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train · 879k rows

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reeddg/training_outputs	reeddg	2024-03-23T03:23:12Z	0	0	null	[ "tensorboard", "trl", "sft", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:finetune:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2024-03-23T02:39:27Z	--- base_model: NousResearch/Llama-2-7b-chat-hf tags: - trl - sft - generated_from_trainer model-index: - name: training_outputs 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. --> # training_outputs This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.13.3
Monkey/phi-2-qlora	Monkey	2024-03-23T03:21:47Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "region:us" ]	null	2024-03-23T03:21:40Z	--- library_name: peft base_model: microsoft/phi-2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0
0x0daughter1/mistral_lollipop3	0x0daughter1	2024-03-23T03:19:14Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T03:15:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Eva1209/HW1	Eva1209	2024-03-23T03:00:18Z	30	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	2024-03-23T01:22:09Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: 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. --> # 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: 1.1607 - Matthews Correlation: 0.5443 ## 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: 1.4175716836316547e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 20 - 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------------------:\| \| 0.5335 \| 1.0 \| 2138 \| 0.5571 \| 0.4299 \| \| 0.4842 \| 2.0 \| 4276 \| 0.9165 \| 0.4857 \| \| 0.3472 \| 3.0 \| 6414 \| 1.0049 \| 0.5239 \| \| 0.2567 \| 4.0 \| 8552 \| 0.9978 \| 0.5604 \| \| 0.1409 \| 5.0 \| 10690 \| 1.1607 \| 0.5443 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
CrCry/PS2filter	CrCry	2024-03-23T02:58:25Z	0	0	null	[ "license:other", "region:us" ]	null	2024-03-23T02:40:34Z	--- license: other license_name: weights-licenses.md license_link: https://github.com/miamcpherson/ps2?tab=License-1-ov-file# ---
TingK/hw-1	TingK	2024-03-23T02:58:13Z	10	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	2024-03-23T01:24:54Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: hw-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hw-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8745 - Matthews Correlation: 0.5275 ## 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5162 \| 1.0 \| 535 \| 0.4590 \| 0.4377 \| \| 0.3431 \| 2.0 \| 1070 \| 0.4641 \| 0.5202 \| \| 0.2294 \| 3.0 \| 1605 \| 0.6410 \| 0.5180 \| \| 0.1719 \| 4.0 \| 2140 \| 0.7804 \| 0.5248 \| \| 0.1267 \| 5.0 \| 2675 \| 0.8745 \| 0.5275 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
namhd1209/my_awesome_model	namhd1209	2024-03-23T02:53:55Z	116	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	2024-03-23T01:53:15Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2313 - Accuracy: 0.9306 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2244 \| 1.0 \| 1563 \| 0.1941 \| 0.9258 \| \| 0.1464 \| 2.0 \| 3126 \| 0.2313 \| 0.9306 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Jerry81920/hw-1	Jerry81920	2024-03-23T02:50:49Z	4	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	2024-03-23T01:22:08Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: hw-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hw-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8827 - Matthews Correlation: 0.5411 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.9903037928023155e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5285 \| 1.0 \| 535 \| 0.4772 \| 0.4537 \| \| 0.3124 \| 2.0 \| 1070 \| 0.5604 \| 0.4833 \| \| 0.1995 \| 3.0 \| 1605 \| 0.7028 \| 0.5330 \| \| 0.1139 \| 4.0 \| 2140 \| 0.8827 \| 0.5411 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
korigami/HW1-20240323	korigami	2024-03-23T02:50:40Z	12	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	2024-03-23T01:22:10Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: HW1-20240323 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. --> # HW1-20240323 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8256 - Matthews Correlation: 0.5573 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.0479079329448482e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 6 - 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.524 \| 1.0 \| 535 \| 0.4653 \| 0.4910 \| \| 0.3431 \| 2.0 \| 1070 \| 0.4880 \| 0.4902 \| \| 0.2376 \| 3.0 \| 1605 \| 0.5751 \| 0.5230 \| \| 0.1684 \| 4.0 \| 2140 \| 0.7772 \| 0.5430 \| \| 0.1287 \| 5.0 \| 2675 \| 0.8256 \| 0.5573 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Kingsman1977/AIA_LLM_B_Work1	Kingsman1977	2024-03-23T02:47:58Z	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", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-23T01:25:58Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: AIA_LLM_B_Work1 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. --> # AIA_LLM_B_Work1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8415 - Matthews Correlation: 0.5445 ## 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.519 \| 1.0 \| 535 \| 0.4553 \| 0.4563 \| \| 0.3452 \| 2.0 \| 1070 \| 0.4619 \| 0.5242 \| \| 0.2344 \| 3.0 \| 1605 \| 0.6638 \| 0.4943 \| \| 0.1782 \| 4.0 \| 2140 \| 0.8306 \| 0.5235 \| \| 0.1256 \| 5.0 \| 2675 \| 0.8415 \| 0.5445 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
niryuu/tinyllama-task1572_samsum_summary-v1	niryuu	2024-03-23T02:46:20Z	191	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T02:44:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lgodwangl/new_04mm	lgodwangl	2024-03-23T02:46:17Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T02:43:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kevintf/AIA_B_HW1	kevintf	2024-03-23T02:46:15Z	4	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	2024-03-23T01:23:08Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: AIA_B_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. --> # AIA_B_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: 0.7819 - Matthews Correlation: 0.5382 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.9425471811084e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 22 - 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| No log \| 1.0 \| 268 \| 0.5090 \| 0.4244 \| \| 0.431 \| 2.0 \| 536 \| 0.4669 \| 0.5179 \| \| 0.431 \| 3.0 \| 804 \| 0.5552 \| 0.5260 \| \| 0.1892 \| 4.0 \| 1072 \| 0.7417 \| 0.5158 \| \| 0.1892 \| 5.0 \| 1340 \| 0.7819 \| 0.5382 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
KellyKelly/homework1	KellyKelly	2024-03-23T02:44:32Z	4	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	2024-03-23T01:22:10Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: homework1 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. --> # homework1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8388 - Matthews Correlation: 0.5347 ## 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.524 \| 1.0 \| 535 \| 0.4618 \| 0.4244 \| \| 0.3493 \| 2.0 \| 1070 \| 0.4673 \| 0.5250 \| \| 0.2315 \| 3.0 \| 1605 \| 0.6676 \| 0.5179 \| \| 0.1729 \| 4.0 \| 2140 \| 0.7878 \| 0.5189 \| \| 0.1263 \| 5.0 \| 2675 \| 0.8388 \| 0.5347 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
niryuu/tinyllama-task1357_xlsum_summary_generation-v1	niryuu	2024-03-23T02:37:36Z	190	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T02:35:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AIA-tclin/hw-1	AIA-tclin	2024-03-23T02:36:06Z	106	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	2024-03-23T01:22:07Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: hw-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hw-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8330 - Matthews Correlation: 0.5321 ## 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5177 \| 1.0 \| 535 \| 0.4552 \| 0.4537 \| \| 0.3422 \| 2.0 \| 1070 \| 0.4733 \| 0.5083 \| \| 0.2322 \| 3.0 \| 1605 \| 0.6302 \| 0.5100 \| \| 0.1723 \| 4.0 \| 2140 \| 0.7842 \| 0.5318 \| \| 0.1294 \| 5.0 \| 2675 \| 0.8330 \| 0.5321 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
niryuu/tinyllama-task1291_multi_news_summarization-v1	niryuu	2024-03-23T02:26:52Z	192	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T02:25:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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niryuu/tinyllama-task1290_xsum_summarization-v1	niryuu	2024-03-23T02:23:31Z	194	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T02:21:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sarak7/H10_323_166_v3	sarak7	2024-03-23T02:23:16Z	190	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T02:22:05Z	--- 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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zzttbrdd/sn6_000g	zzttbrdd	2024-03-23T02:22:53Z	134	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T02:20:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Ansible-Model/santacoder-finetuned-alanstack-ec2	Ansible-Model	2024-03-23T02:22:35Z	7	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-22T14:47:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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chiaying0608/demo	chiaying0608	2024-03-23T02:21:24Z	106	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	2024-03-23T01:27:47Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: demo 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. --> # demo This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4509 - Matthews Correlation: 0.5279 ## 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5259 \| 1.0 \| 535 \| 0.4498 \| 0.4628 \| \| 0.3468 \| 2.0 \| 1070 \| 0.4509 \| 0.5279 \| \| 0.2403 \| 3.0 \| 1605 \| 0.5986 \| 0.5220 \| \| 0.1757 \| 4.0 \| 2140 \| 0.7999 \| 0.5111 \| \| 0.1248 \| 5.0 \| 2675 \| 0.8358 \| 0.5181 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
IrisLiou/hw-1	IrisLiou	2024-03-23T02:18:21Z	108	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	2024-03-23T01:23:18Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: hw-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hw-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6083 - Matthews Correlation: 0.5293 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5197 \| 1.0 \| 535 \| 0.4569 \| 0.4618 \| \| 0.3424 \| 2.0 \| 1070 \| 0.4682 \| 0.5221 \| \| 0.2348 \| 3.0 \| 1605 \| 0.6083 \| 0.5293 \| \| 0.1821 \| 4.0 \| 2140 \| 0.8065 \| 0.5156 \| \| 0.131 \| 5.0 \| 2675 \| 0.8186 \| 0.5286 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
grantdi/phi-2-role-play	grantdi	2024-03-23T02:14:56Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-23T02:14:48Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi-2-role-play results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-role-play This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
kennethge123/bigbench_entailedpolarity-gpt2	kennethge123	2024-03-23T02:13:45Z	164	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-classification", "generated_from_trainer", "dataset:bigbench", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T04:46:34Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer datasets: - bigbench metrics: - accuracy model-index: - name: bigbench_entailedpolarity-gpt2 results: - task: name: Text Classification type: text-classification dataset: name: bigbench type: bigbench config: entailed_polarity split: train args: entailed_polarity metrics: - name: Accuracy type: accuracy value: 0.9166666666666666 --- <!-- 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. --> # bigbench_entailedpolarity-gpt2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the bigbench dataset. It achieves the following results on the evaluation set: - Loss: 1.0213 - Accuracy: 0.9167 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 24 \| 0.5201 \| 0.875 \| \| No log \| 2.0 \| 48 \| 0.3512 \| 0.875 \| \| No log \| 3.0 \| 72 \| 0.4245 \| 0.8333 \| \| No log \| 4.0 \| 96 \| 0.3220 \| 0.9167 \| \| No log \| 5.0 \| 120 \| 0.3962 \| 0.875 \| \| No log \| 6.0 \| 144 \| 0.5551 \| 0.875 \| \| No log \| 7.0 \| 168 \| 0.8597 \| 0.875 \| \| No log \| 8.0 \| 192 \| 0.4610 \| 0.9583 \| \| No log \| 9.0 \| 216 \| 0.9003 \| 0.9167 \| \| No log \| 10.0 \| 240 \| 0.8778 \| 0.9167 \| \| No log \| 11.0 \| 264 \| 0.9036 \| 0.9167 \| \| No log \| 12.0 \| 288 \| 0.9188 \| 0.9167 \| \| No log \| 13.0 \| 312 \| 1.0192 \| 0.9167 \| \| No log \| 14.0 \| 336 \| 0.9984 \| 0.9167 \| \| No log \| 15.0 \| 360 \| 0.9718 \| 0.9167 \| \| No log \| 16.0 \| 384 \| 0.9882 \| 0.9167 \| \| No log \| 17.0 \| 408 \| 1.0189 \| 0.9167 \| \| No log \| 18.0 \| 432 \| 1.0210 \| 0.9167 \| \| No log \| 19.0 \| 456 \| 1.0211 \| 0.9167 \| \| No log \| 20.0 \| 480 \| 1.0213 \| 0.9167 \| ### Framework versions - Transformers 4.35.2 - Pytorch 1.10.1+cu102 - Datasets 2.15.0 - Tokenizers 0.15.0
nitsuai/llama-2-70b-Guanaco-QLoRA-GGUF	nitsuai	2024-03-23T02:09:32Z	17	0	transformers	[ "transformers", "gguf", "llama", "llama-2", "text-classification", "en", "base_model:Mikael110/llama-2-70b-guanaco-qlora", "base_model:quantized:Mikael110/llama-2-70b-guanaco-qlora", "license:other", "region:us" ]	text-classification	2024-03-23T02:09:31Z	--- language: - en license: other tags: - llama-2 model_name: Llama2 70B Guanaco QLoRA base_model: Mikael110/llama-2-70b-guanaco-qlora inference: false model_creator: Mikael110 model_type: llama pipeline_tag: text-classification prompt_template: '### Human: {prompt} ### Assistant: ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama2 70B Guanaco QLoRA - GGUF - Model creator: [Mikael110](https://huggingface.co/Mikael110) - Original model: [Llama2 70B Guanaco QLoRA](https://huggingface.co/Mikael110/llama-2-70b-guanaco-qlora) <!-- description start --> ## Description This repo contains GGUF format model files for [Mikael110's Llama2 70b Guanaco QLoRA](https://huggingface.co/Mikael110/llama-2-70b-guanaco-qlora). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-fp16) * [Mikael110's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Mikael110/llama-2-70b-guanaco-qlora) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Guanaco ``` ### Human: {prompt} ### Assistant: ``` <!-- prompt-template end --> <!-- licensing start --> ## Licensing The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Mikael110's Llama2 70b Guanaco QLoRA](https://huggingface.co/Mikael110/llama-2-70b-guanaco-qlora). <!-- licensing end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [llama-2-70b-guanaco-qlora.Q2_K.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q2_K.gguf) \| Q2_K \| 2 \| 29.28 GB\| 31.78 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [llama-2-70b-guanaco-qlora.Q3_K_S.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 29.92 GB\| 32.42 GB \| very small, high quality loss \| \| [llama-2-70b-guanaco-qlora.Q3_K_M.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 33.19 GB\| 35.69 GB \| very small, high quality loss \| \| [llama-2-70b-guanaco-qlora.Q3_K_L.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 36.15 GB\| 38.65 GB \| small, substantial quality loss \| \| [llama-2-70b-guanaco-qlora.Q4_0.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q4_0.gguf) \| Q4_0 \| 4 \| 38.87 GB\| 41.37 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [llama-2-70b-guanaco-qlora.Q4_K_S.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 39.07 GB\| 41.57 GB \| small, greater quality loss \| \| [llama-2-70b-guanaco-qlora.Q4_K_M.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 41.42 GB\| 43.92 GB \| medium, balanced quality - recommended \| \| [llama-2-70b-guanaco-qlora.Q5_0.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q5_0.gguf) \| Q5_0 \| 5 \| 47.46 GB\| 49.96 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [llama-2-70b-guanaco-qlora.Q5_K_S.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 47.46 GB\| 49.96 GB \| large, low quality loss - recommended \| \| [llama-2-70b-guanaco-qlora.Q5_K_M.gguf](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF/blob/main/llama-2-70b-guanaco-qlora.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 48.75 GB\| 51.25 GB \| large, very low quality loss - recommended \| \| llama-2-70b-guanaco-qlora.Q6_K.gguf \| Q6_K \| 6 \| 56.59 GB\| 59.09 GB \| very large, extremely low quality loss \| \| llama-2-70b-guanaco-qlora.Q8_0.gguf \| Q8_0 \| 8 \| 73.29 GB\| 75.79 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ### Q6_K and Q8_0 files are split and require joining Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files. <details> <summary>Click for instructions regarding Q6_K and Q8_0 files</summary> ### q6_K Please download: * `llama-2-70b-guanaco-qlora.Q6_K.gguf-split-a` * `llama-2-70b-guanaco-qlora.Q6_K.gguf-split-b` ### q8_0 Please download: * `llama-2-70b-guanaco-qlora.Q8_0.gguf-split-a` * `llama-2-70b-guanaco-qlora.Q8_0.gguf-split-b` To join the files, do the following: Linux and macOS: ``` cat llama-2-70b-guanaco-qlora.Q6_K.gguf-split-* > llama-2-70b-guanaco-qlora.Q6_K.gguf && rm llama-2-70b-guanaco-qlora.Q6_K.gguf-split-* cat llama-2-70b-guanaco-qlora.Q8_0.gguf-split-* > llama-2-70b-guanaco-qlora.Q8_0.gguf && rm llama-2-70b-guanaco-qlora.Q8_0.gguf-split-* ``` Windows command line: ``` COPY /B llama-2-70b-guanaco-qlora.Q6_K.gguf-split-a + llama-2-70b-guanaco-qlora.Q6_K.gguf-split-b llama-2-70b-guanaco-qlora.Q6_K.gguf del llama-2-70b-guanaco-qlora.Q6_K.gguf-split-a llama-2-70b-guanaco-qlora.Q6_K.gguf-split-b COPY /B llama-2-70b-guanaco-qlora.Q8_0.gguf-split-a + llama-2-70b-guanaco-qlora.Q8_0.gguf-split-b llama-2-70b-guanaco-qlora.Q8_0.gguf del llama-2-70b-guanaco-qlora.Q8_0.gguf-split-a llama-2-70b-guanaco-qlora.Q8_0.gguf-split-b ``` </details> <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF and below it, a specific filename to download, such as: llama-2-70b-guanaco-qlora.q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub>=0.17.1 ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF llama-2-70b-guanaco-qlora.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF llama-2-70b-guanaco-qlora.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m llama-2-70b-guanaco-qlora.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Human: {prompt}\n### Assistant:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model from Python using ctransformers #### First install the package ```bash # Base ctransformers with no GPU acceleration pip install ctransformers>=0.2.24 # Or with CUDA GPU acceleration pip install ctransformers[cuda]>=0.2.24 # Or with ROCm GPU acceleration CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers ``` #### Simple example code to load one of these GGUF models ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/llama-2-70b-Guanaco-QLoRA-GGUF", model_file="llama-2-70b-guanaco-qlora.q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here's guides on using llama-cpp-python or ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Mikael110's Llama2 70b Guanaco QLoRA <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Llama2 70b Guanaco QLoRA - fp16 - Model creator: [Mikael110](https://huggingface.co/Mikael110) - Original model: [Llama2 70b Guanaco QLoRA](https://huggingface.co/Mikael110/llama-2-70b-guanaco-qlora) # Mikael110's Llama2 70b Guanaco QLoRA fp16 These files are pytorch format fp16 model files for [Mikael110's Llama2 70b Guanaco QLoRA](https://huggingface.co/Mikael110/llama-2-70b-guanaco-qlora). It is the result of merging and/or converting the source repository to float16. ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGML) * [Merged fp16 model, for GPU inference and further conversions](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-fp16) * [Mikael110's original QLoRA adapter](https://huggingface.co/Mikael110/llama-2-70b-guanaco-qlora) ## Prompt template: Guanaco ``` ### Human: {prompt} ### Assistant: ``` <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz. Patreon special mentions: Slarti, Chadd, John Detwiler, Pieter, zynix, K, Mano Prime, ReadyPlayerEmma, Ai Maven, Leonard Tan, Edmond Seymore, Joseph William Delisle, Luke @flexchar, Fred von Graf, Viktor Bowallius, Rishabh Srivastava, Nikolai Manek, Matthew Berman, Johann-Peter Hartmann, ya boyyy, Greatston Gnanesh, Femi Adebogun, Talal Aujan, Jonathan Leane, terasurfer, David Flickinger, William Sang, Ajan Kanaga, Vadim, Artur Olbinski, Raven Klaugh, Michael Levine, Oscar Rangel, Randy H, Cory Kujawski, RoA, Dave, Alex, Alexandros Triantafyllidis, Fen Risland, Eugene Pentland, vamX, Elle, Nathan LeClaire, Khalefa Al-Ahmad, Rainer Wilmers, subjectnull, Junyu Yang, Daniel P. Andersen, SuperWojo, LangChain4j, Mandus, Kalila, Illia Dulskyi, Trenton Dambrowitz, Asp the Wyvern, Derek Yates, Jeffrey Morgan, Deep Realms, Imad Khwaja, Pyrater, Preetika Verma, biorpg, Gabriel Tamborski, Stephen Murray, Spiking Neurons AB, Iucharbius, Chris Smitley, Willem Michiel, Luke Pendergrass, Sebastain Graf, senxiiz, Will Dee, Space Cruiser, Karl Bernard, Clay Pascal, Lone Striker, transmissions 11, webtim, WelcomeToTheClub, Sam, theTransient, Pierre Kircher, chris gileta, John Villwock, Sean Connelly, Willian Hasse Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Mikael110's Llama2 70b Guanaco QLoRA This is a Llama-2 version of [Guanaco](https://huggingface.co/timdettmers/guanaco-65b). It was finetuned from the base [Llama-70b](https://huggingface.co/meta-llama/Llama-2-70b-hf) model using the official training scripts found in the [QLoRA repo](https://github.com/artidoro/qlora). I wanted it to be as faithful as possible and therefore changed nothing in the training script beyond the model it was pointing to. The model prompt is therefore also the same as the original Guanaco model. This repo contains the QLoRA adapter. A 7b version of the adapter can be found [here](https://huggingface.co/Mikael110/llama-2-7b-guanaco-qlora). A 13b version of the adapter can be found [here](https://huggingface.co/Mikael110/llama-2-13b-guanaco-qlora). Legal Disclaimer: This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind. <!-- original-model-card end -->
AlignmentResearch/robust_llm_pythia-yelp-410m-mz-ada-v3	AlignmentResearch	2024-03-23T01:53:45Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-deduped", "base_model:finetune:EleutherAI/pythia-410m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T00:41:21Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-410m-deduped model-index: - name: robust_llm_pythia-yelp-410m-mz-ada-v3 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. --> # robust_llm_pythia-yelp-410m-mz-ada-v3 This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka	BEE-spoke-data	2024-03-23T01:51:36Z	10	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:BEE-spoke-data/allNLI-sbert", "base_model:BEE-spoke-data/bert-plus-L8-4096-v1.0", "base_model:finetune:BEE-spoke-data/bert-plus-L8-4096-v1.0", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-07T21:34:43Z	--- base_model: BEE-spoke-data/bert-plus-L8-4096-v1.0 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: apache-2.0 widget: - source_sentence: How to discreetly optimize operating expenses? sentences: - Strategies for quietly reducing overhead costs - Subtle ways to cut down on operating expenses - >- Implementing technology to save on operating costs without broad announcements - Lowering daily business expenses through unnoticed efficiencies - >- Minimizing operational expenditures in small businesses without drawing attention datasets: - BEE-spoke-data/allNLI-sbert language: - en --- # BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. - this was finetuned at 512 ctx (allNLI is all short-ctx examples) but the base model supports 4096 - Matryoshka dims: [768, 512, 256, 128, 64] <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka') model = AutoModel.from_pretrained('BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka) ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8663 with parameters: ``` {'batch_size': 32} ``` Loss*: `sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters: ``` {'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 216, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 867, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ```
AhmedAtef22/qlora_quizzer-llama2	AhmedAtef22	2024-03-23T01:40:50Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-03-23T01:40:45Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-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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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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sarak7/H10_323_166_v2	sarak7	2024-03-23T01:38:34Z	190	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T01:37:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MiguelZoo/Miguel	MiguelZoo	2024-03-23T01:21:09Z	0	0	null	[ "region:us" ]	null	2024-03-23T00:49:49Z	import gradio as gr import spaces import json import re import random import numpy as np from gradio_client import Client
mrxna/Marc-Spector	mrxna	2024-03-23T01:14:46Z	0	0	null	[ "region:us" ]	null	2024-03-23T00:42:47Z	--- language: - en tags: - nsfw - fiction - moon knight - adventure - steven grant - khonshu - coffee - fate - chance - first meeting - coffee spill license: afl-3.0 pipeline_tag: text-generation
sarak7/H10_323_166_v1	sarak7	2024-03-23T01:11:56Z	190	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T01:10:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Bakobiibizo/nimble-bert	Bakobiibizo	2024-03-23T01:08:39Z	80	0	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-21T21:57:05Z	## TextAttack Model Card This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 5e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9699473684210527, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2	bartowski	2024-03-23T01:07:27Z	0	1	transformers	[ "transformers", "mergekit", "merge", "text-generation", "base_model:berkeley-nest/Starling-LM-7B-alpha", "base_model:merge:berkeley-nest/Starling-LM-7B-alpha", "base_model:cognitivecomputations/WestLake-7B-v2-laser", "base_model:merge:cognitivecomputations/WestLake-7B-v2-laser", "base_model:mistralai/Mistral-7B-v0.1", "base_model:merge:mistralai/Mistral-7B-v0.1", "base_model:mlabonne/AlphaMonarch-7B", "base_model:merge:mlabonne/AlphaMonarch-7B", "base_model:senseable/garten2-7b", "base_model:merge:senseable/garten2-7b", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T00:55:39Z	--- base_model: - mistralai/Mistral-7B-v0.1 - berkeley-nest/Starling-LM-7B-alpha - mlabonne/AlphaMonarch-7B - cognitivecomputations/WestLake-7B-v2-laser - senseable/garten2-7b library_name: transformers tags: - mergekit - merge license: cc-by-nc-4.0 model-index: - name: Starling_Monarch_Westlake_Garten-7B-v0.1 results: - task: type: text-generation name: Text Generation dataset: name: EQ-Bench type: eq-bench config: EQ-Bench split: v2.1 args: num_few_shot: 3 metrics: - type: acc_norm value: 80.01 name: self-reported source: url: https://github.com/EQ-bench/EQ-Bench name: EQ-Bench v2.1 - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 67.92 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.16 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Starling_Monarch_Westlake_Garten-7B-v0.1 Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.16">turboderp's ExLlamaV2 v0.0.16</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.8 GB \| 11.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.2 GB \| 8.6 GB \| 10.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2/tree/5_0) \| 5.0 \| 6.0 \| 6.0 GB \| 7.4 GB \| 9.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.3 GB \| 6.7 GB \| 8.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.7 GB \| 6.1 GB \| 8.1 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2 Starling_Monarch_Westlake_Garten-7B-v0.1-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Starling_Monarch_Westlake_Garten-7B-v0.1-exl2`: ```shell mkdir Starling_Monarch_Westlake_Garten-7B-v0.1-exl2 huggingface-cli download bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2 --local-dir Starling_Monarch_Westlake_Garten-7B-v0.1-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir Starling_Monarch_Westlake_Garten-7B-v0.1-exl2-6_5 huggingface-cli download bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2 --revision 6_5 --local-dir Starling_Monarch_Westlake_Garten-7B-v0.1-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir Starling_Monarch_Westlake_Garten-7B-v0.1-exl2-6.5 huggingface-cli download bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-exl2 --revision 6_5 --local-dir Starling_Monarch_Westlake_Garten-7B-v0.1-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
leejaymin/Llama-2-13b-chat-hf-AWQ	leejaymin	2024-03-23T00:57:10Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2307.09288", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-22T15:21:34Z	--- language: - en license: llama2 tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 7B base_model: meta-llama/Llama-2-7b-hf inference: false model_creator: Meta model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: Jemin Lee <!-- header end --> # Llama 2 7B - AWQ - Model creator: [Meta](https://huggingface.co/meta-llama) - Original model: [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) <!-- description start --> ## Description This repo contains AWQ model files for [Meta's Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference. It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-7B-GGUF) * [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-7b-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files and AWQ parameters For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM. Models are released as sharded safetensors files. \| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \| \| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Llama-2-7B-AWQ/tree/main) \| 4 \| 128 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 3.89 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-use-from-vllm start --> ## Serving this model from vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - When using vLLM as a server, pass the `--quantization awq` parameter, for example: ```shell python3 python -m vllm.entrypoints.api_server --model TheBloke/Llama-2-7B-AWQ --quantization awq ``` When using vLLM from Python code, pass the `quantization=awq` parameter, for example: ```python from vllm import LLM, SamplingParams prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/Llama-2-7B-AWQ", quantization="awq") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-python start --> ## How to use this AWQ model from Python code ### Install the necessary packages Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later ```shell pip3 install autoawq ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### You can then try the following example code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer model_name_or_path = "TheBloke/Llama-2-7B-AWQ" # Load model model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True, trust_remote_code=True, safetensors=True) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' print("\n\n* Generate:") tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() # Generate output generation_output = model.generate( tokens, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, max_new_tokens=512 ) print("Output: ", tokenizer.decode(generation_output[0])) # Inference can also be done using transformers' pipeline from transformers import pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm). [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781). <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> <!-- footer end --> # Original model card: Meta's Llama 2 7B # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\| --
bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF	bartowski	2024-03-23T00:52:41Z	69	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "text-generation", "base_model:berkeley-nest/Starling-LM-7B-alpha", "base_model:merge:berkeley-nest/Starling-LM-7B-alpha", "base_model:cognitivecomputations/WestLake-7B-v2-laser", "base_model:merge:cognitivecomputations/WestLake-7B-v2-laser", "base_model:mistralai/Mistral-7B-v0.1", "base_model:merge:mistralai/Mistral-7B-v0.1", "base_model:mlabonne/AlphaMonarch-7B", "base_model:merge:mlabonne/AlphaMonarch-7B", "base_model:senseable/garten2-7b", "base_model:merge:senseable/garten2-7b", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T00:40:48Z	--- base_model: - mistralai/Mistral-7B-v0.1 - berkeley-nest/Starling-LM-7B-alpha - mlabonne/AlphaMonarch-7B - cognitivecomputations/WestLake-7B-v2-laser - senseable/garten2-7b library_name: transformers tags: - mergekit - merge license: cc-by-nc-4.0 model-index: - name: Starling_Monarch_Westlake_Garten-7B-v0.1 results: - task: type: text-generation name: Text Generation dataset: name: EQ-Bench type: eq-bench config: EQ-Bench split: v2.1 args: num_few_shot: 3 metrics: - type: acc_norm value: 80.01 name: self-reported source: url: https://github.com/EQ-bench/EQ-Bench name: EQ-Bench v2.1 - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 67.92 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.16 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp Quantizations of Starling_Monarch_Westlake_Garten-7B-v0.1 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2440">b2440</a> for quantization. Original model: https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q8_0.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q8_0.gguf) \| Q8_0 \| 7.69GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q6_K.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q6_K.gguf) \| Q6_K \| 5.94GB \| Very high quality, near perfect, recommended. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q5_K_M.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q5_K_M.gguf) \| Q5_K_M \| 5.13GB \| High quality, very usable. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q5_K_S.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q5_K_S.gguf) \| Q5_K_S \| 4.99GB \| High quality, very usable. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q5_0.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q5_0.gguf) \| Q5_0 \| 4.99GB \| High quality, older format, generally not recommended. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q4_K_M.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q4_K_M.gguf) \| Q4_K_M \| 4.36GB \| Good quality, similar to 4.25 bpw. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q4_K_S.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q4_K_S.gguf) \| Q4_K_S \| 4.14GB \| Slightly lower quality with small space savings. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-IQ4_NL.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-IQ4_NL.gguf) \| IQ4_NL \| 4.15GB \| Good quality, similar to Q4_K_S, new method of quanting, \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-IQ4_XS.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-IQ4_XS.gguf) \| IQ4_XS \| 3.94GB \| Decent quality, new method with similar performance to Q4. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q4_0.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q4_0.gguf) \| Q4_0 \| 4.10GB \| Decent quality, older format, generally not recommended. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-IQ3_M.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-IQ3_M.gguf) \| IQ3_M \| 3.28GB \| Medium-low quality, new method with decent performance. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-IQ3_S.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-IQ3_S.gguf) \| IQ3_S \| 3.18GB \| Lower quality, new method with decent performance, recommended over Q3 quants. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q3_K_L.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q3_K_L.gguf) \| Q3_K_L \| 3.82GB \| Lower quality but usable, good for low RAM availability. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q3_K_M.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q3_K_M.gguf) \| Q3_K_M \| 3.51GB \| Even lower quality. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q3_K_S.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q3_K_S.gguf) \| Q3_K_S \| 3.16GB \| Low quality, not recommended. \| \| [Starling_Monarch_Westlake_Garten-7B-v0.1-Q2_K.gguf](https://huggingface.co/bartowski/Starling_Monarch_Westlake_Garten-7B-v0.1-GGUF/blob/main/Starling_Monarch_Westlake_Garten-7B-v0.1-Q2_K.gguf) \| Q2_K \| 2.71GB \| Extremely low quality, not recommended. 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ahmedelsayed/NeuralPipe-9B-Passthrough	ahmedelsayed	2024-03-23T00:49:28Z	10	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:merge:OpenPipe/mistral-ft-optimized-1218", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T00:44:15Z	--- tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B base_model: - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B --- # NeuralPipe-7B-passthrough NeuralPipe-7B-passthrough is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - sources: - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [24, 32] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "ahmedelsayed/NeuralPipe-7B-passthrough" 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"]) ```
0x0daughter1/mistral_lollipop2	0x0daughter1	2024-03-23T00:47:24Z	134	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T00:44:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
DUAL-GPO/phi-2-gpo-test-longest-iter-3	DUAL-GPO	2024-03-23T00:36:16Z	1	0	peft	[ "peft", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-23T00:05:03Z	--- license: mit library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: microsoft/phi-2 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-gpo-test-longest-iter-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-gpo-test-longest-iter-3 This model is a fine-tuned version of [DUAL-GPO/phi-2-gpo-test-longest-iter-2](https://huggingface.co/DUAL-GPO/phi-2-gpo-test-longest-iter-2) on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 0.0105 - Rewards/chosen: 0.0036 - Rewards/rejected: 0.0021 - Rewards/accuracies: 0.5235 - Rewards/margins: 0.0015 - Logps/rejected: -278.5249 - Logps/chosen: -306.1786 - Logits/rejected: 0.0859 - Logits/chosen: -0.0119 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.0102 \| 1.6 \| 100 \| 0.0108 \| 0.0012 \| 0.0014 \| 0.4830 \| -0.0002 \| -278.6008 \| -306.4147 \| 0.0907 \| -0.0070 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.2.1+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
PaulSvGr/mistral-sina	PaulSvGr	2024-03-23T00:24:34Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-03-23T00:23:07Z	--- tags: - autotrain - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
0x0daughter1/mistral_lollipop	0x0daughter1	2024-03-23T00:12:58Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-22T20:00:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Lourdes/mistral_7b_v0.2-instruct-title-generation-2000-3	Lourdes	2024-03-23T00:10:48Z	76	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-23T00:02:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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UCFSD-LLMote/Med-Quad-gptq-4bit-mk3	UCFSD-LLMote	2024-03-23T00:10:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-22T23:31:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jspr/bts_mistral_7b_v5_32k_merged	jspr	2024-03-22T23:58:07Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-13T21:48:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Herry443/Mistral-7B-KNUT-ref-en-mmlu-0.2	Herry443	2024-03-22T23:55:36Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-22T23:32:58Z	--- 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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kghanlon/green_as_train_context_roberta-large_20e	kghanlon	2024-03-22T23:40:50Z	11	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:58:29Z	--- license: mit base_model: FacebookAI/roberta-large tags: - generated_from_trainer model-index: - name: green_as_train_context_roberta-large_20e 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. --> # green_as_train_context_roberta-large_20e This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4371 - Val Accuracy: 0.8913 - Val Precision: 0.7554 - Val Recall: 0.5910 - Val F1: 0.6632 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Val Accuracy \| Val Precision \| Val Recall \| Val F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------------:\|:-------------:\|:----------:\|:------:\| \| 0.1908 \| 1.0 \| 1012 \| 0.4035 \| 0.8904 \| 0.7844 \| 0.5448 \| 0.6430 \| \| 0.152 \| 2.0 \| 2024 \| 0.4631 \| 0.8930 \| 0.7440 \| 0.6235 \| 0.6784 \| \| 0.12 \| 3.0 \| 3036 \| 0.5046 \| 0.8879 \| 0.7028 \| 0.6605 \| 0.6810 \| \| 0.0757 \| 4.0 \| 4048 \| 0.7762 \| 0.8902 \| 0.7438 \| 0.6003 \| 0.6644 \| \| 0.0557 \| 5.0 \| 5060 \| 0.8961 \| 0.8846 \| 0.7273 \| 0.5802 \| 0.6455 \| \| 0.0319 \| 6.0 \| 6072 \| 0.8864 \| 0.8916 \| 0.7338 \| 0.6296 \| 0.6777 \| \| 0.0235 \| 7.0 \| 7084 \| 0.8025 \| 0.8902 \| 0.7348 \| 0.6157 \| 0.6700 \| \| 0.0125 \| 8.0 \| 8096 \| 1.1034 \| 0.8916 \| 0.7559 \| 0.5926 \| 0.6644 \| \| 0.0114 \| 9.0 \| 9108 \| 1.1414 \| 0.8882 \| 0.7422 \| 0.5864 \| 0.6552 \| \| 0.0147 \| 10.0 \| 10120 \| 1.2555 \| 0.8902 \| 0.7401 \| 0.6065 \| 0.6667 \| \| 0.0068 \| 11.0 \| 11132 \| 1.2923 \| 0.8879 \| 0.7526 \| 0.5679 \| 0.6473 \| \| 0.0112 \| 12.0 \| 12144 \| 1.3150 \| 0.8890 \| 0.8024 \| 0.5139 \| 0.6265 \| \| 0.0059 \| 13.0 \| 13156 \| 1.1883 \| 0.8899 \| 0.7396 \| 0.6049 \| 0.6655 \| \| 0.0056 \| 14.0 \| 14168 \| 1.3822 \| 0.8871 \| 0.7824 \| 0.5216 \| 0.6259 \| \| 0.0029 \| 15.0 \| 15180 \| 1.4309 \| 0.8888 \| 0.7741 \| 0.5448 \| 0.6395 \| \| 0.0021 \| 16.0 \| 16192 \| 1.3541 \| 0.8916 \| 0.7529 \| 0.5972 \| 0.6661 \| \| 0.004 \| 17.0 \| 17204 \| 1.3666 \| 0.8907 \| 0.7384 \| 0.6142 \| 0.6706 \| \| 0.0022 \| 18.0 \| 18216 \| 1.4396 \| 0.8896 \| 0.7525 \| 0.5818 \| 0.6562 \| \| 0.0028 \| 19.0 \| 19228 \| 1.4340 \| 0.8910 \| 0.7539 \| 0.5910 \| 0.6626 \| \| 0.0001 \| 20.0 \| 20240 \| 1.4371 \| 0.8913 \| 0.7554 \| 0.5910 \| 0.6632 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
dmusingu/wav2vec2-large-xls-r-300m-luganda-cv-14	dmusingu	2024-03-22T23:30:59Z	106	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-22T00:59:33Z	--- 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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research-dump/Llama-2-13b-chat-hf_fold_1	research-dump	2024-03-22T23:29:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-19T17:37:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Beniuv/poca-SoccerTwos	Beniuv	2024-03-22T23:17:59Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-03-22T23:17:47Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Beniuv/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
research-dump/Llama-2-13b-chat-hf_fold_0	research-dump	2024-03-22T23:17:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-19T17:31:12Z	--- 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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UCFSD-LLMote/Med-Quad-gptq-4bit-mk2	UCFSD-LLMote	2024-03-22T23:12:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-22T23:11:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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abdelhakimDZ/phi-2-role-play	abdelhakimDZ	2024-03-22T23:10:06Z	2	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-22T23:09:56Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi-2-role-play results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-role-play This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
language-and-voice-lab/talromur_e_loudnorm_xvector_finetune_fastspeech2	language-and-voice-lab	2024-03-22T23:06:34Z	2	0	espnet	[ "espnet", "audio", "text-to-speech", "en", "dataset:talromur", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	text-to-speech	2024-03-22T23:05:58Z	--- tags: - espnet - audio - text-to-speech language: en datasets: - talromur license: cc-by-4.0 --- ## ESPnet2 TTS model ### `language-and-voice-lab/talromur_e_loudnorm_xvector_finetune_fastspeech2` This model was trained by G-Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout d0047402e830a3c53e8b590064af4bf70415fb3b pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model language-and-voice-lab/talromur_e_loudnorm_xvector_finetune_fastspeech2 ``` ## TTS config <details><summary>expand</summary> ``` config: ./conf/tuning/finetune_xvector_fastspeech2.yaml print_config: false log_level: INFO drop_last_iter: false dry_run: false iterator_type: sequence valid_iterator_type: null output_dir: exp/tts_finetune_e_loudnorm_xvector_fastspeech2 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 8 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false use_adapter: false adapter: lora save_strategy: all adapter_conf: {} pretrain_path: null init_param: - /users/home/gunnaro/talromur_1and2_spk_avg_xvector_fastspeech2/exp/tts_xvector_fastspeech2_spk_avg_combined/valid.loss.ave_5best.pth:tts:tts ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 800 batch_size: 20 valid_batch_size: null batch_bins: 4500000 valid_batch_bins: null train_shape_file: - exp/tts_stats_e/train/text_shape.phn - exp/tts_stats_e/train/speech_shape valid_shape_file: - exp/tts_stats_e/valid/text_shape.phn - exp/tts_stats_e/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending shuffle_within_batch: false sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] chunk_default_fs: null train_data_path_and_name_and_type: - - dump/raw/train_e/text - text - text - - data/train_e/durations - durations - text_int - - dump/raw/train_e/wav.scp - speech - sound - - dump/xvector/train_e/xvector.scp - spembs - kaldi_ark valid_data_path_and_name_and_type: - - dump/raw/dev_e/text - text - text - - data/dev_e/durations - durations - text_int - - dump/raw/dev_e/wav.scp - speech - sound - - dump/xvector/dev_e/xvector.scp - spembs - kaldi_ark allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 allow_multi_rates: false valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.1 scheduler: noamlr scheduler_conf: model_size: 384 warmup_steps: 4000 token_list: - <blank> - <unk> - a - r - sil - I - t - n - s - D - Y - E - l - v - m - h - k - j - G - T - f - p - 'E:' - c - i - 'au:' - 'O:' - 'a:' - ei - 'i:' - r_0 - t_h - O - k_h - ou - ai - '9' - au - 'I:' - 'ou:' - u - 'ei:' - N - l_0 - 'u:' - n_0 - '9:' - 'ai:' - 9i - c_h - p_h - x - C - '9i:' - 'Y:' - J - N_0 - m_0 - Oi - Yi - J_0 - spn - '1' - '7' - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null feats_extract: fbank feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null fs: 22050 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/tts_stats_e/train/feats_stats.npz tts: fastspeech2 tts_conf: adim: 384 aheads: 2 elayers: 4 eunits: 1536 dlayers: 4 dunits: 1536 positionwise_layer_type: conv1d positionwise_conv_kernel_size: 3 duration_predictor_layers: 2 duration_predictor_chans: 256 duration_predictor_kernel_size: 3 postnet_layers: 5 postnet_filts: 5 postnet_chans: 256 use_masking: true use_scaled_pos_enc: true encoder_normalize_before: true decoder_normalize_before: true reduction_factor: 1 init_type: xavier_uniform init_enc_alpha: 1.0 init_dec_alpha: 1.0 transformer_enc_dropout_rate: 0.2 transformer_enc_positional_dropout_rate: 0.2 transformer_enc_attn_dropout_rate: 0.2 transformer_dec_dropout_rate: 0.2 transformer_dec_positional_dropout_rate: 0.2 transformer_dec_attn_dropout_rate: 0.2 pitch_predictor_layers: 5 pitch_predictor_chans: 256 pitch_predictor_kernel_size: 5 pitch_predictor_dropout: 0.5 pitch_embed_kernel_size: 1 pitch_embed_dropout: 0.0 stop_gradient_from_pitch_predictor: true energy_predictor_layers: 2 energy_predictor_chans: 256 energy_predictor_kernel_size: 3 energy_predictor_dropout: 0.5 energy_embed_kernel_size: 1 energy_embed_dropout: 0.0 stop_gradient_from_energy_predictor: false spk_embed_dim: 512 spk_embed_integration_type: add pitch_extract: dio pitch_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 f0max: 400 f0min: 80 reduction_factor: 1 pitch_normalize: global_mvn pitch_normalize_conf: stats_file: exp/tts_stats_e/train/pitch_stats.npz energy_extract: energy energy_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 win_length: null reduction_factor: 1 energy_normalize: global_mvn energy_normalize_conf: stats_file: exp/tts_stats_e/train/energy_stats.npz required: - output_dir - token_list version: '202402' distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
language-and-voice-lab/talromur_f_loudnorm_xvector_finetune_fastspeech2	language-and-voice-lab	2024-03-22T23:03:43Z	4	0	espnet	[ "espnet", "audio", "text-to-speech", "en", "dataset:talromur", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	text-to-speech	2024-03-22T23:03:35Z	--- tags: - espnet - audio - text-to-speech language: en datasets: - talromur license: cc-by-4.0 --- ## ESPnet2 TTS model ### `language-and-voice-lab/talromur_f_loudnorm_xvector_finetune_fastspeech2` This model was trained by G-Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout d0047402e830a3c53e8b590064af4bf70415fb3b pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model language-and-voice-lab/talromur_f_loudnorm_xvector_finetune_fastspeech2 ``` ## TTS config <details><summary>expand</summary> ``` config: ./conf/tuning/finetune_xvector_fastspeech2.yaml print_config: false log_level: INFO drop_last_iter: false dry_run: false iterator_type: sequence valid_iterator_type: null output_dir: exp/tts_finetune_f_loudnorm_xvector_fastspeech2 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 8 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false use_adapter: false adapter: lora save_strategy: all adapter_conf: {} pretrain_path: null init_param: - /users/home/gunnaro/talromur_1and2_spk_avg_xvector_fastspeech2/exp/tts_xvector_fastspeech2_spk_avg_combined/valid.loss.ave_5best.pth:tts:tts ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 800 batch_size: 20 valid_batch_size: null batch_bins: 4500000 valid_batch_bins: null train_shape_file: - exp/tts_stats_f/train/text_shape.phn - exp/tts_stats_f/train/speech_shape valid_shape_file: - exp/tts_stats_f/valid/text_shape.phn - exp/tts_stats_f/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending shuffle_within_batch: false sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] chunk_default_fs: null train_data_path_and_name_and_type: - - dump/raw/train_f/text - text - text - - data/train_f/durations - durations - text_int - - dump/raw/train_f/wav.scp - speech - sound - - dump/xvector/train_f/xvector.scp - spembs - kaldi_ark valid_data_path_and_name_and_type: - - dump/raw/dev_f/text - text - text - - data/dev_f/durations - durations - text_int - - dump/raw/dev_f/wav.scp - speech - sound - - dump/xvector/dev_f/xvector.scp - spembs - kaldi_ark allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 allow_multi_rates: false valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.1 scheduler: noamlr scheduler_conf: model_size: 384 warmup_steps: 4000 token_list: - <blank> - <unk> - a - r - sil - I - t - n - s - D - Y - E - l - v - m - h - k - j - G - T - f - p - 'E:' - c - i - 'au:' - 'O:' - 'a:' - ei - 'i:' - r_0 - t_h - O - k_h - ou - ai - '9' - au - 'I:' - 'ou:' - u - 'ei:' - N - l_0 - 'u:' - n_0 - '9:' - 'ai:' - 9i - c_h - p_h - x - C - '9i:' - 'Y:' - J - N_0 - m_0 - Oi - Yi - J_0 - spn - '1' - '7' - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null feats_extract: fbank feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null fs: 22050 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/tts_stats_f/train/feats_stats.npz tts: fastspeech2 tts_conf: adim: 384 aheads: 2 elayers: 4 eunits: 1536 dlayers: 4 dunits: 1536 positionwise_layer_type: conv1d positionwise_conv_kernel_size: 3 duration_predictor_layers: 2 duration_predictor_chans: 256 duration_predictor_kernel_size: 3 postnet_layers: 5 postnet_filts: 5 postnet_chans: 256 use_masking: true use_scaled_pos_enc: true encoder_normalize_before: true decoder_normalize_before: true reduction_factor: 1 init_type: xavier_uniform init_enc_alpha: 1.0 init_dec_alpha: 1.0 transformer_enc_dropout_rate: 0.2 transformer_enc_positional_dropout_rate: 0.2 transformer_enc_attn_dropout_rate: 0.2 transformer_dec_dropout_rate: 0.2 transformer_dec_positional_dropout_rate: 0.2 transformer_dec_attn_dropout_rate: 0.2 pitch_predictor_layers: 5 pitch_predictor_chans: 256 pitch_predictor_kernel_size: 5 pitch_predictor_dropout: 0.5 pitch_embed_kernel_size: 1 pitch_embed_dropout: 0.0 stop_gradient_from_pitch_predictor: true energy_predictor_layers: 2 energy_predictor_chans: 256 energy_predictor_kernel_size: 3 energy_predictor_dropout: 0.5 energy_embed_kernel_size: 1 energy_embed_dropout: 0.0 stop_gradient_from_energy_predictor: false spk_embed_dim: 512 spk_embed_integration_type: add pitch_extract: dio pitch_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 f0max: 400 f0min: 80 reduction_factor: 1 pitch_normalize: global_mvn pitch_normalize_conf: stats_file: exp/tts_stats_f/train/pitch_stats.npz energy_extract: energy energy_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 win_length: null reduction_factor: 1 energy_normalize: global_mvn energy_normalize_conf: stats_file: exp/tts_stats_f/train/energy_stats.npz required: - output_dir - token_list version: '202402' distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
VatsaDev/MathText-models	VatsaDev	2024-03-22T22:56:12Z	0	1	null	[ "en", "dataset:VatsaDev/mathtext", "license:mit", "region:us" ]	null	2024-03-21T17:39:38Z	--- license: mit datasets: - VatsaDev/mathtext language: - en --- Models for mathtext, teaching transformers math, all trained on kaggle - (+1) model, didnt add, but 2m - Add - lost original, retrained model, 18m - sub - lost original, but 10m - multiply model - 15m - divide model, too gibberish to add, but 18m Difficulty of task rating (easiest to hardest): +1>add>mul>sub>div
AlignmentResearch/robust_llm_pythia-tt-1b-mz-ada-v3-ch-141000	AlignmentResearch	2024-03-22T22:52:23Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b-deduped", "base_model:finetune:EleutherAI/pythia-1b-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T22:50:25Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-1b-deduped model-index: - name: robust_llm_pythia-tt-1b-mz-ada-v3-ch-141000 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. --> # robust_llm_pythia-tt-1b-mz-ada-v3-ch-141000 This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
haith1307/q-FrozenLake-v1-4x4-noSlippery	haith1307	2024-03-22T22:50:30Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-22T22:50:25Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="haith1307/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Locutusque/Hyperion-3.0-Mistral-7B-alpha	Locutusque	2024-03-22T22:49:19Z	114	7	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "chemistry", "medical", "en", "dataset:Locutusque/hyperion-v3.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-16T02:45:21Z	--- library_name: transformers tags: - code - chemistry - medical license: apache-2.0 datasets: - Locutusque/hyperion-v3.0 language: - en widget: - example_title: Reasoning text: \| <\|im_start\|>user An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of this increase in rotation? A. Planetary density will decrease. B. Planetary years will become longer. C. Planetary days will become shorter. D. Planetary gravity will become stronger.<\|im_end\|> <\|im_start\|>assistant output: text: \| C. Planetary days will become shorter. The increase in rotation is due to the transfer of angular momentum from the meteorite impact to the planet. This means that more kinetic energy is imparted on the planet, making it spin faster. As a result, the planet's rotational period (the time it takes for the planet to complete one rotation) decreases. So, planetary days will become shorter. A. Planetary density will not change significantly as a result of the meteorite impact. The increase in rotation is primarily an issue of angular momentum transfer, and density is mainly determined by the composition of the planet. B. Planetary years would become longer if the planet took less time to rotate around its axis, since a year is based on how long it takes the planet to revolve around its star. However, meteorite impacts generally affect rotation, not revolution. D. Planetary gravity would remain the same or possibly slightly decrease. A meteorite impact alone does not have a significant enough effect on the mass of a planet to alter its gravitational force appreciably. --- # Hyperion-3.0-Mistral-7B-alpha ## Model Details - Model Name: Locutusque/Hyperion-3.0-Mistral-7B-alpha - Base Model: mistralai/Mistral-7B-v0.1 - Publisher: Locutusque - Model Type: Question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, logical reasoning. - Language: Multi-domain, English language. - License: Apache-2.0 ## Model Description `Locutusque/Hyperion-3.0-Mistral-7B-alpha` is a state-of-the-art language model fine-tuned on the Hyperion-v3.0 dataset for advanced reasoning across scientific domains. This model is designed to handle complex inquiries and instructions, leveraging the diverse and rich information contained in the Hyperion dataset. Its primary use cases include but are not limited to complex question answering, conversational understanding, code generation, medical text comprehension, mathematical reasoning, and logical reasoning. This model is designed to greatly outperform its predecessors. ## Intended Use This model is intended for researchers and practitioners looking for a powerful tool to tackle challenging problems in scientific domains. It can be used in the following scenarios: - AI-driven tutoring systems for science, medicine, mathematics, and computer science. - Assistive tools for professionals requiring fast and accurate domain-specific information retrieval. - Platforms that require conversational AI capabilities with a focus on technical and scientific reasoning. - Automation in code generation and understanding complex programming context. ## Training Data The `Locutusque/Hyperion-3.0-Mistral-7B-alpha` model was fine-tuned on 200,000 examples of the Hyperion-3.0 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks. ## Quants ExLlamaV2: https://huggingface.co/bartowski/Hyperion-3.0-Mistral-7B-alpha-exl2 GGUF: https://huggingface.co/bartowski/Hyperion-3.0-Mistral-7B-alpha-GGUF ## Evaluation Results Zero-shot AGIEval \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value \| \|Stderr\| \|---------------------------------\|-------\|------\|------\|--------\|-----:\|---\|-----:\| \|agieval_nous \|N/A \|none \|None \|acc_norm\|0.3500\|± \|0.0092\| \| \| \|none \|None \|acc \|0.3519\|± \|0.0093\| \| - agieval_aqua_rat \| 1\|none \|None \|acc \|0.2087\|± \|0.0255\| \| \| \|none \|None \|acc_norm\|0.2165\|± \|0.0259\| \| - agieval_logiqa_en \| 1\|none \|None \|acc \|0.3164\|± \|0.0182\| \| \| \|none \|None \|acc_norm\|0.3533\|± \|0.0187\| \| - agieval_lsat_ar \| 1\|none \|None \|acc \|0.2348\|± \|0.0280\| \| \| \|none \|None \|acc_norm\|0.2174\|± \|0.0273\| \| - agieval_lsat_lr \| 1\|none \|None \|acc \|0.3529\|± \|0.0212\| \| \| \|none \|None \|acc_norm\|0.3647\|± \|0.0213\| \| - agieval_lsat_rc \| 1\|none \|None \|acc \|0.4721\|± \|0.0305\| \| \| \|none \|None \|acc_norm\|0.4201\|± \|0.0301\| \| - agieval_sat_en \| 1\|none \|None \|acc \|0.6019\|± \|0.0342\| \| \| \|none \|None \|acc_norm\|0.6117\|± \|0.0340\| \| - agieval_sat_en_without_passage\| 1\|none \|None \|acc \|0.4078\|± \|0.0343\| \| \| \|none \|None \|acc_norm\|0.3835\|± \|0.0340\| \| - agieval_sat_math \| 1\|none \|None \|acc \|0.3091\|± \|0.0312\| \| \| \|none \|None \|acc_norm\|0.2364\|± \|0.0287\| 5 shot CoT MMLU \| Tasks \|Version\| Filter \|n-shot\| Metric \|Value \| \|Stderr\| \|-------------------------------------------------------------\|-------\|----------\|-----:\|-----------\|-----:\|---\|-----:\| \|mmlu_flan_cot_fewshot \|N/A \|get-answer\| 0\|exact_match\|0.5924\|± \|0.0118\| \| - mmlu_flan_cot_fewshot_humanities \|N/A \|get-answer\| 0\|exact_match\|0.5077\|± \|0.0206\| \| - mmlu_flan_cot_fewshot_formal_logic \| 0\|get-answer\| 0\|exact_match\|0.2143\|± \|0.1138\| \| - mmlu_flan_cot_fewshot_high_school_european_history \| 0\|get-answer\| 0\|exact_match\|0.6111\|± \|0.1182\| \| - mmlu_flan_cot_fewshot_high_school_us_history \| 0\|get-answer\| 0\|exact_match\|0.7727\|± \|0.0914\| \| - mmlu_flan_cot_fewshot_high_school_world_history \| 0\|get-answer\| 0\|exact_match\|0.6154\|± \|0.0973\| \| - mmlu_flan_cot_fewshot_international_law \| 0\|get-answer\| 0\|exact_match\|0.9231\|± \|0.0769\| \| - mmlu_flan_cot_fewshot_jurisprudence \| 0\|get-answer\| 0\|exact_match\|0.3636\|± \|0.1521\| \| - mmlu_flan_cot_fewshot_logical_fallacies \| 0\|get-answer\| 0\|exact_match\|0.7222\|± \|0.1086\| \| - mmlu_flan_cot_fewshot_moral_disputes \| 0\|get-answer\| 0\|exact_match\|0.5526\|± \|0.0817\| \| - mmlu_flan_cot_fewshot_moral_scenarios \| 0\|get-answer\| 0\|exact_match\|0.3900\|± \|0.0490\| \| - mmlu_flan_cot_fewshot_philosophy \| 0\|get-answer\| 0\|exact_match\|0.7647\|± \|0.0738\| \| - mmlu_flan_cot_fewshot_prehistory \| 0\|get-answer\| 0\|exact_match\|0.7143\|± \|0.0775\| \| - mmlu_flan_cot_fewshot_professional_law \| 0\|get-answer\| 0\|exact_match\|0.3471\|± \|0.0366\| \| - mmlu_flan_cot_fewshot_world_religions \| 0\|get-answer\| 0\|exact_match\|0.8947\|± \|0.0723\| \| - mmlu_flan_cot_fewshot_other \|N/A \|get-answer\| 0\|exact_match\|0.6921\|± \|0.0240\| \| - mmlu_flan_cot_fewshot_business_ethics \| 0\|get-answer\| 0\|exact_match\|0.9091\|± \|0.0909\| \| - mmlu_flan_cot_fewshot_clinical_knowledge \| 0\|get-answer\| 0\|exact_match\|0.5517\|± \|0.0940\| \| - mmlu_flan_cot_fewshot_college_medicine \| 0\|get-answer\| 0\|exact_match\|0.7727\|± \|0.0914\| \| - mmlu_flan_cot_fewshot_global_facts \| 0\|get-answer\| 0\|exact_match\|0.6000\|± \|0.1633\| \| - mmlu_flan_cot_fewshot_human_aging \| 0\|get-answer\| 0\|exact_match\|0.6522\|± \|0.1015\| \| - mmlu_flan_cot_fewshot_management \| 0\|get-answer\| 0\|exact_match\|0.9091\|± \|0.0909\| \| - mmlu_flan_cot_fewshot_marketing \| 0\|get-answer\| 0\|exact_match\|0.8400\|± \|0.0748\| \| - mmlu_flan_cot_fewshot_medical_genetics \| 0\|get-answer\| 0\|exact_match\|1.0000\|± \|0.0000\| \| - mmlu_flan_cot_fewshot_miscellaneous \| 0\|get-answer\| 0\|exact_match\|0.7791\|± \|0.0450\| \| - mmlu_flan_cot_fewshot_nutrition \| 0\|get-answer\| 0\|exact_match\|0.6667\|± \|0.0833\| \| - mmlu_flan_cot_fewshot_professional_accounting \| 0\|get-answer\| 0\|exact_match\|0.4194\|± \|0.0901\| \| - mmlu_flan_cot_fewshot_professional_medicine \| 0\|get-answer\| 0\|exact_match\|0.6774\|± \|0.0853\| \| - mmlu_flan_cot_fewshot_virology \| 0\|get-answer\| 0\|exact_match\|0.3889\|± \|0.1182\| \| - mmlu_flan_cot_fewshot_social_sciences \|N/A \|get-answer\| 0\|exact_match\|0.6973\|± \|0.0239\| \| - mmlu_flan_cot_fewshot_econometrics \| 0\|get-answer\| 0\|exact_match\|0.3333\|± \|0.1421\| \| - mmlu_flan_cot_fewshot_high_school_geography \| 0\|get-answer\| 0\|exact_match\|0.9091\|± \|0.0627\| \| - mmlu_flan_cot_fewshot_high_school_government_and_politics\| 0\|get-answer\| 0\|exact_match\|0.8095\|± \|0.0878\| \| - mmlu_flan_cot_fewshot_high_school_macroeconomics \| 0\|get-answer\| 0\|exact_match\|0.6279\|± \|0.0746\| \| - mmlu_flan_cot_fewshot_high_school_microeconomics \| 0\|get-answer\| 0\|exact_match\|0.6154\|± \|0.0973\| \| - mmlu_flan_cot_fewshot_high_school_psychology \| 0\|get-answer\| 0\|exact_match\|0.9167\|± \|0.0360\| \| - mmlu_flan_cot_fewshot_human_sexuality \| 0\|get-answer\| 0\|exact_match\|0.5000\|± \|0.1508\| \| - mmlu_flan_cot_fewshot_professional_psychology \| 0\|get-answer\| 0\|exact_match\|0.6667\|± \|0.0572\| \| - mmlu_flan_cot_fewshot_public_relations \| 0\|get-answer\| 0\|exact_match\|0.5833\|± \|0.1486\| \| - mmlu_flan_cot_fewshot_security_studies \| 0\|get-answer\| 0\|exact_match\|0.4444\|± \|0.0975\| \| - mmlu_flan_cot_fewshot_sociology \| 0\|get-answer\| 0\|exact_match\|0.7727\|± \|0.0914\| \| - mmlu_flan_cot_fewshot_us_foreign_policy \| 0\|get-answer\| 0\|exact_match\|0.7273\|± \|0.1408\| \| - mmlu_flan_cot_fewshot_stem \|N/A \|get-answer\| 0\|exact_match\|0.5164\|± \|0.0265\| \| - mmlu_flan_cot_fewshot_abstract_algebra \| 0\|get-answer\| 0\|exact_match\|0.4545\|± \|0.1575\| \| - mmlu_flan_cot_fewshot_anatomy \| 0\|get-answer\| 0\|exact_match\|0.3571\|± \|0.1329\| \| - mmlu_flan_cot_fewshot_astronomy \| 0\|get-answer\| 0\|exact_match\|0.5000\|± \|0.1291\| \| - mmlu_flan_cot_fewshot_college_biology \| 0\|get-answer\| 0\|exact_match\|0.5625\|± \|0.1281\| \| - mmlu_flan_cot_fewshot_college_chemistry \| 0\|get-answer\| 0\|exact_match\|0.3750\|± \|0.1830\| \| - mmlu_flan_cot_fewshot_college_computer_science \| 0\|get-answer\| 0\|exact_match\|0.2727\|± \|0.1408\| \| - mmlu_flan_cot_fewshot_college_mathematics \| 0\|get-answer\| 0\|exact_match\|0.2727\|± \|0.1408\| \| - mmlu_flan_cot_fewshot_college_physics \| 0\|get-answer\| 0\|exact_match\|0.4545\|± \|0.1575\| \| - mmlu_flan_cot_fewshot_computer_security \| 0\|get-answer\| 0\|exact_match\|0.7273\|± \|0.1408\| \| - mmlu_flan_cot_fewshot_conceptual_physics \| 0\|get-answer\| 0\|exact_match\|0.6154\|± \|0.0973\| \| - mmlu_flan_cot_fewshot_electrical_engineering \| 0\|get-answer\| 0\|exact_match\|0.6875\|± \|0.1197\| \| - mmlu_flan_cot_fewshot_elementary_mathematics \| 0\|get-answer\| 0\|exact_match\|0.7317\|± \|0.0701\| \| - mmlu_flan_cot_fewshot_high_school_biology \| 0\|get-answer\| 0\|exact_match\|0.7188\|± \|0.0808\| \| - mmlu_flan_cot_fewshot_high_school_chemistry \| 0\|get-answer\| 0\|exact_match\|0.3636\|± \|0.1050\| \| - mmlu_flan_cot_fewshot_high_school_computer_science \| 0\|get-answer\| 0\|exact_match\|0.6667\|± \|0.1667\| \| - mmlu_flan_cot_fewshot_high_school_mathematics \| 0\|get-answer\| 0\|exact_match\|0.4138\|± \|0.0931\| \| - mmlu_flan_cot_fewshot_high_school_physics \| 0\|get-answer\| 0\|exact_match\|0.2353\|± \|0.1060\| \| - mmlu_flan_cot_fewshot_high_school_statistics \| 0\|get-answer\| 0\|exact_match\|0.4348\|± \|0.1057\| \| - mmlu_flan_cot_fewshot_machine_learning \| 0\|get-answer\| 0\|exact_match\|0.3636\|± \|0.1521\| \| Groups \|Version\| Filter \|n-shot\| Metric \|Value \| \|Stderr\| \|----------------------------------------\|-------\|----------\|-----:\|-----------\|-----:\|---\|-----:\| \|mmlu_flan_cot_fewshot \|N/A \|get-answer\| 0\|exact_match\|0.5924\|± \|0.0118\| \| - mmlu_flan_cot_fewshot_humanities \|N/A \|get-answer\| 0\|exact_match\|0.5077\|± \|0.0206\| \| - mmlu_flan_cot_fewshot_other \|N/A \|get-answer\| 0\|exact_match\|0.6921\|± \|0.0240\| \| - mmlu_flan_cot_fewshot_social_sciences\|N/A \|get-answer\| 0\|exact_match\|0.6973\|± \|0.0239\| \| - mmlu_flan_cot_fewshot_stem \|N/A \|get-answer\| 0\|exact_match\|0.5164\|± \|0.0265\| ## How to Use ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Locutusque/Hyperion-3.0-Mistral-7B-alpha" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # For a text generation task input_text = "<\|im_start\|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<\|im_end\|>\n<\|im_start\|>assistant\n" input_ids = tokenizer.encode(input_text, return_tensors="pt") # Generate a response outputs = model.generate(input_ids, max_length=200, num_return_sequences=1, temperature=0.8, top_p=0.95, top_k=40, repetition_penalty=1.1) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Known Limitations The diversity of the dataset could lead to inconsistencies in the model's responses due to variations in data formatting and annotation quality. This model is also very compliant, it will respond to any request. Please make sure to build upon this model with DPO if you plan on using it for enterprise-level deployment. ## Licensing Information This model is released under the Apache-2.0 license.
AlignmentResearch/robust_llm_pythia-tt-1b-mz-ada-v3-ch-135000	AlignmentResearch	2024-03-22T22:48:14Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b-deduped", "base_model:finetune:EleutherAI/pythia-1b-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T22:46:22Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-1b-deduped model-index: - name: robust_llm_pythia-tt-1b-mz-ada-v3-ch-135000 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. --> # robust_llm_pythia-tt-1b-mz-ada-v3-ch-135000 This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
Rahman1/ft-mistral-7b-v01	Rahman1	2024-03-22T22:47:25Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-03-22T22:46:40Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: ft-mistral-7b-v01 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. --> # ft-mistral-7b-v01 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the OpenAssistant/oasst_top1_2023-08-25 dataset. It achieves the following results on the evaluation set: - Loss: 2.6703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 5 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.3784 \| 1.0 \| 90 \| 2.3305 \| \| 1.5259 \| 2.0 \| 180 \| 2.6703 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.15.0 - Tokenizers 0.15.2
mole-code/llama_index-starcoderbase-1b-fft	mole-code	2024-03-22T22:41:23Z	189	0	transformers	[ "transformers", "safetensors", "gpt_bigcode", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T19:57:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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B2111797/recipe_v1_lr5e-4_wu1000_epo2	B2111797	2024-03-22T22:35:46Z	134	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-22T22:35:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
AlignmentResearch/robust_llm_pythia-tt-1b-mz-ada-v3-ch-120000	AlignmentResearch	2024-03-22T22:35:08Z	106	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b-deduped", "base_model:finetune:EleutherAI/pythia-1b-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T22:33:09Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-1b-deduped model-index: - name: robust_llm_pythia-tt-1b-mz-ada-v3-ch-120000 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. --> # robust_llm_pythia-tt-1b-mz-ada-v3-ch-120000 This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-1b-mz-ada-v3-ch-123000	AlignmentResearch	2024-03-22T22:34:15Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b-deduped", "base_model:finetune:EleutherAI/pythia-1b-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T22:32:14Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-1b-deduped model-index: - name: robust_llm_pythia-tt-1b-mz-ada-v3-ch-123000 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. --> # robust_llm_pythia-tt-1b-mz-ada-v3-ch-123000 This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-1b-mz-ada-v3-ch-112000	AlignmentResearch	2024-03-22T22:32:11Z	106	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b-deduped", "base_model:finetune:EleutherAI/pythia-1b-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T22:30:08Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-1b-deduped model-index: - name: robust_llm_pythia-tt-1b-mz-ada-v3-ch-112000 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. --> # robust_llm_pythia-tt-1b-mz-ada-v3-ch-112000 This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
jkcg/dreambooth-sdxl-jkcg	jkcg	2024-03-22T22:26:42Z	11	1	diffusers	[ "diffusers", "autotrain", "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	2024-03-22T17:19:19Z	--- tags: - autotrain - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: jkcgchry license: openrail++ --- # AutoTrain SDXL LoRA DreamBooth - jkcg/dreambooth-sdxl-jkcg <Gallery /> ## Model description These are jkcg/dreambooth-sdxl-jkcg 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: None. ## Trigger words You should use jkcgchry to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](jkcg/dreambooth-sdxl-jkcg/tree/main) them in the Files & versions tab.
AlignmentResearch/robust_llm_pythia-tt-1b-mz-ada-v3-ch-106000	AlignmentResearch	2024-03-22T22:20:50Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b-deduped", "base_model:finetune:EleutherAI/pythia-1b-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T22:18:46Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-1b-deduped model-index: - name: robust_llm_pythia-tt-1b-mz-ada-v3-ch-106000 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. --> # robust_llm_pythia-tt-1b-mz-ada-v3-ch-106000 This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
rajevan123/STS-Lora-Fine-Tuning-Capstone-electra-model-auto-cross-testing-123-final-pipes-value-error-solve	rajevan123	2024-03-22T22:19:24Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:rv2307/electra-small-ner", "base_model:adapter:rv2307/electra-small-ner", "license:apache-2.0", "region:us" ]	null	2024-03-22T19:51:30Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - accuracy base_model: rv2307/electra-small-ner model-index: - name: STS-Lora-Fine-Tuning-Capstone-electra-model-auto-cross-testing-123-final-pipes-value-error-solve 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. --> # STS-Lora-Fine-Tuning-Capstone-electra-model-auto-cross-testing-123-final-pipes-value-error-solve This model is a fine-tuned version of [rv2307/electra-small-ner](https://huggingface.co/rv2307/electra-small-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7183 - Accuracy: 0.2727 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 180 \| 1.7288 \| 0.2429 \| \| No log \| 2.0 \| 360 \| 1.7230 \| 0.2502 \| \| 1.7009 \| 3.0 \| 540 \| 1.7216 \| 0.2676 \| \| 1.7009 \| 4.0 \| 720 \| 1.7216 \| 0.2705 \| \| 1.7009 \| 5.0 \| 900 \| 1.7183 \| 0.2748 \| \| 1.6754 \| 6.0 \| 1080 \| 1.7186 \| 0.2748 \| \| 1.6754 \| 7.0 \| 1260 \| 1.7178 \| 0.2741 \| \| 1.6754 \| 8.0 \| 1440 \| 1.7184 \| 0.2748 \| \| 1.6715 \| 9.0 \| 1620 \| 1.7183 \| 0.2741 \| \| 1.6715 \| 10.0 \| 1800 \| 1.7183 \| 0.2727 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Owhslp/nous_researcher_tuning_2_31	Owhslp	2024-03-22T22:18:57Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-12T20:40:20Z	--- library_name: transformers tags: [] --- # Model Card fodedeedr Model 0ID asdadadas <!-- Provide a quick summary of what the model is/does. --> last dadadadada ## Model Detalpils ### 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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AlignmentResearch/robust_llm_pythia-tt-410m-mz-ada-v3-ch-135000	AlignmentResearch	2024-03-22T22:13:04Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-deduped", "base_model:finetune:EleutherAI/pythia-410m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T22:12:14Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-410m-deduped model-index: - name: robust_llm_pythia-tt-410m-mz-ada-v3-ch-135000 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. --> # robust_llm_pythia-tt-410m-mz-ada-v3-ch-135000 This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-410m-mz-ada-v3-ch-132000	AlignmentResearch	2024-03-22T22:11:05Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-deduped", "base_model:finetune:EleutherAI/pythia-410m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T22:10:08Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-410m-deduped model-index: - name: robust_llm_pythia-tt-410m-mz-ada-v3-ch-132000 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. --> # robust_llm_pythia-tt-410m-mz-ada-v3-ch-132000 This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
hamzamurtaza/final_xml_prompt_m	hamzamurtaza	2024-03-22T22:10:34Z	0	0	peft	[ "peft", "region:us" ]	null	2024-03-22T22:08:04Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
sho28/taxi	sho28	2024-03-22T22:06:35Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-22T22:06:34Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.77 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="sho28/taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
dinalt/walsh-1-7b	dinalt	2024-03-22T22:06:33Z	105	0	transformers	[ "transformers", "safetensors", "walsh-causal-v1", "text-generation", "conversational", "custom_code", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:wikipedia", "arxiv:2203.00555", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2024-03-12T09:09:48Z	--- license: mit datasets: - togethercomputer/RedPajama-Data-1T - wikipedia pipeline_tag: text-generation --- Hadamard-Walsh 1.7B is an experimental model using a new positional encoder. The encoder represents absolute positions by using a combination of rows from the Hadamard-Walsh matrix (https://en.wikipedia.org/wiki/Hadamard_code). Each row corresponds to a binary digit is the positional code, where the presence of a row codes for a 1 and the absence, a zero. While training, the base offset in the sequence is randomly chosen for each batch. The result is that the model is very proficient at sequences much longer than those seen in training. The encoding scheme was devised when I was performing experiments to determine the degree to which various positional encodings schemes interfere with the information carrying capacity of embeddings. This particular scheme did exceptionally well. It was both highly resilient to interference as well as minimally interfering with the embeddings. As a follow-on experiment, I adapted the encoder to work with my transformer implementation and found that it performs exceptionally well when directly compared with other popular positional encoding schemes. The model has had approximately three weeks of pretraining on six RTX4090s. It seems to be doing remarkably well when compared with my other model of similar size and training, but using ALiBi positional encodings and SwiGLU. I have also noted an unusual loss pattern, where evaluation loss shows large punctuated drops. I can only speculate, but my suspicion is that the random offset of the positional encoder may have a regularizing effect on training. The attention patterns are also quite "interesting." [TODO: add images of attention patterns]. ### Model Details: - Model Dimension: 2048 - Hidden Layers: 32 - Attention Heads: 32 - Feedforward Dimension: 8192 - Feedforward Network Type: Conventional MLP with GeLU activation - Vocabulary Size: 32000 - Max Sequence Length: 16K (14-bit absolute positional encoding via Walsh matrix) - Weight Initialization: DeepNet, https://arxiv.org/abs/2203.00555 - Pretraining Datasets: RedPajama-Data-1T, mostly "books" and some Wikipedia. ### Loading: The model implementation is all my own, so you will need to use "trust_remote_code" to load the model. ``` from transformers import ( AutoTokenizer, AutoModelForCausalLM, ) model_id = "dinalt/walsh-1-7b" model = AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, # flash_attention_2 requires bfloat16 or float16 torch_dtype=torch.bfloat16, # One of ["flash_attention_2", "sdpa", "eager"] attn_implementation="flash_attention_2", ) tokenizer = AutoTokenizer.from_pretrained(model_id) ``` The model has been tested with text-generation-webui, which needs to be started with the "--trust-remote-code" flag.
AlignmentResearch/robust_llm_pythia-tt-410m-mz-ada-v3-ch-120000	AlignmentResearch	2024-03-22T22:05:52Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-deduped", "base_model:finetune:EleutherAI/pythia-410m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T22:05:00Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-410m-deduped model-index: - name: robust_llm_pythia-tt-410m-mz-ada-v3-ch-120000 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. --> # robust_llm_pythia-tt-410m-mz-ada-v3-ch-120000 This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-410m-mz-ada-v3-ch-106000	AlignmentResearch	2024-03-22T21:58:56Z	108	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-deduped", "base_model:finetune:EleutherAI/pythia-410m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:58:05Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-410m-deduped model-index: - name: robust_llm_pythia-tt-410m-mz-ada-v3-ch-106000 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. --> # robust_llm_pythia-tt-410m-mz-ada-v3-ch-106000 This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
mychen76/mistral-7b-merged-slerp	mychen76	2024-03-22T21:57:25Z	47	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-09T23:02:03Z	--- license: apache-2.0 tags: - merge --- # mistral-7b-merged-slerp mistral-7b-merged-slerp is a merge of the following models: ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "mychen76/mistral-7b-merged-slerp" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "why the sky is blue."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mychen76__mistral-7b-merged-slerp) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|71.09\| \|AI2 Reasoning Challenge (25-Shot)\|67.75\| \|HellaSwag (10-Shot) \|86.17\| \|MMLU (5-Shot) \|64.05\| \|TruthfulQA (0-shot) \|59.85\| \|Winogrande (5-shot) \|80.19\| \|GSM8k (5-shot) \|68.54\|
mychen76/mistral-7b-merged-dare	mychen76	2024-03-22T21:57:01Z	49	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-09T23:22:50Z	--- license: apache-2.0 tags: - merge --- # mistral-7b-merged-dare mistral-7b-merged-dare is a merge of the following models: ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 - model: samir-fama/SamirGPT-v1 parameters: density: 0.53 weight: 0.4 - model: abacusai/Slerp-CM-mist-dpo parameters: density: 0.53 weight: 0.3 - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2 parameters: density: 0.53 weight: 0.3 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "mayacinka/West-Ramen-7Bx4" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mychen76__mistral-7b-merged-dare) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|73.46\| \|AI2 Reasoning Challenge (25-Shot)\|69.71\| \|HellaSwag (10-Shot) \|87.05\| \|MMLU (5-Shot) \|65.07\| \|TruthfulQA (0-shot) \|63.24\| \|Winogrande (5-shot) \|81.61\| \|GSM8k (5-shot) \|73.01\|
mychen76/mistral-7b-merged-passthrough	mychen76	2024-03-22T21:56:39Z	48	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-09T23:03:36Z	--- license: apache-2.0 tags: - merge --- # mistral-7b-merged-passthrough mistral-7b-merged-passthrough is a merge of the following models: ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - sources: - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [24, 32] merge_method: passthrough dtype: bfloat16 ```
mychen76/mistral-7b-merged-dare_6x7	mychen76	2024-03-22T21:55:09Z	48	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T05:05:04Z	--- license: apache-2.0 tags: - merge --- # mistral-7b-merged-dare-v2 mistral-7b-merged-dare-v2 is a merge of the following models: ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 - model: samir-fama/SamirGPT-v1 parameters: density: 0.53 weight: 0.4 - model: abacusai/Slerp-CM-mist-dpo parameters: density: 0.53 weight: 0.3 - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2 parameters: density: 0.53 weight: 0.3 - model: Weyaxi/Einstein-v4-7B parameters: density: 0.53 weight: 0.3 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "mistral-7b-merged-dare_6x7" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Why the sky is blue"}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mychen76__mistral-7b-merged-dare_6x7) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|73.46\| \|AI2 Reasoning Challenge (25-Shot)\|69.62\| \|HellaSwag (10-Shot) \|87.04\| \|MMLU (5-Shot) \|65.18\| \|TruthfulQA (0-shot) \|66.98\| \|Winogrande (5-shot) \|80.58\| \|GSM8k (5-shot) \|71.34\|
mychen76/biomistral_medqa_v1	mychen76	2024-03-22T21:52:15Z	79	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:2402.10373", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-21T16:09:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> Finetuned "BioMistral/BioMistral-7B" with MedQA dataset. ## Model Details A Collection of Open-Source Pretrained Large Language Models for Medical Domains finetuned with MedQA dataset. ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: mychen76 - Model type: BioMedical - Finetuned from model: BioMistral/BioMistral-7B ### Model Sources [optional] <!-- Provide the basic links for the model. --> - dataset: MedQA dataset ## How to Get Started with the Model Use the code below to get started with the model. <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Load Model: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig base_model_id = "mychen76/biomistral_medqa_v1" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config) tokenizer = AutoTokenizer.from_pretrained( base_model_id, add_eos_token=True, add_bos_token=True, ) ## Uses ``` * Information * ``` eval_prompt = """From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer: ### Question type: information ### Question: What are the genetic changes related to X-linked lymphoproliferative disease ? ### Answer: """ model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda") ft_model.eval() with torch.no_grad(): print(eval_tokenizer.decode(ft_model.generate(model_input, max_new_tokens=300)[0], skip_special_tokens=True)) ``` result: ``` From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer: ### Question type: information ### Question: What are the genetic changes related to X-linked lymphoproliferative disease ? ### Answer: X-linked lymphoproliferative disease (XLP) is a rare primary immunodeficiency syndrome. XLP is caused by mutations in SH2D1A gene, which encodes the cytoplasmic signaling protein SLAM-associated protein ( client protein-SLAM). SLAM is a member of the signaling lymphocytic activation molecule family of receptors, which are involved in the regulation of lymphocyte activation and proliferation. The SLAM receptor is expressed on the surface of B and T lymphocytes, natural killer cells, and monocytes. Mutations in SH2D1A gene lead to impaired signaling through the SLAM receptor, resulting in a deficiency in the activation and proliferation of B and T lymphocytes. This leads to a decrease in the number of B and T lymphocytes, resulting in a weakened immune response. ``` * Frequency * ``` eval_prompt = """From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer: ### Question type: frequency ### Question: How many people are affected by Smith-Lemli-Opitz syndrome ? ### Answer: """ model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda") ft_model.eval() with torch.no_grad(): print(eval_tokenizer.decode(ft_model.generate(model_input, max_new_tokens=300)[0], skip_special_tokens=True)) ``` result: ``` From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer: ### Question type: frequency ### Question: How many people are affected by Smith-Lemli-Opitz syndrome ? ### Answer: Smith-Lemli-Opitz syndrome (SLOS) is a rare autosomal recessive disorder of human development. It is characterized by a wide range of symptoms, including growth and developmental delay, intellectual disability, characteristic facial features, and congenital heart defects. The prevalence of SLOS is estimated to be 1 in 15,000 to 1 in 25,000 live births. ``` * Symptons * ``` eval_prompt = """From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer: ### Question type: symptoms ### Question: What are the symptoms of Norrie disease ? ### Answer: """ model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda") ft_model.eval() with torch.no_grad(): print(eval_tokenizer.decode(ft_model.generate(model_input, max_new_tokens=300)[0], skip_special_tokens=True)) ``` Result: ``` Setting `pad_token_id` to `eos_token_id`:2 for open-end generation. From the MedQuad MedicalQA Dataset: Given the following medical question and question type, provide an accurate answer: ### Question type: symptoms ### Question: What are the symptoms of Norrie disease ? ### Answer: Norrie disease is a rare, X-linked recessive disorder of the blood vessels. It is characterized by a variety of symptoms, including glaucoma, mental retardation, seizures, and deafness. ``` ### Out-of-Scope Use images <!-- 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. [More Information Needed] ## Training Details ### Training Data - dataset:** keivalya/MedQuad-MedicalQnADataset <!-- 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. --> ## Citation Arxiv : https://arxiv.org/abs/2402.10373 @misc{labrak2024biomistral, title={BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains}, author={Yanis Labrak and Adrien Bazoge and Emmanuel Morin and Pierre-Antoine Gourraud and Mickael Rouvier and Richard Dufour}, year={2024}, eprint={2402.10373}, archivePrefix={arXiv}, primaryClass={cs.CL} }
AlignmentResearch/robust_llm_pythia-tt-160m-mz-ada-v3-ch-138000	AlignmentResearch	2024-03-22T21:51:19Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m-deduped", "base_model:finetune:EleutherAI/pythia-160m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:50:54Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m-deduped model-index: - name: robust_llm_pythia-tt-160m-mz-ada-v3-ch-138000 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. --> # robust_llm_pythia-tt-160m-mz-ada-v3-ch-138000 This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-160m-mz-ada-v3-ch-126000	AlignmentResearch	2024-03-22T21:46:59Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m-deduped", "base_model:finetune:EleutherAI/pythia-160m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:46:29Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m-deduped model-index: - name: robust_llm_pythia-tt-160m-mz-ada-v3-ch-126000 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. --> # robust_llm_pythia-tt-160m-mz-ada-v3-ch-126000 This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-160m-mz-ada-v3-ch-117000	AlignmentResearch	2024-03-22T21:44:47Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m-deduped", "base_model:finetune:EleutherAI/pythia-160m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:44:24Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m-deduped model-index: - name: robust_llm_pythia-tt-160m-mz-ada-v3-ch-117000 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. --> # robust_llm_pythia-tt-160m-mz-ada-v3-ch-117000 This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
research-dump/Llama-2-7b-chat-hf_fold_3	research-dump	2024-03-22T21:44:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-19T15:49:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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research-dump/Llama-2-7b-chat-hf_fold_1	research-dump	2024-03-22T21:41:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-19T15:44:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AlignmentResearch/robust_llm_pythia-tt-160m-mz-ada-v3-ch-106000	AlignmentResearch	2024-03-22T21:41:25Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m-deduped", "base_model:finetune:EleutherAI/pythia-160m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:40:57Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m-deduped model-index: - name: robust_llm_pythia-tt-160m-mz-ada-v3-ch-106000 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. --> # robust_llm_pythia-tt-160m-mz-ada-v3-ch-106000 This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-160m-mz-ada-v3-ch-103000	AlignmentResearch	2024-03-22T21:39:28Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m-deduped", "base_model:finetune:EleutherAI/pythia-160m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:38:58Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m-deduped model-index: - name: robust_llm_pythia-tt-160m-mz-ada-v3-ch-103000 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. --> # robust_llm_pythia-tt-160m-mz-ada-v3-ch-103000 This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
haibotamiu/out	haibotamiu	2024-03-22T21:39:27Z	2	0	transformers	[ "transformers", "pytorch", "albert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:albert/albert-base-v2", "base_model:finetune:albert/albert-base-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-03-14T02:07:40Z	--- license: apache-2.0 base_model: albert-base-v2 tags: - question-answering - generated_from_trainer datasets: - squad model-index: - name: out results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # out This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad 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: 256 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.10.1 - Tokenizers 0.14.1
UCFSD-LLMote/Med-Quad-gptq-4bit	UCFSD-LLMote	2024-03-22T21:39:17Z	0	1	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-22T21:38:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
okeowo1014/fingerprintgrecognizer	okeowo1014	2024-03-22T21:39:01Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2024-03-22T06:10:06Z	--- library_name: keras --- ## 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: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| False \| \| is_legacy_optimizer \| False \| \| learning_rate \| 0.0010000000474974513 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \|
AlGM93/rl_course_vizdoom_health_gathering_supreme	AlGM93	2024-03-22T21:35:43Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-22T21:35:31Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 8.96 +/- 2.89 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r AlGM93/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
AF6ECHO/rl_course_vizdoom_health_gathering_supreme	AF6ECHO	2024-03-22T21:34:32Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-22T21:34:24Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.35 +/- 4.78 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r AF6ECHO/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
AlignmentResearch/robust_llm_pythia-tt-70m-mz-ada-v3-ch-132000	AlignmentResearch	2024-03-22T21:33:54Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:33:43Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-70m-deduped model-index: - name: robust_llm_pythia-tt-70m-mz-ada-v3-ch-132000 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. --> # robust_llm_pythia-tt-70m-mz-ada-v3-ch-132000 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-70m-mz-ada-v3-ch-126000	AlignmentResearch	2024-03-22T21:32:54Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:32:31Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-70m-deduped model-index: - name: robust_llm_pythia-tt-70m-mz-ada-v3-ch-126000 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. --> # robust_llm_pythia-tt-70m-mz-ada-v3-ch-126000 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-70m-mz-ada-v3-ch-123000	AlignmentResearch	2024-03-22T21:32:52Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:32:30Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-70m-deduped model-index: - name: robust_llm_pythia-tt-70m-mz-ada-v3-ch-123000 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. --> # robust_llm_pythia-tt-70m-mz-ada-v3-ch-123000 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
mole-code/lancedb-starcoderbase-1b-fft	mole-code	2024-03-22T21:31:41Z	187	0	transformers	[ "transformers", "safetensors", "gpt_bigcode", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T19:17:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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AlignmentResearch/robust_llm_pythia-tt-70m-mz-ada-v3-ch-109000	AlignmentResearch	2024-03-22T21:29:35Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-22T21:29:21Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-70m-deduped model-index: - name: robust_llm_pythia-tt-70m-mz-ada-v3-ch-109000 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. --> # robust_llm_pythia-tt-70m-mz-ada-v3-ch-109000 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
rshrott/vit-base-renovation2	rshrott	2024-03-22T21:28:51Z	12	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:renovation", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-20T02:51:13Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - renovation metrics: - accuracy model-index: - name: vit-base-renovation2 results: - task: name: Image Classification type: image-classification dataset: name: renovations type: renovation config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.6666666666666666 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-renovation2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the renovations dataset. It achieves the following results on the evaluation set: - Loss: 0.8273 - Accuracy: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.359 \| 0.2 \| 25 \| 1.2074 \| 0.4658 \| \| 1.1384 \| 0.4 \| 50 \| 1.1213 \| 0.5205 \| \| 1.0866 \| 0.6 \| 75 \| 0.9746 \| 0.6301 \| \| 1.1787 \| 0.81 \| 100 \| 1.0523 \| 0.5662 \| \| 0.9242 \| 1.01 \| 125 \| 0.9543 \| 0.6256 \| \| 0.7945 \| 1.21 \| 150 \| 0.9200 \| 0.6119 \| \| 0.8379 \| 1.41 \| 175 \| 0.8447 \| 0.6712 \| \| 0.7253 \| 1.61 \| 200 \| 0.8642 \| 0.6575 \| \| 0.6344 \| 1.81 \| 225 \| 0.8443 \| 0.6438 \| \| 0.6521 \| 2.02 \| 250 \| 0.8273 \| 0.6667 \| \| 0.3627 \| 2.22 \| 275 \| 0.8653 \| 0.6712 \| \| 0.2523 \| 2.42 \| 300 \| 0.8748 \| 0.6895 \| \| 0.363 \| 2.62 \| 325 \| 0.8407 \| 0.6849 \| \| 0.3433 \| 2.82 \| 350 \| 0.9696 \| 0.6484 \| \| 0.2874 \| 3.02 \| 375 \| 0.9290 \| 0.6804 \| \| 0.1682 \| 3.23 \| 400 \| 0.9713 \| 0.6575 \| \| 0.1575 \| 3.43 \| 425 \| 0.9963 \| 0.6804 \| \| 0.0822 \| 3.63 \| 450 \| 0.9473 \| 0.7123 \| \| 0.1678 \| 3.83 \| 475 \| 0.9788 \| 0.7032 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

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