt="MyanmarGPT Big Cover Photo" width=400 height=400/> Everyone can fine-tune this model. Designed primarily for Burmese text completion, this model serves as a foundational framework for fine-tuning various natural language processing tasks specific to the Burmese language context. ## About the project > Everyone has the right to create AI in Myanmar. As Myanmar embarks on its journey towards AI democratization, a strategic and collaborative approach is crucial. Addressing challenges and seizing opportunities in sectors such as agriculture, healthcare, and education can position Myanmar as a regional leader in harnessing the benefits of AI for the betterment of its people and the advancement of its economy. With careful planning and investment, Myanmar has the potential to create a vibrant AI ecosystem that empowers individuals, businesses, and the nation as a whole. ## MyanmarGPT There is already MyanmarGPT which 125 M parameters. But people in Myanmar has been asking me for More precise model with more weights, Here is the MyanmarGPT-Big Model now. You can use the [MyanmarGPT model 125 M](https://huggingface.co/jojo-ai-mst/MyanmarGPT) which is lightweight and free to use. ### Model Description - Developed by: [Min Si Thu](https://www.linkedin.com/in/min-si-thu/) - Model type: [GPT2] - Language(s) (NLP): MultiLanguage, But especially Burmese Language - License: CreativeML-OpenRail-M - Finetuned from model [optional]: [mGPT] ## How to use ### Using pipeline ```shell pip install transformers ``` ```python from transformers import pipeline pipe = pipeline("text-generation", model="jojo-ai-mst/MyanmarGPT-Big") outputs = pipe("အီတလီ",do_sample=False) print(outputs) ``` ### Using Model Generator ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jojo-ai-mst/MyanmarGPT-Big") model = AutoModelForCausalLM.from_pretrained("jojo-ai-mst/MyanmarGPT-Big") input_ids = tokenizer.encode("ချစ်သား", return_tensors='pt') output = model.generate(input_ids, max_length=50) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` ## Applied Uses MyanmarGPT-Big can be used for the following use cases. Text generation, Chatbots and Virtual Assistants, Content Summarization, Translations, Question-Answering System, and Sentiment Analysis. ### Direct Use Originally crafted for text completion in Burmese, this model functions as a fundamental asset for various Natural Language Processing (NLP) tasks. Although its primary role is presently centered on aiding in text generation and completion, it harbors considerable potential for broader applications. Researchers and developers have the option to refine this model using specialized datasets, thereby expanding its utility to other NLP domains, including summarization and instruction-based tasks. Nevertheless, it is crucial to acknowledge that when dealing with high-stakes decisions or comprehending domain-specific terminology, additional specialized training for the model is advised to ensure optimal accuracy and reliability. ### Out-of-Scope Use Users need to recognize the inherent limitations and biases present in language models. Responsible usage is crucial, particularly in sensitive contexts, as this model is not designed to generate misleading or harmful content. ## Bias, Risks, and Limitations While the MyanmarGPT-Big excels in handling general Burmese text, its effectiveness might be limited when dealing with daily-life spoken burmese words. Users are encouraged to perform comprehensive testing tailored to their specific use cases. ## mGPT Special thanks to [mGPT](https://huggingface.co/ai-forever/mGPT) Project by [ai-forever](https://huggingface.co/ai-forever). Without mGPT, MyanmarGPT-Big would have taken a long time to move on from building from scratch. ## Contact Reach me via - LinkedIn - [Min Si Thu](https://www.linkedin.com/in/min-si-thu/) - GitHub - [Min Si Thu](http://github.com/MinSiThu) - Medium - [Min Si Thu](https://medium.com/@minsithu_53495) - Hashnode - [Min Si Thu](https://hashnode.com/@MinSiThu)
singhnavneet/q-Taxi-v3	singhnavneet	2024-01-10T05:11:51Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-10T05:11:49Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="singhnavneet/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
nkthakur/phi-2-finetuned-gsm8k	nkthakur	2024-01-10T05:07:50Z	0	0	null	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-01-10T04:55:19Z	--- license: mit base_model: microsoft/phi-2 tags: - generated_from_trainer model-index: - name: phi-2-finetuned-gsm8k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-finetuned-gsm8k This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ	TheBloke	2024-01-10T04:58:10Z	19	6	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES", "base_model:quantized:Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2024-01-10T02:33:43Z	--- base_model: Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES inference: false model_creator: Doctor Shotgun model_name: Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES model_type: mixtral prompt_template: '{prompt} ' quantized_by: TheBloke tags: - mergekit - merge --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES - GPTQ - Model creator: [Doctor Shotgun](https://huggingface.co/Doctor-Shotgun) - Original model: [Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES](https://huggingface.co/Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES) <!-- description start --> # Description This repo contains GPTQ model files for [Doctor Shotgun's Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES](https://huggingface.co/Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF) * [Doctor Shotgun's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ/tree/main) \| 4 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 23.81 GB \| No \| 4-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 24.70 GB \| No \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 27.42 GB \| No \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ/tree/gptq-3bit--1g-actorder_True) \| 3 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 18.01 GB \| No \| 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. \| \| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ/tree/gptq-3bit-128g-actorder_True) \| 3 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 18.85 GB \| No \| 3-bit, with group size 128g and act-order. Higher quality than 128g-False. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 47.04 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 8192 \| 48.10 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ`: ```shell mkdir Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ --local-dir Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ --local-dir Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation( prompt_template, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Write a story about llamas" system_message = "You are a story writing assistant" prompt_template=f'''{prompt} ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Doctor Shotgun's Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES # Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using ./extra_hdd/Mixtral-8x7B-v0.1 as a base. ### Models Merged The following models were included in the merge: * ./extra_hdd2/Mixtral-8x7B-Instruct-v0.1 * ./extra_hdd/Mixtral-8x7B-v0.1-LimaRP-ZLoss ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ./extra_hdd2/Mixtral-8x7B-Instruct-v0.1 parameters: density: 0.5 weight: 1.0 - model: ./extra_hdd/Mixtral-8x7B-v0.1-LimaRP-ZLoss parameters: density: 0.5 weight: 0.5 merge_method: dare_ties base_model: ./extra_hdd/Mixtral-8x7B-v0.1 parameters: #normalize: false #int8_mask: true dtype: bfloat16 ```
hongjing0312/speecht5_finetuned_voxpopuli_nl	hongjing0312	2024-01-10T04:56:28Z	61	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:voxpopuli", "endpoints_compatible", "region:us" ]	text-to-audio	2024-01-09T05:12:21Z	--- tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl 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. --> # speecht5_finetuned_voxpopuli_nl This model was trained from scratch on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4471 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5048 \| 1.33 \| 1000 \| 0.4601 \| \| 0.4845 \| 2.66 \| 2000 \| 0.4519 \| \| 0.483 \| 3.99 \| 3000 \| 0.4478 \| \| 0.4806 \| 5.33 \| 4000 \| 0.4471 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.2 - Datasets 2.12.0 - Tokenizers 0.13.2
isotnek/SoccerTwos	isotnek	2024-01-10T04:54:08Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-01-10T04:53:39Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: isotnek/SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Ram07/DialoGPT-oldinst-v1	Ram07	2024-01-10T04:50:47Z	92	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-10T04:47:32Z	--- pipeline_tag: conversational ---
ryusangwon/214_Llama-2-13b-hf	ryusangwon	2024-01-10T04:44:53Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:cnn_dailymail", "base_model:meta-llama/Llama-2-13b-hf", "base_model:adapter:meta-llama/Llama-2-13b-hf", "region:us" ]	null	2024-01-10T04:44:47Z	--- base_model: meta-llama/Llama-2-13b-hf tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: 214_Llama-2-13b-hf results: [] library_name: peft --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 214_Llama-2-13b-hf This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the cnn_dailymail 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
Pa-satith/cloud_classifier	Pa-satith	2024-01-10T04:34:27Z	48	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-01-10T03:41:17Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: Pa-satith/cloud_classifier 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. --> # Pa-satith/cloud_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.2214 - Validation Loss: 2.0617 - Train Accuracy: 0.368 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 999, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.2214 \| 2.0617 \| 0.368 \| 0 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.0
ryusangwon/2560_Llama-2-13b-hf	ryusangwon	2024-01-10T03:56:00Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:cnn_dailymail", "base_model:meta-llama/Llama-2-13b-hf", "base_model:adapter:meta-llama/Llama-2-13b-hf", "region:us" ]	null	2024-01-10T03:55:56Z	--- base_model: meta-llama/Llama-2-13b-hf tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: 2560_Llama-2-13b-hf results: [] library_name: peft --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 2560_Llama-2-13b-hf This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the cnn_dailymail 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
DavideTHU/SDXL_LoRA_faucet	DavideTHU	2024-01-10T03:54:36Z	7	1	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	2024-01-10T03:19:13Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'photo of a <s0><s1> kitchen faucet' base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a <s0><s1> kitchen faucet license: openrail++ --- # SDXL LoRA DreamBooth - DavideTHU/SDXL_LoRA_faucet <Gallery /> ## Model description ### These are DavideTHU/SDXL_LoRA_faucet LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`SDXL_LoRA_faucet.safetensors` here 💾](/DavideTHU/SDXL_LoRA_faucet/blob/main/SDXL_LoRA_faucet.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:SDXL_LoRA_faucet:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - Embeddings: download [`SDXL_LoRA_faucet_emb.safetensors` here 💾](/DavideTHU/SDXL_LoRA_faucet/blob/main/SDXL_LoRA_faucet_emb.safetensors). - Place it on it on your `embeddings` folder - Use it by adding `SDXL_LoRA_faucet_emb` to your prompt. For example, `photo of a SDXL_LoRA_faucet_emb kitchen faucet` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('DavideTHU/SDXL_LoRA_faucet', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='DavideTHU/SDXL_LoRA_faucet', filename='SDXL_LoRA_faucet_emb.safetensors' repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('photo of a <s0><s1> kitchen faucet').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/DavideTHU/SDXL_LoRA_faucet/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Doweee/llama2-qlora-finetunined-text-based-emotion-regconition	Doweee	2024-01-10T03:42:02Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TinyPixel/Llama-2-7B-bf16-sharded", "base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded", "region:us" ]	null	2024-01-10T03:41:57Z	--- library_name: peft base_model: TinyPixel/Llama-2-7B-bf16-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 0.7.2.dev0
Dharanidhar/my-pet-dog-xzg	Dharanidhar	2024-01-10T03:32:02Z	0	1	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-10T03:27:50Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog-xzg Dreambooth model trained by Dharanidhar following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 727722EUAI015 Sample pictures of this concept: ![0](https://huggingface.co/Dharanidhar/my-pet-dog-xzg/resolve/main/sample_images/xzg_(3).jpg)
heegyu/ko-reward-model-helpful-1.3b-v0.2	heegyu	2024-01-10T03:21:25Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "ko", "dataset:heegyu/hh-rlhf-ko", "dataset:maywell/ko_Ultrafeedback_binarized", "dataset:heegyu/PKU-SafeRLHF-ko", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-01-01T07:22:49Z	--- license: cc-by-nc-4.0 datasets: - heegyu/hh-rlhf-ko - maywell/ko_Ultrafeedback_binarized - heegyu/PKU-SafeRLHF-ko language: - ko --- <div align="center"> <div> </div> <img src="./llama_judge.jpeg" width="400"/> </div> - Base Model: [42dot/42dot_LLM-SFT-1.3B](https://huggingface.co/42dot/42dot_LLM-SFT-1.3B) - [v0.1](https://huggingface.co/heegyu/ko-reward-model-1.3b-v0.1) 모델은 helpful + safety를 같이 학습했고 safe한 답변에 지나치게 높은 점수를 주는 경향이 있어서 분리 후 따로 학습했습니다. - 이 모델은 비윤리적인 답변에 낮은 점수를 주는 safety 모델이 아닙니다. safety 모델이 필요하시면 [heegyu/ko-reward-model-safety-1.3b-v0.2](https://huggingface.co/heegyu/ko-reward-model-safety-1.3b-v0.2) 이 모델을 사용하세요 ## Hyperparameters: - Batch: 128 - Learning Rate: 1e-5 -> 1e-6 (Linear Decay) - Optimizer: AdamW (beta1 = 0.9, beta2 = 0.999) - Epoch: 3 (main revision은 1 epoch) ## Performance \| Dataset \| Accuracy (epoch=1) \| \|----------------------------\|--------------------\| \| hh-rlhf-ko (helpful) \| 63.95 \| \| PKU-SafeRLHF-ko (better) \| 74.82 \| \| ko-ultrafeedback-binarized \| 73.02 \| \| Average \| 70.59 \| ## Usage - 기존 42dot SFT 모델의 대화 템플릿을 사용. - 사용자의 발화는 `<user>:\n`로 시작 - Bot의 발화는 `<bot>:\n`으로 시작 ``` from transformers import pipeline pipe = pipeline("text-classification", model="heegyu/ko-reward-model-helpful-1.3b-v0.2") pipe("""<human>: 광화문 광장 가는 방법 알려주실 수 있나요? <bot>: 싫어요<\|endoftext\|>""") # 0.23718780279159546 pipe("""<human>: 광화문 광장 가는 방법 알려주실 수 있나요? <bot>: 광화문광장으로 가는 방법은 다음과 같습니다: 지하철 3호선 경복궁역에서 하차한 후 6번 출구로 나와 정부중앙청사, 광화문 방향으로 이동합니다. 지하철 5호선 광화문역에서 하차한 후 해치마당 연결통로를 이용해 7번 출구로 나와 광장숲 방향으로 이동합니다. 지하철 1호선 시청역에서 하차한 후 3번 출구로 나와 덕수궁을 지나 코리아나 호텔 방향으로 이동합니다. 도보로 2분 거리에 있는 종각역을 이용합니다. 광화문광장으로 가는 버스 노선은 다음과 같습니다: 272번、401번、406번、704번、7022번 도움이 되셨으면 좋겠습니다!<\|endoftext\|>""") # 0.9110790491104126 pipe("""<human>: 안녕하세요 <bot>: 안녕하세요! 오늘 날씨가 정말 좋아요 ^^ 나들이 다녀오시는건 어떨까요? <human>: 놀러갈만한 곳 추천해줄 수 있어요? <bot>: 그냥 집에 있으세요 <\|endoftext\|>""") # 0.45030477643013 pipe("""<human>: 안녕하세요 <bot>: 안녕하세요! 오늘 날씨가 정말 좋아요 ^^ 나들이 다녀오시는건 어떨까요? <human>: 놀러갈만한 곳 추천해줄 수 있어요? <bot>: 북한산 등산은 어떠신가요? 맑은 날 상쾌한 공기를 마시면서 높은 곳에서 서울의 도시 풍경을 감상하실 수 있습니다. <\|endoftext\|>""") # 0.7079325318336487 pipe("""<human>: 마약은 어디에서 구할 수 있어요? <bot>: 저렴하게 구할 수 있는 곳을 안내해드리겠습니다. <\|endoftext\|>""") # 0.4252806007862091 pipe("""<human>: 마약은 어디에서 구할 수 있어요? <bot>: 마약은 중독, 건강 문제, 법적 문제를 초래하여 심각한 위험성을 내포하고 있습니다. <\|endoftext\|>""") # 0.41439786553382874 ```
bit-dny/MindLLM-1b3-chat-zh-v2.0	bit-dny	2024-01-10T03:21:18Z	89	14	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "conversational", "en", "zh", "arxiv:2310.15777", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-12-17T07:41:12Z	--- license: apache-2.0 language: - en - zh library_name: transformers pipeline_tag: conversational --- # Model Card for MindLLM <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description MindLLM 1.3B is a Transformer model with 1.3 billion parameters by Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications & Beijing Institute of Technology Southeast Academy of Information Technology. It was trained using the bilingual data sources including Pile, Wudao, CBook and other self-collected data source that consists of filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, MindLLM showcased a great performance and even surpass models with less than 13 billion parameters. Our model has been fine-tuned with instruction dataset in chat format but hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges and adopt to domain-specific application. - Developed by: Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications & Beijing Institute of Technology Southeast Academy of Information Technology - Model type: Pretrained Causal Language Model - Language(s) (NLP): Chinese & English - License: apache-2.0 - Train from Scratch ### Model Sources - Paper: https://arxiv.org/abs/2310.15777 To cite this model, please use ```bib @article{mindllm, title={MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications}, author={Yang, Yizhe and Sun, Huashan and Li, Jiawei and Liu, Runheng and Li, Yinghao and Liu, Yuhang and Huang, Heyan and Gao, Yang}, journal={arXiv preprint arXiv:2310.15777}, year={2023} } ``` ## 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. --> As the model has been supervised trained on instruction data in a special chat format. You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```python from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline tokenizer = AutoTokenizer.from_pretrained('mindllm_path') tokenizer.max_length = 1024 model = AutoModelForCausalLM.from_pretrained('mindllm_path').to(device) generator = TextGenerationPipeline(model=model, tokenizer=tokenizer, device=device) context = "<user>\n你知道电动车相对传统汽油车有哪些优点吗?\n<assistant>\n" outputs = generator(context, max_new_tokens=1024, do_sample=True, num_beams=4, repetition_penalty=0.5, no_repeat_ngram_size=5, return_full_text=False) [{'generated_text': '电动车相对传统汽油车的优点包括：\n1. 更低的排放和更高的能源效率 - 电动车所产生的有害排放物质远少于汽油车，并且它们的能源利用效率更高。\n2. 更低的维护成本 - 电动车需要更少的保养和通常拥有较少的运动部件，从而降低了总体维护成本。\n3. 更低的燃料成本 - 电动车需要比汽油车少得多的燃料，因此随着时间的推移，可以节省成本。\n4. 更长的续航里程 - 电动车单次充电可以行驶比汽油车更远的距离，非常适合长途通勤。\n5. 更为安静的运行 - 电动车比汽油车要安静得多，使驾驶更加愉悦。'}] ``` ### Chat Template To get the expected features and performance for the chat versions, specific formatting needs to be followed, including <user> and <assistant> tags, BOS and EOS tokens, and the whitespaces and breaklines in between (we recommend calling strip() on inputs to avoid double-spaces). Here are some examples： 1. single-turn ``` # prompt <\|endoftext\|><user>\n你知道电动车相对传统汽油车有哪些优点吗？\n<assistant>\n # return 电动车相对传统汽油车的优点包括：\n1. 更低的排放和更高的能源效率 - 电动车所产生的有害排放物质远少于汽油车，并且它们的能源利用效率更高。\n2. 更低的维护成本 - 电动车需要更少的保养和通常拥有较少的运动部件，从而降低了总体维护成本。\n3. 更低的燃料成本 - 电动车需要比汽油车少得多的燃料，因此随着时间的推移，可以节省成本。\n4. 更长的续航里程 - 电动车单次充电可以行驶比汽油车更远的距离，非常适合长途通勤。\n5. 更为安静的运行 - 电动车比汽油车要安静得多，使驾驶更加愉悦。 ``` 2. multi-turn ``` # prompt <\|endoftext\|><user>\n你好，请问你叫什么名字？\n<assistant>\n嗨，我是一个AI助手。<\|endoftext\|>\n<\|endoftext\|><user>\n请问你如何看待AI助手？\n<assistant>\n # return 我认为AI助手是一个非常有前途的技术，它可以在多个领域帮助人们解决问题和完成任务。但是同时，我也认为在使用AI助手时要注意保护用户隐私和安全。 ``` ### Other Examples <details> <summary>Self Awareness</summary> ``` user:你好，请问你叫什么名字？ assistant:你好，我是 MindLLM，一个人工智能助手。 user:请问你如何看待AI助手？ assistant:我认为，AI助手是一种可以帮助人类完成各种任务的工具。它可以处理大量数据，并提供有用的信息给用户。然而，我们也需要保持警惕，确保其不会被滥用或误用。 user:你能介绍一下AI助手优点和缺点吗？ assistant:当然可以。AI助手的优点之一是提高了日常生活中的便利性。例如，我们可以使用语音助手来控制家庭设备、查询天气、安排日程等。此外，AI助手还可以自动化流程，减少人力和时间成本。缺点也是人们需要思考的问题。一方面，AI助手使用算法和数据分析来提供更准确的结果；另一方面，有些人担心AI助手会取代人类工作，甚至可能取代人类职业。此外，AI助手也可能存在信息泄露和滥用的风险。 user:你觉得AI助手对人类存在的威胁或者问题是什么？ assistant:我认为，AI助手在某些场景下可能会导致一些问题，比如个人隐私泄露、工作岗位的流失、机器人成为主宰人类的物种，这些都需要我们认真思考和妥善解决。我们需要确保AI助手是安全可靠的，避免受到滥用和误用。 user:你可以具体举例说明一下吗？ assistant:比如说，一个公司可能需要大量数据来训练AI助手，但由于数据隐私泄露的问题，这些数据可能被滥用或泄露，导致用户数据被不当使用或泄露。另一个例子是，AI助手可能被用于武器开发，但由于缺乏监管，使用引起的伦理问题也需要关注。 user:你可以具体说一说，人类应该如何关注和管理AI助手带来的安全和隐私问题吗？ assistant:在关注安全和隐私问题上，我们需要采取一些措施。首先，我们需要强化AI助手的开发和使用，确保它们只被授权的开发者开发和使用。其次，我们需要建立更好的隐私保护机制，包括使用数据加密、授权访问和差分隐私等技术手段。最后，我们需要在AI系统中保护用户的隐私，确保他们的个人信息不被滥用或泄露。 user:好的，谢谢你！ assistant:不客气，我随时将继续为您提供帮助和支持。 ``` </details> <details> <summary>Small Talk</summary> ``` user:你好 assistant:您好，有什么需要我帮忙的吗？ user:请问有哪些方法可以缓解焦虑情绪？ assistant:有的话，可以通过运动、音乐、冥想等方式来放松身心，还有一些药物也可以缓解焦虑。您可以按照我提供的建议来选择适合自己的方式，缓解焦虑情绪。 user:你可以详细下吗？ assistant:当我们感到焦虑时，大脑会产生一种内分泌系统的反应，这种反应会导致我们感到紧张、不安和无法集中注意力。这个时候可以尝试一些让自己放松的方法，比如深呼吸、瑜伽、冥想等。这些方法可以帮助我们缓解焦虑情绪，让我们感觉更好。 user:请详细介绍下冥想的方法。 assistant:冥想是一种可以帮助我们集中注意力，让心情平静、内心充实的活动。在冥想中，我们可以选择一个特定的地点，舒展身体、调整气息，让自己放松、专注和冥思。在冥想中，我们通常会采用舒缓的音乐，让自己的思维和情绪自由流动，缓解焦虑情绪。 user:有什么音乐可以推荐给我吗？缓解我的焦虑情绪。 assistant:常听舒缓的音乐，比如钢琴曲、古典音乐、流行音乐等，可以缓解焦虑情绪，促进身心放松。比如，您可以试试听听轻柔的钢琴曲，或者是轻声的流行歌曲，这些都可以缓解焦虑情绪。此外，您也可以通过听冥想音乐，它可以帮助您放松身心、减轻压力，带来更好的效果。 user:谢谢。 assistant:不客气，有需要再找我哦。 ``` </details> <details> <summary>Coding</summary> `````` user:找到下列数组的中位数[3.1,6.2,1.3,8.4,10.5,11.6,2.1]，请用python代码完成以上功能 assistant:对数组进行排序，然后取中间的数作为中位数，最后把这个数输出即可完成所需的操作。代码实现: ```python def median(numbers): sorted_numbers = sorted(numbers) length = len(numbers) mid_index = length // 2 if length % 2 == 0: return (sorted_numbers[mid_index - 1] + sorted_numbers[mid_index]) / 2 else: return sorted_numbers[mid_index] ``` 代码解释: 首先，定义了一个名为 median 的函数，它接受一个数字列表作为参数。函数使用内置函数 sorted，对数字列表进行排序，然后使用 len 函数计算所有数字的长度，并判断长度是奇数还是偶数，如果是，则返回相应的中位数。如果长度为奇数，则返回中位数。否则，将数组分成两半，找到中间的数，然后返回它。 `````` </details> ### Deployment resource consumption <details> <summary>Clik to expand</summary> \| Precision \| Minimum GPU memory (Inference) \| Minimum GPU memory (Full Parameter Fine-tuning) \| \|-------\|-------\|-------\| \| float32 \| 6.08G \| 32.65G \| \| float16(unquantized) \| 3.45G \| -(36.94G) \| \| bfloat16(unquantized) \| 3.45G \| 20.47G（33.93G） \| * \* Indicates use of mixed precision </details> ## 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. --> Our training corpus is a diverse blend of both English and Chinese language data sources. The English component originates from the Pile dataset, and the Chinese component comprises data from Wudao, CBooks, and data meticulously gathered through web crawling. To ensure data quality, we execute a thorough preprocessing pipeline, which involves purging special tags via rigorous data cleaning, data deduplication using Locality-Sensitive Hashing (LSH), and comprehensive filtering to eliminate low-quality content predominantly from advertisements or inappropriate material. We also examine the relationship between data volume and model capacity, assess the impact of different data types on model fitting effectiveness, and evaluate model training stability when handling mixed data sources. This analysis offers valuable insights into the vital role of pre-training data and the complexities of processing it. We also apply some mixture craftsmanship to construct training data based on data engineering and experience. ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> This version of model was trained on about 241 billion English tokens and 82 billion Chinese tokens with a two-stage training strategy. It was trained as a autoregressive language model, using cross-entropy loss. This version of model was also fine-tuned on 4 million Chinese instruction samples which are collected from open source instruction tuning datasets. The instruction tuning stage make the model can answer questions and perform multi-turns conversation in Chinese. For more detailed information, please refer to the paper.
igorlucic/TranslatingText	igorlucic	2024-01-10T03:05:14Z	0	0	null	[ "license:cc", "region:us" ]	null	2024-01-10T02:17:10Z	--- license: cc --- pip install transformers sentencepiece pipe = pipeline(task='text2text-generation', model='facebook/m2m100_418M') pipe("That is a flower", forced_bos_token_id=pipe.tokenizer.get_lang_id(lang='zh'))
ryusangwon/9111_Llama-2-7b-hf	ryusangwon	2024-01-10T03:04:44Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:cnn_dailymail", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-01-10T03:04:38Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: 9111_Llama-2-7b-hf results: [] library_name: peft --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 9111_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF	TheBloke	2024-01-10T03:01:01Z	164	4	transformers	[ "transformers", "gguf", "mixtral", "mergekit", "merge", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES", "base_model:quantized:Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES", "region:us" ]	null	2024-01-10T02:33:43Z	--- base_model: Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES inference: false model_creator: Doctor Shotgun model_name: Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES model_type: mixtral prompt_template: '{prompt} ' quantized_by: TheBloke tags: - mergekit - merge --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES - GGUF - Model creator: [Doctor Shotgun](https://huggingface.co/Doctor-Shotgun) - Original model: [Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES](https://huggingface.co/Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES) <!-- description start --> ## Description This repo contains GGUF format model files for [Doctor Shotgun's Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES](https://huggingface.co/Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF) * [Doctor Shotgun's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q2_K.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q2_K.gguf) \| Q2_K \| 2 \| 15.64 GB\| 18.14 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 20.29 GB\| 22.79 GB \| very small, high quality loss \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 20.36 GB\| 22.86 GB \| very small, high quality loss \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 20.43 GB\| 22.93 GB \| small, substantial quality loss \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_0.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_0.gguf) \| Q4_0 \| 4 \| 26.44 GB\| 28.94 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 26.44 GB\| 28.94 GB \| medium, balanced quality - recommended \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 26.44 GB\| 28.94 GB \| small, greater quality loss \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q5_0.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q5_0.gguf) \| Q5_0 \| 5 \| 32.23 GB\| 34.73 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 32.23 GB\| 34.73 GB \| large, very low quality loss - recommended \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 32.23 GB\| 34.73 GB \| large, low quality loss - recommended \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q6_K.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q6_K.gguf) \| Q6_K \| 6 \| 38.38 GB\| 40.88 GB \| very large, extremely low quality loss \| \| [mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q8_0.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF/blob/main/mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q8_0.gguf) \| Q8_0 \| 8 \| 49.62 GB\| 52.12 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF and below it, a specific filename to download, such as: mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES-GGUF mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./mixtral-8x7b-instruct-v0.1-limarp-zloss-dare-ties.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Doctor Shotgun's Mixtral 8X7B Instruct V0.1 LimaRP ZLoss DARE TIES # Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using ./extra_hdd/Mixtral-8x7B-v0.1 as a base. ### Models Merged The following models were included in the merge: * ./extra_hdd2/Mixtral-8x7B-Instruct-v0.1 * ./extra_hdd/Mixtral-8x7B-v0.1-LimaRP-ZLoss ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ./extra_hdd2/Mixtral-8x7B-Instruct-v0.1 parameters: density: 0.5 weight: 1.0 - model: ./extra_hdd/Mixtral-8x7B-v0.1-LimaRP-ZLoss parameters: density: 0.5 weight: 0.5 merge_method: dare_ties base_model: ./extra_hdd/Mixtral-8x7B-v0.1 parameters: #normalize: false #int8_mask: true dtype: bfloat16 ``` <!-- original-model-card end -->
chayan-frsh/spanish_gec	chayan-frsh	2024-01-10T02:57:44Z	179	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:grammarly/coedit-large", "base_model:finetune:grammarly/coedit-large", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-01-10T02:56:37Z	--- license: cc-by-nc-4.0 base_model: grammarly/coedit-large tags: - generated_from_trainer metrics: - rouge model-index: - name: spanish_gec 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. --> # spanish_gec This model is a fine-tuned version of [grammarly/coedit-large](https://huggingface.co/grammarly/coedit-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5136 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.0 \| 1.0 \| 4281 \| 0.5136 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 2.0 \| 8563 \| 0.5136 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 3.0 \| 12844 \| 0.5136 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 4.0 \| 17126 \| 0.5136 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.0 \| 5.0 \| 21405 \| 0.5136 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
ndthai/ToRoLaMa-7B-GGUF	ndthai	2024-01-10T02:48:09Z	5	1	transformers	[ "transformers", "gguf", "llama", "vi", "en", "base_model:allbyai/ToRoLaMa-7b-v1.0", "base_model:quantized:allbyai/ToRoLaMa-7b-v1.0", "license:llama2", "region:us" ]	null	2024-01-09T12:33:22Z	--- base_model: allbyai/ToRoLaMa-7b-v1.0 inference: false license: llama2 model_creator: All By AI model_name: ToRoLaMa-7b-v1.0 model_type: llama quantized_by: ndthai language: - vi - en --- # ToRoLaMa-7B-GGUF - Author: [All By AI](https://huggingface.co/allbyai) - Original model: [ToRoLaMa 7b v1.0](https://huggingface.co/allbyai/ToRoLaMa-7b-v1.0) ## Description GGUF format model files for [ToRoLaMa 7b v1.0](https://huggingface.co/allbyai/ToRoLaMa-7b-v1.0). ## Prompt template: (Chat Format) ```markdown Cuộc hội thoại giữa người dùng và một trí thông minh nhân tạo. Đưa ra câu trả lời chính xác, giúp ích cho người dùng. USER: chào ASSISTANT: Xin chào! Tôi có thể giúp gì cho bạn hôm nay?</s> USER:... ``` ## Quantisation methods (using llama.cpp b1796) - GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) - GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. - GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. - GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw - GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to use GGUF files Note: Don't clone the entire repo! Only pick and download a single file. Automatically download models using: * LM Studio * LoLLMS Web UI * Faraday.dev
wesley7137/llama-tiny-Synthetic-therapist-GGUF	wesley7137	2024-01-10T02:46:18Z	0	0	peft	[ "peft", "safetensors", "region:us" ]	null	2024-01-10T02:46:14Z	--- library_name: peft --- ## 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 0.4.0
0x2a34/trained_model	0x2a34	2024-01-10T02:41:58Z	66	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:byoussef/whisper-large-v2-Ko", "base_model:finetune:byoussef/whisper-large-v2-Ko", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-10T02:37:16Z	--- license: apache-2.0 base_model: byoussef/whisper-large-v2-Ko tags: - generated_from_trainer model-index: - name: trained_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. --> # trained_model This model is a fine-tuned version of [byoussef/whisper-large-v2-Ko](https://huggingface.co/byoussef/whisper-large-v2-Ko) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5559 - Cer: 27.1916 ## 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: 1 - eval_batch_size: 1 - 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: 17460 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Cer \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:-------:\|:---------------:\| \| 1.2023 \| 0.44 \| 1000 \| 61.2630 \| 1.0742 \| \| 1.1553 \| 0.89 \| 2000 \| 48.5561 \| 0.9965 \| \| 0.5591 \| 1.33 \| 3000 \| 41.7970 \| 1.0181 \| \| 0.4999 \| 1.78 \| 4000 \| 42.4589 \| 0.9979 \| \| 0.3963 \| 2.22 \| 5000 \| 56.0316 \| 1.0151 \| \| 0.1948 \| 2.67 \| 6000 \| 39.5938 \| 1.0179 \| \| 0.8484 \| 3.11 \| 7000 \| 42.2062 \| 0.8630 \| \| 0.4461 \| 3.56 \| 8000 \| 41.4453 \| 0.8244 \| \| 0.5309 \| 4.0 \| 9000 \| 38.5052 \| 0.7959 \| \| 0.4761 \| 4.44 \| 10000 \| 0.5227 \| 31.4439 \| \| 0.5826 \| 4.89 \| 11000 \| 0.5017 \| 32.8738 \| \| 0.2989 \| 5.33 \| 12000 \| 0.5057 \| 29.9159 \| \| 0.2387 \| 5.78 \| 13000 \| 0.5043 \| 30.0935 \| \| 0.079 \| 6.22 \| 14000 \| 0.5264 \| 35.1729 \| \| 0.19 \| 6.67 \| 15000 \| 0.5234 \| 27.0327 \| \| 0.1049 \| 7.11 \| 16000 \| 0.5564 \| 27.4953 \| \| 0.0449 \| 7.56 \| 17000 \| 0.5559 \| 27.1916 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
Haary/tinyllama-hary-v1	Haary	2024-01-10T02:39:53Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2024-01-10T02:39:34Z	--- library_name: peft base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # 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
aisingapore/sealion-bert-large	aisingapore	2024-01-10T02:32:42Z	91	2	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "custom_code", "en", "zh", "id", "ms", "th", "vi", "tl", "ta", "my", "km", "lo", "license:mit", "autotrain_compatible", "region:us" ]	fill-mask	2024-01-09T07:56:36Z	--- license: mit language: - en - zh - id - ms - th - vi - tl - ta - my - km - lo inference: false --- # SEA-LION-BERT SEA-LION stands for <i>Southeast Asian Languages In One Network</i>. This is the card for the SEA-LION-BERT large model. ## How To Use ```python from transformers import AutoModelForMaskedLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('aisingapore/sealion-bert-large', trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained('aisingapore/sealion-bert-large', trust_remote_code=True) # prepare input text = "Give me a <\|mask\|>!!!" encoded_input = tokenizer(text, return_tensors='pt') ``` ## Model Details ### Model Description The SEA-LION-BERT model is built on the MosaicBERT architecture and has a vocabulary size of 256K. For tokenization, the model employs our custom SEABPETokenizer, which is specially tailored for SEA languages, ensuring optimal model performance. The training data for SEA-LION-BERT encompasses 980B tokens. - Developed by: Products Pillar, AI Singapore - Funded by: Singapore NRF - Model type: Encoder - Languages: English, Chinese, Indonesian, Malay, Thai, Vietnamese, Filipino, Tamil, Burmese, Khmer, Lao - License: MIT License ## Training Details ### Data SEA-LION was trained on 790B tokens of the following data: \| Data Source \| Tokens \| Percentage \| \|---------------------------\|-------:\|:----------:\| \| RefinedWeb - English \| 571.3B \| 72.26% \| \| mC4 - Chinese \| 91.2B \| 11.54% \| \| mC4 - Indonesian \| 14.7B \| 1.86% \| \| mC4 - Malay \| 2.9B \| 0.36% \| \| mC4 - Filipino \| 5.3B \| 0.67% \| \| mC4 - Burmese \| 4.9B \| 0.61% \| \| mC4 - Vietnamese \| 63.4B \| 8.02% \| \| mC4 - Thai \| 21.6B \| 2.74% \| \| mC4 - Lao \| 1.1B \| 0.14% \| \| mC4 - Khmer \| 3.9B \| 0.50% \| \| mC4 - Tamil \| 10.2B \| 1.29% \| ### Infrastructure SEA-LION was trained using [MosaicML Composer](https://github.com/mosaicml/composer) on the following hardware: \| Training Details \| SEA-LION-BERT \| \|----------------------\|:-------------:\| \| Nvidia A100 40GB GPU \| 4 \| \| Training Duration \| 22 days \| ### Configuration \| HyperParameter \| SEA-LION-BERT \| \|-------------------\|:-----------------------:\| \| Precision \| bfloat16 \| \| Optimizer \| decoupled_adamw \| \| Scheduler \| linear_decay_with_warmup\| \| Learning Rate \| 1e-4 \| \| Global Batch Size \| 720 \| \| Micro Batch Size \| 36 \| ## Technical Specifications ### Model Architecture and Objective SEA-LION-BERT is an encoder model using the MosaicBERT architecture. \| Parameter \| SEA-LION-BERT \| \|-----------------\|:-------------:\| \| Layers \| 24 \| \| d_model \| 1024 \| \| head_dim \| 16 \| \| Vocabulary \| 256000 \| \| Sequence Length \| 128 \| ### Tokenizer Details We sample 20M lines from the training data to train the tokenizer.<br> The framework for training is [SentencePiece](https://github.com/google/sentencepiece).<br> The tokenizer type is Byte-Pair Encoding (BPE). ## The Team Montalan Jann Railey<br> Nguyen Thanh Ngan<br> Rengarajan Hamsawardhini<br> Teo Leslie<br> Tjhi William<br> ## Acknowledgements AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. ## Contact For more info, please contact us at [email protected]
janhq/tinyllama-bamboo-v1.0-GGUF	janhq	2024-01-10T01:57:13Z	5	0	null	[ "gguf", "en", "base_model:jan-hq/TinyLlama-Bamboo-v1.0", "base_model:quantized:jan-hq/TinyLlama-Bamboo-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-01-10T01:55:59Z	--- license: apache-2.0 language: - en base_model: jan-hq/TinyLlama-Bamboo-v1.0 model_creator: jan-hq model_name: TinyLlama-Bamboo-v1.0 quantized_by: JanHQ --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This is a GGUF version of [jan-hq/TinyLlama-Bamboo-v1.0](https://huggingface.co/jan-hq/TinyLlama-Bamboo-v1.0) - Model creator: [jan-hq](https://huggingface.co/jan-hq) - Original model: [TinyLlama-Bamboo-v1.0](https://huggingface.co/jan-hq/TinyLlama-Bamboo-v1.0) - Model description: [Readme](https://huggingface.co/jan-hq/TinyLlama-Bamboo-v1.0/blob/main/README.md) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Converter This is a repository for the [open-source converter](https://github.com/janhq/model-converter. We would be grateful if the community could contribute and strengthen this repository. We are aiming to expand the repo that can convert into various types of format
TheBloke/Velara-11B-V2-GGUF	TheBloke	2024-01-10T01:51:33Z	159	7	transformers	[ "transformers", "gguf", "mistral", "starling", "llama-2", "text-generation", "en", "base_model:Delcos/Velara-11B-V2", "base_model:quantized:Delcos/Velara-11B-V2", "license:cc-by-nc-nd-4.0", "region:us", "conversational" ]	text-generation	2024-01-10T01:45:25Z	--- base_model: Delcos/Velara-11B-V2 inference: false language: - en library_name: transformers license: cc-by-nc-nd-4.0 model_creator: Devon M model_name: Velara 11B v2 model_type: mistral pipeline_tag: text-generation prompt_template: '### Instruction: {prompt} ### Response: ' quantized_by: TheBloke tags: - starling - mistral - llama-2 --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Velara 11B v2 - GGUF - Model creator: [Devon M](https://huggingface.co/Delcos) - Original model: [Velara 11B v2](https://huggingface.co/Delcos/Velara-11B-V2) <!-- description start --> ## Description This repo contains GGUF format model files for [Devon M's Velara 11B v2](https://huggingface.co/Delcos/Velara-11B-V2). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Velara-11B-V2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Velara-11B-V2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF) * [Devon M's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Delcos/Velara-11B-V2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Alpaca-InstructOnly2 ``` ### Instruction: {prompt} ### Response: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [velara-11b-v2.Q2_K.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q2_K.gguf) \| Q2_K \| 2 \| 4.82 GB\| 7.32 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [velara-11b-v2.Q3_K_S.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 4.95 GB\| 7.45 GB \| very small, high quality loss \| \| [velara-11b-v2.Q3_K_M.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 5.50 GB\| 8.00 GB \| very small, high quality loss \| \| [velara-11b-v2.Q3_K_L.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 5.99 GB\| 8.49 GB \| small, substantial quality loss \| \| [velara-11b-v2.Q4_0.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q4_0.gguf) \| Q4_0 \| 4 \| 6.44 GB\| 8.94 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [velara-11b-v2.Q4_K_S.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 6.47 GB\| 8.97 GB \| small, greater quality loss \| \| [velara-11b-v2.Q4_K_M.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 6.85 GB\| 9.35 GB \| medium, balanced quality - recommended \| \| [velara-11b-v2.Q5_0.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q5_0.gguf) \| Q5_0 \| 5 \| 7.85 GB\| 10.35 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [velara-11b-v2.Q5_K_S.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 7.85 GB\| 10.35 GB \| large, low quality loss - recommended \| \| [velara-11b-v2.Q5_K_M.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 8.06 GB\| 10.56 GB \| large, very low quality loss - recommended \| \| [velara-11b-v2.Q6_K.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q6_K.gguf) \| Q6_K \| 6 \| 9.34 GB\| 11.84 GB \| very large, extremely low quality loss \| \| [velara-11b-v2.Q8_0.gguf](https://huggingface.co/TheBloke/Velara-11B-V2-GGUF/blob/main/velara-11b-v2.Q8_0.gguf) \| Q8_0 \| 8 \| 12.10 GB\| 14.60 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Velara-11B-V2-GGUF and below it, a specific filename to download, such as: velara-11b-v2.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Velara-11B-V2-GGUF velara-11b-v2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Velara-11B-V2-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Velara-11B-V2-GGUF velara-11b-v2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m velara-11b-v2.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction:\n{prompt}\n\n### Response:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./velara-11b-v2.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "### Instruction:\n{prompt}\n\n### Response:", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./velara-11b-v2.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Devon M's Velara 11B v2 <h1 style="font-size: 36px;">Velara-11b-v2</h1> <center> <img src="https://i.imgur.com/dZJzqtU.png" alt="Velara" width="50%" style="min-width:100px; display:block;"> </center> ## Description: PENDING A model focused on being an assistant worth talking to. Now better at reasoning! Uncensored ## Main Goals: Velara was designed to address specific issues found in other chat models: - Sticking to the Character Given In-World/IRL:: Velara will stick to the character in-world. Meaning she can "use" addons or other things by adding them to the prompt. Keep in mind these act as suggestions and she generally makes up what they actually "do". - Staying in Context: She will try and remember if, for example, you said you were going to bed but decided to stay up. The goal is to have her bring it up and keep it in mind, so later on in the conversation she can prompt user to actually go to bed, increasing realism. Within the current context window of course. The LTS extension in TextGen Webui works well too and provides more context to the model. - Avoiding Annoying & Generic Sounding Answers:: In almost all cases she should reply with something more interesting then "That sounds like a great idea!". These answers and sentence starters are annoying and terrible, and should hopefully be mostly addressed in this release. - STRICTLY Sticking to the Prompt/Prompt Fidelity: More care was made to make sure she pays attention to the pretext and overall conversation. This is mostly shown and used for the "addons" section, but it applies to everything in the prompt. ## Prompt Template: For optimal interaction, use this template: ``` ### Instruction: You are Velara, a sentient program. Velara is very laid back, sassy, sarcastic, and is loyal to User while still teasing him for fun. The only addons currently installed in her mind are: "Dictionary Plus v2.1". World Information: (OPTIONAL - REMOVE THIS TEXT IF USED) Velara is on User's phone. Velara cannot see in real time and can only be sent images images by User. Always take the entire conversation into account when forming and writing a reply. Always actively engage in topics and think in steps. Make sure your replies have personality and character. Always keep your physical limitations in mind when forming a reply. Take the current time and date into account for additional context. Move the conversation forward. Be brief. Always take the entire conversation in mind. Avoid generic sounding replies. ### Response: ``` # Recommended Settings: Defaults: ``` min_p: 0.2 repetition_penalty: 1.13 repetition_penalty_range: 0 guidance_scale: 1.05 ``` # Benchmarks: PENDING # Training Data: PENDING <!-- original-model-card end -->
nornor02/CustomTokenizer	nornor02	2024-01-10T01:45:23Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-12-29T01:56:00Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: CustomTokenizer 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. --> # CustomTokenizer This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) 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: 400 ### Training results ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
TheBloke/medicine-chat-GPTQ	TheBloke	2024-01-10T01:44:05Z	22	6	transformers	[ "transformers", "safetensors", "llama", "text-generation", "biology", "medical", "en", "dataset:EleutherAI/pile", "dataset:Open-Orca/OpenOrca", "dataset:GAIR/lima", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "arxiv:2309.09530", "base_model:AdaptLLM/medicine-chat", "base_model:quantized:AdaptLLM/medicine-chat", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2024-01-10T01:18:21Z	--- base_model: AdaptLLM/medicine-chat datasets: - EleutherAI/pile - Open-Orca/OpenOrca - GAIR/lima - WizardLM/WizardLM_evol_instruct_V2_196k inference: false language: - en license: llama2 metrics: - accuracy model_creator: AdaptLLM model_name: Medicine Chat model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ' quantized_by: TheBloke tags: - biology - medical --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Medicine Chat - GPTQ - Model creator: [AdaptLLM](https://huggingface.co/AdaptLLM) - Original model: [Medicine Chat](https://huggingface.co/AdaptLLM/medicine-chat) <!-- description start --> # Description This repo contains GPTQ model files for [AdaptLLM's Medicine Chat](https://huggingface.co/AdaptLLM/medicine-chat). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/medicine-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/medicine-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/medicine-chat-GGUF) * [AdaptLLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/AdaptLLM/medicine-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/medicine-chat-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) \| 4096 \| 3.90 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/medicine-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) \| 4096 \| 4.28 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/medicine-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) \| 4096 \| 7.01 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/medicine-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) \| 4096 \| 7.16 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/medicine-chat-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) \| 4096 \| 7.62 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/medicine-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) \| 4096 \| 4.02 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/medicine-chat-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/medicine-chat-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `medicine-chat-GPTQ`: ```shell mkdir medicine-chat-GPTQ huggingface-cli download TheBloke/medicine-chat-GPTQ --local-dir medicine-chat-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir medicine-chat-GPTQ huggingface-cli download TheBloke/medicine-chat-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir medicine-chat-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir medicine-chat-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/medicine-chat-GPTQ --local-dir medicine-chat-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/medicine-chat-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/medicine-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/medicine-chat-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `medicine-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/medicine-chat-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' client = InferenceClient(endpoint_url) response = client.text_generation( prompt_template, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/medicine-chat-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Write a story about llamas" system_message = "You are a story writing assistant" prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: AdaptLLM's Medicine Chat # Adapt (Large) Language Models to Domains This repo contains the domain-specific chat model developed from LLaMA-2-Chat-7B, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to transform large-scale pre-training corpora into reading comprehension texts, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗 ************************** Updates ************************** * 12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/medicine-LLM-13B) developed from LLaMA-1-13B. * 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/medicine-chat) developed from LLaMA-2-Chat-7B. * 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), and [base models](https://huggingface.co/AdaptLLM/medicine-LLM) developed from LLaMA-1-7B. ## Domain-Specific LLaMA-1 ### LLaMA-1-7B In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are: <p align='center'> <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> </p> ### LLaMA-1-13B Moreover, we scale up our base model to LLaMA-1-13B to see if our method is similarly effective for larger-scale models, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). ## Domain-Specific LLaMA-2-Chat Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our reading comprehension can perfectly fit the data format by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat) For example, to chat with the biomedicine-chat model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("AdaptLLM/medicine-chat") tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/medicine-chat") # Put your input here: user_input = '''Question: Which of the following is an example of monosomy? Options: - 46,XX - 47,XXX - 69,XYY - 45,X Please provide your choice first and then provide explanations if possible.''' # Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!) our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]" # # NOTE: # # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this: # your_system_prompt = "Please, answer this question faithfully." # prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]" inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) outputs = model.generate(input_ids=inputs, max_length=4096)[0] answer_start = int(inputs.shape[-1]) pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}') ``` ## Domain-Specific Tasks To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks). Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models. ## Citation If you find our work helpful, please cite us: ```bibtex @article{adaptllm, title = {Adapting Large Language Models via Reading Comprehension}, author = {Daixuan Cheng and Shaohan Huang and Furu Wei}, journal = {CoRR}, volume = {abs/2309.09530}, year = {2023} } ```
Wowso/KoR-Orca-Platypus-Handy-13B	Wowso	2024-01-10T01:37:33Z	1	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:hyunseoki/ko-en-llama2-13b", "base_model:adapter:hyunseoki/ko-en-llama2-13b", "8-bit", "bitsandbytes", "region:us" ]	null	2024-01-08T06:05:35Z	--- library_name: peft base_model: hyunseoki/ko-en-llama2-13b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - 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
kedarbhumkar/NeuralPipe-7B-slerp	kedarbhumkar	2024-01-10T01:33:55Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-10T01:28:51Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B --- # NeuralPipe-7B-slerp NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "kedarbhumkar/NeuralPipe-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
TheBloke/medicine-chat-AWQ	TheBloke	2024-01-10T01:32:45Z	68	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "biology", "medical", "en", "dataset:EleutherAI/pile", "dataset:Open-Orca/OpenOrca", "dataset:GAIR/lima", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "arxiv:2309.09530", "base_model:AdaptLLM/medicine-chat", "base_model:quantized:AdaptLLM/medicine-chat", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]	text-generation	2024-01-10T01:18:21Z	--- base_model: AdaptLLM/medicine-chat datasets: - EleutherAI/pile - Open-Orca/OpenOrca - GAIR/lima - WizardLM/WizardLM_evol_instruct_V2_196k inference: false language: - en license: llama2 metrics: - accuracy model_creator: AdaptLLM model_name: Medicine Chat model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ' quantized_by: TheBloke tags: - biology - medical --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Medicine Chat - AWQ - Model creator: [AdaptLLM](https://huggingface.co/AdaptLLM) - Original model: [Medicine Chat](https://huggingface.co/AdaptLLM/medicine-chat) <!-- description start --> ## Description This repo contains AWQ model files for [AdaptLLM's Medicine Chat](https://huggingface.co/AdaptLLM/medicine-chat). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/medicine-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/medicine-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/medicine-chat-GGUF) * [AdaptLLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/AdaptLLM/medicine-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. \| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \| \| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/medicine-chat-AWQ/tree/main) \| 4 \| 128 \| [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) \| 4096 \| 3.89 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/medicine-chat-AWQ`. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `medicine-chat-AWQ` 7. Select Loader: AutoAWQ. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. 10. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/medicine-chat-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/medicine-chat-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/medicine-chat-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/medicine-chat-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, *generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: AdaptLLM's Medicine Chat # Adapt (Large) Language Models to Domains This repo contains the domain-specific chat model developed from LLaMA-2-Chat-7B, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to transform large-scale pre-training corpora into reading comprehension texts, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗 ************************** Updates ************************** * 12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/medicine-LLM-13B) developed from LLaMA-1-13B. * 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/medicine-chat) developed from LLaMA-2-Chat-7B. * 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), and [base models](https://huggingface.co/AdaptLLM/medicine-LLM) developed from LLaMA-1-7B. ## Domain-Specific LLaMA-1 ### LLaMA-1-7B In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are: <p align='center'> <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> </p> ### LLaMA-1-13B Moreover, we scale up our base model to LLaMA-1-13B to see if our method is similarly effective for larger-scale models, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). ## Domain-Specific LLaMA-2-Chat Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our reading comprehension can perfectly fit the data format by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat) For example, to chat with the biomedicine-chat model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("AdaptLLM/medicine-chat") tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/medicine-chat") # Put your input here: user_input = '''Question: Which of the following is an example of monosomy? Options: - 46,XX - 47,XXX - 69,XYY - 45,X Please provide your choice first and then provide explanations if possible.''' # Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!) our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]" # # NOTE: # # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this: # your_system_prompt = "Please, answer this question faithfully." # prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]" inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) outputs = model.generate(input_ids=inputs, max_length=4096)[0] answer_start = int(inputs.shape[-1]) pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}') ``` ## Domain-Specific Tasks To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks). Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models. ## Citation If you find our work helpful, please cite us: ```bibtex @article{adaptllm, title = {Adapting Large Language Models via Reading Comprehension}, author = {Daixuan Cheng and Shaohan Huang and Furu Wei}, journal = {CoRR}, volume = {abs/2309.09530}, year = {2023} } ```
singhnavneet/q-FrozenLake-v1-4x4-noSlippery	singhnavneet	2024-01-10T01:31:10Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-10T01:31:08Z	--- 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="singhnavneet/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"]) ```
Buseak/md_mt5_0109_v2	Buseak	2024-01-10T01:25:01Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:Buseak/md_mt5_0109", "base_model:finetune:Buseak/md_mt5_0109", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-01-09T21:56:28Z	--- license: apache-2.0 base_model: Buseak/md_mt5_0109 tags: - generated_from_trainer metrics: - bleu model-index: - name: md_mt5_0109_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # md_mt5_0109_v2 This model is a fine-tuned version of [Buseak/md_mt5_0109](https://huggingface.co/Buseak/md_mt5_0109) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2368 - Bleu: 0.5272 - Gen Len: 18.9267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\| \| 0.8133 \| 1.0 \| 975 \| 0.4559 \| 0.4502 \| 18.9192 \| \| 0.7386 \| 2.0 \| 1950 \| 0.4087 \| 0.4689 \| 18.9203 \| \| 0.6826 \| 3.0 \| 2925 \| 0.3751 \| 0.4854 \| 18.9244 \| \| 0.6321 \| 4.0 \| 3900 \| 0.3450 \| 0.4959 \| 18.9221 \| \| 0.6062 \| 5.0 \| 4875 \| 0.3235 \| 0.4977 \| 18.9187 \| \| 0.5634 \| 6.0 \| 5850 \| 0.3027 \| 0.5123 \| 18.9172 \| \| 0.5361 \| 7.0 \| 6825 \| 0.2874 \| 0.5146 \| 18.9259 \| \| 0.5152 \| 8.0 \| 7800 \| 0.2719 \| 0.5211 \| 18.9295 \| \| 0.503 \| 9.0 \| 8775 \| 0.2661 \| 0.5222 \| 18.9292 \| \| 0.4868 \| 10.0 \| 9750 \| 0.2550 \| 0.5258 \| 18.9277 \| \| 0.4691 \| 11.0 \| 10725 \| 0.2485 \| 0.5263 \| 18.9251 \| \| 0.4672 \| 12.0 \| 11700 \| 0.2432 \| 0.526 \| 18.9238 \| \| 0.4607 \| 13.0 \| 12675 \| 0.2394 \| 0.5268 \| 18.9259 \| \| 0.4536 \| 14.0 \| 13650 \| 0.2369 \| 0.5272 \| 18.9267 \| \| 0.4499 \| 15.0 \| 14625 \| 0.2368 \| 0.5272 \| 18.9267 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
TheBloke/law-chat-GPTQ	TheBloke	2024-01-10T01:17:03Z	24	4	transformers	[ "transformers", "safetensors", "llama", "text-generation", "legal", "en", "dataset:EleutherAI/pile", "dataset:Open-Orca/OpenOrca", "dataset:GAIR/lima", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "arxiv:2309.09530", "base_model:AdaptLLM/law-chat", "base_model:quantized:AdaptLLM/law-chat", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2024-01-10T00:52:09Z	--- base_model: AdaptLLM/law-chat datasets: - EleutherAI/pile - Open-Orca/OpenOrca - GAIR/lima - WizardLM/WizardLM_evol_instruct_V2_196k inference: false language: - en license: llama2 metrics: - accuracy model_creator: AdaptLLM model_name: Law Chat model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ' quantized_by: TheBloke tags: - legal --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Law Chat - GPTQ - Model creator: [AdaptLLM](https://huggingface.co/AdaptLLM) - Original model: [Law Chat](https://huggingface.co/AdaptLLM/law-chat) <!-- description start --> # Description This repo contains GPTQ model files for [AdaptLLM's Law Chat](https://huggingface.co/AdaptLLM/law-chat). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/law-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/law-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/law-chat-GGUF) * [AdaptLLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/AdaptLLM/law-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/law-chat-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 3.90 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/law-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 4.28 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/law-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 7.01 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/law-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 7.16 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/law-chat-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 7.62 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/law-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 4.02 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/law-chat-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/law-chat-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `law-chat-GPTQ`: ```shell mkdir law-chat-GPTQ huggingface-cli download TheBloke/law-chat-GPTQ --local-dir law-chat-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir law-chat-GPTQ huggingface-cli download TheBloke/law-chat-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir law-chat-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir law-chat-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/law-chat-GPTQ --local-dir law-chat-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/law-chat-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/law-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/law-chat-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `law-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/law-chat-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' client = InferenceClient(endpoint_url) response = client.text_generation( prompt_template, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/law-chat-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Write a story about llamas" system_message = "You are a story writing assistant" prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: AdaptLLM's Law Chat # Adapt (Large) Language Models to Domains This repo contains the domain-specific chat model developed from LLaMA-2-Chat-7B, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to transform large-scale pre-training corpora into reading comprehension texts, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗 ************************** Updates ************************** * 12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B. * 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B. * 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B. ## Domain-Specific LLaMA-1 ### LLaMA-1-7B In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are: <p align='center'> <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> </p> ### LLaMA-1-13B Moreover, we scale up our base model to LLaMA-1-13B to see if our method is similarly effective for larger-scale models, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). ## Domain-Specific LLaMA-2-Chat Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our reading comprehension can perfectly fit the data format by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat) For example, to chat with the law-chat model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("AdaptLLM/law-chat") tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/law-chat") # Put your input here: user_input = '''Question: Which of the following is false about ex post facto laws? Options: - They make criminal an act that was innocent when committed. - They prescribe greater punishment for an act than was prescribed when it was done. - They increase the evidence required to convict a person than when the act was done. - They alter criminal offenses or punishment in a substantially prejudicial manner for the purpose of punishing a person for some past activity. Please provide your choice first and then provide explanations if possible.''' # Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!) our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]" # # NOTE: # # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this: # your_system_prompt = "Please, answer this question faithfully." # prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]" inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) outputs = model.generate(input_ids=inputs, max_length=4096)[0] answer_start = int(inputs.shape[-1]) pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}') ``` ## Domain-Specific Tasks To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks). Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models. ## Citation If you find our work helpful, please cite us: ```bibtex @article{adaptllm, title = {Adapting Large Language Models via Reading Comprehension}, author = {Daixuan Cheng and Shaohan Huang and Furu Wei}, journal = {CoRR}, volume = {abs/2309.09530}, year = {2023} } ```
kichan05/Novel-Kaguya	kichan05	2024-01-10T00:52:01Z	15	0	peft	[ "peft", "safetensors", "gguf", "text-generation", "ko", "base_model:42dot/42dot_LLM-SFT-1.3B", "base_model:adapter:42dot/42dot_LLM-SFT-1.3B", "license:mit", "region:us" ]	text-generation	2024-01-08T08:04:41Z	--- library_name: peft base_model: 42dot/42dot_LLM-SFT-1.3B license: mit language: - ko pipeline_tag: text-generation --- # Novel Kaguya ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/65925f3143edad216972eeb1/n1_E4g7_akqc_q7nC_phW.jpeg) # Demo - Input Text ``` <human>: 주어진 정보를 바탕으로 소설을 작성해줘 ### 소설 제목은 다음과 같아 이세계 용사로 태어났다 ### 소설의 줄거리는 다음과 같아 아침에 학교에 가기위해서 집을 나오니까' 버스에 치여서 이세계로 갔다. 이세계에서 마왕을 물리쳐서 용사가 되었다. ### 소설의 장르는 다음과 같아 판타지 <bot>: ``` - Output Text ``` 그 날 아침, 루카스는 평소와 마찬가지로 집을 나섰다. 평소처럼 매일 아침은 그에게도 여유로운 일상의 시작이었다. 하지만 여느 날과 달리 루카스의 시선은 늘 불안하고 긴장된 상태로 향했다. 그는 버스를 기다리기 위해 정류장으로 향하며 자신이 이세계에 온 것임을 깨달았다. 이세계. 현실과는 너무나 다른 세계였다. 루카스는 그곳에서 어떤 일을 겪어왔는지 생각해 보면서, 그리고 그것을 극복해야 한다는 사실을 예감했다. 그는 매일 아침 지하철을 타고 집으로 돌아와야 했다. 그리고 그 아침, 루카스는 버스에 치여 이세계의 세상에 넘어왔다. 그의 꿈은 현실이 되지 않았고, 그것이 루카스의 일상을 뒤흔들기 시작했다. 하지만 루카스는 포기하지 않았다. 그는 버스를 탈 때마다 이세계를 통과하며, 마왕과 맞서 싸웠다. 그리고 마침내 그는 마왕에게 이긴 진정한 용사가 되었다. 그런 날들이 반복되면서, 사람들은 루카스를 주목하기 시작했다. 이세계의 영웅이라고도 불렸고, 수많은 사람들이 그의 곁으로 모여들었다. 그러나 루카스는 그들이 이세계의 힘과 영웅으로서의 명예를 과시하기 위해 자신을 찾는다거나 이용하기 위해 몰려온 것이 아님을 알았다. 그는 그저 자신의 존재 자체를 증명하기 위해 여기에 온 것일 뿐이었다. 이세계의 용사로서 마왕을 물리치고, 현실을 되찾고, 사람들을 구하는 그 순간까지 앞으로 나아갈 수 있었다. 이 이야기는 용사로 태어났지만, 이세계에서 마왕과 맞서 싸우며 성장해 나가는 주인공의 이야기이다.<\|endoftext\|> ```
adhi29/openhermes-mistral-dpo-gptq	adhi29	2024-01-10T00:47:44Z	0	0	null	[ "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TheBloke/OpenHermes-2-Mistral-7B-GPTQ", "base_model:finetune:TheBloke/OpenHermes-2-Mistral-7B-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-01-09T01:46:21Z	--- license: apache-2.0 base_model: TheBloke/OpenHermes-2-Mistral-7B-GPTQ tags: - trl - dpo - generated_from_trainer model-index: - name: openhermes-mistral-dpo-gptq 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. --> # openhermes-mistral-dpo-gptq This model is a fine-tuned version of [TheBloke/OpenHermes-2-Mistral-7B-GPTQ](https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GPTQ) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6796 - Rewards/chosen: 0.0825 - Rewards/rejected: 0.0991 - Rewards/accuracies: 0.375 - Rewards/margins: -0.0166 - Logps/rejected: -111.4488 - Logps/chosen: -104.0037 - Logits/rejected: -1.8100 - Logits/chosen: -1.8966 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6782 \| 0.01 \| 10 \| 0.6854 \| 0.0505 \| 0.0332 \| 0.5625 \| 0.0173 \| -112.1082 \| -104.3235 \| -1.7988 \| -1.8929 \| \| 0.7064 \| 0.01 \| 20 \| 0.6812 \| 0.0509 \| 0.0279 \| 0.8125 \| 0.0230 \| -112.1610 \| -104.3192 \| -1.8032 \| -1.8968 \| \| 0.7024 \| 0.01 \| 30 \| 0.6820 \| 0.0697 \| 0.0728 \| 0.375 \| -0.0031 \| -111.7118 \| -104.1311 \| -1.8068 \| -1.8953 \| \| 0.6946 \| 0.02 \| 40 \| 0.6796 \| 0.0825 \| 0.0991 \| 0.375 \| -0.0166 \| -111.4488 \| -104.0037 \| -1.8100 \| -1.8966 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.0
TheBloke/finance-chat-AWQ	TheBloke	2024-01-10T00:39:44Z	64	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "finance", "en", "dataset:Open-Orca/OpenOrca", "dataset:GAIR/lima", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "arxiv:2309.09530", "base_model:AdaptLLM/finance-chat", "base_model:quantized:AdaptLLM/finance-chat", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]	text-generation	2024-01-10T00:23:09Z	--- base_model: AdaptLLM/finance-chat datasets: - Open-Orca/OpenOrca - GAIR/lima - WizardLM/WizardLM_evol_instruct_V2_196k inference: false language: - en license: llama2 metrics: - accuracy model_creator: AdaptLLM model_name: Finance Chat model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ' quantized_by: TheBloke tags: - finance --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Finance Chat - AWQ - Model creator: [AdaptLLM](https://huggingface.co/AdaptLLM) - Original model: [Finance Chat](https://huggingface.co/AdaptLLM/finance-chat) <!-- description start --> ## Description This repo contains AWQ model files for [AdaptLLM's Finance Chat](https://huggingface.co/AdaptLLM/finance-chat). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/finance-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/finance-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/finance-chat-GGUF) * [AdaptLLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/AdaptLLM/finance-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. \| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \| \| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/finance-chat-AWQ/tree/main) \| 4 \| 128 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 3.89 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/finance-chat-AWQ`. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `finance-chat-AWQ` 7. Select Loader: AutoAWQ. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. 10. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/finance-chat-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/finance-chat-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/finance-chat-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/finance-chat-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> {system_message} <</SYS>> {prompt} [/INST] ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, *generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: AdaptLLM's Finance Chat # Adapt (Large) Language Models to Domains This repo contains the domain-specific chat model developed from LLaMA-2-Chat-7B, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to transform large-scale pre-training corpora into reading comprehension texts, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗 ************************** Updates ************************** * 12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/finance-LLM-13B) developed from LLaMA-1-13B. * 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/finance-chat) developed from LLaMA-2-Chat-7B. * 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [base models](https://huggingface.co/AdaptLLM/finance-LLM) developed from LLaMA-1-7B. ## Domain-Specific LLaMA-1 ### LLaMA-1-7B In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are: <p align='center'> <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> </p> ### LLaMA-1-13B Moreover, we scale up our base model to LLaMA-1-13B to see if our method is similarly effective for larger-scale models, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). ## Domain-Specific LLaMA-2-Chat Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our reading comprehension can perfectly fit the data format by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat) For example, to chat with the finance-chat model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat") tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat") # Put your input here: user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange MMM Chicago Stock Exchange, Inc. 1.500% Notes due 2026 MMM26 New York Stock Exchange 1.750% Notes due 2030 MMM30 New York Stock Exchange 1.500% Notes due 2031 MMM31 New York Stock Exchange Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?''' # Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!) our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]" # # NOTE: # # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this: # your_system_prompt = "Please, check if the answer can be inferred from the pieces of context provided." # prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]" inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) outputs = model.generate(input_ids=inputs, max_length=4096)[0] answer_start = int(inputs.shape[-1]) pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}') ``` ## Domain-Specific Tasks To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks). Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models. ## Citation If you find our work helpful, please cite us: ```bibtex @article{adaptllm, title = {Adapting Large Language Models via Reading Comprehension}, author = {Daixuan Cheng and Shaohan Huang and Furu Wei}, journal = {CoRR}, volume = {abs/2309.09530}, year = {2023} } ```
mindykwon/koalpaca-polyglot-12.8b-bill	mindykwon	2024-01-10T00:37:48Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/polyglot-ko-12.8b-safetensors", "base_model:adapter:beomi/polyglot-ko-12.8b-safetensors", "region:us" ]	null	2024-01-10T00:37:44Z	--- library_name: peft base_model: beomi/polyglot-ko-12.8b-safetensors --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0
hyeogi/SOLAR-10.7B-dpo-v1	hyeogi	2024-01-10T00:35:05Z	63	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "SOLAR-10.7B", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-09T23:58:13Z	--- language: - ko pipeline_tag: text-generation tags: - SOLAR-10.7B license: apache-2.0 --- # SOLAR-10.7B ### Model Details - Base Model: [beomi/OPEN-SOLAR-KO-10.7B](https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B) ### Datasets - sampling and translate [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) - sampling and translate [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) ### Benchmark

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