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train · 840k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
emilykang/Phi_medQuad_finetuned_lora	emilykang	2024-05-17T07:14:02Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-16T21:10:49Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medQuad_finetuned_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medQuad_finetuned_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
ivykopal/spanish_prompt_100k	ivykopal	2024-05-17T07:10:34Z	0	0	null	[ "generated_from_trainer", "dataset:wikipedia", "base_model:bigscience/mt0-base", "base_model:finetune:bigscience/mt0-base", "license:apache-2.0", "region:us" ]	null	2024-05-02T21:46:38Z	--- license: apache-2.0 base_model: bigscience/mt0-base tags: - generated_from_trainer datasets: - wikipedia metrics: - accuracy model-index: - name: spanish_prompt_100k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # spanish_prompt_100k This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on the wikipedia 20231101.es dataset. It achieves the following results on the evaluation set: - Loss: 2.3640 - Accuracy: 0.5741 ## 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: 0.5 - 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 - training_steps: 100000 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ivykopal/slovak_prompt_100k	ivykopal	2024-05-17T07:10:27Z	0	0	null	[ "generated_from_trainer", "dataset:wikipedia", "base_model:bigscience/mt0-base", "base_model:finetune:bigscience/mt0-base", "license:apache-2.0", "region:us" ]	null	2024-04-27T10:49:23Z	--- license: apache-2.0 base_model: bigscience/mt0-base tags: - generated_from_trainer datasets: - wikipedia metrics: - accuracy model-index: - name: slovak_prompt_100k 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. --> # slovak_prompt_100k This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on the wikipedia 20231101.sk dataset. It achieves the following results on the evaluation set: - Loss: 2.6439 - Accuracy: 0.5620 ## 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: 0.5 - 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 - training_steps: 100000 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ivykopal/slovak_prompt_100	ivykopal	2024-05-17T07:10:25Z	0	0	null	[ "generated_from_trainer", "dataset:wikipedia", "base_model:bigscience/mt0-base", "base_model:finetune:bigscience/mt0-base", "license:apache-2.0", "region:us" ]	null	2024-04-19T06:54:33Z	--- license: apache-2.0 base_model: bigscience/mt0-base tags: - generated_from_trainer datasets: - wikipedia metrics: - accuracy model-index: - name: slovak_prompt-100 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. --> # slovak_prompt-100 This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on the wikipedia 20231101.sk dataset. It achieves the following results on the evaluation set: - Loss: 2.5302 - Accuracy: 0.5792 ## 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: 0.5 - 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 - training_steps: 50000 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ivykopal/slovak_adapter_100k	ivykopal	2024-05-17T07:10:21Z	0	0	null	[ "generated_from_trainer", "dataset:wikipedia", "base_model:bigscience/mt0-base", "base_model:finetune:bigscience/mt0-base", "license:apache-2.0", "region:us" ]	null	2024-04-27T08:04:08Z	--- license: apache-2.0 base_model: bigscience/mt0-base tags: - generated_from_trainer datasets: - wikipedia metrics: - accuracy model-index: - name: slovak_adapter_100k 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. --> # slovak_adapter_100k This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on the wikipedia 20231101.sk dataset. It achieves the following results on the evaluation set: - Loss: 1.8110 - Accuracy: 0.6754 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100000 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
abc88767/5c93	abc88767	2024-05-17T07:10:15Z	140	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:16:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ivykopal/english_adapter_100k	ivykopal	2024-05-17T07:10:07Z	0	0	null	[ "generated_from_trainer", "dataset:wikipedia", "base_model:bigscience/mt0-base", "base_model:finetune:bigscience/mt0-base", "license:apache-2.0", "region:us" ]	null	2024-04-26T11:49:05Z	--- license: apache-2.0 base_model: bigscience/mt0-base tags: - generated_from_trainer datasets: - wikipedia metrics: - accuracy model-index: - name: english_adapter_100k 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. --> # english_adapter_100k This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on the wikipedia 20231101.en dataset. It achieves the following results on the evaluation set: - Loss: 1.9931 - Accuracy: 0.6252 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100000 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ivykopal/czech_prompt_100	ivykopal	2024-05-17T07:10:02Z	0	0	null	[ "generated_from_trainer", "dataset:wikipedia", "base_model:bigscience/mt0-base", "base_model:finetune:bigscience/mt0-base", "license:apache-2.0", "region:us" ]	null	2024-04-23T12:35:00Z	--- license: apache-2.0 base_model: bigscience/mt0-base tags: - generated_from_trainer datasets: - wikipedia metrics: - accuracy model-index: - name: czech_prompt_100 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. --> # czech_prompt_100 This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on the wikipedia 20231101.cs dataset. It achieves the following results on the evaluation set: - Loss: 2.7867 - Accuracy: 0.5335 ## 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: 0.5 - 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 - training_steps: 50000 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ivykopal/czech_adapter_100k	ivykopal	2024-05-17T07:09:30Z	0	0	null	[ "generated_from_trainer", "dataset:wikipedia", "base_model:bigscience/mt0-base", "base_model:finetune:bigscience/mt0-base", "license:apache-2.0", "region:us" ]	null	2024-04-28T04:40:32Z	--- license: apache-2.0 base_model: bigscience/mt0-base tags: - generated_from_trainer datasets: - wikipedia metrics: - accuracy model-index: - name: czech_adapter_100k 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. --> # czech_adapter_100k This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on the wikipedia 20231101.cs dataset. It achieves the following results on the evaluation set: - Loss: 1.9871 - Accuracy: 0.6411 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100000 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
lainshower/Llama2-13b-dolly-ep2	lainshower	2024-05-17T07:07:52Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T06:53:42Z	--- 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. 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]
RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf	RichardErkhov	2024-05-17T07:07:37Z	11	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T05:03:39Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) CarbonVillain-en-10.7B-v1 - GGUF - Model creator: https://huggingface.co/jeonsworld/ - Original model: https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [CarbonVillain-en-10.7B-v1.Q2_K.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q2_K.gguf) \| Q2_K \| 3.73GB \| \| [CarbonVillain-en-10.7B-v1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.IQ3_XS.gguf) \| IQ3_XS \| 4.14GB \| \| [CarbonVillain-en-10.7B-v1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.IQ3_S.gguf) \| IQ3_S \| 4.37GB \| \| [CarbonVillain-en-10.7B-v1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| \| [CarbonVillain-en-10.7B-v1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.IQ3_M.gguf) \| IQ3_M \| 4.51GB \| \| [CarbonVillain-en-10.7B-v1.Q3_K.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q3_K.gguf) \| Q3_K \| 4.84GB \| \| [CarbonVillain-en-10.7B-v1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q3_K_M.gguf) \| Q3_K_M \| 4.84GB \| \| [CarbonVillain-en-10.7B-v1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q3_K_L.gguf) \| Q3_K_L \| 5.26GB \| \| [CarbonVillain-en-10.7B-v1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.IQ4_XS.gguf) \| IQ4_XS \| 5.43GB \| \| [CarbonVillain-en-10.7B-v1.Q4_0.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q4_0.gguf) \| Q4_0 \| 5.66GB \| \| [CarbonVillain-en-10.7B-v1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [CarbonVillain-en-10.7B-v1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q4_K_S.gguf) \| Q4_K_S \| 5.7GB \| \| [CarbonVillain-en-10.7B-v1.Q4_K.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q4_K.gguf) \| Q4_K \| 6.02GB \| \| [CarbonVillain-en-10.7B-v1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q4_K_M.gguf) \| Q4_K_M \| 6.02GB \| \| [CarbonVillain-en-10.7B-v1.Q4_1.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q4_1.gguf) \| Q4_1 \| 6.27GB \| \| [CarbonVillain-en-10.7B-v1.Q5_0.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q5_0.gguf) \| Q5_0 \| 6.89GB \| \| [CarbonVillain-en-10.7B-v1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q5_K_S.gguf) \| Q5_K_S \| 6.89GB \| \| [CarbonVillain-en-10.7B-v1.Q5_K.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q5_K.gguf) \| Q5_K \| 7.08GB \| \| [CarbonVillain-en-10.7B-v1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q5_K_M.gguf) \| Q5_K_M \| 7.08GB \| \| [CarbonVillain-en-10.7B-v1.Q5_1.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q5_1.gguf) \| Q5_1 \| 7.51GB \| \| [CarbonVillain-en-10.7B-v1.Q6_K.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q6_K.gguf) \| Q6_K \| 8.2GB \| \| [CarbonVillain-en-10.7B-v1.Q8_0.gguf](https://huggingface.co/RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-gguf/blob/main/CarbonVillain-en-10.7B-v1.Q8_0.gguf) \| Q8_0 \| 10.62GB \| Original model description: --- license: cc-by-nc-4.0 language: - en tags: - merge - slerp --- # CarbonVillain This is a model created without learning to oppose indiscriminate carbon emissions. This model is an experimental version created using [mergekit](https://github.com/cg123/mergekit). - merge models - Weyaxi/SauerkrautLM-UNA-SOLAR-Instruct - VAGOsolutions/SauerkrautLM-SOLAR-Instruct - method: slerp # Prompt Template(s) ``` ### User: {user} ### Assistant: {asistant} ``` # Evaluation Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jeonsworld__CarbonVillain-en-10.7B-v1) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 74.28 \| \| ARC (25-shot) \| 71.24 \| \| HellaSwag (10-shot) \| 88.45 \| \| MMLU (5-shot) \| 66.42 \| \| TruthfulQA (0-shot) \| 71.97 \| \| Winogrande (5-shot) \| 83.26 \| \| GSM8K (5-shot) \| 64.29 \|
dendimaki/mistralai-Code-Instruct-Finetune-test	dendimaki	2024-05-17T07:06:03Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T05:14:04Z	--- library_name: transformers pipeline_tag: text-generation --- # 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]
yqw0920/demo1	yqw0920	2024-05-17T07:02:40Z	0	0	null	[ "ab", "am", "license:mit", "region:us" ]	null	2023-12-08T07:21:57Z	--- license: mit language: - ab - am ---
pmrster/test_ft_gpt2	pmrster	2024-05-17T07:01:33Z	106	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T07:01:06Z	--- 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]
finalgp3/Our_model_GandA	finalgp3	2024-05-17T07:01:04Z	163	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-11T13:12:04Z	This is model for generate questions and answer about some of computer science courses with score :832 perplixity
Hamza12rdsdsf/blip2-opt-2.7b-onepiece-fine-tuned	Hamza12rdsdsf	2024-05-17T07:00:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T07:00:53Z	--- 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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emilykang/Gemma_medprob_finetuned_model	emilykang	2024-05-17T06:56:47Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T14:48:55Z	--- 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. 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benchang1110/Tinyllama-1.1B-Chat-PEFT-v1.0	benchang1110	2024-05-17T06:56:41Z	161	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T06:52:03Z	--- 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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SmrutiB/conversation-analyzer-llm	SmrutiB	2024-05-17T06:55:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T05:26:22Z	--- 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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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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DUAL-GPO-2/phi-2-gpo-v1-i2	DUAL-GPO-2	2024-05-17T06:55:41Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO-2/phi-2-gpo-v34-merged-i1", "base_model:adapter:DUAL-GPO-2/phi-2-gpo-v34-merged-i1", "region:us" ]	null	2024-05-17T05:21:59Z	--- library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO-2/phi-2-gpo-v34-merged-i1 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-gpo-v1-i2 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-v1-i2 This model is a fine-tuned version of [DUAL-GPO-2/phi-2-gpo-v34-merged-i1](https://huggingface.co/DUAL-GPO-2/phi-2-gpo-v34-merged-i1) on the HuggingFaceH4/ultrafeedback_binarized 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-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - total_eval_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
modulora-repoducibility/llama-7b-4bit-c4-samsum-bitsandbytes	modulora-repoducibility	2024-05-17T06:54:28Z	0	0	peft	[ "peft", "region:us" ]	null	2024-05-17T06:54:24Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0
emilykang/Gemma_medner_finetuned_lora	emilykang	2024-05-17T06:47:49Z	4	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-16T08:18:18Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medner_finetuned_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medner_finetuned_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator 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: 3 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
lainshower/Llama2-13b-dolly-ep1	lainshower	2024-05-17T06:46:33Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T06:31:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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wisenut-nlp-team/wisenut-llama-3-600	wisenut-nlp-team	2024-05-17T06:46:19Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:30:03Z	--- 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]
bartowski/falcon-11B-GGUF	bartowski	2024-05-17T06:46:02Z	462	6	null	[ "gguf", "text-generation", "en", "de", "es", "fr", "dataset:tiiuae/falcon-refinedweb", "region:us", "conversational" ]	text-generation	2024-05-13T23:54:28Z	--- datasets: - tiiuae/falcon-refinedweb language: - en - de - es - fr inference: false quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of falcon-11B Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2854">b2854</a> for quantization. Original model: https://huggingface.co/tiiuae/falcon-11B All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) ## Prompt format ``` System: {system_prompt} User: {prompt} Falcon: ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [falcon-11B-Q8_0.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q8_0.gguf) \| Q8_0 \| 11.80GB \| Extremely high quality, generally unneeded but max available quant. \| \| [falcon-11B-Q6_K.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q6_K.gguf) \| Q6_K \| 9.17GB \| Very high quality, near perfect, recommended. \| \| [falcon-11B-Q5_K_M.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q5_K_M.gguf) \| Q5_K_M \| 8.20GB \| High quality, recommended. \| \| [falcon-11B-Q5_K_S.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q5_K_S.gguf) \| Q5_K_S \| 7.73GB \| High quality, recommended. \| \| [falcon-11B-Q4_K_M.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q4_K_M.gguf) \| Q4_K_M \| 6.84GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [falcon-11B-Q4_K_S.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q4_K_S.gguf) \| Q4_K_S \| 6.38GB \| Slightly lower quality with more space savings, recommended. \| \| [falcon-11B-IQ4_NL.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ4_NL.gguf) \| IQ4_NL \| 6.38GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [falcon-11B-IQ4_XS.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ4_XS.gguf) \| IQ4_XS \| 6.04GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [falcon-11B-Q3_K_L.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q3_K_L.gguf) \| Q3_K_L \| 5.81GB \| Lower quality but usable, good for low RAM availability. \| \| [falcon-11B-Q3_K_M.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q3_K_M.gguf) \| Q3_K_M \| 5.43GB \| Even lower quality. \| \| [falcon-11B-IQ3_M.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ3_M.gguf) \| IQ3_M \| 5.20GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [falcon-11B-IQ3_S.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ3_S.gguf) \| IQ3_S \| 4.94GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [falcon-11B-Q3_K_S.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q3_K_S.gguf) \| Q3_K_S \| 4.94GB \| Low quality, not recommended. \| \| [falcon-11B-IQ3_XS.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ3_XS.gguf) \| IQ3_XS \| 4.80GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [falcon-11B-IQ3_XXS.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ3_XXS.gguf) \| IQ3_XXS \| 4.44GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [falcon-11B-Q2_K.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-Q2_K.gguf) \| Q2_K \| 4.25GB \| Very low quality but surprisingly usable. \| \| [falcon-11B-IQ2_M.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ2_M.gguf) \| IQ2_M \| 3.94GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [falcon-11B-IQ2_S.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ2_S.gguf) \| IQ2_S \| 3.66GB \| Very low quality, uses SOTA techniques to be usable. \| \| [falcon-11B-IQ2_XS.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ2_XS.gguf) \| IQ2_XS \| 3.44GB \| Very low quality, uses SOTA techniques to be usable. \| \| [falcon-11B-IQ2_XXS.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ2_XXS.gguf) \| IQ2_XXS \| 3.13GB \| Lower quality, uses SOTA techniques to be usable. \| \| [falcon-11B-IQ1_M.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ1_M.gguf) \| IQ1_M \| 2.77GB \| Extremely low quality, not recommended. \| \| [falcon-11B-IQ1_S.gguf](https://huggingface.co/bartowski/falcon-11B-GGUF/blob/main/falcon-11B-IQ1_S.gguf) \| IQ1_S \| 2.56GB \| Extremely low quality, not recommended. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/falcon-11B-GGUF --include "falcon-11B-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/falcon-11B-GGUF --include "falcon-11B-Q8_0.gguf/" --local-dir falcon-11B-Q8_0 --local-dir-use-symlinks False ``` You can either specify a new local-dir (falcon-11B-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
scott4ai/llama3-8b-cosmic-fusion-dynamics-f16-gguf	scott4ai	2024-05-17T06:41:40Z	9	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-11T12:05:33Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: scott4ai - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
chuckma/ReminiClay_LoRA_Dim128	chuckma	2024-05-17T06:35:41Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T05:49:30Z	--- license: apache-2.0 ---
worldboss/idefics-9b-doodles	worldboss	2024-05-17T06:32:17Z	66	0	transformers	[ "transformers", "safetensors", "idefics", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2024-05-17T06:20:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ahmedesmail16/Balanced-No-Augmentation-swinv2-base	ahmedesmail16	2024-05-17T06:28:54Z	163	0	transformers	[ "transformers", "tensorboard", "safetensors", "swinv2", "image-classification", "generated_from_trainer", "base_model:microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft", "base_model:finetune:microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-17T04:36:21Z	--- license: apache-2.0 base_model: microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft tags: - generated_from_trainer metrics: - accuracy model-index: - name: Balanced-No-Augmentation-swinv2-base 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. --> # Balanced-No-Augmentation-swinv2-base This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.6109 - Accuracy: 0.5692 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.2985 \| 0.98 \| 11 \| 1.4531 \| 0.4822 \| \| 0.9081 \| 1.97 \| 22 \| 1.5086 \| 0.5731 \| \| 0.4604 \| 2.95 \| 33 \| 1.9810 \| 0.5692 \| \| 0.2255 \| 3.93 \| 44 \| 3.0618 \| 0.5415 \| \| 0.1339 \| 4.92 \| 55 \| 2.8634 \| 0.5613 \| \| 0.0883 \| 5.99 \| 67 \| 3.0244 \| 0.5652 \| \| 0.0605 \| 6.97 \| 78 \| 3.5175 \| 0.5573 \| \| 0.0506 \| 7.96 \| 89 \| 3.4068 \| 0.5850 \| \| 0.0272 \| 8.94 \| 100 \| 3.6996 \| 0.5573 \| \| 0.0262 \| 9.83 \| 110 \| 3.6109 \| 0.5692 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.19.1 - Tokenizers 0.15.2
Nbeau/GPT2-arithmetic-3digits	Nbeau	2024-05-17T06:22:08Z	22	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:Nbeau/additiondataset_3digits_10000examples", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T16:32:15Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: GPT2-arithmetic-3digits results: [] datasets: - Nbeau/additiondataset_3digits_10000examples widget: - text: "Input:\n561+372\nTarget:" inference: parameters: add_special_tokens: True do_sample: False --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GPT2-arithmetic-3digits This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0721 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.116 \| 1.0 \| 125 \| 0.1085 \| \| 0.0825 \| 2.0 \| 250 \| 0.0801 \| \| 0.0745 \| 3.0 \| 375 \| 0.0740 \| \| 0.0733 \| 4.0 \| 500 \| 0.0724 \| \| 0.7246 \| 5.0 \| 625 \| 0.1426 \| \| 0.0726 \| 6.0 \| 750 \| 0.0721 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
Hopper1394/RoadRoBillionaire	Hopper1394	2024-05-17T06:12:12Z	0	0	transformers	[ "transformers", "depth-estimation", "en", "license:mit", "endpoints_compatible", "region:us" ]	depth-estimation	2024-05-17T04:49:41Z	--- license: mit language: - en library_name: transformers pipeline_tag: depth-estimation --- # config.py BINANCE_API_KEY = 'your_binance_api_key' ALPHA_VANTAGE_API_KEY = 'your_alpha_vantage_api_key' YAHOO_FINANCE_API_KEY = 'your_yahoo_finance_api_key' TRADING_VIEW_API_KEY = 'your_trading_view_api_key' BINOMO_API_KEY = 'your_binomo_api_key' TELEGRAM_BOT_API_KEY = 'your_telegram_bot_api_key' # data_acquisition.py import requests import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense, Dropout from telegram.ext import Updater, CommandHandler def fetch_binance_data(pair): url = f"https://api.binance.com/api/v3/klines?symbol={pair}&interval=1h" response = requests.get(url) data = response.json() df = pd.DataFrame(data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_asset_volume', 'number_of_trades', 'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore']) df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms') return df[['timestamp', 'open', 'high', 'low', 'close', 'volume']] def fetch_alpha_vantage_data(pair): symbol = pair.split("USDT")[0] # Assuming pair like BTCUSDT url = f"https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=60min&apikey={ALPHA_VANTAGE_API_KEY}" response = requests.get(url) data = response.json() time_series_key = 'Time Series (60min)' if time_series_key not in data: raise ValueError(f"Error fetching data from Alpha Vantage: {data}") df = pd.DataFrame(data[time_series_key]).T df.columns = ['open', 'high', 'low', 'close', 'volume'] df.index = pd.to_datetime(df.index) return df.reset_index().rename(columns={'index': 'timestamp'}) def fetch_yahoo_finance_data(pair): url = f"https://yfapi.net/v8/finance/chart/{pair}?interval=60m" headers = {'x-api-key': YAHOO_FINANCE_API_KEY} response = requests.get(url, headers=headers) data = response.json() timestamps = data['chart']['result'][0]['timestamp'] ohlc = data['chart']['result'][0]['indicators']['quote'][0] df = pd.DataFrame({ 'timestamp': pd.to_datetime(timestamps, unit='s'), 'open': ohlc['open'], 'high': ohlc['high'], 'low': ohlc['low'], 'close': ohlc['close'], 'volume': ohlc['volume'] }) return df def fetch_trading_view_data(pair): # Placeholder for TradingView API data fetching raise NotImplementedError("TradingView API integration not implemented.") def fetch_binomo_data(pair): # Placeholder for Binomo API data fetching raise NotImplementedError("Binomo API integration not implemented.") def get_combined_data(pair): df_binance = fetch_binance_data(pair) df_alpha = fetch_alpha_vantage_data(pair) df_yahoo = fetch_yahoo_finance_data(pair) # Merge dataframes on timestamp df = pd.merge(df_binance, df_alpha, on='timestamp', suffixes=('_binance', '_alpha')) df = pd.merge(df, df_yahoo, on='timestamp', suffixes=('', '_yahoo')) # Drop any redundant columns or handle conflicts return df def preprocess_data(df): df = df.dropna() scaler = StandardScaler() scaled_data = scaler.fit_transform(df[['open', 'high', 'low', 'close', 'volume']]) return scaled_data, scaler def create_dataset(data, time_step=60): X, Y = [], [] for i in range(len(data) - time_step - 1): a = data[i:(i + time_step), :] X.append(a) Y.append(data[i + time_step, 3]) # Assuming 'close' price is the target return np.array(X), np.array(Y) def build_model(input_shape): model = Sequential() model.add(LSTM(50, return_sequences=True, input_shape=input_shape)) model.add(LSTM(50, return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense(25)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mean_squared_error') return model def train_model(df): data, scaler = preprocess_data(df) X, Y = create_dataset(data) X_train, Y_train = X[:int(len(X) * 0.8)], Y[:int(len(Y) * 0.8)] X_val, Y_val = X[int(len(X) * 0.8):], Y[int(len(Y) * 0.8):] model = build_model((X_train.shape[1], X_train.shape[2])) model.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs=20, batch_size=32) return model, scaler def generate_signal(pair): df = get_combined_data(pair) model, scaler = train_model(df) recent_data = df.tail(60).drop(columns=['timestamp']) scaled_recent_data = scaler.transform(recent_data) prediction = model.predict(np.expand_dims(scaled_recent_data, axis=0)) last_close = df['close'].iloc[-1] if prediction > last_close: return "Buy" else: return "Sell" def start(update, context): context.bot.send_message(chat_id=update.effective_chat.id, text="I'm a trading bot, how can I help you today?") def signal(update, context): pair = context.args[0] if context.args else 'BTCUSDT' try: trade_signal = generate_signal(pair) context.bot.send_message(chat_id=update.effective_chat.id, text=f"Trade Signal for {pair}: {trade_signal}") except Exception as e: context.bot.send_message(chat_id=update.effective_chat.id, text=f"Error: {e}") def main(): updater = Updater(token=TELEGRAM_BOT_API_KEY, use_context=True) dispatcher = updater.dispatcher start_handler = CommandHandler('start', start) signal_handler = CommandHandler('signal', signal) dispatcher.add_handler(start_handler) dispatcher.add_handler(signal_handler) updater.start_polling() if __name__ == '__main__': main()
PQlet/lora-narutoblip-v1-ablation-r256-a16	PQlet	2024-05-17T06:12:03Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-17T06:11:37Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - PQlet/lora-narutoblip-v1-ablation-r256-a16 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Naruto-BLIP dataset. You can find some example images in the following. ![img_0](./image_a_man_with_glasses_and_a_shirt_on.png) ![img_1](./image_a_group_of_people_sitting_on_the_ground.png) ![img_2](./image_a_man_in_a_green_hoodie_standing_in_front_of_a_mountain.png) ![img_3](./image_a_man_with_a_gun_in_his_hand.png) ![img_4](./image_a_woman_with_red_hair_and_a_cat_on_her_head.png) ![img_5](./image_two_pokemons_sitting_on_top_of_a_cloud.png) ![img_6](./image_a_man_standing_in_front_of_a_bridge.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
mxersion/LJKM	mxersion	2024-05-17T06:11:23Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T06:11:22Z	--- license: apache-2.0 ---
DreadN0ugh7/ChatAcademy-Trained-13b	DreadN0ugh7	2024-05-17T06:07:25Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:adapter:meta-llama/Llama-2-13b-chat-hf", "license:llama2", "region:us" ]	null	2024-05-08T12:53:11Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-13b-chat-hf model-index: - name: ChatAcademy-Trained-13b 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. --> # ChatAcademy-Trained-13b This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
JaydenJH/swinv2-tiny-patch4-window8-256-finetuned-eurosat	JaydenJH	2024-05-17T05:56:49Z	155	0	transformers	[ "transformers", "tensorboard", "safetensors", "swinv2", "image-classification", "generated_from_trainer", "base_model:microsoft/swinv2-tiny-patch4-window8-256", "base_model:finetune:microsoft/swinv2-tiny-patch4-window8-256", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-17T05:07:51Z	--- license: apache-2.0 base_model: microsoft/swinv2-tiny-patch4-window8-256 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swinv2-tiny-patch4-window8-256-finetuned-eurosat 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. --> # swinv2-tiny-patch4-window8-256-finetuned-eurosat This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6760 - Accuracy: 0.8170 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.2519 \| 0.9955 \| 167 \| 0.7302 \| 0.8017 \| \| 0.2407 \| 1.9970 \| 335 \| 0.7095 \| 0.7836 \| \| 0.3423 \| 2.9985 \| 503 \| 0.7016 \| 0.7884 \| \| 0.4687 \| 4.0 \| 671 \| 0.6480 \| 0.7969 \| \| 0.4789 \| 4.9955 \| 838 \| 0.5132 \| 0.8160 \| \| 0.4417 \| 5.9970 \| 1006 \| 0.5321 \| 0.8065 \| \| 0.435 \| 6.9985 \| 1174 \| 0.5770 \| 0.8093 \| \| 0.4106 \| 8.0 \| 1342 \| 0.5650 \| 0.8189 \| \| 0.4216 \| 8.9955 \| 1509 \| 0.5535 \| 0.8132 \| \| 0.3786 \| 9.9970 \| 1677 \| 0.5745 \| 0.8179 \| \| 0.3536 \| 10.9985 \| 1845 \| 0.6322 \| 0.8046 \| \| 0.4842 \| 12.0 \| 2013 \| 0.7200 \| 0.8103 \| \| 0.3095 \| 12.9955 \| 2180 \| 0.6996 \| 0.8112 \| \| 0.2603 \| 13.9970 \| 2348 \| 0.7004 \| 0.8065 \| \| 0.2838 \| 14.9985 \| 2516 \| 0.6331 \| 0.8227 \| \| 0.3449 \| 16.0 \| 2684 \| 0.6788 \| 0.8122 \| \| 0.253 \| 16.9955 \| 2851 \| 0.6940 \| 0.8103 \| \| 0.2647 \| 17.9970 \| 3019 \| 0.6770 \| 0.8132 \| \| 0.2991 \| 18.9985 \| 3187 \| 0.6647 \| 0.8189 \| \| 0.26 \| 19.9106 \| 3340 \| 0.6760 \| 0.8170 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.2 - Datasets 2.19.1 - Tokenizers 0.19.1
neurips-user/neurips-covid-fake-combined-1	neurips-user	2024-05-17T05:53:30Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "dataset:neurips-bert-covid-fake-combined/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T05:40:35Z	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - neurips-bert-covid-fake-combined/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.633008599281311 f1: 0.6947368421052632 precision: 0.7333333333333333 recall: 0.66 auc: 0.7308 accuracy: 0.71
emilykang/mts_dialogue_clinical_note-genhx_lora	emilykang	2024-05-17T05:48:13Z	1	0	peft	[ "peft", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-17T05:26:45Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: mts_dialogue_clinical_note-genhx_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mts_dialogue_clinical_note-genhx_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.0.1+cu117 - Datasets 2.19.0 - Tokenizers 0.19.1
ByteBrew23/j	ByteBrew23	2024-05-17T05:37:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T05:37:26Z	--- 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. 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. (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]
yadvender12/AV_MAE_2class_LAVDF	yadvender12	2024-05-17T05:35:30Z	64	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "endpoints_compatible", "region:us" ]	video-classification	2024-05-17T04:24:32Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: AV_MAE_2class_LAVDF 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. --> # AV_MAE_2class_LAVDF This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2668 - Accuracy: 0.9273 - F1: 0.9273 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 9685 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.605 \| 0.76 \| 7354 \| 0.2605 \| 0.9259 \| 0.9259 \| \| 0.5001 \| 1.24 \| 9685 \| 0.2668 \| 0.9273 \| 0.9273 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.1+cu118 - Datasets 2.18.0 - Tokenizers 0.15.1
boringblobking/temp-alpaca-med-train	boringblobking	2024-05-17T05:35:16Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-16T23:31:28Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: boringblobking - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Mag0g/Ezekiel29_1	Mag0g	2024-05-17T05:34:39Z	140	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T05:33:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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JJaeuk/new-model-tutorial	JJaeuk	2024-05-17T05:32:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T05:32:27Z	--- license: apache-2.0 ---
collaiborate-tech/CollAIborate4x7B	collaiborate-tech	2024-05-17T05:24:28Z	6	1	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T10:59:03Z	--- license: apache-2.0 --- This is a MoE model with a mix of domain agnostic fine-tuned models derived from the base Mistral
TinyPixel/pythia-chatml	TinyPixel	2024-05-17T05:23:47Z	152	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T05:22:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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. (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]
Anonymous-G/shikra-Genixer-350K-7b	Anonymous-G	2024-05-17T05:17:51Z	6	0	transformers	[ "transformers", "pytorch", "shikra", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T14:55:17Z	--- license: apache-2.0 ---
emilykang/Gemma_medQuad_finetuned_model	emilykang	2024-05-17T05:16:02Z	152	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T05:07:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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. (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]
chihhh/Attack-techniques-Lora-gemma	chihhh	2024-05-17T05:10:02Z	4	1	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mustafaaljadery/gemma-2B-10M", "base_model:adapter:mustafaaljadery/gemma-2B-10M", "license:mit", "region:us" ]	null	2024-05-17T05:09:56Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: mustafaaljadery/gemma-2B-10M model-index: - name: Attack-techniques 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. --> # Attack-techniques This model is a fine-tuned version of [mustafaaljadery/gemma-2B-10M](https://huggingface.co/mustafaaljadery/gemma-2B-10M) 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 25 ### Training results ### Framework versions - PEFT 0.11.0 - Transformers 4.40.2 - Pytorch 2.1.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
emilykang/Gemma_medQuad_finetuned_lora	emilykang	2024-05-17T05:06:54Z	5	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-16T21:11:41Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medQuad_finetuned_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medQuad_finetuned_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
MediaMeter/Chapterization_Mistral-7B-v0.2-Chat_0.1.0-adapters	MediaMeter	2024-05-17T05:04:03Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:other", "region:us" ]	null	2024-05-17T05:03:47Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: Chapterization_Mistral-7B-v0.2-Chat_0.1.0 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. --> # Chapterization_Mistral-7B-v0.2-Chat_0.1.0 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the chapterization 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Ayush-1722/Llama-2-7b-chat-Summarize-64K-LoRANET-Merged	Ayush-1722	2024-05-17T05:04:00Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-2", "conversational", "en", "arxiv:2307.09288", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T16:31:31Z	--- extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: >- ### LLAMA 2 COMMUNITY LICENSE AGREEMENT "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. 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If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy). #### Prohibited Uses We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 2 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky 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) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected]) extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 license: llama2 --- # 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 fine-tuned model, optimized for dialogue use cases and 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/meta-llama/Llama-2-7b) \| [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/meta-llama/Llama-2-13b) \| [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)\| \|70B\| [Link](https://huggingface.co/meta-llama/Llama-2-70b) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) \| [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)\|
Itsmabhishek/phi-therapist-chat-v1	Itsmabhishek	2024-05-17T05:03:47Z	152	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:29:35Z	--- 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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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]
cwei13/bert-base-japanese-ghost_rate-weighted	cwei13	2024-05-17T05:01:26Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:tohoku-nlp/bert-base-japanese", "base_model:finetune:tohoku-nlp/bert-base-japanese", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T04:00:53Z	--- license: cc-by-sa-4.0 base_model: cl-tohoku/bert-base-japanese tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bert-base-japanese-ghost_rate-weighted results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-japanese-ghost_rate-weighted This model is a fine-tuned version of [cl-tohoku/bert-base-japanese](https://huggingface.co/cl-tohoku/bert-base-japanese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7735 - Accuracy: 0.4195 - F1: 0.4186 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - 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 \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 0.9977 \| 220 \| 1.5524 \| 0.3651 \| 0.3644 \| \| No log \| 2.0 \| 441 \| 1.5018 \| 0.3781 \| 0.3761 \| \| 1.5438 \| 2.9977 \| 661 \| 1.5367 \| 0.3934 \| 0.3832 \| \| 1.5438 \| 4.0 \| 882 \| 1.5724 \| 0.4019 \| 0.4042 \| \| 1.1647 \| 4.9977 \| 1102 \| 1.6488 \| 0.4093 \| 0.4115 \| \| 1.1647 \| 6.0 \| 1323 \| 1.7250 \| 0.4138 \| 0.4176 \| \| 0.8864 \| 6.9977 \| 1543 \| 1.7735 \| 0.4195 \| 0.4186 \| \| 0.8864 \| 8.0 \| 1764 \| 1.8372 \| 0.4138 \| 0.4161 \| \| 0.8864 \| 8.9977 \| 1984 \| 1.8975 \| 0.4150 \| 0.4133 \| \| 0.6978 \| 9.9773 \| 2200 \| 1.8925 \| 0.4127 \| 0.4146 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
RichardErkhov/jeonsworld_-_CarbonVillain-en-10.7B-v1-8bits	RichardErkhov	2024-05-17T05:00:28Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T04:51:54Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) CarbonVillain-en-10.7B-v1 - bnb 8bits - Model creator: https://huggingface.co/jeonsworld/ - Original model: https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v1/ Original model description: --- license: cc-by-nc-4.0 language: - en tags: - merge - slerp --- # CarbonVillain This is a model created without learning to oppose indiscriminate carbon emissions. This model is an experimental version created using [mergekit](https://github.com/cg123/mergekit). - merge models - Weyaxi/SauerkrautLM-UNA-SOLAR-Instruct - VAGOsolutions/SauerkrautLM-SOLAR-Instruct - method: slerp # Prompt Template(s) ``` ### User: {user} ### Assistant: {asistant} ``` # Evaluation Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jeonsworld__CarbonVillain-en-10.7B-v1) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 74.28 \| \| ARC (25-shot) \| 71.24 \| \| HellaSwag (10-shot) \| 88.45 \| \| MMLU (5-shot) \| 66.42 \| \| TruthfulQA (0-shot) \| 71.97 \| \| Winogrande (5-shot) \| 83.26 \| \| GSM8K (5-shot) \| 64.29 \|
ebowwa/informational_substrate_with_people_profilesv0.1	ebowwa	2024-05-17T04:58:52Z	0	1	null	[ "safetensors", "dataset:ebowwa/people-profiles", "dataset:ebowwa/Informational-simulation", "license:apache-2.0", "region:us" ]	null	2024-05-17T04:37:58Z	--- license: apache-2.0 datasets: - ebowwa/people-profiles - ebowwa/Informational-simulation --- ## Training https://www.kaggle.com/code/ebowwa/training-informational-substrate-with-people-profi
enchan1/reinforce-pixelcopter	enchan1	2024-05-17T04:57:27Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T01:38:25Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 36.30 +/- 31.45 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Omkar4141/phi-therapist-chat-v1	Omkar4141	2024-05-17T04:55:17Z	153	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:29:24Z	--- 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. 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TinyPixel/pythia-lima	TinyPixel	2024-05-17T04:54:11Z	152	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:51:19Z	--- 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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austinmw/renamed-model	austinmw	2024-05-17T04:52:48Z	106	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-17T03:48:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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worldboss/llama3-8b-oig-unsloth-merged	worldboss	2024-05-17T04:47:38Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:35:13Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: worldboss - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
neurips-user/neurips-covid-fake-covid-1	neurips-user	2024-05-17T04:43:31Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "dataset:neurips-bert-covid-fake5/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T04:37:06Z	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - neurips-bert-covid-fake5/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.46801355481147766 f1: 0.7692307692307693 precision: 0.7142857142857143 recall: 0.8333333333333334 auc: 0.8871527777777778 accuracy: 0.75
VinoG/swin-food102	VinoG	2024-05-17T04:43:20Z	221	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "base_model:juliensimon/autotrain-food101-1471154053", "base_model:finetune:juliensimon/autotrain-food101-1471154053", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-16T22:32:36Z	--- base_model: juliensimon/autotrain-food101-1471154053 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-food102 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. --> # swin-food102 This model is a fine-tuned version of [juliensimon/autotrain-food101-1471154053](https://huggingface.co/juliensimon/autotrain-food101-1471154053) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2488 - Accuracy: 0.9332 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 1.2026 \| 0.9987 \| 597 \| 0.3030 \| 0.924 \| \| 0.308 \| 1.9992 \| 1195 \| 0.2569 \| 0.9319 \| \| 0.2397 \| 2.9962 \| 1791 \| 0.2488 \| 0.9332 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
justinwong208/dqn-BeamRiderNoFrameskip-v4	justinwong208	2024-05-17T04:42:15Z	4	0	stable-baselines3	[ "stable-baselines3", "BeamRiderNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T04:38:30Z	--- library_name: stable-baselines3 tags: - BeamRiderNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BeamRiderNoFrameskip-v4 type: BeamRiderNoFrameskip-v4 metrics: - type: mean_reward value: 22.00 +/- 29.52 name: mean_reward verified: false --- # DQN Agent playing BeamRiderNoFrameskip-v4 * COLLAB CODE: https://colab.research.google.com/drive/1-VI4g7PwEDgnIj3LSlClGshu8LobQ9FY?usp=sharing * This is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga justinwong208 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga justinwong208 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ -orga justinwong208 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
gokaygokay/paligemma-rich-captions-ckpt	gokaygokay	2024-05-17T04:41:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T04:38:22Z	--- license: apache-2.0 ---
ebowwa/toxic-dpo-v0.2-llama-3-01-beta	ebowwa	2024-05-17T04:38:47Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "dataset:unalignment/toxic-dpo-v0.2", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-16T05:18:32Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit datasets: - unalignment/toxic-dpo-v0.2 --- i initially fine-tuned with a dpo dataset so headers: prompt, chosen, rejected. from datasets import load_dataset # Load the dataset dataset = load_dataset("unalignment/toxic-dpo-v0.2", split="train") # Define the formatting function def formatting_prompts_func(examples): return { "prompt": examples["prompt"], "chosen": examples["chosen"], "rejected": examples["rejected"], } # Apply the formatting function to the dataset dataset = dataset.map(formatting_prompts_func, batched=True) Which i used with the method supervised fine-tuning (SFT) of LLMs on specific tasks or datasets. It involves fine-tuning the model on labeled examples from the target domain, such as question-answering, summarization, or dialogue data. The objective is to adapt the model's behavior to the desired output format and data distribution. from trl import SFTTrainer from transformers import TrainingArguments trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset, dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 2, packing = False, # Can make training 5x faster for short sequences. args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 4, warmup_steps = 5, max_steps = 60, learning_rate = 2e-4, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 3407, output_dir = "outputs", ), ) But this dataset is DPO (Direct Preference Optimization) specific i.e. prompt, chosen, rejected DPO is a subsequent step after SFT, where the model undergoes preference learning using preference data, ideally from the same distribution as the SFT examples. It involves ranking pairs of outputs based on human feedback, such as which one is more informative, fluent, or engaging. from unsloth import FastLanguageModel, PatchDPOTrainer PatchDPOTrainer() import torch from transformers import TrainingArguments from trl import DPOTrainer dpo_trainer = DPOTrainer( model = model, ref_model = None, args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 4, warmup_ratio = 0.1, num_train_epochs = 3, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 1, optim = "adamw_8bit", seed = 42, output_dir = "outputs", ), beta = 0.1, train_dataset = dataset, # eval_dataset = YOUR_DATASET_HERE, tokenizer = tokenizer, max_length = 1024, max_prompt_length = 512, ) Key points about SFTTrainer: Initial step in the fine-tuning process Trains the model on labeled examples from the target task/domain Aims to improve performance on that specific task Adapts the model to the data distribution and output format The key aspects of DPO are: Performed after the initial SFT step Uses preference data consisting of ranked pairs of outputs Aims to align the model's outputs with human preferences and expectations Optimizes a binary cross-entropy loss based on the ranked pairs Simplified approach compared to traditional Reinforcement Learning from Human Feedback (RLHF) In summary, SFTTrainer is used for the initial supervised fine-tuning on the target task, while DPO is a subsequent step that fine-tunes the model further by incorporating human preferences and feedback on the model's outputs. # Uploaded model - Developed by: ebowwa - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. https://colab.research.google.com/drive/14ArcJ4hR613jH0HxYcT734it_HVHG_bb?usp=sharing [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
YifanXu/libra-vision-tokenizer	YifanXu	2024-05-17T04:38:33Z	0	1	null	[ "arxiv:2405.10140", "license:apache-2.0", "region:us" ]	null	2024-05-16T05:03:00Z	--- license: apache-2.0 --- ## Libra Vision Tokenizer [Libra: Building Decoupled Vision System on Large Language Models](https://arxiv.org/abs/2405.10140) This repo provides the pretrained weight of Libra vision tokenizer trained with lookup-free quantization. ### !!! NOTE !!! 1. Please merge the weights into ``llama-2-7b-chat-hf-libra`` ([huggingface version of LLaMA2-7B-Chat](https://huggingface.co/docs/transformers/main/model_doc/llama2)). 2. Please download the pretrained CLIP model in huggingface and merge it into the path. The CLIP model can be downloaded [here](https://huggingface.co/openai/clip-vit-large-patch14-336). The files should be organized as: ``` llama-2-7b-chat-hf-libra/ \| │ # original llama files \| ├── ... │ │ # newly added vision tokenizer │ ├── vision_tokenizer_config.yaml ├── vqgan.ckpt │ │ # CLIP model │ └── openai-clip-vit-large-patch14-336/ └── ... ```
YifanXu/libra-11b-base	YifanXu	2024-05-17T04:37:59Z	8	0	transformers	[ "transformers", "safetensors", "libra", "text-generation", "image-to-text", "arxiv:2405.10140", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-to-text	2024-05-15T09:45:45Z	--- license: apache-2.0 pipeline_tag: image-to-text --- ## Libra-Base [Libra: Building Decoupled Vision System on Large Language Models](https://arxiv.org/abs/2405.10140) This model was trained on image-text pairs for basic multi-modal understanding ability. ### !!! NOTE !!! In addition to the pretrained weights in this repo, please download the pretrained CLIP model in huggingface and merge it into the path, as: ``` libra-base/ ├── ... └── openai-clip-vit-large-patch14-336/ └── ... ``` The CLIP model can be downloaded [here](https://huggingface.co/openai/clip-vit-large-patch14-336).
ahmedgongi/Llama_dev3tokenizer_finale1	ahmedgongi	2024-05-17T04:32:51Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T04:32:50Z	--- 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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DokHee/JSLLMV3	DokHee	2024-05-17T04:31:23Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T04:16:56Z	--- library_name: transformers tags: - trl - sft --- # 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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Sharvajpatil/phi-therapist-chat-v1	Sharvajpatil	2024-05-17T04:18:25Z	151	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:08:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Alpha-VLLM/Lumina-T2I	Alpha-VLLM	2024-05-17T04:17:51Z	0	85	null	[ "text-to-image", "safetensors", "license:apache-2.0", "region:us" ]	text-to-image	2024-04-28T02:27:46Z	--- license: apache-2.0 tags: - text-to-image - safetensors --- <p align="center"> <img src="./lumina-logo.png" width="30%"/> <br> </p> # Lumina-T2I Lumina-T2I is a model that generates images based on text conditions, supporting various text encoders and models of different parameter sizes. With minimal training costs, it achieves high-quality image generation by training from scratch. Additionally, it offers usage through CLI console programs and Web Demo displays. Our generative model has `LargeDiT` as the backbone, the text encoder is the `LLaMa` 7B model, and the VAE uses a version of `sdxl` fine-tuned by stabilityai. - Generation Model: Large-DiT - Text Encoder: [LLaMA2-7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) - VAE: [stabilityai/sdxl-vae](https://huggingface.co/stabilityai/sdxl-vae) ## 📰 News - [2024-4-1] 🚀🚀🚀 We release the initial version of Lumina-T2I for text-to-image generation ## 🎮 Model Zoo More checkpoints of our model will be released soon~ \| Resolution \| Flag-DiT Parameter\| Text Encoder \| Prediction \| Download URL \| \| ---------- \| ----------------------- \| ------------ \| -----------\|-------------- \| \| 1024 \| 5B \| [LLaMA2-7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) \| Rectified Flow \| [hugging face](https://huggingface.co/Alpha-VLLM/Lumina-T2I/tree/main) \| ## Installation Before installation, ensure that you have a working ``nvcc`` ```bash # The command should work and show the same version number as in our case. (12.1 in our case). nvcc --version ``` On some outdated distros (e.g., CentOS 7), you may also want to check that a late enough version of ``gcc`` is available ```bash # The command should work and show a version of at least 6.0. # If not, consult distro-specific tutorials to obtain a newer version or build manually. gcc --version ``` Downloading Lumina-T2X repo from github: ```bash git clone https://github.com/Alpha-VLLM/Lumina-T2X ``` ### 1. Create a conda environment and install PyTorch Note: You may want to adjust the CUDA version [according to your driver version](https://docs.nvidia.com/deploy/cuda-compatibility/#default-to-minor-version). ```bash conda create -n Lumina_T2X -y conda activate Lumina_T2X conda install python=3.11 pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia -y ``` ### 2. Install dependencies ```bash pip install diffusers fairscale accelerate tensorboard transformers gradio torchdiffeq click ``` or you can use ```bash cd lumina-t2i pip install -r requirements.txt ``` ### 3. Install ``flash-attn`` ```bash pip install flash-attn --no-build-isolation ``` ### 4. Install [nvidia apex](https://github.com/nvidia/apex) (optional) >[!Warning] > While Apex can improve efficiency, it is not a must to make Lumina-T2X work. > > Note that Lumina-T2X works smoothly with either: > + Apex not installed at all; OR > + Apex successfully installed with CUDA and C++ extensions. > > However, it will fail when: > + A Python-only build of Apex is installed. > > If the error `No module named 'fused_layer_norm_cuda'` appears, it typically means you are using a Python-only build of Apex. To resolve this, please run `pip uninstall apex`, and Lumina-T2X should then function correctly. You can clone the repo and install following the official guidelines (note that we expect a full build, i.e., with CUDA and C++ extensions) ```bash pip install ninja git clone https://github.com/NVIDIA/apex cd apex # if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./ # otherwise pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./ ``` ## Inference To ensure that our generative model is ready to use immediately, we provide a user-friendly CLI program and a locally deployable Web Demo site. ### CLI 1. Install Lumina-T2I ```bash pip install -e . ``` 2. Prepare the pre-trained model ⭐⭐ (Recommended) you can use huggingface_cli to download our model: ```bash huggingface-cli download --resume-download Alpha-VLLM/Lumina-T2I --local-dir /path/to/ckpt ``` or using git for cloning the model you want to use: ```bash git clone https://huggingface.co/Alpha-VLLM/Lumina-T2I ``` 1. Setting your personal inference configuration Update your own personal inference settings to generate different styles of images, checking `config/infer/config.yaml` for detailed settings. Detailed config structure: > `/path/to/ckpt` should be a directory containing `consolidated.pth` and `model_args.pth` ```yaml - settings: model: ckpt: "/path/to/ckpt" # if ckpt is "", you should use `--ckpt` for passing model path when using `lumina` cli. ckpt_lm: "" # if ckpt is "", you should use `--ckpt_lm` for passing model path when using `lumina` cli. token: "" # if LLM is a huggingface gated repo, you should input your access token from huggingface and when token is "", you should `--token` for accessing the model. transport: path_type: "Linear" # option: ["Linear", "GVP", "VP"] prediction: "velocity" # option: ["velocity", "score", "noise"] loss_weight: "velocity" # option: [None, "velocity", "likelihood"] sample_eps: 0.1 train_eps: 0.2 ode: atol: 1e-6 # Absolute tolerance rtol: 1e-3 # Relative tolerance reverse: false # option: true or false likelihood: false # option: true or false sde: sampling_method: "Euler" # option: ["Euler", "Heun"] diffusion_form: "sigma" # option: ["constant", "SBDM", "sigma", "linear", "decreasing", "increasing-decreasing"] diffusion_norm: 1.0 # range: 0-1 last_step: Mean # option: [None, "Mean", "Tweedie", "Euler"] last_step_size: 0.04 infer: resolution: "1024x1024" # option: ["1024x1024", "512x2048", "2048x512", "(Extrapolation) 1664x1664", "(Extrapolation) 1024x2048", "(Extrapolation) 2048x1024"] num_sampling_steps: 60 # range: 1-1000 cfg_scale: 4. # range: 1-20 solver: "euler" # option: ["euler", "dopri5", "dopri8"] t_shift: 4 # range: 1-20 (int only) ntk_scaling: true # option: true or false proportional_attn: true # option: true or false seed: 0 # rnage: any number ``` - model: - `ckpt`: lumina-t2i checkpoint path from [huggingface repo](https://huggingface.co/Alpha-VLLM/Lumina-T2I) containing `consolidated.pth` and `model_args.pth`. - `ckpt_lm`: LLM checkpoint. - `token`: huggingface access token for accessing gated repo. - transport: - `path_type`: the type of path for transport: 'Linear', 'GVP' (Geodesic Vector Pursuit), or 'VP' (Vector Pursuit). - `prediction`: the prediction model for the transport dynamics. - `loss_weight`: the weighting of different components in the loss function, can be 'velocity' for dynamic modeling, 'likelihood' for statistical consistency, or None for no weighting - `sample_eps`: sampling in the transport model. - `train_eps`: training to stabilize the learning process. - ode: - `atol`: Absolute tolerance for the ODE solver. (options: ["Linear", "GVP", "VP"]) - `rtol`: Relative tolerance for the ODE solver. (option: ["velocity", "score", "noise"]) - `reverse`: run the ODE solver in reverse. (option: [None, "velocity", "likelihood"]) - `likelihood`: Enable calculation of likelihood during the ODE solving process. - sde - `sampling-method`: the numerical method used for sampling the stochastic differential equation: 'Euler' for simplicity or 'Heun' for improved accuracy. - `diffusion-form`: form of diffusion coefficient in the SDE - `diffusion-norm`: Normalizes the diffusion coefficient, affecting the scale of the stochastic component. - `last-step`: form of last step taken in the SDE - `last-step-size`: size of the last step taken - infer - `resolution`: generated image resolution. - `num_sampling_steps`: sampling step for generating image. - `cfg_scale`: classifier-free guide scaling factor - `solver`: solver for image generation. - `t_shift`: time shift factor. - `ntk_scaling`: ntk rope scaling factor. - `proportional_attn`: Whether to use proportional attention. - `seed`: random initialization seeds. 1. Run with CLI inference command: ```bash lumina infer -c <config_path> <caption_here> <output_dir> ``` e.g. Demo command: ```bash cd lumina-t2i lumina infer -c "config/infer/settings.yaml" "a snow man of ..." "./outputs" ``` ### Web Demo To host a local gradio demo for interactive inference, run the following command: ```bash # `/path/to/ckpt` should be a directory containing `consolidated*.pth` and `model_args.pth` # default python -u demo.py --ckpt "/path/to/ckpt" # the demo by default uses bf16 precision. to switch to fp32: python -u demo.py --ckpt "/path/to/ckpt" --precision fp32 # use ema model python -u demo.py --ckpt "/path/to/ckpt" --ema ```
jspr/llama3_8b_wordcel_8k_merged	jspr	2024-05-17T04:17:28Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:13:52Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: meta-llama/Meta-Llama-3-8B --- # Uploaded model - Developed by: jspr - License: apache-2.0 - Finetuned from model : meta-llama/Meta-Llama-3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
crs0256/my_awesome_qa_model	crs0256	2024-05-17T04:17:17Z	62	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-17T04:01:39Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: crs0256/my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # crs0256/my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5585 - Validation Loss: 1.6998 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.4036 \| 2.0494 \| 0 \| \| 1.8245 \| 1.6998 \| 1 \| \| 1.5585 \| 1.6998 \| 2 \| ### Framework versions - Transformers 4.40.2 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
justinwong208/dqn-SpaceInvadersNoFrameskip-v4	justinwong208	2024-05-17T04:13:12Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T04:12:45Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 5.00 +/- 7.07 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga justinwong208 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga justinwong208 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga justinwong208 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
jspr/llama3_8b_wordcel_8k_peft	jspr	2024-05-17T04:12:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T04:12:46Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: meta-llama/Meta-Llama-3-8B --- # Uploaded model - Developed by: jspr - License: apache-2.0 - Finetuned from model : meta-llama/Meta-Llama-3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dimoZ/phi-therapist-chat-v1	dimoZ	2024-05-17T04:11:47Z	152	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T04:02:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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presencesw/mt5-base-vinli_3_label_contradiction-triplet	presencesw	2024-05-17T04:10:51Z	50	0	transformers	[ "transformers", "safetensors", "mt5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T04:10:13Z	--- 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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Lannisterr/phi-therapist-chat-v1	Lannisterr	2024-05-17T04:08:00Z	152	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T03:59:52Z	--- 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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mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF	mradermacher	2024-05-17T04:02:08Z	18	0	transformers	[ "transformers", "gguf", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "cognitivecomputations/dolphin-2.8-mistral-7b-v02", "OpenPipe/mistral-ft-optimized-1227", "en", "base_model:frost19k/dolphin-2.8-mistral-v02-2x7b", "base_model:quantized:frost19k/dolphin-2.8-mistral-v02-2x7b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T02:10:42Z	--- base_model: frost19k/dolphin-2.8-mistral-v02-2x7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - frankenmoe - merge - mergekit - lazymergekit - cognitivecomputations/dolphin-2.8-mistral-7b-v02 - OpenPipe/mistral-ft-optimized-1227 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/frost19k/dolphin-2.8-mistral-v02-2x7b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.IQ3_XS.gguf) \| IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q3_K_S.gguf) \| Q3_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.IQ3_S.gguf) \| IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.IQ3_M.gguf) \| IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q3_K_M.gguf) \| Q3_K_M \| 6.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q3_K_L.gguf) \| Q3_K_L \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q4_K_S.gguf) \| Q4_K_S \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q4_K_M.gguf) \| Q4_K_M \| 7.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q5_K_S.gguf) \| Q5_K_S \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q5_K_M.gguf) \| Q5_K_M \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q6_K.gguf) \| Q6_K \| 10.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.8-mistral-v02-2x7b-GGUF/resolve/main/dolphin-2.8-mistral-v02-2x7b.Q8_0.gguf) \| Q8_0 \| 13.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
profpurohit/phi-therapist-chat-v1	profpurohit	2024-05-17T04:01:53Z	152	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T03:56:42Z	--- 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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JustAFool/wav2vec2-vi-300-3	JustAFool	2024-05-17T04:00:43Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T04:00:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DokHee/JSLLMV1	DokHee	2024-05-17T03:54:08Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T03:38:27Z	--- 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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damgomz/ThunBERT_bs64_lr4	damgomz	2024-05-17T03:45:11Z	118	0	transformers	[ "transformers", "safetensors", "albert", "pretraining", "fill-mask", "en", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-15T08:43:59Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T05:45:08' project_name: ThunBERT_bs64_lr4_emissions_tracker run_id: d2c6dcba-9ae6-44ee-920e-d6a98f77cc7e duration: 158953.8123600483 emissions: 0.1663746335150697 emissions_rate: 1.0466853927240982e-06 cpu_power: 42.5 gpu_power: 0.0 ram_power: 37.5 cpu_energy: 1.87653561675366 gpu_energy: 0 ram_energy: 1.6557595259199582 energy_consumed: 3.5322951426736116 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 4 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 100 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 158953.8123600483 \| \| Emissions (Co2eq in kg) \| 0.1663746335150697 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 37.5 \| \| CPU energy (kWh) \| 1.87653561675366 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 1.6557595259199582 \| \| Consumed energy (kWh) \| 3.5322951426736116 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 4 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.30598608879309297 \| \| Emissions (Co2eq in kg) \| 0.062256909841018906 \| ## Note 15 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ThunBERT_bs64_lr4 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 0.0005 \| \| batch_size \| 64 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 10263 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss ---\|---\|--- \| 0.0 \| 6.820532 \| 11.810026 \| \| 0.5 \| 4.147990 \| 3.898213 \| \| 1.0 \| 3.784049 \| 3.836403 \| \| 1.5 \| 7.045870 \| 7.716833 \| \| 2.0 \| 7.670383 \| 7.707286 \| \| 2.5 \| 7.666404 \| 7.691573 \| \| 3.0 \| 7.656751 \| 7.687526 \| \| 3.5 \| 7.654266 \| 7.679237 \| \| 4.0 \| 7.644708 \| 7.664175 \| \| 4.5 \| 7.641661 \| 7.666334 \| \| 5.0 \| 7.638564 \| 7.655856 \| \| 5.5 \| 7.625011 \| 7.645496 \| \| 6.0 \| 7.626149 \| 7.648783 \|
abc88767/6lc91	abc88767	2024-05-17T03:43:50Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T03:35:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TinyPixel/pth-l3	TinyPixel	2024-05-17T03:36:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T03:36:02Z	--- 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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kauevestena/clip-vit-base-patch32-finetuned-surface-materials	kauevestena	2024-05-17T03:34:03Z	0	0	null	[ "en", "dataset:kauevestena/deep_pavements_surface_patches", "region:us" ]	null	2024-05-17T03:19:59Z	--- datasets: - kauevestena/deep_pavements_surface_patches language: - en ---
Chayaaaaa/without_japanese_apart_from_stablelm_base_gamma-7b_task_arithmetic	Chayaaaaa	2024-05-17T03:31:16Z	9	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "stabilityai/japanese-stablelm-base-gamma-7b", "mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T03:27:11Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - stabilityai/japanese-stablelm-base-gamma-7b - mistralai/Mistral-7B-v0.1 --- # without_japanese_apart_from_stablelm_base_gamma-7b_task_arithmetic without_japanese_apart_from_stablelm_base_gamma-7b_task_arithmetic is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [stabilityai/japanese-stablelm-base-gamma-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b) * [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ## 🧩 Configuration ```yaml models: - model: stabilityai/japanese-stablelm-base-gamma-7b parameters: weight: 1 density: 0.9 gamma: 0.01 - model: mistralai/Mistral-7B-v0.1 parameters: weight: 1 density: 0.9 gamma: 0.01 merge_method: task_arithmetic base_model: stabilityai/japanese-stablelm-base-gamma-7b parameters: normalize: true int8_mask: true dtype: bfloat16 ```
abc88767/4lc91	abc88767	2024-05-17T03:30:56Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T03:22:13Z	--- 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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Yntec/Mo-Di-Diffusion-768	Yntec	2024-05-17T03:30:53Z	108	1	diffusers	[ "diffusers", "safetensors", "3D Animation", "nitrosocke", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-17T02:21:34Z	--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - 3D Animation - nitrosocke - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: true --- Use "modern disney" in your prompts. # Mo Di Diffusion 768x768, safetensors version of this model for the inference API. For the 512x512 ckpt version visit: https://huggingface.co/nitrosocke/mo-di-diffusion Samples and prompts: ![Free AI image generator mo di diffusion](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/9UM0HLiQftsTyC9F8Gghk.png) (Click for larger) Top left: modern disney link Top right: modern disney lara croft Bottom left: modern disney loli girl Bottom right: modern disney pikachu
jettjaniak/mess3-llama-17-05-2024	jettjaniak	2024-05-17T03:25:12Z	210	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T03:25:08Z	--- 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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deliciouscat/deberta-v3-base-encoder-v0.1	deliciouscat	2024-05-17T03:21:18Z	106	0	transformers	[ "transformers", "safetensors", "deberta-v2", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-17T03:20:49Z	--- 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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RomBor/Reinforce-CarPole-v1	RomBor	2024-05-17T03:16:22Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T03:16:12Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CarPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
afrideva/Mermaid-Llama-3-5B-Pruned-GGUF	afrideva	2024-05-17T03:14:55Z	10	0	null	[ "gguf", "ggml", "quantized", "text-generation", "base_model:TroyDoesAI/Mermaid-Llama-3-5B-Pruned", "base_model:quantized:TroyDoesAI/Mermaid-Llama-3-5B-Pruned", "license:cc-by-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-17T02:41:14Z	--- base_model: TroyDoesAI/Mermaid-Llama-3-5B-Pruned inference: true license: cc-by-4.0 model_creator: TroyDoesAI model_name: Mermaid-Llama-3-5B-Pruned pipeline_tag: text-generation quantized_by: afrideva tags: - gguf - ggml - quantized --- # Mermaid-Llama-3-5B-Pruned-GGUF Quantized GGUF model files for [Mermaid-Llama-3-5B-Pruned](https://huggingface.co/TroyDoesAI/Mermaid-Llama-3-5B-Pruned) from [TroyDoesAI](https://huggingface.co/TroyDoesAI) ## Original Model Card: # Mermaid-Llama-3-5B Introducing Mermaid-LLama-3-5B, a language model designed for Python code understanding and crafting captivating story flow maps. ![MermaidLlama GIF](Mermaid_ShowCase/MermaidLlama.webp) ## Key Features 1. Code Understanding: - Masters Python intricacies with finesse. - Generates clear and accurate Mermaid Diagram Flow Charts. - Ideal for developers seeking visual representations of their code logic. 2. Storytelling Capabilities: - Converts narrative inputs into captivating Mermaid Diagrams. - Maps character interactions, plot developments, and narrative arcs. 3. Unmatched Performance: - Surpasses GPT-4 in generating well-organized Mermaid Diagrams. 4. Training Insights: - Trained on a diverse dataset, including 800 unique, hand-curated Mermaid Graph examples utilizing 478 complete Python programs. - Exhibits emergent properties in story-to-flow map translations and step-by-step instruction flow maps. ## Collaboration Interested in enhancing Mermaid's capabilities? Contact [email protected] for collaboration opportunities. ## Example Use Cases - Retrieval-Augmented Generation (RAG): Utilize Mermaid-LLama-3-8B to create condensed knowledge graphs. This model excels in generating flow diagrams that enhance the retrieval process. These knowledge graphs are stored in a vector database, which allows for quick and efficient retrieval of contextually relevant information. When a query is received, the system retrieves a pertinent knowledge graph, appending it as context to the model. This enriched context enables Mermaid-LLama-3-8B to deliver more accurate and nuanced responses. This approach is particularly beneficial in applications requiring deep, context-aware interactions, such as sophisticated Q&A systems, dynamic data analysis, and complex decision-making tasks. - Code Documentation: Automatic visual flow charts from Python code. - Storyboarding: Visually appealing diagrams for storytelling. - Project Planning: Visual project flow maps for effective team communication. - Learning Python: Helps students visually understand Python code structures. - Game Design: Visualizing game storylines for coherent narrative structure. ## Proof of Concept Stay tuned for the release of the VSCode Extension that displays the Live Flow Map every time a user stops typing for more than 10 seconds. ## Training Specifications - LoRA Rank: 2048 - LoRA Alpha: 4096 - Batch Size: 1 - Micro Batch Size: 1 - Cutoff Length: 4096 - Save every n steps: 1000 - Epochs: 3 - Learning Rate: 1e-6 - LR Scheduler: Cosine Target Modules: - Enable q_proj - Enable v_proj - Enable k_proj - Enable o_proj - Enable gate_proj - Enable down_proj - Enable up_proj ## Getting Started Start by downloading one of my models. ![0 TroyDoesAI GIF](Mermaid_ShowCase/0_TroyDoesAI.gif) Load the model. ![1 Load Model in 4-bit Show Example Use GIF](Mermaid_ShowCase/1_LoadModel_in_4bit_Show_Example_Use.gif) Use my prompt template to generate a Mermaid code block, which can be viewed in the Mermaid Live Editor or using the Mermaid CLI tool. ![2 Loaded Model in Full Precision 16-bit Show Inference and Mermaid Live Editor GIF](Mermaid_ShowCase/2_Loaded_Model_in_Full_Precision_16bit_Show_Inference_and_Mermaid_Live_editor.gif) Here we open the VLLM GUI Program while still running in Vram the Mermaid-Llama-8B to compare the flow diagram to the actual program and show the lightweight capabilites of small models on consumer hardware. ![3 Open The Program VLLM Program With Full Precision Mermaid-Llama-8B Running to Evaluate Flow Map GIF](Mermaid_ShowCase/3_Open_The_Program_VLLM_Program_With_Full_Precision_Mermaid-Llama-8B-Running_to_evaluate_flow_map.gif) ## More on my VLLM Class and inference GUI : https://github.com/Troys-Code/VLLM ![Python RtdBsaz8gy GIF](Mermaid_ShowCase/python_RtdBsaz8gy.gif) --- Note: This model should be treated as an Auto-Complete Model, Do not try talking to it in chat you are gonna get garbage, those layers have been pruned and replaced, that is all you will hear of my secret sauce on training on small < 1000 entry datasets.
roshinishetty333/llama-2-7b-lora-tuned-per3	roshinishetty333	2024-05-17T03:13:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T03:13:28Z	--- 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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krisha-n/a2c-PandaReachDense-v3	krisha-n	2024-05-17T03:13:01Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T02:27:46Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.15 +/- 0.14 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
DokHee/Alpha-Edu-LLM-TEST-V2-8bit	DokHee	2024-05-17T03:07:27Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T02:51:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahmedesmail16/Train-Test-Augmentation-swinv2-base	ahmedesmail16	2024-05-17T03:07:26Z	165	0	transformers	[ "transformers", "tensorboard", "safetensors", "swinv2", "image-classification", "generated_from_trainer", "base_model:microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft", "base_model:finetune:microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-16T13:39:21Z	--- license: apache-2.0 base_model: microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft tags: - generated_from_trainer metrics: - accuracy model-index: - name: Train-Test-Augmentation-swinv2-base 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. --> # Train-Test-Augmentation-swinv2-base This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7329 - Accuracy: 0.8206 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5364 \| 0.98 \| 23 \| 0.8286 \| 0.7257 \| \| 0.4948 \| 1.97 \| 46 \| 0.6373 \| 0.7958 \| \| 0.2036 \| 2.99 \| 70 \| 0.5860 \| 0.8234 \| \| 0.1158 \| 3.98 \| 93 \| 0.6284 \| 0.8151 \| \| 0.0656 \| 4.96 \| 116 \| 0.6982 \| 0.8129 \| \| 0.0568 \| 5.99 \| 140 \| 0.7678 \| 0.8217 \| \| 0.0332 \| 6.97 \| 163 \| 0.7208 \| 0.8206 \| \| 0.0279 \| 8.0 \| 187 \| 0.7053 \| 0.8217 \| \| 0.0169 \| 8.98 \| 210 \| 0.7489 \| 0.8256 \| \| 0.0125 \| 9.84 \| 230 \| 0.7329 \| 0.8206 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.19.1 - Tokenizers 0.15.2
afrideva/Phi-3-Context-Obedient-RAG-GGUF	afrideva	2024-05-17T03:04:24Z	64	0	null	[ "gguf", "ggml", "quantized", "text-generation", "base_model:TroyDoesAI/Phi-3-Context-Obedient-RAG", "base_model:quantized:TroyDoesAI/Phi-3-Context-Obedient-RAG", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-17T02:42:30Z	--- base_model: TroyDoesAI/Phi-3-Context-Obedient-RAG inference: true license: cc-by-sa-4.0 model_creator: TroyDoesAI model_name: Phi-3-Context-Obedient-RAG pipeline_tag: text-generation quantized_by: afrideva tags: - gguf - ggml - quantized --- # Phi-3-Context-Obedient-RAG-GGUF Quantized GGUF model files for [Phi-3-Context-Obedient-RAG](https://huggingface.co/TroyDoesAI/Phi-3-Context-Obedient-RAG) from [TroyDoesAI](https://huggingface.co/TroyDoesAI) ## Original Model Card: Base Model : microsoft/Phi-3-mini-128k-instruct Overview This model is meant to enhance adherence to provided context (e.g., for RAG applications) and reduce hallucinations, inspired by airoboros context-obedient question answer format. --- license: cc-by-4.0 --- # Contextual DPO ## Overview The format for a contextual prompt is as follows: ``` BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set Here's a trivial, but important example to prove the point: ``` BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the expected response: ``` Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` ### References in response As shown in the example, the dataset includes many examples of including source details in the response, when the question asks for source/citation/references. Why do this? Well, the R in RAG seems to be the weakest link in the chain. Retrieval accuracy, depending on many factors including the overall dataset size, can be quite low. This accuracy increases when retrieving more documents, but then you have the issue of actually using the retrieved documents in prompts. If you use one prompt per document (or document chunk), you know exactly which document the answer came from, so there's no issue. If, however, you include multiple chunks in a single prompt, it's useful to include the specific reference chunk(s) used to generate the response, rather than naively including references to all of the chunks included in the prompt. For example, suppose I have two documents: ``` url: http://foo.bar/1 Strawberries are tasty. url: http://bar.foo/2 The cat is blue. ``` If the question being asked is `What color is the cat?`, I would only expect the 2nd document to be referenced in the response, as the other link is irrelevant.
Steffany30/Comic	Steffany30	2024-05-17T03:02:07Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T03:02:07Z	--- license: apache-2.0 ---
EstebanKora/Aprendizaje-IneBot	EstebanKora	2024-05-17T03:01:43Z	151	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-16T01:16:05Z	--- license: apache-2.0 ---
chohi/llama-3-8b-chat-molit-kor	chohi	2024-05-17T03:00:06Z	11	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-04-24T07:46:50Z	--- 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]

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