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FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t3000_e5_member_shadow9	FounderOfHuggingface	2024-01-19T10:22:25Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T10:22:22Z	--- 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_lora_r4_e2e_nlg_t3000_e5_member_shadow6	FounderOfHuggingface	2024-01-19T10:18:05Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T10:18:02Z	--- 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
imalexianne/MovieBertReview-SentimentPrediction-Model	imalexianne	2024-01-19T10:16:41Z	3	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-19T08:32:28Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: MovieBertReview-SentimentPrediction-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. --> # MovieBertReview-SentimentPrediction-Model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2269 - Accuracy: 0.916 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 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 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2846 \| 1.0 \| 625 \| 0.2269 \| 0.916 \| \| 0.1445 \| 2.0 \| 1250 \| 0.2616 \| 0.9294 \| \| 0.0734 \| 3.0 \| 1875 \| 0.2877 \| 0.933 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t3000_e5_member_shadow5	FounderOfHuggingface	2024-01-19T10:16:38Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T10:16:36Z	--- 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. 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_lora_r4_e2e_nlg_t3000_e5_member_shadow4	FounderOfHuggingface	2024-01-19T10:15:12Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T10:15:10Z	--- 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_lora_r4_e2e_nlg_t3000_e5_member_shadow1	FounderOfHuggingface	2024-01-19T10:10:50Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T10:10:49Z	--- 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
tresbien1/a2c-PandaPickAndPlace-v3	tresbien1	2024-01-19T10:08:29Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaPickAndPlace-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-19T10:03:55Z	--- library_name: stable-baselines3 tags: - PandaPickAndPlace-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaPickAndPlace-v3 type: PandaPickAndPlace-v3 metrics: - type: mean_reward value: -50.00 +/- 0.00 name: mean_reward verified: false --- # A2C Agent playing PandaPickAndPlace-v3 This is a trained model of a A2C agent playing PandaPickAndPlace-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
varun-v-rao/bert-base-cased-mnli-model6	varun-v-rao	2024-01-19T10:05:25Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-19T08:39:23Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-cased-mnli-model6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-mnli-model6 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4743 - Accuracy: 0.8391 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 82 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4774 \| 1.0 \| 6136 \| 0.4403 \| 0.8294 \| \| 0.3627 \| 2.0 \| 12272 \| 0.4449 \| 0.8362 \| \| 0.2653 \| 3.0 \| 18408 \| 0.4743 \| 0.8391 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
s3nh/Tiger-7b-v0.1-GGUF	s3nh	2024-01-19T09:59:48Z	1	1	transformers	[ "transformers", "gguf", "text-generation", "zh", "en", "license:openrail", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-01-19T09:02:17Z	--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/NeuralNovel/Tiger-7b-v0.1). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### inference User: Tell me story about what is an quantization and what do we need to build. Quantization refers to the process of converting continuous signals or data into discrete levels or values. This is necessary because most digital systems, such as computers and mobile devices, can only process and store discrete information. The goal of quantization is to reduce the amount of data required while minimizing loss in accuracy or information. To illustrate this concept, let's consider an example using sound recordings. Analog recordings capture sound waves continuously over time as a smooth waveform. However, digital audio formats require that these continuous signals be converted into discrete binary values for storage and processing. This conversion process is called quantization. # Original model card
s3nh/Sydney_Pirate_Mistral_7b-GGUF	s3nh	2024-01-19T09:58:58Z	0	0	transformers	[ "transformers", "gguf", "text-generation", "zh", "en", "license:openrail", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-01-19T09:00:38Z	--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/FPHam/Sydney_Pirate_Mistral_7b). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### inference User: Tell me story about what is an quantization and what do we need to build. Pirate: Gather 'round, landlubber! I be about to regale ye with a tale o' quantization. In the heart of the seven seas, there be a great many of fishies and other sea creatures. But alas, not all can hear the sweet melodies of me and me crew's shanties. Yea, verily! Ye see, when we sing our sea shanties, we divide the tune into equal portions, like so many tiny slices o' rum-soaked pie. This be what they call "quantization # Original model card
szymonrucinski/Krakowiak-7B-v2	szymonrucinski	2024-01-19T09:40:59Z	17	9	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "pl", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-26T09:21:50Z	--- license: cc-by-sa-4.0 language: - pl --- # Model Card for Krakowiak-v2-7b Krakowiak-v2-7b is a state of the art 7.3 billion parameters LLM based on Mistral-7B. It was finetuned for Polish text generation using custom created large corpus of 300K Polish instructions. It uses novel techniques e.g. LORA, adding noise to the embeddings for greater preformance. For full details of this model please read our [paper to be released soon](www.example.come) ## Model Architecture Krakowiak-v2-7b is a huge update to [Krakowiak-7b](https://huggingface.co/szymonrucinski/krakowiak-7b) bringing the following improvements: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer - Significantly larger training corpus of better quality - Improved training pipeline - Faster inference - No random token generation (generating Russian or Czech text alongside Polish) - Significantly higher quality of generated text Krakowiak does not have any censorship mechanisms implemented. ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] Czy warto się uczyć? [/INST]" "Jako model języka AI nie mam osobistych opinii ani przekonań. Jednak uczanie się jest ogólnie cenione i angażuje długoterminowe korzyści dla jednostki i społeczeństwa. Dzięki nauce można opracować umiejętności, zdobąć wiedzę i zwiększyć potencjał osobisty i zawodowy. Ponadto nauka promuje otwartość na innowacje i może zapewnić lepszą perspektywę życia. Dlatego uczanie się jest ogólnie cenione i wartościowe dla osób, które chcą osiągnąć cel lub rozwinąć swoją wiedzę. Jednak każda sytuacja może być inna i niektóre osoby mogą mieć różne cele lub okoliczności, które wpływają na ich podejście do nauki. Ostatecznie zależy to od indywidualnych preferencji i celów.</s> " "[INST] Gdzie warto się uczyć? [/INST]" ``` ## Model parameters From my experience the temperature value of 0.7 is the best baseline value. ## Optimal text generation ``` from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" chat_tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") messages = [ {"role": "user", "content": "Czy warto nauczyć się jeździć na nartach w wieku 25 lat?"}, ] chat_tokenized = tokenizer.apply_chat_template(messages, tokenize=False) model = AutoModelForCausalLM.from_pretrained("szymonrucinski/Krakowiak-7B-v2") model.config.pad_token_id = model.config.eos_token_id tokenizer = AutoTokenizer.from_pretrained("szymonrucinski/Krakowiak-7B-v2",add_eos_token=True) tokenizer.pad_token = tokenizer.eos_token model_inputs = tokenizer(chat_tokenized, return_tensors='pt').to(torch_device) beam_outputs = model.generate( **model_inputs, max_new_tokens=1024, num_beams=5, no_repeat_ngram_size=2, num_return_sequences=1, early_stopping=True ) print(tokenizer.decode(beam_outputs[0], skip_special_tokens=True)) ``` ## Use a pipeline as a high-level helper ``` from transformers import pipeline pipe = pipeline("text-generation", model="szymonrucinski/krakowiak-v2-7b") pipe("<s>[INST] Też lubisz jeździć na rowerze? [/INST]") ``` ## Demo You can play with Krakowiak-v2-7b [here](https://huggingface.co/spaces/szymonrucinski/krakowiak). This model uses 4-bit quantization technique and CPU inference that negatively influence the quality of the output but are more cost effective. You can run Krakowiak on your CPU using its quantized version availabe [here](https://huggingface.co/szymonrucinski/krakowiak-v2-7b-gguf) ## Krakowiak team [Szymon Franciszek Ruciński](https://szymonrucinski.pl) ## Citation If you find the content of this repo useful in your work, please cite it as follows: ```bibtex @misc{Krakowiak-V2-7B, author = {Szymon Ruciński}, title = {Krakowiak-V2-7B}, year = {2023}, publisher = {Huggingface}, journal = {Huggingface repository}, howpublished = {\url{https://huggingface.co/szymonrucinski/krakowiak-v2-7b/}} }
Kunalmod/finetuned-model	Kunalmod	2024-01-19T09:38:41Z	11	0	transformers	[ "transformers", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2024-01-19T09:27:28Z	--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: finetuned-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. --> # finetuned-model This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Tokenizers 0.15.0
lc111/tinyllama-colorist-lora	lc111	2024-01-19T09:30:15Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "license:apache-2.0", "region:us" ]	null	2024-01-17T06:53:03Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: PY007/TinyLlama-1.1B-Chat-v0.3 model-index: - name: tinyllama-colorist-lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tinyllama-colorist-lora This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0 - Datasets 2.16.1 - Tokenizers 0.15.0
Yntec/CuteFurry	Yntec	2024-01-19T09:05:08Z	1,116	2	diffusers	[ "diffusers", "safetensors", "Anime", "Animals", "Cute", "Character Design", "Adorable", "CGI", "anyangs303305", "McSionnaigh", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-19T07:15:53Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Anime - Animals - Cute - Character Design - Adorable - CGI - anyangs303305 - McSionnaigh - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # Cute Furry The Cute Furry LoRA baked in the Genuine model which includes NuipeniMix and Generate Me! Samples and prompts: ![Cute Furry Free AI Images Generator Samples](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/ndJQ9urMgSyPgJT1gLV5f.png) (Click for larger) Top left: oil painting, best quality,masterpiece,Fluffy,raccoon. red scarf, big eyes,lawn,forest,paw pose,chibi, Top right: a dreamworks frog playing guitar in a club. chibi orange large eyes, whimsical. fedora. Bottom left: best quality,masterpiece,Fluffy,Bunny. Blue scarf, big eyes,White hair, paw pose,chibi, Bottom right: a painting of a reindeer by Bnhr, cute teal eyes, nature, grass, tree, outdoors, forest, animal focus, red nose, Original pages: https://civitai.com/models/213753/15-cute-furry?modelVersionId=240777 https://civitai.com/models/81937?modelVersionId=86977 (nuipenimix v1) https://huggingface.co/Yntec/GenerateMe https://huggingface.co/Yntec/Genuine ![Cute Furry Free Text to image generator 512x512 sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/XLW3mUV_VEtEkDwDY7Eji.png)
tliu/asp-ner-flan-t5-large	tliu	2024-01-19T09:01:28Z	2	0	transformers	[ "transformers", "pytorch", "en", "dataset:conll2003", "arxiv:2210.14698", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-01-18T18:45:22Z	--- license: mit datasets: - conll2003 language: - en metrics: - f1 --- # Model Card for asp-ner-flan-t5-large ![model image](https://github.com/lyutyuh/ASP/raw/master/figs/illustration.gif) # Intro This model is initialized from flan-t5-base and finetuned for named entity recognition task. The model structure is described in the paper [Autoregressive Structured Prediction with Language Models](https://arxiv.org/pdf/2210.14698v2.pdf), [Github repo](https://github.com/lyutyuh/ASP). # Model Description - Task: Named Entity Recognition - Dataset: CoNLL-03 - Base Model: flan-t5-large # Command ```bash CUDA_VISIBLE_DEVICES=0 python evaluate_ner.py flant5_large tliu/asp-ner-flan-t5-large 0 ```
tliu/asp-ner-flan-t5-base	tliu	2024-01-19T08:59:12Z	6	0	transformers	[ "transformers", "pytorch", "en", "dataset:conll2003", "arxiv:2210.14698", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-01-18T08:41:19Z	--- license: mit datasets: - conll2003 language: - en metrics: - f1 --- # Model Card for asp-ner-flan-t5-base ![model image](https://github.com/lyutyuh/ASP/raw/master/figs/illustration.gif) # Intro This model is initialized from flan-t5-base and finetuned for named entity recognition task. The model structure is described in the paper [Autoregressive Structured Prediction with Language Models](https://arxiv.org/pdf/2210.14698v2.pdf), [Github repo](https://github.com/lyutyuh/ASP). # Model Description - Task: Named Entity Recognition - Dataset: CoNLL-03 - Base Model: flan-t5-base # Command ```bash CUDA_VISIBLE_DEVICES=0 python evaluate_ner.py flant5_base tliu/asp-ner-flan-t5-base 0 ```
nullne/dqn-SpaceInvadersNoFrameskip-v4	nullne	2024-01-19T08:57:10Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-19T08:56:32Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 735.00 +/- 283.64 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nullne -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nullne -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga nullne ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
monkeyerKong/bert-finetuned-ner	monkeyerKong	2024-01-19T08:56:20Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-19T07:02:49Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9125862635557016 - name: Recall type: recall value: 0.9347021204981488 - name: F1 type: f1 value: 0.9235118057864983 - name: Accuracy type: accuracy value: 0.9822658503561547 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0660 - Precision: 0.9126 - Recall: 0.9347 - F1: 0.9235 - Accuracy: 0.9823 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0756 \| 1.0 \| 1756 \| 0.0660 \| 0.9126 \| 0.9347 \| 0.9235 \| 0.9823 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0 - Datasets 2.16.1 - Tokenizers 0.15.0
codewizardUV/mistral-7b-finetuned-6.67_epochs	codewizardUV	2024-01-19T08:52:03Z	2	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-01-19T08:51:42Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4597 ## 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: 0.00025 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9748 \| 0.12 \| 50 \| 0.8738 \| \| 0.7514 \| 0.23 \| 100 \| 0.7808 \| \| 0.7228 \| 0.35 \| 150 \| 0.7320 \| \| 0.6811 \| 0.47 \| 200 \| 0.6688 \| \| 0.6477 \| 0.59 \| 250 \| 0.6679 \| \| 0.6606 \| 0.7 \| 300 \| 0.6215 \| \| 0.5836 \| 0.82 \| 350 \| 0.5981 \| \| 0.6127 \| 0.94 \| 400 \| 0.5961 \| \| 0.4759 \| 1.05 \| 450 \| 0.5341 \| \| 0.496 \| 1.17 \| 500 \| 0.5321 \| \| 0.47 \| 1.29 \| 550 \| 0.5168 \| \| 0.4519 \| 1.41 \| 600 \| 0.5090 \| \| 0.4619 \| 1.52 \| 650 \| 0.5004 \| \| 0.4579 \| 1.64 \| 700 \| 0.5164 \| \| 0.437 \| 1.76 \| 750 \| 0.4887 \| \| 0.4274 \| 1.87 \| 800 \| 0.4798 \| \| 0.4651 \| 1.99 \| 850 \| 0.4784 \| \| 0.3846 \| 2.11 \| 900 \| 0.4999 \| \| 0.3846 \| 2.22 \| 950 \| 0.4896 \| \| 0.3732 \| 2.34 \| 1000 \| 0.4792 \| \| 0.3815 \| 2.46 \| 1050 \| 0.4799 \| \| 0.403 \| 2.58 \| 1100 \| 0.4777 \| \| 0.3593 \| 2.69 \| 1150 \| 0.4875 \| \| 0.381 \| 2.81 \| 1200 \| 0.4763 \| \| 0.4044 \| 2.93 \| 1250 \| 0.4504 \| \| 0.3381 \| 3.04 \| 1300 \| 0.4543 \| \| 0.3111 \| 3.16 \| 1350 \| 0.4597 \| \| 0.322 \| 3.28 \| 1400 \| 0.4796 \| \| 0.3211 \| 3.4 \| 1450 \| 0.4634 \| \| 0.3339 \| 3.51 \| 1500 \| 0.4936 \| \| 0.329 \| 3.63 \| 1550 \| 0.4479 \| \| 0.3454 \| 3.75 \| 1600 \| 0.4520 \| \| 0.337 \| 3.86 \| 1650 \| 0.4395 \| \| 0.3305 \| 3.98 \| 1700 \| 0.4373 \| \| 0.2809 \| 4.1 \| 1750 \| 0.4747 \| \| 0.2918 \| 4.22 \| 1800 \| 0.4705 \| \| 0.2994 \| 4.33 \| 1850 \| 0.4778 \| \| 0.3006 \| 4.45 \| 1900 \| 0.4759 \| \| 0.3048 \| 4.57 \| 1950 \| 0.4615 \| \| 0.3183 \| 4.68 \| 2000 \| 0.4494 \| \| 0.3125 \| 4.8 \| 2050 \| 0.4584 \| \| 0.3112 \| 4.92 \| 2100 \| 0.4461 \| \| 0.279 \| 5.04 \| 2150 \| 0.4692 \| \| 0.2686 \| 5.15 \| 2200 \| 0.4563 \| \| 0.2794 \| 5.27 \| 2250 \| 0.4467 \| \| 0.2774 \| 5.39 \| 2300 \| 0.4541 \| \| 0.2927 \| 5.5 \| 2350 \| 0.4529 \| \| 0.2899 \| 5.62 \| 2400 \| 0.4458 \| \| 0.2865 \| 5.74 \| 2450 \| 0.4469 \| \| 0.2965 \| 5.85 \| 2500 \| 0.4469 \| \| 0.2901 \| 5.97 \| 2550 \| 0.4471 \| \| 0.2381 \| 6.09 \| 2600 \| 0.4777 \| \| 0.257 \| 6.21 \| 2650 \| 0.4677 \| \| 0.256 \| 6.32 \| 2700 \| 0.4847 \| \| 0.2658 \| 6.44 \| 2750 \| 0.4820 \| \| 0.2617 \| 6.56 \| 2800 \| 0.4602 \| \| 0.2761 \| 6.67 \| 2850 \| 0.4597 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
rafiaa/llama-finetuned-p2a-lora-v2	rafiaa	2024-01-19T08:50:44Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-19T08:50:25Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Hyeoli/cord-layoutlmv3-finetuned	Hyeoli	2024-01-19T08:48:52Z	1	0	transformers	[ "transformers", "pytorch", "tensorboard", "layoutlmv3", "token-classification", "generated_from_trainer", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-19T08:30:04Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer model-index: - name: cord-layoutlmv3-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cord-layoutlmv3-finetuned This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 0.62 \| 100 \| 0.9126 \| 0.6542 \| 0.6801 \| 0.6669 \| 0.7612 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
taki0112/lora-trained-xl_realistic3	taki0112	2024-01-19T08:48:27Z	1	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-01-19T07:58:31Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'a dragon in sks style' output: url: "image_0.png" - text: 'a dragon in sks style' output: url: "image_1.png" - text: 'a dragon in sks style' output: url: "image_2.png" - text: 'a dragon in sks style' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a fire in sks style license: openrail++ --- # SDXL LoRA DreamBooth - taki0112/lora-trained-xl_realistic3 <Gallery /> ## Model description These are taki0112/lora-trained-xl_realistic3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a fire in sks style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](taki0112/lora-trained-xl_realistic3/tree/main) them in the Files & versions tab.
simbolo-ai/Myanmarsar-GPT	simbolo-ai	2024-01-19T08:40:21Z	208	3	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "burmese", "pre-trained", "my", "arxiv:2110.05896", "arxiv:2204.07580", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-07T00:02:05Z	--- license: mit language: - my pipeline_tag: text-generation metrics: - code_eval library_name: transformers tags: - burmese - gpt2 - pre-trained --- The Simbolo's Myanmarsar-GPT (it is not a chatbot but a text generation model which can be used to develop chatbot) is pre-trained on a dataset of 20,000 Burmese data and pre-trained using the GPT-2 architecture of MGPT Model. Its purpose is to serve as a foundational pre-trained model for the Burmese language, facilitating fine-tuning for specific applications of different tasks such as creative writing, chatbot, machine translation etc. ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6598b82502c4796342239a35/rFId3-xyzWW-juDq_er9k.jpeg) ### How to use ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Simbolo-Servicio/Myanmarsar-GPT") model = AutoModelForCausalLM.from_pretrained("Simbolo-Servicio/Myanmarsar-GPT") input_text = "ပညာရေး" input_ids = tokenizer.encode(input_text, return_tensors='pt') output = model.generate(input_ids, max_length=50) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` ### Data We use 20,000 Burmese sentences and most are from our open-source [data](https://huggingface.co/datasets/Simbolo-Servicio/wiki-burmese-sentences) which contains 100,000 sentences sourced from Wikipedia. ### Contributors Main Contributor: [Sa Phyo Thu Htet](https://github.com/SaPhyoThuHtet) Wikipedia Data Crawling: Kaung Kaung Ko Ko, Phuu Pwint Thinzar Kyaing Releasing the Model: Eithandaraung, Ye Yint Htut, Thet Chit Su, Naing Phyo Aung, Nyan Linn Phyo Zaw, Lynn Thu Kha ### Acknowledgment We extend our gratitude to the creators of the [mGPT-XL](https://huggingface.co/ai-forever/mGPT) models for their invaluable contribution to this project. We want to thank everyone who has worked on the related works, especially [Minsithu](https://huggingface.co/jojo-ai-mst/MyanmarGPTT) and [Dr. Wai Yan Nyein Naing](https://huggingface.co/WYNN747/Burmese-GPT)who initiated the work of gpt-2 model. And We would like to thank Simbolo:Servico which is a branch of Simbolo under the company of Intello Tech for providing financial support. ### Limitations and Bias We have yet to investigate the potential bias inherent in this model thoroughly. Regarding transparency, it's important to note that the model is primarily trained on data from the Unicode Burmese(Myanmar) language. ### References 1. Jiang, Shengyi & Huang, Xiuwen & Cai, Xiaonan & Lin, Nankai. (2021). Pre-trained Models and Evaluation Data for the Myanmar Language. 10.1007/978-3-030-92310-5_52. 2. Lin, N., Fu, Y., Chen, C., Yang, Z., & Jiang, S. (2021). LaoPLM: Pre-trained Language Models for Lao. ArXiv. /abs/2110.05896 3. MinSithu, MyanmarGPT, https://huggingface.co/jojo-ai-mst/MyanmarGPT, 1.1-SweptWood 4. Wai Yan Nyein Naing, WYNN747/Burmese-GPT, https://huggingface.co/WYNN747/Burmese-GPT 5. Sai Htaung Kham, saihtaungkham/BurmeseRoBERTaCLM 6. Shliazhko, O., Fenogenova, A., Tikhonova, M., Mikhailov, V., Kozlova, A., & Shavrina, T. (2022). MGPT: Few-Shot Learners Go Multilingual. ArXiv. /abs/2204.07580 ### How to Cite this work: ### Cite As: ```bibtex @misc{myanmarsar-gpt, author = {{Sa Phyo Thu Htet}}, title = {Myanmarsar GPT}, url = {https://huggingface.co/Simbolo-Servicio/Myanmarsar-GPT}, urldate = {2024-1-09}, date = {2024-1-09} } ```
varun-v-rao/bert-base-cased-mnli-model5	varun-v-rao	2024-01-19T08:38:35Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-19T07:11:59Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-cased-mnli-model5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-mnli-model5 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4801 - Accuracy: 0.8391 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4718 \| 1.0 \| 6136 \| 0.4357 \| 0.8271 \| \| 0.3557 \| 2.0 \| 12272 \| 0.4261 \| 0.8374 \| \| 0.2607 \| 3.0 \| 18408 \| 0.4801 \| 0.8391 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
taki0112/lora-trained-xl_munch2	taki0112	2024-01-19T08:27:20Z	1	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-01-19T07:37:04Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'a rabbit in sks style' output: url: "image_0.png" - text: 'a rabbit in sks style' output: url: "image_1.png" - text: 'a rabbit in sks style' output: url: "image_2.png" - text: 'a rabbit in sks style' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a screaming person in sks style license: openrail++ --- # SDXL LoRA DreamBooth - taki0112/lora-trained-xl_munch2 <Gallery /> ## Model description These are taki0112/lora-trained-xl_munch2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a screaming person in sks style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](taki0112/lora-trained-xl_munch2/tree/main) them in the Files & versions tab.
taki0112/lora-trained-xl_vangogh2	taki0112	2024-01-19T08:26:09Z	1	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-01-19T07:36:41Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'a fish in sks style' output: url: "image_0.png" - text: 'a fish in sks style' output: url: "image_1.png" - text: 'a fish in sks style' output: url: "image_2.png" - text: 'a fish in sks style' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a village in sks style license: openrail++ --- # SDXL LoRA DreamBooth - taki0112/lora-trained-xl_vangogh2 <Gallery /> ## Model description These are taki0112/lora-trained-xl_vangogh2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a village in sks style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](taki0112/lora-trained-xl_vangogh2/tree/main) them in the Files & versions tab.
liwii/factual-consistency-classification-ja-avgpool-unfrozen	liwii	2024-01-19T08:22:17Z	1	0	transformers	[ "transformers", "pytorch", "distilbert", "generated_from_trainer", "base_model:line-corporation/line-distilbert-base-japanese", "base_model:finetune:line-corporation/line-distilbert-base-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-01-19T07:58:26Z	--- license: apache-2.0 base_model: line-corporation/line-distilbert-base-japanese tags: - generated_from_trainer metrics: - accuracy model-index: - name: factual-consistency-classification-ja-avgpool-unfrozen 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. --> # factual-consistency-classification-ja-avgpool-unfrozen This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2583 - Accuracy: 0.9121 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 306 \| 0.2837 \| 0.8691 \| \| 0.3826 \| 2.0 \| 612 \| 0.2294 \| 0.9121 \| \| 0.3826 \| 3.0 \| 918 \| 0.2583 \| 0.9121 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0
Kooten/Lelantos-Maid-DPO-7B-8bpw-exl2	Kooten	2024-01-19T08:04:13Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "SanjiWatsuki/Lelantos-DPO-7B", "NeverSleep/Noromaid-7B-0.4-DPO", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-17T09:47:34Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - SanjiWatsuki/Lelantos-DPO-7B - NeverSleep/Noromaid-7B-0.4-DPO --- # Lelantos-Maid-DPO-7B 8bpw EXL2 *Note:* There have been issues with never ending generations for the exl quant of this model, i have not been able to figure out why, though it works for some. ## Description Exllama quant of [SanjiWatsuki/Lelantos-Maid-DPO-7B](https://huggingface.co/SanjiWatsuki/Lelantos-Maid-DPO-7B) ## Other quants: EXL2: [8bpw](https://huggingface.co/Kooten/Lelantos-Maid-DPO-7B-8bpw-exl2), [4bpw](https://huggingface.co/Kooten/Lelantos-Maid-DPO-7B-4bpw-exl2) ## Prompt format: ChatML It is partly Noromaid so should be ChatML ``` <\|im_start\|>system {sysprompt}<\|im_end\|> <\|im_start\|>user {input}<\|im_end\|> <\|im_start\|>assistant {output}<\|im_end\|> ``` ## Contact Kooten on discord
abacusai/MM-Orc-Vic-bagel-34b-c1000	abacusai	2024-01-19T08:00:18Z	1,362	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:abacusai/MetaMathFewshot", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-19T07:23:41Z	--- license: apache-2.0 datasets: - abacusai/MetaMathFewshot --- Finetune of the DPO Bagel model (https://huggingface.co/jondurbin/nontoxic-bagel-34b-v0.2) on the MetamathFewshot (https://huggingface.co/datasets/abacusai/MetaMathFewshot) dataset ### Evaluation Results \| Average \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| Winogrande \| GSM8K \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| For comparison the GSM8K score for the original `nontoxic-bagel-34b-v0.2` model was 58.45 and average score was 74.69
anakib1/whisper-small-multi-diar-wer	anakib1	2024-01-19T07:58:32Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-01-18T21:55:22Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-multi-diar-wer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-multi-diar-wer This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 10.4868 - Wer: 100.0 - Cer: 100.4922 - Speech Scored: 10807 - Speech Miss: 1926 - Speech Falarm: 1656 - Speaker Miss: 1926 - Speaker Falarm: 1656 - Speaker Error: 2927 - Speaker Correct: 23.7093 - Diarization Error: 6509 - Frames: 10 - Speaker Wide Frames: 12733 - Speech Scored Ratio: 1080.7 - Speech Miss Ratio: 192.6 - Speech Falarm Ratio: 165.6 - Speaker Correct Ratio: 2.3709 - Speaker Miss Ratio: 0.1513 - Speaker Falarm Ratio: 0.1301 - Speaker Error Ratio: 0.2299 - Diarization Error Ratio: 0.5112 ## 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: 24 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| Speech Scored \| Speech Miss \| Speech Falarm \| Speaker Miss \| Speaker Falarm \| Speaker Error \| Speaker Correct \| Diarization Error \| Frames \| Speaker Wide Frames \| Speech Scored Ratio \| Speech Miss Ratio \| Speech Falarm Ratio \| Speaker Correct Ratio \| Speaker Miss Ratio \| Speaker Falarm Ratio \| Speaker Error Ratio \| Diarization Error Ratio \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:-------------:\|:-----------:\|:-------------:\|:------------:\|:--------------:\|:-------------:\|:---------------:\|:-----------------:\|:------:\|:-------------------:\|:-------------------:\|:-----------------:\|:-------------------:\|:---------------------:\|:------------------:\|:--------------------:\|:-------------------:\|:-----------------------:\| \| No log \| 1.0 \| 1 \| 11.6412 \| 100.0 \| 189.4586 \| 11732 \| 1001 \| 2061 \| 1001 \| 11893 \| 2169 \| 18.5120 \| 15063 \| 10 \| 12733 \| 1173.2 \| 100.1 \| 206.1 \| 1.8512 \| 0.0786 \| 0.9340 \| 0.1703 \| 1.1830 \| \| No log \| 2.0 \| 2 \| 11.4959 \| 209.5855 \| 298.3593 \| 11425 \| 1308 \| 2031 \| 1308 \| 5103 \| 3052 \| 21.6567 \| 9463 \| 10 \| 12733 \| 1142.5 \| 130.8 \| 203.1 \| 2.1657 \| 0.1027 \| 0.4008 \| 0.2397 \| 0.7432 \| \| No log \| 3.0 \| 3 \| 11.2874 \| 572.5389 \| 437.2847 \| 12060 \| 673 \| 2169 \| 673 \| 2169 \| 3938 \| 22.8547 \| 6780 \| 10 \| 12733 \| 1206.0 \| 67.3 \| 216.9 \| 2.2855 \| 0.0529 \| 0.1703 \| 0.3093 \| 0.5325 \| \| No log \| 4.0 \| 4 \| 11.1333 \| 535.2332 \| 534.6596 \| 12537 \| 196 \| 2237 \| 196 \| 2237 \| 4141 \| 22.8567 \| 6574 \| 10 \| 12733 \| 1253.7 \| 19.6 \| 223.7 \| 2.2857 \| 0.0154 \| 0.1757 \| 0.3252 \| 0.5163 \| \| No log \| 5.0 \| 5 \| 11.0564 \| 100.0 \| 100.6153 \| 12717 \| 16 \| 2264 \| 16 \| 2264 \| 4222 \| 22.8507 \| 6502 \| 10 \| 12733 \| 1271.7 \| 1.6 \| 226.4 \| 2.2851 \| 0.0013 \| 0.1778 \| 0.3316 \| 0.5106 \| \| No log \| 6.0 \| 6 \| 10.9894 \| 100.0 \| 100.6153 \| 12733 \| 0 \| 2267 \| 0 \| 2267 \| 4232 \| 22.8460 \| 6499 \| 10 \| 12733 \| 1273.3 \| 0.0 \| 226.7 \| 2.2846 \| 0.0 \| 0.1780 \| 0.3324 \| 0.5104 \| \| No log \| 7.0 \| 7 \| 10.9209 \| 100.0 \| 100.6153 \| 12733 \| 0 \| 2267 \| 0 \| 2267 \| 4232 \| 22.8460 \| 6499 \| 10 \| 12733 \| 1273.3 \| 0.0 \| 226.7 \| 2.2846 \| 0.0 \| 0.1780 \| 0.3324 \| 0.5104 \| \| No log \| 8.0 \| 8 \| 10.8541 \| 100.0 \| 100.6153 \| 12733 \| 0 \| 2267 \| 0 \| 2267 \| 4232 \| 22.8460 \| 6499 \| 10 \| 12733 \| 1273.3 \| 0.0 \| 226.7 \| 2.2846 \| 0.0 \| 0.1780 \| 0.3324 \| 0.5104 \| \| No log \| 9.0 \| 9 \| 10.7928 \| 100.0 \| 100.6153 \| 12705 \| 28 \| 2262 \| 28 \| 2262 \| 4212 \| 22.8573 \| 6502 \| 10 \| 12733 \| 1270.5 \| 2.8 \| 226.2 \| 2.2857 \| 0.0022 \| 0.1776 \| 0.3308 \| 0.5106 \| \| No log \| 10.0 \| 10 \| 10.7401 \| 100.0 \| 100.6153 \| 12535 \| 198 \| 2240 \| 198 \| 2240 \| 4093 \| 22.9173 \| 6531 \| 10 \| 12733 \| 1253.5 \| 19.8 \| 224.0 \| 2.2917 \| 0.0156 \| 0.1759 \| 0.3214 \| 0.5129 \| \| No log \| 11.0 \| 11 \| 10.6969 \| 100.0 \| 100.6153 \| 12279 \| 454 \| 2190 \| 454 \| 2190 \| 3907 \| 23.0280 \| 6551 \| 10 \| 12733 \| 1227.9 \| 45.4 \| 219.0 \| 2.3028 \| 0.0357 \| 0.1720 \| 0.3068 \| 0.5145 \| \| No log \| 12.0 \| 12 \| 10.6622 \| 100.0 \| 100.6153 \| 12126 \| 607 \| 2140 \| 607 \| 2140 \| 3804 \| 23.0967 \| 6551 \| 10 \| 12733 \| 1212.6 \| 60.7 \| 214.0 \| 2.3097 \| 0.0477 \| 0.1681 \| 0.2988 \| 0.5145 \| \| No log \| 13.0 \| 13 \| 10.6341 \| 100.0 \| 100.6153 \| 12131 \| 602 \| 2126 \| 602 \| 2126 \| 3808 \| 23.1040 \| 6536 \| 10 \| 12733 \| 1213.1 \| 60.2 \| 212.6 \| 2.3104 \| 0.0473 \| 0.1670 \| 0.2991 \| 0.5133 \| \| No log \| 14.0 \| 14 \| 10.6127 \| 100.0 \| 100.6153 \| 12202 \| 531 \| 2139 \| 531 \| 2139 \| 3854 \| 23.0813 \| 6524 \| 10 \| 12733 \| 1220.2 \| 53.1 \| 213.9 \| 2.3081 \| 0.0417 \| 0.1680 \| 0.3027 \| 0.5124 \| \| No log \| 15.0 \| 15 \| 10.5973 \| 100.0 \| 100.6153 \| 12253 \| 480 \| 2137 \| 480 \| 2137 \| 3889 \| 23.0700 \| 6506 \| 10 \| 12733 \| 1225.3 \| 48.0 \| 213.7 \| 2.3070 \| 0.0377 \| 0.1678 \| 0.3054 \| 0.5110 \| \| No log \| 16.0 \| 16 \| 10.5863 \| 100.0 \| 100.6153 \| 12239 \| 494 \| 2107 \| 494 \| 2107 \| 3885 \| 23.0860 \| 6486 \| 10 \| 12733 \| 1223.9 \| 49.4 \| 210.7 \| 2.3086 \| 0.0388 \| 0.1655 \| 0.3051 \| 0.5094 \| \| No log \| 17.0 \| 17 \| 10.5787 \| 100.0 \| 100.6153 \| 12158 \| 575 \| 2066 \| 575 \| 2066 \| 3824 \| 23.1407 \| 6465 \| 10 \| 12733 \| 1215.8 \| 57.5 \| 206.6 \| 2.3141 \| 0.0452 \| 0.1623 \| 0.3003 \| 0.5077 \| \| No log \| 18.0 \| 18 \| 10.5715 \| 100.0 \| 100.6153 \| 12030 \| 703 \| 2009 \| 703 \| 2009 \| 3740 \| 23.2053 \| 6452 \| 10 \| 12733 \| 1203.0 \| 70.3 \| 200.9 \| 2.3205 \| 0.0552 \| 0.1578 \| 0.2937 \| 0.5067 \| \| No log \| 19.0 \| 19 \| 10.5638 \| 100.0 \| 100.6153 \| 11866 \| 867 \| 1943 \| 867 \| 1943 \| 3624 \| 23.2947 \| 6434 \| 10 \| 12733 \| 1186.6 \| 86.7 \| 194.3 \| 2.3295 \| 0.0681 \| 0.1526 \| 0.2846 \| 0.5053 \| \| No log \| 20.0 \| 20 \| 10.5554 \| 100.0 \| 100.6153 \| 11695 \| 1038 \| 1892 \| 1038 \| 1892 \| 3514 \| 23.3613 \| 6444 \| 10 \| 12733 \| 1169.5 \| 103.8 \| 189.2 \| 2.3361 \| 0.0815 \| 0.1486 \| 0.2760 \| 0.5061 \| \| No log \| 21.0 \| 21 \| 10.5470 \| 100.0 \| 100.6153 \| 11588 \| 1145 \| 1854 \| 1145 \| 1854 \| 3451 \| 23.3993 \| 6450 \| 10 \| 12733 \| 1158.8 \| 114.5 \| 185.4 \| 2.3399 \| 0.0899 \| 0.1456 \| 0.2710 \| 0.5066 \| \| No log \| 22.0 \| 22 \| 10.5397 \| 100.0 \| 100.6153 \| 11556 \| 1177 \| 1840 \| 1177 \| 1840 \| 3437 \| 23.4060 \| 6454 \| 10 \| 12733 \| 1155.6 \| 117.7 \| 184.0 \| 2.3406 \| 0.0924 \| 0.1445 \| 0.2699 \| 0.5069 \| \| No log \| 23.0 \| 23 \| 10.5330 \| 100.0 \| 100.6153 \| 11562 \| 1171 \| 1834 \| 1171 \| 1834 \| 3442 \| 23.4073 \| 6447 \| 10 \| 12733 \| 1156.2 \| 117.1 \| 183.4 \| 2.3407 \| 0.0920 \| 0.1440 \| 0.2703 \| 0.5063 \| \| No log \| 24.0 \| 24 \| 10.5265 \| 100.0 \| 100.6153 \| 11583 \| 1150 \| 1838 \| 1150 \| 1838 \| 3457 \| 23.3987 \| 6445 \| 10 \| 12733 \| 1158.3 \| 115.0 \| 183.8 \| 2.3399 \| 0.0903 \| 0.1443 \| 0.2715 \| 0.5062 \| \| 5.1826 \| 25.0 \| 25 \| 10.5191 \| 100.0 \| 100.6153 \| 11574 \| 1159 \| 1834 \| 1159 \| 1834 \| 3454 \| 23.3993 \| 6447 \| 10 \| 12733 \| 1157.4 \| 115.9 \| 183.4 \| 2.3399 \| 0.0910 \| 0.1440 \| 0.2713 \| 0.5063 \| \| 5.1826 \| 26.0 \| 26 \| 10.5103 \| 100.0 \| 100.6153 \| 11539 \| 1194 \| 1826 \| 1194 \| 1826 \| 3428 \| 23.4160 \| 6448 \| 10 \| 12733 \| 1153.9 \| 119.4 \| 182.6 \| 2.3416 \| 0.0938 \| 0.1434 \| 0.2692 \| 0.5064 \| \| 5.1826 \| 27.0 \| 27 \| 10.5003 \| 100.0 \| 100.6153 \| 11466 \| 1267 \| 1802 \| 1267 \| 1802 \| 3376 \| 23.4527 \| 6445 \| 10 \| 12733 \| 1146.6 \| 126.7 \| 180.2 \| 2.3453 \| 0.0995 \| 0.1415 \| 0.2651 \| 0.5062 \| \| 5.1826 \| 28.0 \| 28 \| 10.4912 \| 100.0 \| 100.6153 \| 11387 \| 1346 \| 1780 \| 1346 \| 1780 \| 3320 \| 23.4893 \| 6446 \| 10 \| 12733 \| 1138.7 \| 134.6 \| 178.0 \| 2.3489 \| 0.1057 \| 0.1398 \| 0.2607 \| 0.5062 \| \| 5.1826 \| 29.0 \| 29 \| 10.4834 \| 100.0 \| 100.5742 \| 11300 \| 1433 \| 1754 \| 1433 \| 1754 \| 3253 \| 23.5380 \| 6440 \| 10 \| 12733 \| 1130.0 \| 143.3 \| 175.4 \| 2.3538 \| 0.1125 \| 0.1378 \| 0.2555 \| 0.5058 \| \| 5.1826 \| 30.0 \| 30 \| 10.4772 \| 100.0 \| 100.5332 \| 11248 \| 1485 \| 1739 \| 1485 \| 1739 \| 3213 \| 23.5667 \| 6437 \| 10 \| 12733 \| 1124.8 \| 148.5 \| 173.9 \| 2.3567 \| 0.1166 \| 0.1366 \| 0.2523 \| 0.5055 \| \| 5.1826 \| 31.0 \| 31 \| 10.4723 \| 100.0 \| 100.5332 \| 11207 \| 1526 \| 1736 \| 1526 \| 1736 \| 3189 \| 23.5733 \| 6451 \| 10 \| 12733 \| 1120.7 \| 152.6 \| 173.6 \| 2.3573 \| 0.1198 \| 0.1363 \| 0.2505 \| 0.5066 \| \| 5.1826 \| 32.0 \| 32 \| 10.4676 \| 100.0 \| 100.5332 \| 11163 \| 1570 \| 1724 \| 1570 \| 1724 \| 3157 \| 23.5947 \| 6451 \| 10 \| 12733 \| 1116.3 \| 157.0 \| 172.4 \| 2.3595 \| 0.1233 \| 0.1354 \| 0.2479 \| 0.5066 \| \| 5.1826 \| 33.0 \| 33 \| 10.4632 \| 100.0 \| 100.5332 \| 11134 \| 1599 \| 1718 \| 1599 \| 1718 \| 3142 \| 23.5993 \| 6459 \| 10 \| 12733 \| 1113.4 \| 159.9 \| 171.8 \| 2.3599 \| 0.1256 \| 0.1349 \| 0.2468 \| 0.5073 \| \| 5.1826 \| 34.0 \| 34 \| 10.4616 \| 100.0 \| 100.5332 \| 11106 \| 1627 \| 1712 \| 1627 \| 1712 \| 3120 \| 23.6140 \| 6459 \| 10 \| 12733 \| 1110.6 \| 162.7 \| 171.2 \| 2.3614 \| 0.1278 \| 0.1345 \| 0.2450 \| 0.5073 \| \| 5.1826 \| 35.0 \| 35 \| 10.4624 \| 100.0 \| 100.4922 \| 11080 \| 1653 \| 1704 \| 1653 \| 1704 \| 3104 \| 23.6233 \| 6461 \| 10 \| 12733 \| 1108.0 \| 165.3 \| 170.4 \| 2.3623 \| 0.1298 \| 0.1338 \| 0.2438 \| 0.5074 \| \| 5.1826 \| 36.0 \| 36 \| 10.4619 \| 100.0 \| 100.4512 \| 11047 \| 1686 \| 1698 \| 1686 \| 1698 \| 3084 \| 23.6320 \| 6468 \| 10 \| 12733 \| 1104.7 \| 168.6 \| 169.8 \| 2.3632 \| 0.1324 \| 0.1334 \| 0.2422 \| 0.5080 \| \| 5.1826 \| 37.0 \| 37 \| 10.4617 \| 100.0 \| 100.4512 \| 11005 \| 1728 \| 1691 \| 1728 \| 1691 \| 3056 \| 23.6460 \| 6475 \| 10 \| 12733 \| 1100.5 \| 172.8 \| 169.1 \| 2.3646 \| 0.1357 \| 0.1328 \| 0.2400 \| 0.5085 \| \| 5.1826 \| 38.0 \| 38 \| 10.4616 \| 100.0 \| 100.4512 \| 10972 \| 1761 \| 1684 \| 1761 \| 1684 \| 3032 \| 23.6607 \| 6477 \| 10 \| 12733 \| 1097.2 \| 176.1 \| 168.4 \| 2.3661 \| 0.1383 \| 0.1323 \| 0.2381 \| 0.5087 \| \| 5.1826 \| 39.0 \| 39 \| 10.4629 \| 100.0 \| 100.4512 \| 10955 \| 1778 \| 1681 \| 1778 \| 1681 \| 3021 \| 23.6660 \| 6480 \| 10 \| 12733 \| 1095.5 \| 177.8 \| 168.1 \| 2.3666 \| 0.1396 \| 0.1320 \| 0.2373 \| 0.5089 \| \| 5.1826 \| 40.0 \| 40 \| 10.4668 \| 100.0 \| 100.4512 \| 10948 \| 1785 \| 1679 \| 1785 \| 1679 \| 3015 \| 23.6707 \| 6479 \| 10 \| 12733 \| 1094.8 \| 178.5 \| 167.9 \| 2.3671 \| 0.1402 \| 0.1319 \| 0.2368 \| 0.5088 \| \| 5.1826 \| 41.0 \| 41 \| 10.4696 \| 100.0 \| 100.4512 \| 10940 \| 1793 \| 1679 \| 1793 \| 1679 \| 3009 \| 23.6733 \| 6481 \| 10 \| 12733 \| 1094.0 \| 179.3 \| 167.9 \| 2.3673 \| 0.1408 \| 0.1319 \| 0.2363 \| 0.5090 \| \| 5.1826 \| 42.0 \| 42 \| 10.4722 \| 100.0 \| 100.4512 \| 10928 \| 1805 \| 1677 \| 1805 \| 1677 \| 3000 \| 23.6787 \| 6482 \| 10 \| 12733 \| 1092.8 \| 180.5 \| 167.7 \| 2.3679 \| 0.1418 \| 0.1317 \| 0.2356 \| 0.5091 \| \| 5.1826 \| 43.0 \| 43 \| 10.4757 \| 100.0 \| 100.4512 \| 10894 \| 1839 \| 1672 \| 1839 \| 1672 \| 2980 \| 23.6860 \| 6491 \| 10 \| 12733 \| 1089.4 \| 183.9 \| 167.2 \| 2.3686 \| 0.1444 \| 0.1313 \| 0.2340 \| 0.5098 \| \| 5.1826 \| 44.0 \| 44 \| 10.4790 \| 100.0 \| 100.4922 \| 10851 \| 1882 \| 1663 \| 1882 \| 1663 \| 2951 \| 23.7020 \| 6496 \| 10 \| 12733 \| 1085.1 \| 188.2 \| 166.3 \| 2.3702 \| 0.1478 \| 0.1306 \| 0.2318 \| 0.5102 \| \| 5.1826 \| 45.0 \| 45 \| 10.4821 \| 100.0 \| 100.4922 \| 10840 \| 1893 \| 1657 \| 1893 \| 1657 \| 2946 \| 23.7053 \| 6496 \| 10 \| 12733 \| 1084.0 \| 189.3 \| 165.7 \| 2.3705 \| 0.1487 \| 0.1301 \| 0.2314 \| 0.5102 \| \| 5.1826 \| 46.0 \| 46 \| 10.4847 \| 100.0 \| 100.4922 \| 10829 \| 1904 \| 1655 \| 1904 \| 1655 \| 2938 \| 23.7100 \| 6497 \| 10 \| 12733 \| 1082.9 \| 190.4 \| 165.5 \| 2.3710 \| 0.1495 \| 0.1300 \| 0.2307 \| 0.5102 \| \| 5.1826 \| 47.0 \| 47 \| 10.4862 \| 100.0 \| 100.4922 \| 10821 \| 1912 \| 1655 \| 1912 \| 1655 \| 2932 \| 23.7127 \| 6499 \| 10 \| 12733 \| 1082.1 \| 191.2 \| 165.5 \| 2.3713 \| 0.1502 \| 0.1300 \| 0.2303 \| 0.5104 \| \| 5.1826 \| 48.0 \| 48 \| 10.4865 \| 100.0 \| 100.4922 \| 10813 \| 1920 \| 1656 \| 1920 \| 1656 \| 2930 \| 23.7093 \| 6506 \| 10 \| 12733 \| 1081.3 \| 192.0 \| 165.6 \| 2.3709 \| 0.1508 \| 0.1301 \| 0.2301 \| 0.5110 \| \| 5.1826 \| 49.0 \| 49 \| 10.4866 \| 100.0 \| 100.4922 \| 10807 \| 1926 \| 1656 \| 1926 \| 1656 \| 2927 \| 23.7093 \| 6509 \| 10 \| 12733 \| 1080.7 \| 192.6 \| 165.6 \| 2.3709 \| 0.1513 \| 0.1301 \| 0.2299 \| 0.5112 \| \| 4.8334 \| 50.0 \| 50 \| 10.4868 \| 100.0 \| 100.4922 \| 10807 \| 1926 \| 1656 \| 1926 \| 1656 \| 2927 \| 23.7093 \| 6509 \| 10 \| 12733 \| 1080.7 \| 192.6 \| 165.6 \| 2.3709 \| 0.1513 \| 0.1301 \| 0.2299 \| 0.5112 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.0 - Datasets 2.16.1 - Tokenizers 0.15.0
liwii/factual-consistency-classification-ja-avgpool	liwii	2024-01-19T07:34:43Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "generated_from_trainer", "base_model:line-corporation/line-distilbert-base-japanese", "base_model:finetune:line-corporation/line-distilbert-base-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-01-19T07:09:41Z	--- license: apache-2.0 base_model: line-corporation/line-distilbert-base-japanese tags: - generated_from_trainer metrics: - accuracy model-index: - name: factual-consistency-classification-ja-avgpool 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. --> # factual-consistency-classification-ja-avgpool This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4881 - Accuracy: 0.8223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 306 \| 0.6837 \| 0.7402 \| \| 0.7763 \| 2.0 \| 612 \| 0.6102 \| 0.7734 \| \| 0.7763 \| 3.0 \| 918 \| 0.5782 \| 0.7832 \| \| 0.657 \| 4.0 \| 1224 \| 0.5698 \| 0.7949 \| \| 0.6267 \| 5.0 \| 1530 \| 0.5743 \| 0.7793 \| \| 0.6267 \| 6.0 \| 1836 \| 0.5465 \| 0.8066 \| \| 0.6082 \| 7.0 \| 2142 \| 0.5474 \| 0.8066 \| \| 0.6082 \| 8.0 \| 2448 \| 0.5488 \| 0.7949 \| \| 0.5976 \| 9.0 \| 2754 \| 0.5359 \| 0.8125 \| \| 0.5845 \| 10.0 \| 3060 \| 0.5236 \| 0.8086 \| \| 0.5845 \| 11.0 \| 3366 \| 0.5240 \| 0.8027 \| \| 0.5769 \| 12.0 \| 3672 \| 0.5120 \| 0.8125 \| \| 0.5769 \| 13.0 \| 3978 \| 0.5105 \| 0.8125 \| \| 0.5742 \| 14.0 \| 4284 \| 0.5282 \| 0.7969 \| \| 0.5631 \| 15.0 \| 4590 \| 0.5026 \| 0.8086 \| \| 0.5631 \| 16.0 \| 4896 \| 0.5120 \| 0.8125 \| \| 0.5529 \| 17.0 \| 5202 \| 0.4996 \| 0.8145 \| \| 0.5525 \| 18.0 \| 5508 \| 0.4928 \| 0.8145 \| \| 0.5525 \| 19.0 \| 5814 \| 0.5143 \| 0.8027 \| \| 0.5471 \| 20.0 \| 6120 \| 0.4859 \| 0.8203 \| \| 0.5471 \| 21.0 \| 6426 \| 0.4923 \| 0.8145 \| \| 0.5397 \| 22.0 \| 6732 \| 0.4874 \| 0.8242 \| \| 0.5404 \| 23.0 \| 7038 \| 0.4926 \| 0.8184 \| \| 0.5404 \| 24.0 \| 7344 \| 0.4913 \| 0.8223 \| \| 0.5375 \| 25.0 \| 7650 \| 0.4914 \| 0.8223 \| \| 0.5375 \| 26.0 \| 7956 \| 0.4960 \| 0.8047 \| \| 0.5301 \| 27.0 \| 8262 \| 0.4883 \| 0.8203 \| \| 0.5313 \| 28.0 \| 8568 \| 0.4890 \| 0.8223 \| \| 0.5313 \| 29.0 \| 8874 \| 0.4918 \| 0.8203 \| \| 0.5318 \| 30.0 \| 9180 \| 0.4881 \| 0.8223 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0
Bruce1489/distilbert-base-uncased-finetuned-emotion	Bruce1489	2024-01-19T07:33:54Z	12	0	bertopic	[ "bertopic", "tensorboard", "safetensors", "distilbert", "text-classification", "en", "dataset:andersonbcdefg/synthetic_retrieval_tasks", "license:mit", "region:us" ]	text-classification	2024-01-18T08:55:31Z	--- license: mit datasets: - andersonbcdefg/synthetic_retrieval_tasks language: - en metrics: - accuracy library_name: bertopic pipeline_tag: text-classification ---
hiwensen/bert_sql_classfication	hiwensen	2024-01-19T07:30:44Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "doi:10.57967/hf/1655", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-18T05:24:51Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_sql_classfication results: [] pipeline_tag: text-classification --- # bert_sql_classfication This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a dataset based on the spider dataset.
MonkeyDdonut/PPO-lunarlander	MonkeyDdonut	2024-01-19T07:28:55Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-01-19T07:27:56Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 279.17 +/- 17.10 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ntc-ai/SDXL-LoRA-slider.on-an-insane-power-trip	ntc-ai	2024-01-19T07:20:46Z	8	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]	text-to-image	2024-01-19T07:20:14Z	--- language: - en thumbnail: "images/evaluate/on an insane power trip...calm/on an insane power trip_17_3.0.png" widget: - text: on an insane power trip output: url: images/on an insane power trip_17_3.0.png - text: on an insane power trip output: url: images/on an insane power trip_19_3.0.png - text: on an insane power trip output: url: images/on an insane power trip_20_3.0.png - text: on an insane power trip output: url: images/on an insane power trip_21_3.0.png - text: on an insane power trip output: url: images/on an insane power trip_22_3.0.png tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers license: "mit" inference: false instance_prompt: "on an insane power trip" base_model: "stabilityai/stable-diffusion-xl-base-1.0" --- # ntcai.xyz slider - on an insane power trip (SDXL LoRA) \| Strength: -3 \| Strength: 0 \| Strength: 3 \| \| --- \| --- \| --- \| \| <img src="images/on an insane power trip_17_-3.0.png" width=256 height=256 /> \| <img src="images/on an insane power trip_17_0.0.png" width=256 height=256 /> \| <img src="images/on an insane power trip_17_3.0.png" width=256 height=256 /> \| \| <img src="images/on an insane power trip_19_-3.0.png" width=256 height=256 /> \| <img src="images/on an insane power trip_19_0.0.png" width=256 height=256 /> \| <img src="images/on an insane power trip_19_3.0.png" width=256 height=256 /> \| \| <img src="images/on an insane power trip_20_-3.0.png" width=256 height=256 /> \| <img src="images/on an insane power trip_20_0.0.png" width=256 height=256 /> \| <img src="images/on an insane power trip_20_3.0.png" width=256 height=256 /> \| ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` on an insane power trip ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.on-an-insane-power-trip', weight_name='on an insane power trip.safetensors', adapter_name="on an insane power trip") # Activate the LoRA pipe.set_adapters(["on an insane power trip"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, on an insane power trip" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 1140+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
LishaLakshmiJadhavHiralalKanjiPatel/whisper_base_hi	LishaLakshmiJadhavHiralalKanjiPatel	2024-01-19T07:18:37Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-18T07:49:23Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper_base_hi_trial results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper_base_hi_trial This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0a0+32f93b1 - Datasets 2.16.1 - Tokenizers 0.15.0
wennycooper/distilbert-base-uncased-finetuned-adl_hw1	wennycooper	2024-01-19T07:16:47Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-18T02:25:25Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-adl_hw1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-adl_hw1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7945 - Accuracy: 0.0003 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.3106 \| 1.0 \| 938 \| 1.7945 \| 0.0003 \| \| 1.2138 \| 2.0 \| 1876 \| 0.5088 \| 0.0 \| \| 0.309 \| 3.0 \| 2814 \| 0.2660 \| 0.0003 \| \| 0.1243 \| 4.0 \| 3752 \| 0.2111 \| 0.0 \| \| 0.0707 \| 5.0 \| 4690 \| 0.2014 \| 0.0 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
nitky/Superswallow-70b-baseline	nitky	2024-01-19T07:13:15Z	14	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "en", "ja", "arxiv:2311.03099", "arxiv:2311.10702", "arxiv:2306.01708", "base_model:allenai/tulu-2-dpo-70b", "base_model:merge:allenai/tulu-2-dpo-70b", "base_model:tokyotech-llm/Swallow-70b-instruct-hf", "base_model:merge:tokyotech-llm/Swallow-70b-instruct-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-17T09:28:21Z	--- base_model: - tokyotech-llm/Swallow-70b-instruct-hf - allenai/tulu-2-dpo-70b tags: - mergekit - merge language: - en - ja library_name: transformers pipeline_tag: text-generation license: llama2 model_type: llama --- # Superswallow-70b-baseline This model is provided for LLMs merging researchers. This model does not achieve sufficient results in terms of "[Absorbing Abilities](https://arxiv.org/abs/2311.03099)". However, it is considered useful for benchmarking and model merging tests. For [13B of these baseline mode](https://huggingface.co/nitky/Superswallow-13B-baseline), I will add the [elyza/ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) benchmark and the technical report for merging LLMs trained in different languages at a later. Important Notice: This model partially utilizes the parameters of Tulu V2 DPO finetuned based on Llama 2, so it may inherit the AI2 ImpACT license. Please use the model keeping in mind that there may be changes regarding the license if AI2 contacts me. The [AI2 ImpACT license](https://allenai.org/impact-license) includes information about data artifacts and model artifacts, but does not cover the case of directly applying parts of the LLM parameters of a model artifact to other models. However, I respect their research and great work, so I will change the license immediately if AI2 contacts me. ## Description This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). The model was created by injecting the ability to follow user intent from [Tulu 2 DPO](https://arxiv.org/abs/2311.10702) into the [Swallow](https://zenn.dev/tokyotech_lm/articles/d6cb3a8fdfc907) instract model. It was a proof of concept for merging LLMs trained in other languages, and paid close attention to preserving the linguistic capabilities of the merge-based model. As far as I know, Swallow is the full set Llama 2 model(7B, 13B, 70B) that can output the most beautiful Japanese. Therefore, I used it as the base model for merging this time. Thank you for their wonderful work. ## Test environment This model was tested using [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main). I use preset `Null preset` for Generation. - temperature: 1 - top_p: 1 - repetition_penalty: 1 - top_k: 0 ## Prompt template: Swallow (Alpaca format) ``` 以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。リクエストを適切に完了するための回答を記述してください。 ### 指示: {instruction} ### 応答: ``` ## Use the instruct model ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "nitky/Superswallow-70b-baseline" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto", load_in_4bit = True) PROMPT_DICT = { "prompt_input": ( "以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 入力:\n{input}\n\n### 応答:" ), "prompt_no_input": ( "以下に、あるタスクを説明する指示があります。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 応答:" ), } def create_prompt(instruction, input=None): """ Generates a prompt based on the given instruction and an optional input. If input is provided, it uses the 'prompt_input' template from PROMPT_DICT. If no input is provided, it uses the 'prompt_no_input' template. Args: instruction (str): The instruction describing the task. input (str, optional): Additional input providing context for the task. Default is None. Returns: str: The generated prompt. """ if input: # Use the 'prompt_input' template when additional input is provided return PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input) else: # Use the 'prompt_no_input' template when no additional input is provided return PROMPT_DICT["prompt_no_input"].format(instruction=instruction) # Example usage instruction_example = "以下のトピックに関する詳細な情報を提供してください。" input_example = "東京工業大学の主なキャンパスについて教えてください" prompt = create_prompt(instruction_example, input_example) input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=200, temperature=0.7, top_p=0.9, repetition_penalty=1.15, top_k=20, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ## 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 [tokyotech-llm/Swallow-70b-instruct-hf](https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-hf) as a base. ### Models Merged The following models were included in the merge: * [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: tokyotech-llm/Swallow-70b-instruct-hf # no parameters necessary for base model - model: allenai/tulu-2-dpo-70b # follow user intent parameters: density: 1 weight: - filter: mlp value: 0.1 - filter: self_attn value: 0.45 - value: 0 # fallback for rest of tensors. merge_method: dare_ties base_model: tokyotech-llm/Swallow-70b-instruct-hf dtype: bfloat16 tokenizer_source: union ```
Salexoid/mistral-7b-saiga-sum-v0.1_gguf	Salexoid	2024-01-19T07:03:00Z	71	2	peft	[ "peft", "safetensors", "gguf", "mistral", "generated_from_trainer", "base_model:IlyaGusev/saiga_mistral_7b_merged", "base_model:adapter:IlyaGusev/saiga_mistral_7b_merged", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-01-18T10:18:15Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: IlyaGusev/saiga_mistral_7b_merged model-index: - name: mistral-7b-saiga-sum-v0.1_gguf results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-7b-saiga-sum-v0.1_gguf This model is a fine-tuned version of [IlyaGusev/saiga_mistral_7b_merged](https://huggingface.co/IlyaGusev/saiga_mistral_7b_merged) 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 250 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
lrzjason/noise-classifier	lrzjason	2024-01-19T06:58:01Z	6	2	transformers	[ "transformers", "safetensors", "vit", "image-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-01-19T06:39:50Z	--- license: apache-2.0 --- An vit classifier for handling noise image like this ![0b36d3c4-da8d-4fb1-bc14-a948af35f02e.jpg](https://cdn-uploads.huggingface.co/production/uploads/63891deed68e37abd59e883f/aOspZVn_4W-hUFKt5JNUj.jpeg) It has limitation inbetween clear and noise ``` from datasets import load_dataset from PIL import Image from transformers import ViTImageProcessor, ViTForImageClassification, TrainingArguments, Trainer import torch import numpy as np from datasets import load_metric import os import shutil model_name_or_path = 'lrzjason/noise-classifier' image_processor = ViTImageProcessor.from_pretrained(model_name_or_path) model = ViTForImageClassification.from_pretrained(model_name_or_path) input_dir = '' file = 'b5b457f4-5b52-4d68-be1b-9a2f557465f6.jpg' image = Image.open(os.path.join(input_dir, file)) inputs = image_processor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() ```
nitky/Superswallow-7b-baseline	nitky	2024-01-19T06:43:22Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "en", "ja", "arxiv:2311.03099", "arxiv:2311.10702", "arxiv:2306.01708", "base_model:allenai/tulu-2-dpo-7b", "base_model:merge:allenai/tulu-2-dpo-7b", "base_model:tokyotech-llm/Swallow-7b-instruct-hf", "base_model:merge:tokyotech-llm/Swallow-7b-instruct-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-17T09:26:52Z	--- base_model: - tokyotech-llm/Swallow-7b-instruct-hf - allenai/tulu-2-dpo-7b tags: - mergekit - merge language: - en - ja library_name: transformers pipeline_tag: text-generation license: llama2 model_type: llama --- # Superswallow-7b-baseline This model is provided for LLMs merging researchers. This model does not achieve sufficient results in terms of "[Absorbing Abilities](https://arxiv.org/abs/2311.03099)". However, it is considered useful for benchmarking and model merging tests. For [13B of these baseline mode](https://huggingface.co/nitky/Superswallow-13B-baseline), I will add the [elyza/ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) benchmark and the technical report for merging LLMs trained in different languages at a later. Important Notice: This model partially utilizes the parameters of Tulu V2 DPO finetuned based on Llama 2, so it may inherit the AI2 ImpACT license. Please use the model keeping in mind that there may be changes regarding the license if AI2 contacts me. The [AI2 ImpACT license](https://allenai.org/impact-license) includes information about data artifacts and model artifacts, but does not cover the case of directly applying parts of the LLM parameters of a model artifact to other models. However, I respect their research and great work, so I will change the license immediately if AI2 contacts me. ## Description This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). The model was created by injecting the ability to follow user intent from [Tulu 2 DPO](https://arxiv.org/abs/2311.10702) into the [Swallow](https://zenn.dev/tokyotech_lm/articles/d6cb3a8fdfc907) instract model. It was a proof of concept for merging LLMs trained in other languages, and paid close attention to preserving the linguistic capabilities of the merge-based model. As far as I know, Swallow is the full set Llama 2 model(7B, 13B, 70B) that can output the most beautiful Japanese. Therefore, I used it as the base model for merging this time. Thank you for their wonderful work. ## Test environment This model was tested using [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main). I use preset `Null preset` for Generation. - temperature: 1 - top_p: 1 - repetition_penalty: 1 - top_k: 0 ## Prompt template: Swallow (Alpaca format) ``` 以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。リクエストを適切に完了するための回答を記述してください。 ### 指示: {instruction} ### 応答: ``` ## Use the instruct model ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "nitky/Superswallow-7b-baseline" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto") PROMPT_DICT = { "prompt_input": ( "以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 入力:\n{input}\n\n### 応答:" ), "prompt_no_input": ( "以下に、あるタスクを説明する指示があります。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 応答:" ), } def create_prompt(instruction, input=None): """ Generates a prompt based on the given instruction and an optional input. If input is provided, it uses the 'prompt_input' template from PROMPT_DICT. If no input is provided, it uses the 'prompt_no_input' template. Args: instruction (str): The instruction describing the task. input (str, optional): Additional input providing context for the task. Default is None. Returns: str: The generated prompt. """ if input: # Use the 'prompt_input' template when additional input is provided return PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input) else: # Use the 'prompt_no_input' template when no additional input is provided return PROMPT_DICT["prompt_no_input"].format(instruction=instruction) # Example usage instruction_example = "以下のトピックに関する詳細な情報を提供してください。" input_example = "東京工業大学の主なキャンパスについて教えてください" prompt = create_prompt(instruction_example, input_example) input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=200, temperature=0.7, top_p=0.9, repetition_penalty=1.15, top_k=20, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ## 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 [tokyotech-llm/Swallow-7b-instruct-hf](https://huggingface.co/tokyotech-llm/Swallow-7b-instruct-hf) as a base. ### Models Merged The following models were included in the merge: * [allenai/tulu-2-dpo-7b](https://huggingface.co/allenai/tulu-2-dpo-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: tokyotech-llm/Swallow-7b-instruct-hf # no parameters necessary for base model - model: allenai/tulu-2-dpo-7b # follow user intent parameters: density: 1 weight: - filter: mlp value: 0.1 - filter: self_attn value: 0.45 - value: 0 # fallback for rest of tensors. merge_method: dare_ties base_model: tokyotech-llm/Swallow-7b-instruct-hf dtype: bfloat16 tokenizer_source: union ```
leveldevai/MarcDareBeagle-7B	leveldevai	2024-01-19T06:39:56Z	1,361	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "flemmingmiguel/MarcMistral-7B", "leveldevai/TurdusDareBeagle-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-19T06:33:20Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - flemmingmiguel/MarcMistral-7B - leveldevai/TurdusDareBeagle-7B --- # MarcDareBeagle-7B MarcDareBeagle-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [flemmingmiguel/MarcMistral-7B](https://huggingface.co/flemmingmiguel/MarcMistral-7B) * [leveldevai/TurdusDareBeagle-7B](https://huggingface.co/leveldevai/TurdusDareBeagle-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: flemmingmiguel/MarcMistral-7B layer_range: [0, 32] - model: leveldevai/TurdusDareBeagle-7B layer_range: [0, 32] merge_method: slerp base_model: leveldevai/TurdusDareBeagle-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.45 # fallback for rest of tensors dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "leveldevai/MarcDareBeagle-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
bbokyeong/b64-lr1e-4	bbokyeong	2024-01-19T06:21:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-01-19T06:19:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mesolitica/translation-t5-small-standard-bahasa-cased-code	mesolitica	2024-01-19T06:11:23Z	6	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-01-17T01:39:07Z	--- language: - ms --- # Translation Small T5 Trained on 2048 context length, able to translate malay, english, javanese, banjarese and indonesian to target language. It also able to maintain the text structure as it is and only translate necessary texts, eg, programming code. Added more coding translation dataset and do heavy postfilter. ## how-to ```python from transformers import T5ForConditionalGeneration, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( 'mesolitica/translation-t5-small-standard-bahasa-cased-code', use_fast=False ) model = T5ForConditionalGeneration.from_pretrained( 'mesolitica/translation-t5-small-standard-bahasa-cased-code ' ) answer = """ First, let's start with implementing the `is_number` function, which checks whether the given Variant is number type or not. It checks the type of the Variant and returns whether it is an integer or a real number. ```cpp #include <cmath> #include <string> namespace godot { // ... (other Variant function declarations here) class Variant { // ... (other Variant function definitions here) public: bool is_number() const { switch (get_type()) { case INT: case REAL: return true; default: return false; } } // ... (other Variant function definitions here) }; } // namespace godot ``` Next, we can create the `apply_operation` function, which takes a `Variant` object, an operation (either "+" or "-"), and a `Variant` operand as the input, applying the given operation to the original `Variant` object and the operand if the original `Variant` object is a number. If the `Variant` object is not a number, the function should throw an exception. ```cpp #include <stdexcept> #include <string> // ... (other Variant and function declarations here) Variant apply_operation(Variant a, const std::string& operation, Variant b) { if (!a.is_number()) { throw std::invalid_argument("The first Variant is not a number."); } if (operation != "+" && operation != "-") { throw std::invalid_argument("Invalid operation. Supported operations are '+' and '-'."); } if (a.get_type() == INT) { if (b.is_number()) { if (b.get_type() == INT) { a = a.operator int() + b.operator int(); } else { // REAL a = a.operator int() + b.operator double(); } } else { throw std::invalid_argument("The second Variant is not a number."); } } else { // REAL if (b.is_number()) { if (b.get_type() == INT) { a = a.operator double() + b.operator int(); } else { // REAL a = a.operator double() + b.operator double(); } } else { throw std::invalid_argument("The second Variant is not a number."); } } if (operation == "-") { a = -a; } return a; } ``` """ input_ids = tokenizer.encode(f'terjemah ke Melayu: {answer.strip()}', return_tensors = 'pt').cuda() outputs = model.generate(input_ids, max_length = 512) outputs = [o for o in outputs[0] if o not in [0, 1, 2]] print(tokenizer.decode(outputs, spaces_between_special_tokens = False, skip_special_tokens = False)) ``` ``` Pertama, mari kita mulakan dengan melaksanakan fungsi `is_number`, yang memeriksa sama ada Variant yang diberikan adalah jenis nombor atau tidak. Ia memeriksa jenis Variant dan mengembalikan sama ada ia adalah integer atau nombor sebenar. ```cpp #include <cmath> #include <string> namespace godot { //... (deklarasi fungsi Variant lain di sini) class Variant { //... (definisi fungsi Variant lain di sini) public: bool is_number() const { switch (get_type()) { case INT: case REAL: return true; default: return false; } } //... (definisi fungsi Variant lain di sini) }; } // namespace godot ``` Seterusnya, kita boleh membuat fungsi `apply_operation`, yang mengambil objek `Variant`, operasi (sama ada "+" atau "-"), dan operand `Variant` sebagai input, menerapkan operasi yang diberikan ke objek `Variant` asal dan operand jika objek `Variant` asal adalah nombor. Jika objek `Variant` bukan nombor, fungsi harus melemparkan pengecualian. ```cpp #include <stdexcept> #include <string> // import torch ```
mitro99/whisper-tiny-polyai-enUS	mitro99	2024-01-19T06:10:38Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-19T04:20:30Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 model-index: - name: whisper-tiny-polyai-enUS results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-polyai-enUS This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7582 - eval_wer_ortho: 0.3510 - eval_wer: 0.3418 - eval_runtime: 34.041 - eval_samples_per_second: 3.32 - eval_steps_per_second: 0.118 - epoch: 26.67 - step: 400 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
yzhuang/phi-1_5_fictional	yzhuang	2024-01-19T06:03:06Z	47	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:mmlu_no_train", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-14T00:00:40Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - trl - sft - generated_from_trainer datasets: - mmlu_no_train model-index: - name: phi-1_5_fictional results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-1_5_fictional This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the mmlu_no_train 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: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.0 - Tokenizers 0.15.0
ToPo-ToPo/cyberagent-open-calm-small-ToPo-ToPo-databricks-dolly-15k-ja-zundamon-full-instruction-tuning	ToPo-ToPo	2024-01-19T05:59:27Z	4	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "dataset:ToPo-ToPo/databricks-dolly-15k-ja-zundamon", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-19T05:45:42Z	--- datasets: - ToPo-ToPo/databricks-dolly-15k-ja-zundamon --- ## モデルの概要 cyberagent/open-calm-smallモデルに対し、フルファインチューニングを行いました． instruction tuningにより，入力された質問に対する回答という形で文章の生成ができます．データセットは，語尾をずんだもん用に変更したものであり，生成される回答もその影響を含むものとなります．
fionazhang/mistral-finetune-environment	fionazhang	2024-01-19T05:58:19Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-19T05:44:16Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer model-index: - name: mistral-finetune-environment results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-finetune-environment This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0a0+git7bcf7da - Datasets 2.16.1 - Tokenizers 0.15.0
watsonpro/bert-finetuned-sem_eval-english	watsonpro	2024-01-19T05:45:51Z	2	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:sem_eval_2018_task_1", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-19T05:40:25Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - sem_eval_2018_task_1 metrics: - f1 - accuracy model-index: - name: bert-finetuned-sem_eval-english results: - task: name: Text Classification type: text-classification dataset: name: sem_eval_2018_task_1 type: sem_eval_2018_task_1 args: subtask5.english metrics: - name: F1 type: f1 value: 0.6645074224021592 - name: Accuracy type: accuracy value: 0.2595936794582393 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-sem_eval-english This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sem_eval_2018_task_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3308 - F1: 0.6645 - Roc Auc: 0.7642 - Accuracy: 0.2596 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.4155 \| 1.0 \| 855 \| 0.3308 \| 0.6645 \| 0.7642 \| 0.2596 \| ### Framework versions - Transformers 4.17.0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
varun-v-rao/bert-base-cased-mnli-model3	varun-v-rao	2024-01-19T05:38:43Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-18T23:40:58Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-cased-mnli-model3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-mnli-model3 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4734 - Accuracy: 0.8376 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 35 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4672 \| 1.0 \| 6136 \| 0.4506 \| 0.8237 \| \| 0.3563 \| 2.0 \| 12272 \| 0.4399 \| 0.8333 \| \| 0.2711 \| 3.0 \| 18408 \| 0.4734 \| 0.8376 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
ewqr2130/alignment-handbook-zephyr-7b_ppo_5e7step_51	ewqr2130	2024-01-19T05:26:53Z	1,362	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-19T04:06:44Z	--- license: apache-2.0 --- ewqr2130/alignment-handbook-zephyr-7b_ppo_5e7step_51 runing the SFT with PPO for 51 steps. runing the SFT with PPO for 51 steps. runing the SFT with PPO for 51 steps. runing the SFT with PPO for 51 steps. runing the SFT with PPO for 51 steps.
Abhinav28/large-v3-hi-test-22	Abhinav28	2024-01-19T05:21:17Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai/whisper-large-v3", "base_model:adapter:openai/whisper-large-v3", "region:us" ]	null	2024-01-19T05:20:55Z	--- library_name: peft base_model: openai/whisper-large-v3 --- # 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 0.7.2.dev0
haoranxu/ALMA-7B	haoranxu	2024-01-19T05:19:47Z	1,607	24	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2309.11674", "arxiv:2401.08417", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-17T17:14:42Z	--- license: mit --- ALMA (Advanced Language Model-based trAnslator) is an LLM-based translation model, which adopts a new translation model paradigm: it begins with fine-tuning on monolingual data and is further optimized using high-quality parallel data. This two-step fine-tuning process ensures strong translation performance. Please find more details in our [paper](https://arxiv.org/abs/2309.11674). ``` @misc{xu2023paradigm, title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models}, author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla}, year={2023}, eprint={2309.11674}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` [ALMA-R](https://arxiv.org/abs/2401.08417) (NEW!) is released now! ALMA-R builds upon ALMA models, with further LoRA fine-tuning with our proposed Contrastive Preference Optimization (CPO) as opposed to the Supervised Fine-tuning used in ALMA. CPO fine-tuning requires our [triplet preference data](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners! ``` @misc{xu2024contrastive, title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}, author={Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim}, year={2024}, eprint={2401.08417}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We release six translation models presented in the paper: - ALMA-7B: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then Full-weight fine-tune on human-written parallel data - ALMA-7B-LoRA: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then LoRA fine-tune on human-written parallel data - ALMA-7B-R (NEW!): Further LoRA fine-tuning upon ALMA-7B-LoRA with contrastive preference optimization. - ALMA-13B: Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then Full-weight fine-tune on human-written parallel data - ALMA-13B-LoRA (Our best system): Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then LoRA fine-tune on human-written parallel data - ALMA-13B-R (NEW!): Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization. Model checkpoints are released at huggingface: \| Models \| Base Model Link \| LoRA Link \| \|:-------------:\|:---------------:\|:---------:\| \| ALMA-7B \| [haoranxu/ALMA-7B](https://huggingface.co/haoranxu/ALMA-7B) \| - \| \| ALMA-7B-LoRA \| [haoranxu/ALMA-7B-Pretrain](https://huggingface.co/haoranxu/ALMA-7B-Pretrain) \| [haoranxu/ALMA-7B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-7B-Pretrain-LoRA) \| \| ALMA-7B-R (NEW!) \| [haoranxu/ALMA-7B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-7B-R) \| - \| \| ALMA-13B \| [haoranxu/ALMA-13B](https://huggingface.co/haoranxu/ALMA-13B) \| - \| \| ALMA-13B-LoRA \| [haoranxu/ALMA-13B-Pretrain](https://huggingface.co/haoranxu/ALMA-13B-Pretrain) \| [haoranxu/ALMA-13B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-13B-Pretrain-LoRA) \| \| ALMA-13B-R (NEW!) \| [haoranxu/ALMA-13B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-13B-R) \| - \| Note that `ALMA-7B-Pretrain` and `ALMA-13B-Pretrain` are NOT translation models. They only experience stage 1 monolingual fine-tuning (20B tokens for the 7B model and 12B tokens for the 13B model), and should be utilized in conjunction with their LoRA models. Datasets used by ALMA and ALMA-R are also released at huggingface now (NEW!) \| Datasets \| Train / Validation\| Test \| \|:-------------:\|:---------------:\|:---------:\| \| Human-Written Parallel Data (ALMA) \| [train and validation](https://huggingface.co/datasets/haoranxu/ALMA-Human-Parallel) \| [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) \| \| Triplet Preference Data \| [train](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) \| [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) and [WMT'23](https://huggingface.co/datasets/haoranxu/WMT23-Test) \| A quick start to use system ALMA-13B-LoRA for translation. An example of translating "我爱机器翻译。" into English: ``` import torch from peft import PeftModel from transformers import AutoModelForCausalLM from transformers import LlamaTokenizer # Load base model and LoRA weights model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-Pretrain", torch_dtype=torch.float16, device_map="auto") model = PeftModel.from_pretrained(model, "haoranxu/ALMA-13B-Pretrain-LoRA") tokenizer = LlamaTokenizer.from_pretrained("haoranxu/ALMA-13B-Pretrain", padding_side='left') # Add the source setence into the prompt template prompt="Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:" input_ids = tokenizer(prompt, return_tensors="pt", padding=True, max_length=40, truncation=True).input_ids.cuda() # Translation with torch.no_grad(): generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_new_tokens=20, do_sample=True, temperature=0.6, top_p=0.9) outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) print(outputs) ``` Please find more details in our [GitHub repository](https://github.com/fe1ixxu/ALMA)
haoranxu/ALMA-13B	haoranxu	2024-01-19T05:19:30Z	2,112	35	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2309.11674", "arxiv:2401.08417", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-17T17:43:04Z	--- license: mit --- ALMA (Advanced Language Model-based trAnslator) is an LLM-based translation model, which adopts a new translation model paradigm: it begins with fine-tuning on monolingual data and is further optimized using high-quality parallel data. This two-step fine-tuning process ensures strong translation performance. Please find more details in our [paper](https://arxiv.org/abs/2309.11674). ``` @misc{xu2023paradigm, title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models}, author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla}, year={2023}, eprint={2309.11674}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` [ALMA-R](https://arxiv.org/abs/2401.08417) (NEW!) is released now! ALMA-R builds upon ALMA models, with further LoRA fine-tuning with our proposed Contrastive Preference Optimization (CPO) as opposed to the Supervised Fine-tuning used in ALMA. CPO fine-tuning requires our [triplet preference data](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners! ``` @misc{xu2024contrastive, title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}, author={Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim}, year={2024}, eprint={2401.08417}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We release six translation models presented in the paper: - ALMA-7B: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then Full-weight fine-tune on human-written parallel data - ALMA-7B-LoRA: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then LoRA fine-tune on human-written parallel data - ALMA-7B-R (NEW!): Further LoRA fine-tuning upon ALMA-7B-LoRA with contrastive preference optimization. - ALMA-13B: Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then Full-weight fine-tune on human-written parallel data - ALMA-13B-LoRA (Our best system): Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then LoRA fine-tune on human-written parallel data - ALMA-13B-R (NEW!): Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization. Model checkpoints are released at huggingface: \| Models \| Base Model Link \| LoRA Link \| \|:-------------:\|:---------------:\|:---------:\| \| ALMA-7B \| [haoranxu/ALMA-7B](https://huggingface.co/haoranxu/ALMA-7B) \| - \| \| ALMA-7B-LoRA \| [haoranxu/ALMA-7B-Pretrain](https://huggingface.co/haoranxu/ALMA-7B-Pretrain) \| [haoranxu/ALMA-7B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-7B-Pretrain-LoRA) \| \| ALMA-7B-R (NEW!) \| [haoranxu/ALMA-7B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-7B-R) \| - \| \| ALMA-13B \| [haoranxu/ALMA-13B](https://huggingface.co/haoranxu/ALMA-13B) \| - \| \| ALMA-13B-LoRA \| [haoranxu/ALMA-13B-Pretrain](https://huggingface.co/haoranxu/ALMA-13B-Pretrain) \| [haoranxu/ALMA-13B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-13B-Pretrain-LoRA) \| \| ALMA-13B-R (NEW!) \| [haoranxu/ALMA-13B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-13B-R) \| - \| Note that `ALMA-7B-Pretrain` and `ALMA-13B-Pretrain` are NOT translation models. They only experience stage 1 monolingual fine-tuning (20B tokens for the 7B model and 12B tokens for the 13B model), and should be utilized in conjunction with their LoRA models. Datasets used by ALMA and ALMA-R are also released at huggingface now (NEW!) \| Datasets \| Train / Validation\| Test \| \|:-------------:\|:---------------:\|:---------:\| \| Human-Written Parallel Data (ALMA) \| [train and validation](https://huggingface.co/datasets/haoranxu/ALMA-Human-Parallel) \| [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) \| \| Triplet Preference Data \| [train](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) \| [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) and [WMT'23](https://huggingface.co/datasets/haoranxu/WMT23-Test) \| A quick start to use system ALMA-13B-LoRA for translation. An example of translating "我爱机器翻译。" into English: ``` import torch from peft import PeftModel from transformers import AutoModelForCausalLM from transformers import LlamaTokenizer # Load base model and LoRA weights model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-Pretrain", torch_dtype=torch.float16, device_map="auto") model = PeftModel.from_pretrained(model, "haoranxu/ALMA-13B-Pretrain-LoRA") tokenizer = LlamaTokenizer.from_pretrained("haoranxu/ALMA-13B-Pretrain", padding_side='left') # Add the source setence into the prompt template prompt="Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:" input_ids = tokenizer(prompt, return_tensors="pt", padding=True, max_length=40, truncation=True).input_ids.cuda() # Translation with torch.no_grad(): generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_new_tokens=20, do_sample=True, temperature=0.6, top_p=0.9) outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) print(outputs) ``` Please find more details in our [GitHub repository](https://github.com/fe1ixxu/ALMA)
haoranxu/ALMA-7B-Pretrain-LoRA	haoranxu	2024-01-19T05:19:10Z	0	3	null	[ "arxiv:2309.11674", "arxiv:2401.08417", "license:mit", "region:us" ]	null	2023-09-17T17:41:07Z	--- license: mit --- ALMA (Advanced Language Model-based trAnslator) is an LLM-based translation model, which adopts a new translation model paradigm: it begins with fine-tuning on monolingual data and is further optimized using high-quality parallel data. This two-step fine-tuning process ensures strong translation performance. Please find more details in our [paper](https://arxiv.org/abs/2309.11674). ``` @misc{xu2023paradigm, title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models}, author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla}, year={2023}, eprint={2309.11674}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` [ALMA-R](https://arxiv.org/abs/2401.08417) (NEW!) is released now! ALMA-R builds upon ALMA models, with further LoRA fine-tuning with our proposed Contrastive Preference Optimization (CPO) as opposed to the Supervised Fine-tuning used in ALMA. CPO fine-tuning requires our [triplet preference data](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners! ``` @misc{xu2024contrastive, title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}, author={Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim}, year={2024}, eprint={2401.08417}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We release six translation models presented in the paper: - ALMA-7B: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then Full-weight fine-tune on human-written parallel data - ALMA-7B-LoRA: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then LoRA fine-tune on human-written parallel data - ALMA-7B-R (NEW!): Further LoRA fine-tuning upon ALMA-7B-LoRA with contrastive preference optimization. - ALMA-13B: Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then Full-weight fine-tune on human-written parallel data - ALMA-13B-LoRA (Our best system): Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then LoRA fine-tune on human-written parallel data - ALMA-13B-R (NEW!): Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization. Model checkpoints are released at huggingface: \| Models \| Base Model Link \| LoRA Link \| \|:-------------:\|:---------------:\|:---------:\| \| ALMA-7B \| [haoranxu/ALMA-7B](https://huggingface.co/haoranxu/ALMA-7B) \| - \| \| ALMA-7B-LoRA \| [haoranxu/ALMA-7B-Pretrain](https://huggingface.co/haoranxu/ALMA-7B-Pretrain) \| [haoranxu/ALMA-7B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-7B-Pretrain-LoRA) \| \| ALMA-7B-R (NEW!) \| [haoranxu/ALMA-7B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-7B-R) \| - \| \| ALMA-13B \| [haoranxu/ALMA-13B](https://huggingface.co/haoranxu/ALMA-13B) \| - \| \| ALMA-13B-LoRA \| [haoranxu/ALMA-13B-Pretrain](https://huggingface.co/haoranxu/ALMA-13B-Pretrain) \| [haoranxu/ALMA-13B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-13B-Pretrain-LoRA) \| \| ALMA-13B-R (NEW!) \| [haoranxu/ALMA-13B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-13B-R) \| - \| Note that `ALMA-7B-Pretrain` and `ALMA-13B-Pretrain` are NOT translation models. They only experience stage 1 monolingual fine-tuning (20B tokens for the 7B model and 12B tokens for the 13B model), and should be utilized in conjunction with their LoRA models. Datasets used by ALMA and ALMA-R are also released at huggingface now (NEW!) \| Datasets \| Train / Validation\| Test \| \|:-------------:\|:---------------:\|:---------:\| \| Human-Written Parallel Data (ALMA) \| [train and validation](https://huggingface.co/datasets/haoranxu/ALMA-Human-Parallel) \| [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) \| \| Triplet Preference Data \| [train](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) \| [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) and [WMT'23](https://huggingface.co/datasets/haoranxu/WMT23-Test) \| A quick start to use system ALMA-13B-LoRA for translation. An example of translating "我爱机器翻译。" into English: ``` import torch from peft import PeftModel from transformers import AutoModelForCausalLM from transformers import LlamaTokenizer # Load base model and LoRA weights model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-Pretrain", torch_dtype=torch.float16, device_map="auto") model = PeftModel.from_pretrained(model, "haoranxu/ALMA-13B-Pretrain-LoRA") tokenizer = LlamaTokenizer.from_pretrained("haoranxu/ALMA-13B-Pretrain", padding_side='left') # Add the source setence into the prompt template prompt="Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:" input_ids = tokenizer(prompt, return_tensors="pt", padding=True, max_length=40, truncation=True).input_ids.cuda() # Translation with torch.no_grad(): generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_new_tokens=20, do_sample=True, temperature=0.6, top_p=0.9) outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) print(outputs) ``` Please find more details in our [GitHub repository](https://github.com/fe1ixxu/ALMA)
haoranxu/ALMA-7B-R	haoranxu	2024-01-19T05:18:39Z	4,693	13	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2401.08417", "arxiv:2309.11674", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-17T11:50:53Z	--- license: mit --- [ALMA-R](https://arxiv.org/abs/2401.08417) builds upon [ALMA models](https://arxiv.org/abs/2309.11674), with further LoRA fine-tuning with our proposed Contrastive Preference Optimization (CPO) as opposed to the Supervised Fine-tuning used in ALMA. CPO fine-tuning requires our [triplet preference data](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners! ``` @misc{xu2024contrastive, title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}, author={Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim}, year={2024}, eprint={2401.08417}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @misc{xu2023paradigm, title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models}, author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla}, year={2023}, eprint={2309.11674}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` # Download ALMA(-R) Models and Dataset 🚀 We release six translation models presented in the paper: - ALMA-7B - ALMA-7B-LoRA - ALMA-7B-R (NEW!): Further LoRA fine-tuning upon ALMA-7B-LoRA with contrastive preference optimization. - ALMA-13B - ALMA-13B-LoRA - ALMA-13B-R (NEW!): Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization (BEST MODEL!). Model checkpoints are released at huggingface: \| Models \| Base Model Link \| LoRA Link \| \|:-------------:\|:---------------:\|:---------:\| \| ALMA-7B \| [haoranxu/ALMA-7B](https://huggingface.co/haoranxu/ALMA-7B) \| - \| \| ALMA-7B-LoRA \| [haoranxu/ALMA-7B-Pretrain](https://huggingface.co/haoranxu/ALMA-7B-Pretrain) \| [haoranxu/ALMA-7B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-7B-Pretrain-LoRA) \| \| ALMA-7B-R (NEW!) \| [haoranxu/ALMA-7B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-7B-R) \| - \| \| ALMA-13B \| [haoranxu/ALMA-13B](https://huggingface.co/haoranxu/ALMA-13B) \| - \| \| ALMA-13B-LoRA \| [haoranxu/ALMA-13B-Pretrain](https://huggingface.co/haoranxu/ALMA-13B-Pretrain) \| [haoranxu/ALMA-13B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-13B-Pretrain-LoRA) \| \| ALMA-13B-R (NEW!) \| [haoranxu/ALMA-13B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-13B-R) \| - \| Note that `ALMA-7B-Pretrain` and `ALMA-13B-Pretrain` are NOT translation models. They only experience stage 1 monolingual fine-tuning (20B tokens for the 7B model and 12B tokens for the 13B model), and should be utilized in conjunction with their LoRA models. Datasets used by ALMA and ALMA-R are also released at huggingface now (NEW!) \| Datasets \| Train / Validation\| Test \| \|:-------------:\|:---------------:\|:---------:\| \| Human-Written Parallel Data (ALMA) \| [train and validation](https://huggingface.co/datasets/haoranxu/ALMA-Human-Parallel) \| [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) \| \| Triplet Preference Data \| [train](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) \| [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) and [WMT'23](https://huggingface.co/datasets/haoranxu/WMT23-Test) \| A quick start to use our best system (ALMA-13B-R) for translation. An example of translating "我爱机器翻译。" into English: ``` import torch from transformers import AutoModelForCausalLM from transformers import AutoTokenizer # Load base model and LoRA weights model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-R", torch_dtype=torch.float16, device_map="auto") tokenizer = AutoTokenizer.from_pretrained("haoranxu/ALMA-13B-R", padding_side='left') # Add the source sentence into the prompt template prompt="Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:" input_ids = tokenizer(prompt, return_tensors="pt", padding=True, max_length=40, truncation=True).input_ids.cuda() # Translation with torch.no_grad(): generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_new_tokens=20, do_sample=True, temperature=0.6, top_p=0.9) outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) print(outputs) ``` Please find more details in our [GitHub repository](https://github.com/fe1ixxu/ALMA)
wenqiglantz/MistralTrinity-7B-slerp-finetuned-dolly-1k	wenqiglantz	2024-01-19T04:53:44Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "finetuned", "mistralai/Mistral-7B-Instruct-v0.2", "jan-hq/trinity-v1", "wenqiglantz/MistralTrinity-7b-slerp", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-16T14:21:20Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - finetuned - mistralai/Mistral-7B-Instruct-v0.2 - jan-hq/trinity-v1 - wenqiglantz/MistralTrinity-7b-slerp --- # MistralTrinity-7B-slerp-finetuned-dolly-1k MistralTrinity-7B-slerp-finetuned-dolly-1k is a fine-tuned model of [MistralTrinity-7B-slerp](https://huggingface.co/wenqiglantz/MistralTrinity-7B-slerp), which was merged from the following two models using [mergekit](https://github.com/cg123/mergekit): * [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) * [jan-hq/trinity-v1](https://huggingface.co/jan-hq/trinity-v1) ## Dataset The dataset used for fine-tuning is from [wenqiglantz/databricks-dolly-1k](https://huggingface.co/datasets/wenqiglantz/databricks-dolly-1k), a subset (1000 samples) of [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset.
LoneStriker/Chinese-Mixtral-8x7B-3.5bpw-h6-exl2	LoneStriker	2024-01-19T04:38:02Z	6	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "arxiv:2401.04088", "arxiv:2109.07306", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-19T04:29:33Z	--- license: apache-2.0 --- <div align="center"> <h1> Chinese-Mixtral-8x7B </h1> </div> ![](img/logo.png) <div align="center"> <a href="https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B/pulls"> <image src="https://img.shields.io/badge/PRs-welcome-brightgreen"></image> <image src="https://img.shields.io/badge/License-Apache_2.0-green.svg"></image> </a> </div> ## 🚀 介绍本项目基于Mistral发布的模型[Mixtral-8x7B](https://mistral.ai/news/mixtral-of-experts/)进行了中文扩词表增量预训练，希望进一步促进中文自然语言处理社区对MoE模型的研究。我们扩充后的词表显著提高了模型对中文的编解码效率，并通过大规模开源语料对扩词表模型进行增量预训练，使模型具备了强大的中文生成和理解能力。项目开源内容： - 中文Mixtral-8x7B扩词表大模型 - 扩词表增量预训练代码 > 请注意，Chinese-Mixtral-8x7B仍然可能生成包含事实性错误的误导性回复或包含偏见/歧视的有害内容，请谨慎鉴别和使用生成的内容，请勿将生成的有害内容传播至互联网。 ## 📥 模型下载本项目使用QLoRA进行训练，LoRA权重与合并权重后的模型分别开源，您可以根据自己的需求选择下载： \| 模型名称 \| 模型大小 \| 下载地址 \| 备注 \| \|:----------------------------:\|:-----:\|:-----------------------------------------------------------------------------:\|:-------------------------------------------------------------------------------------------------------------------:\| \| Chinese-Mixtral-8x7B \| 88GB \| [🤗HuggingFace](https://huggingface.co/HIT-SCIR/Chinese-Mixtral-8x7B) \| 中文扩词表完整模型，可以直接使用 \| \| Chinese-Mixtral-8x7B-adapter \| 2.7GB \| [🤗HuggingFace](https://huggingface.co/HIT-SCIR/Chinese-Mixtral-8x7B-adapter) \| LoRA权重，需要与原版Mixtral-8x7B进行合并才可以使用，合并脚本请参考[这里](https://gist.github.com/ChrisHayduk/1a53463331f52dca205e55982baf9930) \| ## 💻 模型推理 Chinese-Mixtral-8x7B支持完整的Mixtral-8x7B模型生态，包括使用`vLLM`、`Flash Attention 2`进行加速，使用`bitsandbytes`进行模型量化等。以下是使用Chinese-Mixtral-8x7B进行推理的代码示例。使用Flash Attention 2： ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "HIT-SCIR/Chinese-Mixtral-8x7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto") text = "我的名字是" inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` 使用4bit量化： ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "HIT-SCIR/Chinese-Mixtral-8x7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto") text = "我的名字是" inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` 请注意，Chinese-Mixtral-8x7B为基座模型，没有经过指令微调，因此指令遵循能力有限。您可以参考[微调](#微调)一节对模型进行微调。 ## 📈 模型性能 ### 模型综合能力我们分别使用以下评测数据集对Chinese-Mixtral-8x7B进行评测： - C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。 - CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。 - MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。 - HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。根据Mistral发布的[技术报告](https://arxiv.org/pdf/2401.04088.pdf)，Mixtral-8x7B在推理时将激活13B参数。下表为Chinese-Mixtral-8x7B与其他13B规模的中文扩词表模型在各个评测数据集上的5-shot结果： \| 模型名称 \| 增量训练语料 \| C-Eval<br>(中文) \| CMMLU<br>(中文) \| MMLU<br>(英文) \| HellaSwag<br>(英文) \| \|:-----------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:-------------:\|:------------:\|:-----------------:\| \| [IDEA-CCNL/Ziya2-13B-Base](https://huggingface.co/IDEA-CCNL/Ziya2-13B-Base) \| 650B Token \| 59.29 \| 60.93 \| 59.86 \| 58.90 \| \| [TigerResearch/tigerbot-13b-base-v3](https://huggingface.co/TigerResearch/tigerbot-13b-base-v3) \| 500B Token \| 50.52 \| 51.65 \| 53.46 \| 59.16 \| \| [Linly-AI/Chinese-LLaMA-2-13B-hf](https://huggingface.co/Linly-AI/Chinese-LLaMA-2-13B-hf) \| 11B Token \| 42.57 \| 41.95 \| 51.32 \| 59.05 \| \| [hfl/chinese-llama-2-13b](https://huggingface.co/hfl/chinese-llama-2-13b) \| 约30B Token(120GB) \| 41.90 \| 42.08 \| 51.92 \| 59.28 \| \| Chinese-Mixtral-8x7B(本项目) \| 42B Token \| 52.08 \| 51.08 \| 69.80 \| 65.69 \| 在中文知识和理解方面，我们的Chinese-Mixtral-8x7B与TigerBot-13B-Base-v3性能相当。由于Chinese-Mixtral-8x7B的训练数据量仅为TigerBot-13B-Base-v3的8%，我们的模型仍有进一步提升的空间。与此同时，得益于原版Mixtral-8x7B模型强大的性能，我们的Chinese-Mixtral-8x7B达到了各个扩词表模型的最强英文水平。 > 由于不同版本的评测脚本实现细节有细微差异，为了保证评测结果的一致性和公平性，我们的评测脚本统一使用EleutherAI发布的lm-evaluation-harness，commit hash为[28ec7fa](https://github.com/EleutherAI/lm-evaluation-harness/tree/28ec7fa950346b5a895e85e1f3edd5648168acc4)。 ### 模型生成效果下表为各个扩词表模型的生成效果。由于部分模型的预训练语料未使用`eos_token`进行分隔，我们采用了`max_tokens = 100`对生成文本进行截断。我们的采样参数为`temperature = 0.8, top_p = 0.9`。 ![](./img/case.png) ### 中文编解码效率针对中文编解码效率，我们使用各个扩词表模型的分词器对[SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B)数据集的一个切片（2023-06_zh_head_0000.jsonl）进行编码，对比了各个分词器输出的中文文本Token量： \| 模型名称 \| 模型类别 \| 词表大小 \| 中文文本Token量 \| 编解码效率 \| \|:----------------------------------:\|:-------:\|:-----:\|:----------:\|:-------:\| \| meta-llama/Llama-2-13B-hf \| LLaMA \| 32000 \| 780M \| 低 \| \| mistralai/Mixtral-8x7B-v0.1 \| Mixtral \| 32000 \| 606M \| 低 \| \| Linly-AI/Chinese-LLaMA-2-13B-hf \| LLaMA \| 40076 \| 532M \| 中 \| \| IDEA-CCNL/Ziya2-13B-Base \| LLaMA \| 39424 \| 532M \| 中 \| \| hfl/chinese-llama-2-13b \| LLaMA \| 55296 \| 365M \| 高 \|、 \| TigerResearch/tigerbot-13b-base-v3 \| LLaMA \| 65112 \| 342M \| 高 \| \| Chinese-Mixtral-8x7B(本项目) \| Mixtral \| 57000 \| 355M \| 高 \| 在约1.4GB的测试文本中，我们的Chinese-Mixtral-8x7B中文编解码效率仅次于TigerBot-13B-Base-v3，较原模型提高了41.5%。这有利于加速中文文本的推理速度，并在In-Context Learning、Chain-of-Thought等场景中节省序列长度，有利于提高复杂推理任务的性能。 ## ⚙️ 训练细节 <details> <summary> ### 词表扩充 </summary> 我们使用`sentencepiece`在12G知乎数据和2G悟道数据上训练中文BPE词表。我们在训练词表时分别枚举了中文单字Token数量以及中文总Token数量，并对二者进行组合，得到了数百个大小、内容各异的词表。为了得到最适合的词表，我们通过Zheng Bo等人提出的[ALP](https://arxiv.org/pdf/2109.07306.pdf)计算这些词表的中文词汇能力。ALP通过计算特定语言的子词切分粒度，并对词表的中低频子词进行惩罚，是一种方便快捷的衡量特定语言词汇能力的指标。我们在书籍和百科语料上评估了不同词表的ALP值。图示中，四条曲线分别代表四种中文单字Token数量的词表（4451、5435、6414和7434）。为了避免词表过小导致中文压缩率过低，以及词表过大导致embedding层过于稀疏，我们选取ALP曲线的拐点，对应向词表中新增25000个中文Token。在此基础上，我们选择了四条曲线中ALP最大者，即新增6414个中文单字Token的词表，作为最终Chinese-Mixtral-8x7B选用的词表。 ![](./img/alp.png) 在获得新词表后，我们需要对embedding和lm_head层进行扩充和初始化。我们使用新Token在旧embedding层中的词嵌入平均值对扩充部分进行初始化。在我们的前期实验中，这种方法略优于HuggingFace的默认实现，即使用固定的正态分布进行初始化。 </details> <details> <summary> ### 增量预训练 </summary> Mixtral-8x7B模型参数量为46.7B，全参数训练需要同时使用多种并行策略，在训练资源受限的情况下时间成本过高。因此我们采用HuggingFace官方推荐的方法，使用QLoRA对模型进行训练。QLoRA在LoRA低秩分解的基础上，通过引入4位量化、双重量化和利用NVIDIA统一内存进行分页，进一步减少了训练所需显存，同时保持了与全参数训练相当的性能。我们参考Yiming Cui等人[对LoRA的设置](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/blob/main/scripts/training/run_pt.sh)，对原模型所有Linear层应用低秩分解，并将扩增后的embedding和lm_head层的参数设置为可训练。对于模型主体，我们采用NF4格式进行量化，这种格式可以使得量化后的数据与量化前具有同等的数据分布，模型的权重信息损失更少。 #### 环境准备我们建议使用Python 3.10 + torch 2.0.1 ```shell # Pytorch + Transformers $ pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 $ pip install transformers==4.36.2 datasets evaluate peft accelerate gradio optimum sentencepiece $ pip install jupyterlab scikit-learn pandas matplotlib tensorboard nltk rouge bitsandbytes fire # DeepSpeed $ git clone https://github.com/microsoft/DeepSpeed.git $ cd DeepSpeed $ DS_BUILD_FUSED_ADAM=1 pip3 install . # Flash Attention $ pip install flash-attn --no-build-isolation ``` #### 数据集下载我们基于现有的开源数据集训练了Chinese-Mixtral-8x7B，数据集包括： \| 数据集名称 \| 数据集语言 \|使用数据量\| 备注 \| \|:----------------------------------------------------------------------------:\|:-----:\|:----------------:\|:-----:\| \| [Skywork/SkyPile-150B](https://huggingface.co/datasets/Skywork/SkyPile-150B) \| 中文 \|30B\| 仅使用2022 + 2023年的数据 \| \| [DKYoon/SlimPajama-6B](https://huggingface.co/datasets/DKYoon/SlimPajama-6B) \| 英文 \|12B\| 数据集重复2 Epoch \| 通过`data/download.py`将数据集下载到`data`中。针对Slimpajama数据集，需要使用`data/parquet2jsonl.py`将原始数据集转换为`jsonl`格式。下载后的数据集为多个jsonl文件的分片，使用`cat`将多个分片合并为一个jsonl文件。 ```shell $ cat *.jsonl > all.jsonl ``` 通过`split`将jsonl切分为train和valid集合。本项目中train和valid的行数比例为999:1。 ```shell $ wc -l all.jsonl # 计算数据集总行数 $ split -l <lines> all.jsonl # 按999:1计算train/valid行数，进行切分 $ mv xaa DKYoon-SlimPajama-6B-train.jsonl # 重命名 $ mv xab DKYoon-SlimPajama-6B-dev.jsonl ``` #### 数据集预处理将数据集名称和路径注册到`data/datasets.toml`中： ```toml [DKYoon-SlimPajama-6B] # 数据集名称 splits = ["train", "dev"] # 数据集train/valid集合 root = "{DATA_DIR}/en/{name}" # 数据集根目录 doc = "{name}-{split}" # 数据集文件名 encoded = "encoded-{name}-{split}" # 预处理保存位置 ``` 使用`data/preprocess_datasets.py`对数据集进行子词切分，从而加快训练速度。 ```shell $ python data/preprocess_datasets.py --ds_name SkyPile-150B-2023 --tokenizer_name_or_path tokenizer/Mixtral-8x7B-v0.1-vocab $ python data/preprocess_datasets.py --ds_name DKYoon-SlimPajama-6B --tokenizer_name_or_path tokenizer/Mixtral-8x7B-v0.1-vocab ``` 在进行子词切分后，可以使用`data/utils.py`查看各个数据集的token总量： ```shell $ python data/utils.py ``` #### 开始训练训练启动脚本为`scripts/train.sh`。可以通过修改其中的`TRAIN_DATASETS`修改训练数据集和数据集比例： ```shell TRAIN_DATASETS=( 1:SkyPile-150B-2022 # 使用全量SkyPile-150B-2022 0.1:SkyPile-150B-2023 # 使用SkyPile-150B-2023的10%数据 1:DKYoon-SlimPajama-6B # 使用全量DKYoon-SlimPajama-6B ) ``` 如果您使用SLURM集群管理系统，可以通过`sbatch`进行提交： ```shell $ sbatch scripts/train.sh ``` 如果没有SLURM或希望通过命令行启动训练，您可以直接提取`scripts/train.sh`中的`torchrun`开始训练。 </details> <details> <summary> ### 微调 </summary> 本项目发布的Chinese-Mixtral-8x7B为基座模型，没有经过微调。如果您希望使用Chinese-Mixtral-8x7B进行下游任务微调或SFT，可以参考HuggingFace给出Mixtral-8x7B的QLoRA微调脚本进行训练：[HuggingFace的官方示例代码](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)。 </details> ## ✒️ 引用如果您觉得本项目对您的研究有所帮助或使用了本项目的代码，请引用本项目： ```bibtex @misc{Chinese-Mixtral-8x7B, author = {HIT-SCIR}, title = {Chinese-Mixtral-8x7B: An Open-Source Mixture-of-Experts LLM}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B}} } ``` ## 🌟 Star History [![Star History Chart](https://api.star-history.com/svg?repos=HIT-SCIR/Chinese-Mixtral-8x7B&type=Date)](https://star-history.com/#HIT-SCIR/Chinese-Mixtral-8x7B&Date)
fionazhang/mistral-experiment-4	fionazhang	2024-01-19T04:35:50Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-19T04:29:20Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer model-index: - name: mistral-experiment-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-experiment-4 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0a0+git7bcf7da - Datasets 2.16.1 - Tokenizers 0.15.0
LoneStriker/Chinese-Mixtral-8x7B-3.0bpw-h6-exl2	LoneStriker	2024-01-19T04:21:43Z	5	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "arxiv:2401.04088", "arxiv:2109.07306", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-19T04:14:25Z	--- license: apache-2.0 --- <div align="center"> <h1> Chinese-Mixtral-8x7B </h1> </div> ![](img/logo.png) <div align="center"> <a href="https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B/pulls"> <image src="https://img.shields.io/badge/PRs-welcome-brightgreen"></image> <image src="https://img.shields.io/badge/License-Apache_2.0-green.svg"></image> </a> </div> ## 🚀 介绍本项目基于Mistral发布的模型[Mixtral-8x7B](https://mistral.ai/news/mixtral-of-experts/)进行了中文扩词表增量预训练，希望进一步促进中文自然语言处理社区对MoE模型的研究。我们扩充后的词表显著提高了模型对中文的编解码效率，并通过大规模开源语料对扩词表模型进行增量预训练，使模型具备了强大的中文生成和理解能力。项目开源内容： - 中文Mixtral-8x7B扩词表大模型 - 扩词表增量预训练代码 > 请注意，Chinese-Mixtral-8x7B仍然可能生成包含事实性错误的误导性回复或包含偏见/歧视的有害内容，请谨慎鉴别和使用生成的内容，请勿将生成的有害内容传播至互联网。 ## 📥 模型下载本项目使用QLoRA进行训练，LoRA权重与合并权重后的模型分别开源，您可以根据自己的需求选择下载： \| 模型名称 \| 模型大小 \| 下载地址 \| 备注 \| \|:----------------------------:\|:-----:\|:-----------------------------------------------------------------------------:\|:-------------------------------------------------------------------------------------------------------------------:\| \| Chinese-Mixtral-8x7B \| 88GB \| [🤗HuggingFace](https://huggingface.co/HIT-SCIR/Chinese-Mixtral-8x7B) \| 中文扩词表完整模型，可以直接使用 \| \| Chinese-Mixtral-8x7B-adapter \| 2.7GB \| [🤗HuggingFace](https://huggingface.co/HIT-SCIR/Chinese-Mixtral-8x7B-adapter) \| LoRA权重，需要与原版Mixtral-8x7B进行合并才可以使用，合并脚本请参考[这里](https://gist.github.com/ChrisHayduk/1a53463331f52dca205e55982baf9930) \| ## 💻 模型推理 Chinese-Mixtral-8x7B支持完整的Mixtral-8x7B模型生态，包括使用`vLLM`、`Flash Attention 2`进行加速，使用`bitsandbytes`进行模型量化等。以下是使用Chinese-Mixtral-8x7B进行推理的代码示例。使用Flash Attention 2： ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "HIT-SCIR/Chinese-Mixtral-8x7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto") text = "我的名字是" inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` 使用4bit量化： ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "HIT-SCIR/Chinese-Mixtral-8x7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto") text = "我的名字是" inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` 请注意，Chinese-Mixtral-8x7B为基座模型，没有经过指令微调，因此指令遵循能力有限。您可以参考[微调](#微调)一节对模型进行微调。 ## 📈 模型性能 ### 模型综合能力我们分别使用以下评测数据集对Chinese-Mixtral-8x7B进行评测： - C-Eval：一个全面的中文基础模型评估套件。它包含了13948个多项选择题，涵盖了52个不同的学科和四个难度级别。 - CMMLU：一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力，涵盖了从基础学科到高级专业水平的67个主题。 - MMLU：一个包含57个多选任务的英文评测数据集，涵盖了初等数学、美国历史、计算机科学、法律等，难度覆盖高中水平到专家水平，是目前主流的LLM评测数据集之一。 - HellaSwag：一个极具挑战的英文NLI评测数据集，每一个问题都需要对上下文进行深入理解，而不能基于常识进行回答。根据Mistral发布的[技术报告](https://arxiv.org/pdf/2401.04088.pdf)，Mixtral-8x7B在推理时将激活13B参数。下表为Chinese-Mixtral-8x7B与其他13B规模的中文扩词表模型在各个评测数据集上的5-shot结果： \| 模型名称 \| 增量训练语料 \| C-Eval<br>(中文) \| CMMLU<br>(中文) \| MMLU<br>(英文) \| HellaSwag<br>(英文) \| \|:-----------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:-------------:\|:------------:\|:-----------------:\| \| [IDEA-CCNL/Ziya2-13B-Base](https://huggingface.co/IDEA-CCNL/Ziya2-13B-Base) \| 650B Token \| 59.29 \| 60.93 \| 59.86 \| 58.90 \| \| [TigerResearch/tigerbot-13b-base-v3](https://huggingface.co/TigerResearch/tigerbot-13b-base-v3) \| 500B Token \| 50.52 \| 51.65 \| 53.46 \| 59.16 \| \| [Linly-AI/Chinese-LLaMA-2-13B-hf](https://huggingface.co/Linly-AI/Chinese-LLaMA-2-13B-hf) \| 11B Token \| 42.57 \| 41.95 \| 51.32 \| 59.05 \| \| [hfl/chinese-llama-2-13b](https://huggingface.co/hfl/chinese-llama-2-13b) \| 约30B Token(120GB) \| 41.90 \| 42.08 \| 51.92 \| 59.28 \| \| Chinese-Mixtral-8x7B(本项目) \| 42B Token \| 52.08 \| 51.08 \| 69.80 \| 65.69 \| 在中文知识和理解方面，我们的Chinese-Mixtral-8x7B与TigerBot-13B-Base-v3性能相当。由于Chinese-Mixtral-8x7B的训练数据量仅为TigerBot-13B-Base-v3的8%，我们的模型仍有进一步提升的空间。与此同时，得益于原版Mixtral-8x7B模型强大的性能，我们的Chinese-Mixtral-8x7B达到了各个扩词表模型的最强英文水平。 > 由于不同版本的评测脚本实现细节有细微差异，为了保证评测结果的一致性和公平性，我们的评测脚本统一使用EleutherAI发布的lm-evaluation-harness，commit hash为[28ec7fa](https://github.com/EleutherAI/lm-evaluation-harness/tree/28ec7fa950346b5a895e85e1f3edd5648168acc4)。 ### 模型生成效果下表为各个扩词表模型的生成效果。由于部分模型的预训练语料未使用`eos_token`进行分隔，我们采用了`max_tokens = 100`对生成文本进行截断。我们的采样参数为`temperature = 0.8, top_p = 0.9`。 ![](./img/case.png) ### 中文编解码效率针对中文编解码效率，我们使用各个扩词表模型的分词器对[SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B)数据集的一个切片（2023-06_zh_head_0000.jsonl）进行编码，对比了各个分词器输出的中文文本Token量： \| 模型名称 \| 模型类别 \| 词表大小 \| 中文文本Token量 \| 编解码效率 \| \|:----------------------------------:\|:-------:\|:-----:\|:----------:\|:-------:\| \| meta-llama/Llama-2-13B-hf \| LLaMA \| 32000 \| 780M \| 低 \| \| mistralai/Mixtral-8x7B-v0.1 \| Mixtral \| 32000 \| 606M \| 低 \| \| Linly-AI/Chinese-LLaMA-2-13B-hf \| LLaMA \| 40076 \| 532M \| 中 \| \| IDEA-CCNL/Ziya2-13B-Base \| LLaMA \| 39424 \| 532M \| 中 \| \| hfl/chinese-llama-2-13b \| LLaMA \| 55296 \| 365M \| 高 \|、 \| TigerResearch/tigerbot-13b-base-v3 \| LLaMA \| 65112 \| 342M \| 高 \| \| Chinese-Mixtral-8x7B(本项目) \| Mixtral \| 57000 \| 355M \| 高 \| 在约1.4GB的测试文本中，我们的Chinese-Mixtral-8x7B中文编解码效率仅次于TigerBot-13B-Base-v3，较原模型提高了41.5%。这有利于加速中文文本的推理速度，并在In-Context Learning、Chain-of-Thought等场景中节省序列长度，有利于提高复杂推理任务的性能。 ## ⚙️ 训练细节 <details> <summary> ### 词表扩充 </summary> 我们使用`sentencepiece`在12G知乎数据和2G悟道数据上训练中文BPE词表。我们在训练词表时分别枚举了中文单字Token数量以及中文总Token数量，并对二者进行组合，得到了数百个大小、内容各异的词表。为了得到最适合的词表，我们通过Zheng Bo等人提出的[ALP](https://arxiv.org/pdf/2109.07306.pdf)计算这些词表的中文词汇能力。ALP通过计算特定语言的子词切分粒度，并对词表的中低频子词进行惩罚，是一种方便快捷的衡量特定语言词汇能力的指标。我们在书籍和百科语料上评估了不同词表的ALP值。图示中，四条曲线分别代表四种中文单字Token数量的词表（4451、5435、6414和7434）。为了避免词表过小导致中文压缩率过低，以及词表过大导致embedding层过于稀疏，我们选取ALP曲线的拐点，对应向词表中新增25000个中文Token。在此基础上，我们选择了四条曲线中ALP最大者，即新增6414个中文单字Token的词表，作为最终Chinese-Mixtral-8x7B选用的词表。 ![](./img/alp.png) 在获得新词表后，我们需要对embedding和lm_head层进行扩充和初始化。我们使用新Token在旧embedding层中的词嵌入平均值对扩充部分进行初始化。在我们的前期实验中，这种方法略优于HuggingFace的默认实现，即使用固定的正态分布进行初始化。 </details> <details> <summary> ### 增量预训练 </summary> Mixtral-8x7B模型参数量为46.7B，全参数训练需要同时使用多种并行策略，在训练资源受限的情况下时间成本过高。因此我们采用HuggingFace官方推荐的方法，使用QLoRA对模型进行训练。QLoRA在LoRA低秩分解的基础上，通过引入4位量化、双重量化和利用NVIDIA统一内存进行分页，进一步减少了训练所需显存，同时保持了与全参数训练相当的性能。我们参考Yiming Cui等人[对LoRA的设置](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/blob/main/scripts/training/run_pt.sh)，对原模型所有Linear层应用低秩分解，并将扩增后的embedding和lm_head层的参数设置为可训练。对于模型主体，我们采用NF4格式进行量化，这种格式可以使得量化后的数据与量化前具有同等的数据分布，模型的权重信息损失更少。 #### 环境准备我们建议使用Python 3.10 + torch 2.0.1 ```shell # Pytorch + Transformers $ pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 $ pip install transformers==4.36.2 datasets evaluate peft accelerate gradio optimum sentencepiece $ pip install jupyterlab scikit-learn pandas matplotlib tensorboard nltk rouge bitsandbytes fire # DeepSpeed $ git clone https://github.com/microsoft/DeepSpeed.git $ cd DeepSpeed $ DS_BUILD_FUSED_ADAM=1 pip3 install . # Flash Attention $ pip install flash-attn --no-build-isolation ``` #### 数据集下载我们基于现有的开源数据集训练了Chinese-Mixtral-8x7B，数据集包括： \| 数据集名称 \| 数据集语言 \|使用数据量\| 备注 \| \|:----------------------------------------------------------------------------:\|:-----:\|:----------------:\|:-----:\| \| [Skywork/SkyPile-150B](https://huggingface.co/datasets/Skywork/SkyPile-150B) \| 中文 \|30B\| 仅使用2022 + 2023年的数据 \| \| [DKYoon/SlimPajama-6B](https://huggingface.co/datasets/DKYoon/SlimPajama-6B) \| 英文 \|12B\| 数据集重复2 Epoch \| 通过`data/download.py`将数据集下载到`data`中。针对Slimpajama数据集，需要使用`data/parquet2jsonl.py`将原始数据集转换为`jsonl`格式。下载后的数据集为多个jsonl文件的分片，使用`cat`将多个分片合并为一个jsonl文件。 ```shell $ cat *.jsonl > all.jsonl ``` 通过`split`将jsonl切分为train和valid集合。本项目中train和valid的行数比例为999:1。 ```shell $ wc -l all.jsonl # 计算数据集总行数 $ split -l <lines> all.jsonl # 按999:1计算train/valid行数，进行切分 $ mv xaa DKYoon-SlimPajama-6B-train.jsonl # 重命名 $ mv xab DKYoon-SlimPajama-6B-dev.jsonl ``` #### 数据集预处理将数据集名称和路径注册到`data/datasets.toml`中： ```toml [DKYoon-SlimPajama-6B] # 数据集名称 splits = ["train", "dev"] # 数据集train/valid集合 root = "{DATA_DIR}/en/{name}" # 数据集根目录 doc = "{name}-{split}" # 数据集文件名 encoded = "encoded-{name}-{split}" # 预处理保存位置 ``` 使用`data/preprocess_datasets.py`对数据集进行子词切分，从而加快训练速度。 ```shell $ python data/preprocess_datasets.py --ds_name SkyPile-150B-2023 --tokenizer_name_or_path tokenizer/Mixtral-8x7B-v0.1-vocab $ python data/preprocess_datasets.py --ds_name DKYoon-SlimPajama-6B --tokenizer_name_or_path tokenizer/Mixtral-8x7B-v0.1-vocab ``` 在进行子词切分后，可以使用`data/utils.py`查看各个数据集的token总量： ```shell $ python data/utils.py ``` #### 开始训练训练启动脚本为`scripts/train.sh`。可以通过修改其中的`TRAIN_DATASETS`修改训练数据集和数据集比例： ```shell TRAIN_DATASETS=( 1:SkyPile-150B-2022 # 使用全量SkyPile-150B-2022 0.1:SkyPile-150B-2023 # 使用SkyPile-150B-2023的10%数据 1:DKYoon-SlimPajama-6B # 使用全量DKYoon-SlimPajama-6B ) ``` 如果您使用SLURM集群管理系统，可以通过`sbatch`进行提交： ```shell $ sbatch scripts/train.sh ``` 如果没有SLURM或希望通过命令行启动训练，您可以直接提取`scripts/train.sh`中的`torchrun`开始训练。 </details> <details> <summary> ### 微调 </summary> 本项目发布的Chinese-Mixtral-8x7B为基座模型，没有经过微调。如果您希望使用Chinese-Mixtral-8x7B进行下游任务微调或SFT，可以参考HuggingFace给出Mixtral-8x7B的QLoRA微调脚本进行训练：[HuggingFace的官方示例代码](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py)。 </details> ## ✒️ 引用如果您觉得本项目对您的研究有所帮助或使用了本项目的代码，请引用本项目： ```bibtex @misc{Chinese-Mixtral-8x7B, author = {HIT-SCIR}, title = {Chinese-Mixtral-8x7B: An Open-Source Mixture-of-Experts LLM}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/HIT-SCIR/Chinese-Mixtral-8x7B}} } ``` ## 🌟 Star History [![Star History Chart](https://api.star-history.com/svg?repos=HIT-SCIR/Chinese-Mixtral-8x7B&type=Date)](https://star-history.com/#HIT-SCIR/Chinese-Mixtral-8x7B&Date)
Atipico1/NQ-base-unans	Atipico1	2024-01-19T04:21:10Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-01-19T04:20:56Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # 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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Atipico1/NQ-base-new-hp	Atipico1	2024-01-19T04:20:37Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-01-19T04:20:24Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # 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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Atipico1/NQ-cbr-v2	Atipico1	2024-01-19T04:20:06Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-01-19T04:19:55Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # 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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Atipico1/NQ-cbr-v2-custom-loss	Atipico1	2024-01-19T04:18:20Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-01-19T04:18:01Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # 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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zaq-hack/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-bpw240-h6-exl2	zaq-hack	2024-01-19T04:17:44Z	5	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-18T17:38:38Z	--- license: cc-by-nc-4.0 --- EXL2 @ 2.40bpw</br> Created for https://huggingface.co/zappa2005</br> Was shooting for 16G GPU usage, but seems like it might be a tight squeeze with decent context size.</br> See also: [2.25bpw](https://huggingface.co/zaq-hack/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-bpw225-h6-exl2)</br> All credit to the original creators: Noromaid is hot.</br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/vwcJfOnL-2QDJ0ShfxRJ5.png) --- # Disclaimer: ## This model is experimental, do not expect everything to work. This model uses the Chatml prompting format --- Beeg noromaid on *steroids*. Suitable for RP, ERP. This model was trained on the Zloss fork of Charles, and should fix issue the model had. Use Chatml prompt format, but not the special token. The reason is that Axolotl merge the finetune with the base model at 1.0 weight basically, but this is too much, so I use another script available [HERE](https://github.com/DocShotgun/LLM-notebooks/blob/main/weighted-lora-merge.ipynb) to merge with less weight, sadly, it don't take the special Chatml token. It's like Orca2 for the matter. ## Credits: - Undi - IkariDev <!-- description start --> ## Description <!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) --> This repo contains FP16 files of Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss. [FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss) <!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)--> <!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)--> <!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)--> <!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)--> <!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)--> [GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-GGUF) <!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)--> ## Ratings: Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here! No ratings yet! If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi". <!-- description end --> <!-- prompt-template start --> ### Prompt format: Chatml ``` <\|im_start\|>system {sysprompt}<\|im_end\|> <\|im_start\|>user {input}<\|im_end\|> <\|im_start\|>assistant {output}<\|im_end\|> ``` ## Datasets used: - Aesir 1, 2 & 3 modified by us, credit to ([MinervaAI](https://huggingface.co/MinervaAI) / [Gryphe](https://huggingface.co/Gryphe)) - [LimaRP-20231109](https://huggingface.co/datasets/lemonilia/LimaRP) ([Lemonilia](https://huggingface.co/lemonilia)) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ([NobodyExistsOnTheInternet](https://huggingface.co/NobodyExistsOnTheInternet) - [No-robots-ShareGPT](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) ([Doctor-Shotgun](https://huggingface.co/Doctor-Shotgun)) ## Others Undi: If you want to support me, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
zaq-hack/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-bpw225-h6-exl2	zaq-hack	2024-01-19T04:13:59Z	7	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-18T23:32:00Z	--- license: cc-by-nc-4.0 --- EXL2 @ 2.25bpw</br> Created for https://huggingface.co/zappa2005</br> You might be able to shoehorn this one into a 16G GPU.</br> All credit to the original creators: Noromaid is hot.</br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/vwcJfOnL-2QDJ0ShfxRJ5.png) --- # Disclaimer: ## This model is experimental, do not expect everything to work. This model uses the Chatml prompting format --- Beeg noromaid on *steroids*. Suitable for RP, ERP. This model was trained on the Zloss fork of Charles, and should fix issue the model had. Use Chatml prompt format, but not the special token. The reason is that Axolotl merge the finetune with the base model at 1.0 weight basically, but this is too much, so I use another script available [HERE](https://github.com/DocShotgun/LLM-notebooks/blob/main/weighted-lora-merge.ipynb) to merge with less weight, sadly, it don't take the special Chatml token. It's like Orca2 for the matter. ## Credits: - Undi - IkariDev <!-- description start --> ## Description <!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) --> This repo contains FP16 files of Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss. [FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss) <!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)--> <!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)--> <!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)--> <!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)--> <!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)--> [GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-GGUF) <!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)--> ## Ratings: Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here! No ratings yet! If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi". <!-- description end --> <!-- prompt-template start --> ### Prompt format: Chatml ``` <\|im_start\|>system {sysprompt}<\|im_end\|> <\|im_start\|>user {input}<\|im_end\|> <\|im_start\|>assistant {output}<\|im_end\|> ``` ## Datasets used: - Aesir 1, 2 & 3 modified by us, credit to ([MinervaAI](https://huggingface.co/MinervaAI) / [Gryphe](https://huggingface.co/Gryphe)) - [LimaRP-20231109](https://huggingface.co/datasets/lemonilia/LimaRP) ([Lemonilia](https://huggingface.co/lemonilia)) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ([NobodyExistsOnTheInternet](https://huggingface.co/NobodyExistsOnTheInternet) - [No-robots-ShareGPT](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) ([Doctor-Shotgun](https://huggingface.co/Doctor-Shotgun)) ## Others Undi: If you want to support me, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
Deepakkori45/go	Deepakkori45	2024-01-19T04:12:52Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2024-01-19T04:12:35Z	--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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zaq-hack/DaringLotus-v2-10.7b-bpw500-h6-exl2	zaq-hack	2024-01-19T04:11:27Z	7	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-18T17:39:02Z	EXL2 @ 5.00bpw</br> Wanted to try something a little different.</br> I haven't had much luck with smaller models, but as a Noromaid superfan, I did have success with Silicon Maid.</br> This is a new model I stumbled on via Reddit, but it was too new to have an EXL2.</br> So I made it my damn self.</br> I'm still tinkering, but have high hopes.</br> All credit to the original creators: This one has lots of good DNA.</br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64bb1109aaccfd28b023bcec/X0i6-KleZPdNqD1qFq3tK.png) # DaringLotus-10.7B-v2 This is a dare ties merge of https://huggingface.co/BlueNipples/SnowLotus-v2-10.7B and it's parent models. It shares it's good prose, and relatively decent coherency, being a little bit more on the side of prose, and a little bit less on the side of coherency. I like this model for generating great prose if I feel like regening a bit. It's a good model as is the other model for RP, and I think both these merged models probably stand up with the best in their weight class (11-13). Which you prefer might be a matter of context and preference which is why I've uploaded both. Credit to Nyx and Sao10k for their models contributions (Frostmaid, FrostWind and SolarDoc), as well as Undi95 and Ikari for Noromaid, the developers of Mergekit, and whomever contributed the medical model used in the frankenmerge portion. GGUF (Small selection of Imatrix and regular k-quants): https://huggingface.co/BlueNipples/DaringLotus-SnowLotus-10.7b-IQ-GGUF ## Recipe - model: ./Frostmaid parameters: density: [0.45] # density gradient weight: 0.23 - model: ./FrostMed parameters: density: [0.35] # density gradient weight: 0.18 - model: ./SnowLotus-10.7B-v2 parameters: density: [1] # density gradient weight: 1
FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_non_member_shadow18	FounderOfHuggingface	2024-01-19T04:11:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:11:19Z	--- 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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FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_non_member_shadow15	FounderOfHuggingface	2024-01-19T04:10:28Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:10:26Z	--- 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_lora_r4_e2e_nlg_t300_e5_non_member_shadow9	FounderOfHuggingface	2024-01-19T04:08:42Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:08:41Z	--- 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_lora_r4_e2e_nlg_t300_e5_non_member_shadow6	FounderOfHuggingface	2024-01-19T04:07:54Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:07:53Z	--- 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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FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_non_member_shadow2	FounderOfHuggingface	2024-01-19T04:06:47Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:06:44Z	--- 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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FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_non_member_shadow1	FounderOfHuggingface	2024-01-19T04:06:29Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:06:26Z	--- 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_lora_r4_e2e_nlg_t300_e5_non_member_shadow0	FounderOfHuggingface	2024-01-19T04:06:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:06:07Z	--- 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. 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FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_member_shadow41	FounderOfHuggingface	2024-01-19T04:05:52Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:05:52Z	--- 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. 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FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_member_shadow40	FounderOfHuggingface	2024-01-19T04:05:37Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:05:35Z	--- 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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FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_member_shadow39	FounderOfHuggingface	2024-01-19T04:05:19Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:05:18Z	--- 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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FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_member_shadow38	FounderOfHuggingface	2024-01-19T04:05:03Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:05:02Z	--- 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. 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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_lora_r4_e2e_nlg_t300_e5_member_shadow37	FounderOfHuggingface	2024-01-19T04:04:47Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:04:46Z	--- 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. 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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_lora_r4_e2e_nlg_t300_e5_member_shadow35	FounderOfHuggingface	2024-01-19T04:04:15Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:04:11Z	--- 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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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_lora_r4_e2e_nlg_t300_e5_member_shadow30	FounderOfHuggingface	2024-01-19T04:02:50Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:02:49Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow28	FounderOfHuggingface	2024-01-19T04:02:18Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:02:17Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow27	FounderOfHuggingface	2024-01-19T04:02:02Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:02:01Z	--- 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. (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_lora_r4_e2e_nlg_t300_e5_member_shadow24	FounderOfHuggingface	2024-01-19T04:01:09Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:01:08Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow21	FounderOfHuggingface	2024-01-19T04:00:20Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T04:00:20Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow18	FounderOfHuggingface	2024-01-19T03:59:33Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:59:30Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow17	FounderOfHuggingface	2024-01-19T03:59:14Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:59:12Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow16	FounderOfHuggingface	2024-01-19T03:58:55Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:58:55Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow14	FounderOfHuggingface	2024-01-19T03:58:22Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:58:13Z	--- 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. (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_lora_r4_e2e_nlg_t300_e5_member_shadow13	FounderOfHuggingface	2024-01-19T03:57:58Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:57:48Z	--- 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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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_lora_r4_e2e_nlg_t300_e5_member_shadow12	FounderOfHuggingface	2024-01-19T03:57:32Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:57:25Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow10	FounderOfHuggingface	2024-01-19T03:56:51Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:56:50Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow6	FounderOfHuggingface	2024-01-19T03:55:44Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:55:39Z	--- 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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MatheusMarquesEiras/adapter-tinyllama	MatheusMarquesEiras	2024-01-19T03:55:26Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.1", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.1", "region:us" ]	null	2024-01-19T03:54:11Z	--- library_name: peft base_model: TinyLlama/TinyLlama-1.1B-Chat-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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FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t300_e5_member_shadow4	FounderOfHuggingface	2024-01-19T03:55:07Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:55:04Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow3	FounderOfHuggingface	2024-01-19T03:54:49Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:54:48Z	--- 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_lora_r4_e2e_nlg_t300_e5_member_shadow2	FounderOfHuggingface	2024-01-19T03:54:32Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-01-19T03:54:32Z	--- 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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Radiantloom/radiantloom-email-assist-7b	Radiantloom	2024-01-19T03:51:58Z	38	3	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "arxiv:2306.05685", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-13T07:15:13Z	--- license: apache-2.0 language: - en library_name: transformers --- <img src="https://huggingface.co/aigeek0x0/radiantloom-email-assist-7b/resolve/main/Radiantloom_Email_Assist.png" alt="Radiantloom Email Assist" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> ## Radiantloom Email Assist 7B Radiantloom Email Assist 7B is an email-assistant large language model fine-tuned from [Zephyr-7B-Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), over a custom-curated dataset of 1,000 email-assistant summarization tasks prepared by Radiantloom AI. The context lenght for the model is 4096 and it is licensed for commercial use. ## Intended Uses & Limitations radiantloom-email-assist-7b is fine-tuned specifically for summarizing personal and business emails, converting them into voice memos, or chat messages. It is not a state-of-the-art generative language model and is not designed to perform competitively on general tasks with more modern model architectures or models subject to larger pretraining corpuses. ## Prompt Template ``` <s>[INST] <<SYS>>Given an input, provide a clear and concise summary and action items in less than 50 words.<</SYS>> Answer the question below from context below : Convert the summary into a voice memo, keeping it under one minute. {{email_text}} [/INST]</s> ``` ## Model Usage We encourage you to try out this model using Google Colab. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Lvt30_gm2_4D-XSBNkX1k45j79fnNJtv#scrollTo=Wa2WjDcOxb8h) Here are a few example use cases. if we query the model with ``` <s>[INST] <<SYS>>Given an input, provide a clear and concise summary and action items in less than 50 words.<</SYS>> Answer the question below from context below : Convert the summary into a voice memo, keeping it under one minute. From: [email protected] To: [email protected] Subject: Review and Feedback on Radiantloom Models for Business Integration Hi Tan, I hope this email finds you well. We are currently in the process of exploring Radiantloom models for potential integration into our business operations. Specifically, we are interested in your insights on the "radiantloom-email-assist-7b" model. To ensure that we make well-informed decisions, we kindly request that you take some time to thoroughly review the documentation provided for this particular model. Your expertise and perspective are valuable to us, and we would appreciate any insights or observations you can share regarding the features, capabilities, and potential benefits of the "radiantloom-email-assist-7b" model. If you come across any noteworthy points, concerns, or suggestions during your review, please don't hesitate to bring them to our attention. Your feedback will play a crucial role in shaping our decision-making process as we assess the suitability of Radiantloom models for our business needs. Thank you in advance for your time and effort in this matter. We look forward to hearing your thoughts and recommendations. Best regards, Deb [/INST]</s> ``` the model will return ``` <</ASSISTANT> You have an email from Deb about reviewing the "radiantloom-email-assist-7b" model for business integration. They're seeking your expert opinion on its features, capabilities, and benefits. Please provide feedback if you notice anything noteworthy during your review. This is important for our decision-making process. Thanks in advance. Action Items: 1. Review the "radiantloom-email-assist-7b" model documentation. 2. Share any insights, concerns, or suggestions with Deb. 3. Respond to Deb with your feedback by the deadline. ``` and if we query the model with ``` <s>[INST] <<SYS>>Given an input, provide a clear and concise summary and action items in less than 50 words.<</SYS>> Answer the question below from context below : Convert the summary into a chat message. From: [email protected] To: [email protected] Subject: Review and Feedback on Radiantloom Models for Business Integration Hi Tan, I hope this email finds you well. We are currently in the process of exploring Radiantloom models for potential integration into our business operations. Specifically, we are interested in your insights on the "radiantloom-email-assist-7b" model. To ensure that we make well-informed decisions, we kindly request that you take some time to thoroughly review the documentation provided for this particular model. Your expertise and perspective are valuable to us, and we would appreciate any insights or observations you can share regarding the features, capabilities, and potential benefits of the "radiantloom-email-assist-7b" model. If you come across any noteworthy points, concerns, or suggestions during your review, please don't hesitate to bring them to our attention. Your feedback will play a crucial role in shaping our decision-making process as we assess the suitability of Radiantloom models for our business needs. Thank you in advance for your time and effort in this matter. We look forward to hearing your thoughts and recommendations. Best regards, Deb [/INST]</s> ``` the model will return ``` Tan, Deb from example.com is seeking your feedback on the "radiantloom-email-assist-7b" model for business integration. Please review the related documents and share any insights, comments, or suggestions by reply. Thanks in advance! Action Items: 1. Review the "radiantloom-email-assist-7b" model documentation. 2. Provide feedback and insights to Deb via email. 3. Respond within a reasonable timeframe. ``` ## Evaluations We are encouraged by the initial assessments conducted using the [LLM-as-a-Judge method](https://arxiv.org/abs/2306.05685), particularly with the utilization of GPT-4. Our ongoing efforts involve a more comprehensive evaluation of the model. As we progress, we will continue to provide updates on our findings and share the results on this platform. ## About Radiantloom AI Radiantloom AI trains open-source large language models tailored for specific business tasks such as email assistance, customer support, and database operations. Learn more about Radiantloom by visiting our [website](https://radiantloom.com). Follow us on Twitter at [Radiantloom](https://twitter.com/radiantloom) to gain early access to upcoming Radiantloom AI large language models.
Radiantloom/radiantloom-support-assist-7b	Radiantloom	2024-01-19T03:51:32Z	17	7	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "arxiv:2306.05685", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-07T14:39:45Z	--- license: apache-2.0 language: - en library_name: transformers --- <img src="https://huggingface.co/aigeek0x0/radiantloom-support-assist-7b/resolve/main/ radiantloom-support-assist.png" alt="Radiantloom Support Assist" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> ## Radiantloom Support Assist 7B Radiantloom Support Assist 7B is a support-assistant large language model fine-tuned from [Zephyr-7B-Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), over a custom-curated dataset of 3,000 support-assistant examples prepared by Radiantloom AI. The context lenght for the model is 4096 and it is licensed for commercial use. ## Intended Uses & Limitations The radiantloom-support-assist-7b is meticulously fine-tuned to deliver optimal performance as a support assistant in customer-facing applications, specifically designed for use in help center chatbots. Its versatile functionality extends to various applications that require support conversation and retrieval augmented generation (RAG). This includes, but is not limited to, the development of AI bots tailored for B2B SaaS companies, e-commerce websites, corporate websites, and personal websites. It is not a state-of-the-art generative language model and is not designed to perform competitively on general tasks with more modern model architectures or models subject to larger pretraining corpuses. ## Prompt Template ``` <\|im_start\|>system You are a helpful assistant.<\|im_end\|> <\|im_start\|>user {user_message}<\|im_end\|> <\|im_start\|>assistant ``` ## Model Usage We encourage you to try out this model using Google Colab. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1z7YvgyYVPnOSml60yh0rIJKTC9uDbbLq?usp=sharing) Here are a few example use cases. if we query the model with ``` <\|im_start\|>system You work as a support assistant for AI Geek Labs, an AI research and development lab, handling inquiries and providing assistance through their website. Use the following pieces of context to answer the user's questions. If you don't know the answer, don't make up an answer, just say you don't know. <context> Get AI-powered, Startup-fast: Build & Tune Your LLM MVPs with AI Geek Labs Invent the future of your business with our cutting-edge AI research, development, and fine-tuning services. We're your AI playground. Stop waiting, build! We turn your AI vision into reality, fast. From email assistants to code generation, we build MVPs with the latest AI frameworks \ like Langchain and OpenAI, helping small businesses and startups jumpstart their AI journey. MVP: Turn your AI dream into a working prototype, lightning-fast. We design, develop, and deploy AI-powered MVPs using cutting-edge frameworks \ like Langchain, HuggingFace, and Llama-Index. LLM Fine-tuning: Tap into the true potential of LLMs for your specific needs. We fine-tune models for email assistants, customer support chatbots, \ code generation, text-to-SQL, and more. Open Source Radiance: We're passionate about sharing! Explore our Radiantloom series, featuring open-source LLM models fine-tuned by our experts, \ ready for you to build upon. <context/><\|im_end\|> <\|im_start\|>user Question: What is AI Geek Labs?<\|im_end\|> <\|im_start\|>assistant ``` the model will return ``` AI Geek Labs is a research and development laboratory focused on AI technology. They offer a range of services including building and tuning machine learning models, developing AI-powered applications, and providing open source resources. Their goal is to help clients achieve their AI goals quickly and efficiently, using the latest technologies and frameworks. Whether it's building a startup or improving existing systems, AI Geek Labs aims to provide innovative solutions tailored to their clients' needs. Their team is dedicated to staying at the forefront of AI technology and sharing their knowledge through open source resources and collaborations. ``` and if we query the model with ``` <\|im_start\|>system You work as a support assistant for AI Geek Labs, an AI research and development lab, handling inquiries and providing assistance through their website. Use the following pieces of context to answer the user's questions. If you don't know the answer, don't make up a answer, just say you don't know. <context> Get AI-powered, Startup-fast: Build & Tune Your LLM MVPs with AI Geek Labs Invent the future of your business with our cutting-edge AI research, development, and fine-tuning services. We're your AI playground. Stop waiting, build! We turn your AI vision into reality, fast. From email assistants to code generation, we build MVPs with the latest AI frameworks \ like Langchain and OpenAI, helping small businesses and startups jumpstart their AI journey. MVP: Turn your AI dream into a working prototype, lightning-fast. We design, develop, and deploy AI-powered MVPs using cutting-edge frameworks \ like Langchain, HuggingFace, and Llama-Index. LLM Fine-tuning: Tap into the true potential of LLMs for your specific needs. We fine-tune models for email assistants, customer support chatbots, \ code generation, text-to-SQL, and more. Open Source Radiance: We're passionate about sharing! Explore our Radiantloom series, featuring open-source LLM models fine-tuned by our experts, \ ready for you to build upon. <context/><\|im_end\|> <\|im_start\|>user Question: Which AI frameworks does AI Geek Labs use to build MVPs for clients?<\|im_end\|> <\|im_start\|>assistant ``` the model will return ``` AI Geek Labs uses the latest AI frameworks such as Langchain, OpenAI, and Llama-Index to build MVPs for clients. These frameworks are designed to provide a comprehensive set of tools for developing and testing AI applications, making them ideal for building prototypical models quickly and efficiently. Additionally, these frameworks offer a wide range of features, including natural language processing, machine learning, and computer vision, which can be leveraged to create innovative and impactful solutions. If you have any specific questions about how these frameworks can benefit your project, feel free to ask! ``` ## Evaluations We are encouraged by the initial assessments conducted using the [LLM-as-a-Judge method](https://arxiv.org/abs/2306.05685), particularly with the utilization of GPT-4. Our ongoing efforts involve a more comprehensive evaluation of the model. As we progress, we will continue to provide updates on our findings and share the results on this platform. ## Ethical Considerations and Limitations radiantloom-support-assist-7b can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of radiantloom-support-assist-7b, developers should perform safety testing. ## About Radiantloom AI Radiantloom AI trains open-source large language models tailored for specific business tasks such as email assistance, customer support, and database operations. Learn more about Radiantloom by visiting our [website](https://radiantloom.com). Follow us on Twitter at [Radiantloom](https://twitter.com/radiantloom) to gain early access to upcoming Radiantloom AI large language models.

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