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modelId stringlengths 5 122	author stringlengths 2 42	last_modified unknown	likes int64 0 10.9k	library_name stringclasses 238 values	tags sequencelengths 1 2.16k	pipeline_tag stringclasses 48 values	createdAt unknown	card stringlengths 1 901k
fleonce/dnrti-t5-base	fleonce	"2024-06-20T09:39:37Z"	0	transformers	[ "transformers", "safetensors", "iter", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T09:36:45Z"	--- 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]
DBangshu/gemma_e5_4_4	DBangshu	"2024-06-20T09:41:26Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-20T09:38:43Z"	--- 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. 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]
AmanCode22/CHATGEM	AmanCode22	"2024-06-20T09:43:22Z"	0	transformers	[ "transformers", "text-generation", "en", "dataset:OpenGVLab/ShareGPT-4o", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-20T09:40:19Z"	--- datasets: - OpenGVLab/ShareGPT-4o language: - en metrics: - accuracy library_name: transformers pipeline_tag: text-generation ---
abdfajar707/llama3_8B_lora_model_rkp_v3	abdfajar707	"2024-06-20T09:41:56Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-20T09:41:43Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: abdfajar707 - 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)
limaatulya/my_awesome_billsum_model_36	limaatulya	"2024-06-20T09:46:35Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-20T09:45:40Z"	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model_36 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model_36 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4601 - Rouge1: 0.9721 - Rouge2: 0.8819 - Rougel: 0.9256 - Rougelsum: 0.9271 - Gen Len: 4.9167 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 12 \| 1.9874 \| 0.4145 \| 0.2913 \| 0.3883 \| 0.3891 \| 17.6042 \| \| No log \| 2.0 \| 24 \| 1.4300 \| 0.4322 \| 0.3091 \| 0.4061 \| 0.4068 \| 17.0833 \| \| No log \| 3.0 \| 36 \| 0.9451 \| 0.5076 \| 0.3886 \| 0.4814 \| 0.48 \| 14.75 \| \| No log \| 4.0 \| 48 \| 0.6345 \| 0.8401 \| 0.7297 \| 0.7858 \| 0.7884 \| 7.625 \| \| No log \| 5.0 \| 60 \| 0.5226 \| 0.9591 \| 0.8586 \| 0.8998 \| 0.9042 \| 5.125 \| \| No log \| 6.0 \| 72 \| 0.4907 \| 0.9701 \| 0.8736 \| 0.9129 \| 0.9167 \| 4.8958 \| \| No log \| 7.0 \| 84 \| 0.4783 \| 0.9701 \| 0.8736 \| 0.9129 \| 0.9167 \| 4.8958 \| \| No log \| 8.0 \| 96 \| 0.4697 \| 0.9721 \| 0.8819 \| 0.9256 \| 0.9271 \| 4.9167 \| \| No log \| 9.0 \| 108 \| 0.4627 \| 0.9721 \| 0.8819 \| 0.9256 \| 0.9271 \| 4.9167 \| \| No log \| 10.0 \| 120 \| 0.4601 \| 0.9721 \| 0.8819 \| 0.9256 \| 0.9271 \| 4.9167 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
zakariyafirachine/Recommendation_using_t5___	zakariyafirachine	"2024-06-20T09:47:01Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-20T09:46:01Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
IsakNordgren/Llama-3-mistral-sum	IsakNordgren	"2024-06-20T09:46:24Z"	0	null	[ "merge", "mergekit", "lazymergekit", "Labagaite/mistral-Summarizer-7b-instruct-v0.2", "base_model:Labagaite/mistral-Summarizer-7b-instruct-v0.2", "region:us" ]	null	"2024-06-20T09:46:23Z"	--- base_model: - Labagaite/mistral-Summarizer-7b-instruct-v0.2 tags: - merge - mergekit - lazymergekit - Labagaite/mistral-Summarizer-7b-instruct-v0.2 --- # Llama-3-mistral-sum Llama-3-mistral-sum is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Labagaite/mistral-Summarizer-7b-instruct-v0.2](https://huggingface.co/Labagaite/mistral-Summarizer-7b-instruct-v0.2) ## 🧩 Configuration ```yaml models: - model: AI-Sweden-Models/Llama-3-8B-instruct # No parameters necessary for base model - model: Labagaite/mistral-Summarizer-7b-instruct-v0.2 parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: AI-Sweden-Models/Llama-3-8B-instruct parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "IsakNordgren/Llama-3-mistral-sum" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
satish99017/gpt2-reuters-tokenizer	satish99017	"2024-06-20T09:46:57Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T09:46:56Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
ChatK/my_awesome_eli5_clm-model	ChatK	"2024-06-20T09:47:05Z"	0	null	[ "region:us" ]	null	"2024-06-20T09:47:05Z"	Entry not found
Ksgk-fy/genius_helper_v1_Maria_product_v1	Ksgk-fy	"2024-06-20T10:40:34Z"	0	null	[ "safetensors", "region:us" ]	null	"2024-06-20T09:50:33Z"	Entry not found
starfishvenus/melili-experiment	starfishvenus	"2024-06-20T09:57:27Z"	0	null	[ "license:unknown", "region:us" ]	null	"2024-06-20T09:53:20Z"	--- license: unknown ---
Jamin20/Baseline_DE	Jamin20	"2024-06-20T09:56:34Z"	0	null	[ "region:us" ]	null	"2024-06-20T09:56:34Z"	Entry not found
Jamin20/Audio-based_Augment_DE	Jamin20	"2024-06-20T09:57:20Z"	0	null	[ "region:us" ]	null	"2024-06-20T09:57:19Z"	Entry not found
JamesSpray/llama-2-7b-chat-bnb-4bit-ift-dpo-002	JamesSpray	"2024-06-20T09:58:31Z"	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T09:57:24Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Jamin20/SpecAugment_L_DE	Jamin20	"2024-06-20T09:57:30Z"	0	null	[ "region:us" ]	null	"2024-06-20T09:57:29Z"	Entry not found
Jamin20/MixSpeech_DE	Jamin20	"2024-06-20T09:57:44Z"	0	null	[ "region:us" ]	null	"2024-06-20T09:57:43Z"	Entry not found
Jamin20/Audio-based_Augment_SpecAugment_L_DE	Jamin20	"2024-06-20T09:57:53Z"	0	null	[ "region:us" ]	null	"2024-06-20T09:57:53Z"	Entry not found
Jamin20/Audio-based_Augment_MixSpeech_DE	Jamin20	"2024-06-20T09:58:03Z"	0	null	[ "region:us" ]	null	"2024-06-20T09:58:03Z"	Entry not found
Jamin20/MixSpeech_SpecAugment_L_DE	Jamin20	"2024-06-20T09:58:15Z"	0	null	[ "region:us" ]	null	"2024-06-20T09:58:14Z"	Entry not found
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-100-percent-low-med-high-nv-embed	AdamKasumovic	"2024-06-20T10:00:50Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-20T09:58:33Z"	--- base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
nianlong/memsum-cnndm-summarization	nianlong	"2024-06-20T10:01:37Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-20T10:00:43Z"	--- license: apache-2.0 ---
cosmic-God/Exp1	cosmic-God	"2024-06-20T10:01:48Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:01:48Z"	Entry not found
morturr/flan-t5-small-amazon-text-classification-2024-06-20	morturr	"2024-06-20T11:16:44Z"	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-classification	"2024-06-20T10:02:05Z"	--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer model-index: - name: flan-t5-small-amazon-text-classification-2024-06-20 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. --> # flan-t5-small-amazon-text-classification-2024-06-20 This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.2 - Pytorch 2.3.1+cu121 - Datasets 2.10.1 - Tokenizers 0.15.2
creativeforce/model-change-fsnv	creativeforce	"2024-06-20T10:28:40Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:02:08Z"	Entry not found
Floriankidev/swin-tiny-patch4-window7-224-finetuned-eurosat-finetuned-eurosat	Floriankidev	"2024-06-20T10:47:16Z"	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-20T10:02:45Z"	Entry not found
basakdemirok/bert-base-multilingual-cased-off_detect_v0_seed42	basakdemirok	"2024-06-20T10:28:22Z"	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-20T10:04:08Z"	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: basakdemirok/bert-base-multilingual-cased-off_detect_v0_seed42 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. --> # basakdemirok/bert-base-multilingual-cased-off_detect_v0_seed42 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1350 - Validation Loss: 0.4174 - Train F1: 0.6091 - Epoch: 3 ## 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': 7488, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.4017 \| 0.3430 \| 0.5471 \| 0 \| \| 0.2964 \| 0.3350 \| 0.6146 \| 1 \| \| 0.2104 \| 0.3516 \| 0.5968 \| 2 \| \| 0.1350 \| 0.4174 \| 0.6091 \| 3 \| ### Framework versions - Transformers 4.41.2 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.19.1
jihyunnn/sceejayRVC	jihyunnn	"2024-06-20T10:05:13Z"	0	null	[ "license:unknown", "region:us" ]	null	"2024-06-20T10:05:13Z"	--- license: unknown ---
varun-v-rao/gpt2-large-bn-adapter-7.42M-snli-model3	varun-v-rao	"2024-06-20T13:47:44Z"	0	null	[ "tensorboard", "region:us" ]	null	"2024-06-20T10:06:39Z"	Entry not found
AshuDon/Ashu_Voice	AshuDon	"2024-06-20T10:15:40Z"	0	null	[ "license:unlicense", "region:us" ]	null	"2024-06-20T10:07:33Z"	--- license: unlicense ---
alex-abb/Classifier	alex-abb	"2024-06-20T10:08:39Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-20T10:08:39Z"	--- license: mit ---
ILKT/2024-06-20_12-08-45	ILKT	"2024-06-20T10:11:18Z"	0	transformers	[ "transformers", "safetensors", "ILKT", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	"2024-06-20T10:10:09Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
hanungaddi/k3_yolov10	hanungaddi	"2024-06-20T10:11:31Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:10:41Z"	Entry not found
Wwzl/modely	Wwzl	"2024-06-20T10:11:13Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-20T10:11:13Z"	--- license: apache-2.0 ---
FazleHasan191/paligemma_attire_200_448	FazleHasan191	"2024-06-21T08:10:46Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-06-20T10:14:31Z"	Entry not found
manbeast3b/KinoInferL25	manbeast3b	"2024-06-20T10:14:56Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:14:51Z"	Entry not found
richardlastrucci/m2m100-afr-zul	richardlastrucci	"2024-06-20T11:29:21Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "m2m_100", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-06-20T10:16:43Z"	Entry not found
Kitajiang/qwen2_fine_3	Kitajiang	"2024-06-20T10:17:11Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:17:10Z"	Entry not found
Kitajiang/qwen2_fine_4	Kitajiang	"2024-06-20T10:17:33Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:17:33Z"	Entry not found
mandarchaudharii/maintenancebot_gptadded	mandarchaudharii	"2024-06-20T10:29:59Z"	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T10:18:35Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
MohamedAshour1993/Bid_Master_v1	MohamedAshour1993	"2024-06-20T10:45:11Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-06-20T10:22:36Z"	Entry not found
njaana/phi3-mini-new-model-lora-adapters	njaana	"2024-06-20T10:23:49Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-20T10:23:33Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: njaana - License: apache-2.0 - Finetuned from model : unsloth/phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
FazleHasan191/paligemma_attire_200_224	FazleHasan191	"2024-06-20T11:43:53Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-06-20T10:23:36Z"	Entry not found
mrr-codes/q-FrozenLake-v1-4x4-noSlippery	mrr-codes	"2024-06-20T10:28:29Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-20T10:28:25Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="mrr-codes/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
soye/vaiv_llm_contest3	soye	"2024-06-21T14:33:08Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:30:07Z"	Entry not found
varun-v-rao/gpt2-large-bn-adapter-7.42M-squad-model2	varun-v-rao	"2024-06-20T15:57:06Z"	0	null	[ "tensorboard", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:openai-community/gpt2-large", "license:mit", "region:us" ]	null	"2024-06-20T10:30:20Z"	--- license: mit base_model: openai-community/gpt2-large tags: - generated_from_trainer datasets: - varun-v-rao/squad model-index: - name: gpt2-large-bn-adapter-7.42M-squad-model2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-large-bn-adapter-7.42M-squad-model2 This model is a fine-tuned version of [openai-community/gpt2-large](https://huggingface.co/openai-community/gpt2-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 15 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
BRIAN12682/Natural-Language-Explanations	BRIAN12682	"2024-06-20T10:31:50Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:31:50Z"	Entry not found
GAI-LLM/myungdonggil_2	GAI-LLM	"2024-06-20T10:33:00Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:33:00Z"	Entry not found
rnaveensrinivas/phi-2-retrained_network_corpus	rnaveensrinivas	"2024-06-20T10:35:54Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:35:54Z"	Entry not found
sleephashira/Accident-LLAMA3-Unsloth	sleephashira	"2024-06-20T10:37:32Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-20T10:37:02Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: sleephashira - 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)
rnaveensrinivas/gemma-2b-it-retrained_network_corpus	rnaveensrinivas	"2024-06-20T11:08:15Z"	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:google/gemma-2b-it", "license:gemma", "region:us" ]	null	"2024-06-20T10:39:42Z"	--- license: gemma library_name: peft tags: - generated_from_trainer base_model: google/gemma-2b-it model-index: - name: gemma-2b-it-retrained_network_corpus 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-2b-it-retrained_network_corpus This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3035 ## 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: 1 - 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: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.9983 \| 1.0 \| 28 \| 2.3757 \| \| 2.1934 \| 2.0 \| 56 \| 2.3035 \| \| 1.6321 \| 3.0 \| 84 \| 2.4507 \| \| 1.1753 \| 4.0 \| 112 \| 2.6984 \| \| 0.8011 \| 5.0 \| 140 \| 2.9980 \| \| 0.5162 \| 6.0 \| 168 \| 3.2588 \| \| 0.304 \| 7.0 \| 196 \| 3.7305 \| \| 0.1822 \| 8.0 \| 224 \| 4.0858 \| \| 0.1148 \| 9.0 \| 252 \| 4.3105 \| \| 0.0855 \| 10.0 \| 280 \| 4.5450 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
jamemcd/output	jamemcd	"2024-06-20T10:40:32Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:40:32Z"	Entry not found
BurakKarakurt/llama-3-8b-Instruct-bnb-4bit-unsloth.Q4_K_M.gguf	BurakKarakurt	"2024-06-20T10:41:33Z"	1	null	[ "license:llama3", "region:us" ]	null	"2024-06-20T10:41:33Z"	--- license: llama3 ---
basakdemirok/bert-base-multilingual-cased-off_detect_v01_seed42	basakdemirok	"2024-06-20T11:23:12Z"	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-20T10:41:47Z"	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: basakdemirok/bert-base-multilingual-cased-off_detect_v01_seed42 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. --> # basakdemirok/bert-base-multilingual-cased-off_detect_v01_seed42 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0510 - Validation Loss: 0.5725 - Train F1: 0.5959 - Epoch: 3 ## 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': 14256, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.3607 \| 0.3331 \| 0.5487 \| 0 \| \| 0.2104 \| 0.3987 \| 0.5794 \| 1 \| \| 0.1029 \| 0.5215 \| 0.5816 \| 2 \| \| 0.0510 \| 0.5725 \| 0.5959 \| 3 \| ### Framework versions - Transformers 4.41.2 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.19.1
pavan01729/Ai_doctor_llama3	pavan01729	"2024-06-20T10:58:10Z"	0	null	[ "safetensors", "license:mit", "region:us" ]	null	"2024-06-20T10:44:47Z"	--- license: mit --- llama3 fine tuned on : Shekswess/medical_llama3_instruct_dataset_short dataset. 100 steps training
danielkosyra/polynomial_1450_7e-4_32b_w0.2	danielkosyra	"2024-06-20T10:45:39Z"	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-20T10:45:20Z"	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: polynomial_1450_7e-4_32b_w0.2 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. --> # polynomial_1450_7e-4_32b_w0.2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8711 ## 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: 0.0007 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 10 - total_train_batch_size: 320 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 250 - training_steps: 1450 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 9.0501 \| 0.2058 \| 50 \| 7.2516 \| \| 6.6334 \| 0.4117 \| 100 \| 6.1191 \| \| 5.8403 \| 0.6175 \| 150 \| 5.5171 \| \| 5.347 \| 0.8234 \| 200 \| 5.0809 \| \| 4.9621 \| 1.0292 \| 250 \| 4.7655 \| \| 4.5909 \| 1.2351 \| 300 \| 4.4418 \| \| 4.3142 \| 1.4409 \| 350 \| 4.1684 \| \| 4.0577 \| 1.6468 \| 400 \| 3.8857 \| \| 3.7934 \| 1.8526 \| 450 \| 3.6317 \| \| 3.5603 \| 2.0585 \| 500 \| 3.4786 \| \| 3.3743 \| 2.2643 \| 550 \| 3.3722 \| \| 3.3003 \| 2.4702 \| 600 \| 3.2932 \| \| 3.2338 \| 2.6760 \| 650 \| 3.2353 \| \| 3.1788 \| 2.8818 \| 700 \| 3.1763 \| \| 3.0774 \| 3.0877 \| 750 \| 3.1289 \| \| 2.9735 \| 3.2935 \| 800 \| 3.0953 \| \| 2.9351 \| 3.4994 \| 850 \| 3.0626 \| \| 2.9367 \| 3.7052 \| 900 \| 3.0310 \| \| 2.9088 \| 3.9111 \| 950 \| 3.0032 \| \| 2.7944 \| 4.1169 \| 1000 \| 2.9830 \| \| 2.7402 \| 4.3228 \| 1050 \| 2.9669 \| \| 2.7293 \| 4.5286 \| 1100 \| 2.9475 \| \| 2.7184 \| 4.7345 \| 1150 \| 2.9275 \| \| 2.7029 \| 4.9403 \| 1200 \| 2.9098 \| \| 2.6065 \| 5.1462 \| 1250 \| 2.9024 \| \| 2.5699 \| 5.3520 \| 1300 \| 2.8938 \| \| 2.5511 \| 5.5578 \| 1350 \| 2.8836 \| \| 2.5503 \| 5.7637 \| 1400 \| 2.8756 \| \| 2.5435 \| 5.9695 \| 1450 \| 2.8711 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
saltdurian/solartest1	saltdurian	"2024-06-20T10:48:47Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-20T10:48:47Z"	--- license: mit ---
mdiamore/intern_math_redo2	mdiamore	"2024-06-21T00:09:22Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-06-20T10:50:16Z"	Entry not found
CezarFY/whisper-tiny-cfy	CezarFY	"2024-06-20T17:35:51Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-06-20T10:52:14Z"	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-cfy results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[451:] args: en-US metrics: - name: Wer type: wer value: 0.3184257602862254 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-cfy This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6293 - Wer Ortho: 0.3192 - Wer: 0.3184 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.0011 \| 17.2414 \| 500 \| 0.6293 \| 0.3192 \| 0.3184 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
heesun1/difu_model_test	heesun1	"2024-06-20T10:59:57Z"	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "diffusers:StableDiffusionInstructPix2PixPipeline", "region:us" ]	null	"2024-06-20T10:52:38Z"	Entry not found
jjdp8rjj/jjdp8rjj	jjdp8rjj	"2024-06-20T10:56:32Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:56:32Z"	Entry not found
ShapeKapseln33/SummerKeto22	ShapeKapseln33	"2024-06-20T11:01:02Z"	0	null	[ "region:us" ]	null	"2024-06-20T10:57:15Z"	Summer Keto + ACV Gummies France -Uni : L’ajustement et la perte de poids sont devenus populaires récemment. Les gens réfléchissent souvent beaucoup à leur apparence et à leur comportement. Les médias sociaux sont la principale raison pour laquelle tant d’individus réussissent dans le monde moderne, car ils sous-tendent tout. Actuellement, plus de 25 % des personnes s'intéressent au vlogging, et tout le monde souhaite se tenir au courant des dernières modes, visiter des endroits exotiques et apprendre de nouvelles choses en général. [Cliquez ici pour acheter maintenant sur le site officiel de Summer Keto ACV Gummies](https://adtocart.xyz/summer-keto-fr) Aujourd’hui, vous trouverez de nombreux produits de perte de poids sur les marchés en ligne qui promettent faussement de vous donner une silhouette mince en quelques semaines. Les clients qui sont piégés dans ces produits contrefaits peuvent obtenir des résultats négatifs sur leur corps. Ils ne sont pas sans danger pour une utilisation à long terme en raison de la présence d'ingrédients et d'arômes artificiels. Maintenant, vous pouvez essayer Summer Keto ACV Gummies au Royaume-Uni pour réduire le poids supplémentaire du corps. Ces bonbons gélifiés peuvent aider à perdre les kilos en trop du corps en 4 à 5 semaines et à affiner votre silhouette. Ce blog décrit en détail Summer Keto ACV Gummies 250 mg avec ses ingrédients, sa composition, sa formule, son fonctionnement, ses études médicales, ses avantages et sa posologie. ##Summer Keto ACV Gummies en bref Summer Keto + ACV Gummies 250 mg sont des gummies naturels de perte de poids composés d'ingrédients biologiques et d'éléments naturels. Ils peuvent aider à éliminer les graisses tenaces du corps et à donner une silhouette élancée en quelques semaines. En dehors de cela, le produit peut également brûler des graisses pour produire de l’énergie au lieu de glucides. Ce produit naturel de perte de poids peut améliorer la santé mentale et améliorer la concentration mentale au cours de certaines semaines. ##Ingrédients clés des bonbons gélifiés Summer Keto ACV Les principaux ingrédients des Summer Keto ACV Gummies au Royaume-Uni comprennent des cétones avancées et des extraits de pommes. Ces bonbons gélifiés peuvent également contenir des extraits d’herbes, de plantes et de fruits. En outre, le produit peut également contenir d’autres nutriments et minéraux importants en quantités modérées. Chaque ingrédient utilisé dans la fabrication de ce produit est testé en laboratoire par des experts médicaux et des scientifiques. Ces bonbons gélifiés ne peuvent pas contenir d'arômes, de colorants, de conservateurs artificiels, de parabènes, de stimulants ou de charges. Ils sont exempts de produits chimiques, synthétiques ou gluten. Prendre ces bonbons quotidiennement est sans danger pour le corps et ne peut provoquer aucun effet secondaire tel que des maux de tête ou des migraines. [Cliquez ici pour acheter maintenant sur le site officiel de Summer Keto ACV Gummies](https://adtocart.xyz/summer-keto-fr) ##Comment sont fabriqués les gummies ? Chaque gummie est fabriqué sous la supervision des meilleures équipes médicales et de bons scientifiques. Ce produit est fabriqué à l’aide des dernières techniques et méthodes. Il est préparé selon les meilleures pratiques et normes de l’industrie. Les Summer Keto + ACV Gummies sont recommandés par les meilleurs médecins et diététistes du monde entier. C’est un produit cliniquement prouvé, efficace pour réduire l’obésité et l’excès de poids dans le corps. ##Que dit l’étude médicale sur les Summer Keto ACV Gummies ? La dernière enquête sur la santé montre qu'environ 26 % des adultes souffrent d'obésité et de prise de poids. 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Jozaita/code-search-net-tokenizer	Jozaita	"2024-06-20T10:57:30Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T10:57:29Z"	--- 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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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]
Eka-Korn/my_awesome_billsum_model	Eka-Korn	"2024-06-20T11:07:56Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-20T10:57:44Z"	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4965 - Rouge1: 0.1462 - Rouge2: 0.0533 - Rougel: 0.121 - Rougelsum: 0.121 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.7872 \| 0.1335 \| 0.0418 \| 0.1138 \| 0.1137 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.5750 \| 0.143 \| 0.0536 \| 0.1199 \| 0.1201 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5135 \| 0.1437 \| 0.0501 \| 0.1186 \| 0.1186 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.4965 \| 0.1462 \| 0.0533 \| 0.121 \| 0.121 \| 19.0 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
ASHreyash/MediGenie-llama3-8b-instruct	ASHreyash	"2024-06-20T11:10:49Z"	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T10:57:48Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DBangshu/Base_gemma_e5_3_1	DBangshu	"2024-06-20T11:01:00Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-20T10:58:26Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sataayu/molt5-augmented-default-1-large-smiles2caption	sataayu	"2024-06-20T14:35:26Z"	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-06-20T10:59:17Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jamesohe/casJoinUs-Llama3-8B-Epo100TextG1-R1e5-adapter	jamesohe	"2024-06-20T10:59:59Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T10:59:22Z"	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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varun-v-rao/bart-large-bn-adapter-3.17M-squad-model2	varun-v-rao	"2024-06-20T13:55:34Z"	0	null	[ "tensorboard", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:facebook/bart-large", "license:apache-2.0", "region:us" ]	null	"2024-06-20T11:03:55Z"	--- license: apache-2.0 base_model: facebook/bart-large tags: - generated_from_trainer datasets: - varun-v-rao/squad model-index: - name: bart-large-bn-adapter-3.17M-squad-model2 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. --> # bart-large-bn-adapter-3.17M-squad-model2 This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
joecheriross/whisper-small-hi	joecheriross	"2024-06-20T11:05:36Z"	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-06-20T11:04:18Z"	--- language: - hi license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper-tiny joe v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper-tiny joe v1 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
gitfreder/brain-tumor-classifier	gitfreder	"2024-06-20T11:23:21Z"	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "image-classification", "en", "license:mit", "region:us" ]	image-classification	"2024-06-20T11:06:33Z"	--- tags: - model_hub_mixin - pytorch_model_hub_mixin license: mit language: - en metrics: - accuracy pipeline_tag: image-classification --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: CNN Model to clasifying brain tumor - Library: [More Information Needed] - Docs: [More Information Needed] - Dataset [Brain Tumor MRI](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset) - Test loss: 0.0212 - Test accuracy: 0.9657
jamesohe/casJoinUs-Gemma-V2-7B-ep100-adapter	jamesohe	"2024-06-20T11:08:55Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T11:08:23Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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jhoppanne/Dogs-Breed-Image-Classification-V0	jhoppanne	"2024-06-21T14:36:06Z"	0	transformers	[ "transformers", "safetensors", "resnet", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/resnet-50", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-20T11:11:16Z"	--- license: apache-2.0 base_model: microsoft/resnet-50 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Dogs-Breed-Image-Classification-V0 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.7444120505344995 --- <!-- 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. --> # Dogs-Breed-Image-Classification-V0 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.8210 - Accuracy: 0.7444 ## Model description This model was trained using dataset from [Kaggle - Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset) Quotes from the website: The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age. citation: Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex] Secondary: J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex] ## Intended uses & limitations This model is fined tune solely for classifiying 120 species of dogs. ## Training and evaluation data 75% training data, 25% testing data. ## 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 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 13.4902 \| 1.0 \| 515 \| 4.7822 \| 0.0104 \| \| 4.7159 \| 2.0 \| 1030 \| 4.6822 \| 0.0323 \| \| 4.6143 \| 3.0 \| 1545 \| 4.5940 \| 0.0554 \| \| 4.4855 \| 4.0 \| 2060 \| 4.5027 \| 0.0935 \| \| 4.36 \| 5.0 \| 2575 \| 4.3961 \| 0.1239 \| \| 4.2198 \| 6.0 \| 3090 \| 4.3112 \| 0.1528 \| \| 4.0882 \| 7.0 \| 3605 \| 4.1669 \| 0.1747 \| \| 3.9314 \| 8.0 \| 4120 \| 4.0775 \| 0.2021 \| \| 3.7863 \| 9.0 \| 4635 \| 3.9487 \| 0.2310 \| \| 3.6511 \| 10.0 \| 5150 \| 3.9028 \| 0.2466 \| \| 3.5168 \| 11.0 \| 5665 \| 3.8635 \| 0.2626 \| \| 3.3999 \| 12.0 \| 6180 \| 3.7550 \| 0.2767 \| \| 3.3037 \| 13.0 \| 6695 \| 3.6973 \| 0.2884 \| \| 3.1613 \| 14.0 \| 7210 \| 3.6315 \| 0.3037 \| \| 3.0754 \| 15.0 \| 7725 \| 3.4839 \| 0.3188 \| \| 2.9441 \| 16.0 \| 8240 \| 3.4406 \| 0.3302 \| \| 2.8579 \| 17.0 \| 8755 \| 3.3528 \| 0.3406 \| \| 2.7531 \| 18.0 \| 9270 \| 3.3132 \| 0.3472 \| \| 2.6477 \| 19.0 \| 9785 \| 3.2736 \| 0.3567 \| \| 2.5422 \| 20.0 \| 10300 \| 3.1950 \| 0.3756 \| \| 2.4629 \| 21.0 \| 10815 \| 3.1174 \| 0.4004 \| \| 2.3735 \| 22.0 \| 11330 \| 2.9916 \| 0.4225 \| \| 2.2436 \| 23.0 \| 11845 \| 2.9205 \| 0.4509 \| \| 2.1578 \| 24.0 \| 12360 \| 2.9197 \| 0.4689 \| \| 2.0671 \| 25.0 \| 12875 \| 2.8196 \| 0.4866 \| \| 1.9902 \| 26.0 \| 13390 \| 2.7117 \| 0.4961 \| \| 1.8737 \| 27.0 \| 13905 \| 2.7129 \| 0.5078 \| \| 1.7945 \| 28.0 \| 14420 \| 2.6654 \| 0.5143 \| \| 1.7092 \| 29.0 \| 14935 \| 2.6273 \| 0.5301 \| \| 1.6228 \| 30.0 \| 15450 \| 2.5407 \| 0.5454 \| \| 1.5744 \| 31.0 \| 15965 \| 2.5412 \| 0.5559 \| \| 1.4761 \| 32.0 \| 16480 \| 2.4658 \| 0.5658 \| \| 1.4084 \| 33.0 \| 16995 \| 2.4247 \| 0.5673 \| \| 1.2624 \| 34.0 \| 17510 \| 2.3766 \| 0.5758 \| \| 1.2066 \| 35.0 \| 18025 \| 2.2879 \| 0.5843 \| \| 1.124 \| 36.0 \| 18540 \| 2.2039 \| 0.5872 \| \| 1.074 \| 37.0 \| 19055 \| 2.2469 \| 0.5965 \| \| 0.9937 \| 38.0 \| 19570 \| 2.1575 \| 0.6011 \| \| 0.9418 \| 39.0 \| 20085 \| 2.0854 \| 0.6122 \| \| 0.8812 \| 40.0 \| 20600 \| 1.9991 \| 0.6254 \| \| 0.819 \| 41.0 \| 21115 \| 2.0161 \| 0.6312 \| \| 0.771 \| 42.0 \| 21630 \| 1.9253 \| 0.6375 \| \| 0.7128 \| 43.0 \| 22145 \| 1.9412 \| 0.6390 \| \| 0.6434 \| 44.0 \| 22660 \| 1.8463 \| 0.6509 \| \| 0.6138 \| 45.0 \| 23175 \| 1.8163 \| 0.6650 \| \| 0.5325 \| 46.0 \| 23690 \| 1.7881 \| 0.6710 \| \| 0.498 \| 47.0 \| 24205 \| 1.7526 \| 0.6744 \| \| 0.4565 \| 48.0 \| 24720 \| 1.7155 \| 0.6859 \| \| 0.4109 \| 49.0 \| 25235 \| 1.6874 \| 0.6946 \| \| 0.3681 \| 50.0 \| 25750 \| 1.7386 \| 0.6997 \| \| 0.3306 \| 51.0 \| 26265 \| 1.6578 \| 0.7104 \| \| 0.2913 \| 52.0 \| 26780 \| 1.6641 \| 0.7104 \| \| 0.2598 \| 53.0 \| 27295 \| 1.6823 \| 0.7162 \| \| 0.2311 \| 54.0 \| 27810 \| 1.6835 \| 0.7157 \| \| 0.2115 \| 55.0 \| 28325 \| 1.6581 \| 0.7206 \| \| 0.1843 \| 56.0 \| 28840 \| 1.6286 \| 0.7274 \| \| 0.1668 \| 57.0 \| 29355 \| 1.6358 \| 0.7225 \| \| 0.1483 \| 58.0 \| 29870 \| 1.6422 \| 0.7250 \| \| 0.132 \| 59.0 \| 30385 \| 1.6618 \| 0.7284 \| \| 0.1164 \| 60.0 \| 30900 \| 1.6894 \| 0.7262 \| \| 0.1043 \| 61.0 \| 31415 \| 1.6923 \| 0.7276 \| \| 0.0937 \| 62.0 \| 31930 \| 1.6627 \| 0.7323 \| \| 0.0826 \| 63.0 \| 32445 \| 1.6280 \| 0.7342 \| \| 0.0743 \| 64.0 \| 32960 \| 1.6204 \| 0.7366 \| \| 0.0638 \| 65.0 \| 33475 \| 1.6890 \| 0.7383 \| \| 0.0603 \| 66.0 \| 33990 \| 1.6967 \| 0.7335 \| \| 0.0491 \| 67.0 \| 34505 \| 1.6975 \| 0.7306 \| \| 0.0459 \| 68.0 \| 35020 \| 1.7242 \| 0.7337 \| \| 0.0416 \| 69.0 \| 35535 \| 1.7019 \| 0.7374 \| \| 0.0382 \| 70.0 \| 36050 \| 1.7098 \| 0.7381 \| \| 0.0378 \| 71.0 \| 36565 \| 1.7188 \| 0.7383 \| \| 0.0326 \| 72.0 \| 37080 \| 1.8212 \| 0.7376 \| \| 0.0323 \| 73.0 \| 37595 \| 1.7965 \| 0.7393 \| \| 0.0299 \| 74.0 \| 38110 \| 1.7934 \| 0.7301 \| \| 0.0259 \| 75.0 \| 38625 \| 1.7799 \| 0.7335 \| \| 0.0276 \| 76.0 \| 39140 \| 1.8456 \| 0.7301 \| \| 0.0257 \| 77.0 \| 39655 \| 1.8551 \| 0.7391 \| \| 0.0234 \| 78.0 \| 40170 \| 1.7780 \| 0.7391 \| \| 0.0222 \| 79.0 \| 40685 \| 1.8216 \| 0.7362 \| \| 0.0195 \| 80.0 \| 41200 \| 1.8333 \| 0.7352 \| \| 0.0214 \| 81.0 \| 41715 \| 1.8526 \| 0.7430 \| \| 0.0207 \| 82.0 \| 42230 \| 1.8581 \| 0.7364 \| \| 0.0171 \| 83.0 \| 42745 \| 1.8329 \| 0.7393 \| \| 0.0175 \| 84.0 \| 43260 \| 1.8841 \| 0.7396 \| \| 0.0165 \| 85.0 \| 43775 \| 1.8381 \| 0.7345 \| \| 0.0152 \| 86.0 \| 44290 \| 1.8192 \| 0.7379 \| \| 0.0168 \| 87.0 \| 44805 \| 1.8538 \| 0.7388 \| \| 0.0158 \| 88.0 \| 45320 \| 1.8390 \| 0.7371 \| \| 0.0181 \| 89.0 \| 45835 \| 1.8555 \| 0.7374 \| \| 0.0142 \| 90.0 \| 46350 \| 1.7987 \| 0.7352 \| \| 0.0147 \| 91.0 \| 46865 \| 1.8446 \| 0.7427 \| \| 0.0142 \| 92.0 \| 47380 \| 1.8210 \| 0.7444 \| \| 0.0124 \| 93.0 \| 47895 \| 1.8233 \| 0.7405 \| \| 0.0128 \| 94.0 \| 48410 \| 1.8517 \| 0.7393 \| \| 0.0135 \| 95.0 \| 48925 \| 1.8408 \| 0.7413 \| \| 0.0122 \| 96.0 \| 49440 \| 1.8153 \| 0.7396 \| \| 0.0141 \| 97.0 \| 49955 \| 1.8645 \| 0.7432 \| \| 0.0121 \| 98.0 \| 50470 \| 1.8526 \| 0.7430 \| \| 0.0124 \| 99.0 \| 50985 \| 1.8693 \| 0.7388 \| \| 0.0113 \| 100.0 \| 51500 \| 1.8051 \| 0.7427 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.15.0 - Tokenizers 0.15.1
MohamedAhmedAE/Tiny-Gemma-Medical-Train	MohamedAhmedAE	"2024-06-20T11:38:23Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-20T11:12:44Z"	Entry not found
DBangshu/gemma_e5_5_4	DBangshu	"2024-06-20T11:15:26Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-20T11:12:46Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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RanchiZhao/MiniCPM-2B-sft-fp32	RanchiZhao	"2024-06-20T11:14:33Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-20T11:14:33Z"	--- license: mit ---
balaramas/asr_bangla	balaramas	"2024-06-20T11:23:34Z"	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-06-20T11:15:14Z"	--- license: apache-2.0 ---
Ramikan-BR/TiamaPY-LORA-v31	Ramikan-BR	"2024-06-20T11:16:51Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-chat-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-20T11:15:48Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-chat-bnb-4bit --- # Uploaded model - Developed by: Ramikan-BR - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-chat-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)
GN20403/distilbert	GN20403	"2024-06-20T11:17:39Z"	0	null	[ "region:us" ]	null	"2024-06-20T11:17:39Z"	Entry not found
Chahat7874/wav2vec2-large-xls-r-300m-hindi_telugu-colab	Chahat7874	"2024-06-20T13:51:23Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T11:19:09Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
bezzam/diffusercam-mirflickr-unet4M-unrolled-admm5-unet4M	bezzam	"2024-06-20T11:21:07Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-20T11:20:38Z"	--- license: mit ---
karthikmit/openai-whisper-medium-LORA	karthikmit	"2024-06-20T15:30:44Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T11:21:07Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
abdullah1010/merged_model_V2	abdullah1010	"2024-06-20T11:29:54Z"	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-06-20T11:22:06Z"	Entry not found
bezzam/diffusercam-mirflickr-mwdn-8M	bezzam	"2024-06-20T11:23:11Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-20T11:22:46Z"	--- license: mit ---
hcy5561/distilbert-base-uncased-finetuned-ner	hcy5561	"2024-06-20T11:27:22Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2024-06-20T11:23:13Z"	Entry not found
majoh837/openchat_viz	majoh837	"2024-06-20T11:23:29Z"	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:openchat/openchat-3.5-0106", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-20T11:23:28Z"	--- base_model: openchat/openchat-3.5-0106 language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: majoh837 - License: apache-2.0 - Finetuned from model : openchat/openchat-3.5-0106 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mogmyij/Llama2-7b-BoolQ-full-LoRA	mogmyij	"2024-06-20T11:24:54Z"	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	"2024-06-20T11:24:46Z"	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf datasets: - generator model-index: - name: Llama2-7b-BoolQ-full-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. --> # Llama2-7b-BoolQ-full-LoRA This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.1372 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.4039 \| 0.9787 \| 23 \| 1.3046 \| \| 1.2571 \| 2.0 \| 47 \| 1.1492 \| \| 1.121 \| 2.9362 \| 69 \| 1.1372 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
Benjoe19/Test	Benjoe19	"2024-06-20T11:29:56Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-20T11:29:56Z"	--- license: mit ---
qazimbhat1/crystal-chat-general	qazimbhat1	"2024-06-20T11:33:19Z"	0	transformers	[ "transformers", "pytorch", "llava_crystal", "text-generation", "custom_code", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-20T11:32:30Z"	Entry not found
Goshgosh/Anyline	Goshgosh	"2024-06-20T11:51:09Z"	0	null	[ "region:us" ]	null	"2024-06-20T11:33:32Z"	Entry not found
valerielucro/mistral_gsm8k_sft_cot	valerielucro	"2024-06-20T11:35:47Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T11:35:35Z"	--- 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. 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]
gsar78/tokenizer_BPE_en_el	gsar78	"2024-06-20T11:36:51Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-20T11:36:51Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
NotAiLOL/test_llama_3_8b_step_60	NotAiLOL	"2024-06-20T11:48:37Z"	0	peft	[ "peft", "safetensors", "text-generation-inference", "transformers", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "region:us" ]	null	"2024-06-20T11:36:52Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit library_name: peft --- # Uploaded model - Developed by: NotAiLOL - 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)
Boostaro155/PharmaflexRX455	Boostaro155	"2024-06-20T11:37:22Z"	0	null	[ "region:us" ]	null	"2024-06-20T11:36:56Z"	# Pharmaflex RX 리뷰 경험 – Pharma Flex RX 효능, 성분 공식 가격, 구매 Pharmaflex RX 리뷰 경험 - 가장 인기 있는 영양 보충제 Pharma Flex Rx는 관절 건강을 유지하고 증진하기 위한 것입니다. 제조사는 필러가 없는 모든 천연 성분을 특징으로 한다고 말합니다. ## [Pharmaflex RX 공식 웹사이트에서 지금 구매하려면 여기를 클릭하세요.](https://adtocart.xyz/pharmaflex-rx) ## Pharma Flex RX는 어떻게 작동하나요? 이미 언급했듯이 Pharma Flex RX는 만성 관절통을 장기적으로 치료하는 데 사용할 수 있는 최고의 제품입니다. 이 솔루션은 연골의 자연적인 윤활을 촉진하여 관절 부종과 자극을 줄이는 데 도움이 됩니다. Pharma Flex RX는 나이가 들수록 뼈의 밀도가 잃기 시작하기 때문에 더 나은 건강과 웰빙을 위해 힘과 활력을 회복하는 데 도움이 됩니다. 경험적으로 뒷받침되는 이 공식은 세포 수준에서 가장 효과적으로 작용하여 안전하고 유용하며 오래 지속되는 결과를 제공할 수 있습니다. PharmaFlex는 연골 완충 및 관절 윤활을 최적화하는 동시에 더 나은 신체 기능과 움직임을 촉진합니다. Pharma Flex RX는 신체의 자연적인 혈류를 증가시켜 관절에 영양을 공급하고 비타민을 공급하며 신체적 웰빙을 향상시킵니다. ## Pharma Flex RX에는 어떤 성분이 포함되어 있나요? Pharma Flex RX는 안전하고 효과적인 솔루션을 제공하기 위해 유기적으로 재배된 천연 물질로만 구성되어 있습니다. 이는 제품을 매우 효과적이고 안전하게 만드는 요소 중 하나입니다. 이러한 구성 요소는 다음과 같습니다. 이 공식의 가장 강력한 성분 중 하나는 심황 뿌리 추출물입니다. 강황뿌리추출물이라는 유기물질이 염증으로 인한 만성관절통을 즉각적으로 완화시켜줍니다. 강황 뿌리의 작용 방식은 관절을 확대하고 염증을 일으키는 유해한 사이토카인 단백질의 축적을 방지하는 것입니다. 그 결과 관절염으로 인한 부종과 염증을 감소시키고 관절염 발병을 지연시킨다. 여러 자생 식물에 존재하는 천연 유기 황인 메틸설포닐메탄은 일반적으로 신체의 만성 통증과 불편함을 감소시키는 것으로 알려져 있습니다. 메틸설포닐메탄은 또한 신체의 유연성을 높이는 데 도움이 됩니다. 좋은 관절 통증 완화를 제공하는 또 다른 활성 성분은 글루코사민 황산염입니다. 연골의 쿠셔닝과 윤활을 촉진하고 관절 기능을 향상시키는 역할을 합니다. 글루코사민 황산염은 기능성 관절 퇴행을 예방합니다. 메이요 클리닉(Mayo Clinic) 웹사이트에 따르면, 이 필수 성분을 보충제로 복용하면 연골 변성을 늦추어 관절 불편함을 줄이는 데 도움이 됩니다. PharmaFlex의 주요 활성 성분은 세틸로, 이는 염증과 만성 관절통을 줄이는 데 도움이 되는 다양한 특성을 가지고 있습니다. 심각한 관절 손상에 대한 방어력을 강화합니다. 파인애플의 천연 화학물질은 브로멜라인이라고 합니다. 진통제와 항염증제 특성이 강력하게 결합되어 장기간 동안 급성 및 만성 통증을 완화합니다. ## Pharma Flex RX 혜택 Pharma Flex RX는 주요 성분이 몸 전체를 치유하고 갑작스러운 통증을 오래 지속시키는 경이로움을 제공하기 때문에 관절 통증과 불안을 치료하는 데 놀라운 효과가 있습니다. 다음은 이점을 요약한 것입니다. 주의 깊게 읽어보시기 바랍니다. 심한 관절 통증 완화에 도움이 됩니다. ### 근육 치유에 도움 불안, 긴장, 절망감과 같은 정신적 불안정을 감소시킵니다. ## [Pharmaflex RX 공식 웹사이트에서 지금 구매하려면 여기를 클릭하세요.](https://adtocart.xyz/pharmaflex-rx)
jihyunnn/repo_name	jihyunnn	"2024-06-20T11:37:00Z"	0	null	[ "license:unknown", "region:us" ]	null	"2024-06-20T11:37:00Z"	--- license: unknown ---
codingninja/w2v2-punjabi-asr-v2	codingninja	"2024-06-20T11:37:07Z"	0	null	[ "region:us" ]	null	"2024-06-20T11:37:07Z"	Entry not found
rebaiAhmad/q-FrozenLake-v1-4x4-noSlippery	rebaiAhmad	"2024-06-20T11:37:10Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-20T11:37:08Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="rebaiAhmad/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
varun-v-rao/gpt2-large-lora-2.95M-squad-model2	varun-v-rao	"2024-06-20T15:17:49Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:openai-community/gpt2-large", "license:mit", "endpoints_compatible", "text-generation-inference", "region:us" ]	question-answering	"2024-06-20T11:37:58Z"	--- license: mit base_model: openai-community/gpt2-large tags: - generated_from_trainer datasets: - varun-v-rao/squad model-index: - name: gpt2-large-lora-2.95M-squad-model2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-large-lora-2.95M-squad-model2 This model is a fine-tuned version of [openai-community/gpt2-large](https://huggingface.co/openai-community/gpt2-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 36 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
YahiaABbas/ddpm-butterflies-128	YahiaABbas	"2024-06-20T11:38:21Z"	0	null	[ "region:us" ]	null	"2024-06-20T11:38:21Z"	Entry not found
xcf-t/q-FrozenLake-v1-4x4-noSlippery	xcf-t	"2024-06-20T11:39:18Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-20T11:39:16Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="xcf-t/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
krvstxne/krvstxne	krvstxne	"2024-06-20T11:39:43Z"	0	null	[ "region:us" ]	null	"2024-06-20T11:39:43Z"	Entry not found