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modelId stringlengths 5 122	author stringlengths 2 42	last_modified unknown	likes int64 0 11k	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt unknown	card stringlengths 1 901k
rrricharrrd/q-FrozenLake-v1-4x4-noSlippery	rrricharrrd	"2024-06-10T13:32:05Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-10T13:32:02Z"	--- 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="rrricharrrd/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"]) ```
rrricharrrd/q-Taxi-v3	rrricharrrd	"2024-06-10T13:34:16Z"	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-10T13:34:14Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.44 +/- 2.79 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="rrricharrrd/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
DownwardSpiral33/gpt2-imdb-pos-4c2-d6-reward-256_0_2-rewardprompts-2024.06.10.13.28	DownwardSpiral33	"2024-06-10T13:36:04Z"	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-10T13:35:49Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
yaraksen/komod_no_sftmx_4_3_full	yaraksen	"2024-06-28T14:09:20Z"	0	null	[ "safetensors", "region:us" ]	null	"2024-06-10T13:35:50Z"	Entry not found
mnlp-2024/dpo_lora_mcqa	mnlp-2024	"2024-06-10T18:18:43Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-10T13:38:37Z"	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Daxuxu36/General-TinyBERT-enwiki-20240301	Daxuxu36	"2024-06-10T13:43:07Z"	0	transformers	[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]	null	"2024-06-10T13:41:37Z"	Entry not found
wwhwhan/tt	wwhwhan	"2024-06-13T16:22:40Z"	0	null	[ "region:us" ]	null	"2024-06-10T13:42:18Z"	Entry not found
stibiu/llama-tweet	stibiu	"2024-06-10T15:56:30Z"	0	null	[ "tensorboard", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]	null	"2024-06-10T13:42:42Z"	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B tags: - generated_from_trainer model-index: - name: llama-tweet 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. --> # llama-tweet This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0190 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.334 \| 0.0075 \| 20 \| 2.9963 \| \| 1.4622 \| 0.0149 \| 40 \| 1.4038 \| \| 1.5365 \| 0.0224 \| 60 \| 1.1542 \| \| 1.0904 \| 0.0298 \| 80 \| 1.1056 \| \| 0.5901 \| 0.0373 \| 100 \| 1.1066 \| \| 1.2178 \| 0.0447 \| 120 \| 1.0601 \| \| 0.8568 \| 0.0522 \| 140 \| 1.0527 \| \| 1.4505 \| 0.0596 \| 160 \| 1.0579 \| \| 1.026 \| 0.0671 \| 180 \| 1.0441 \| \| 0.5462 \| 0.0745 \| 200 \| 1.0531 \| \| 1.212 \| 0.0820 \| 220 \| 1.0366 \| \| 0.8079 \| 0.0895 \| 240 \| 1.0311 \| \| 1.466 \| 0.0969 \| 260 \| 1.0333 \| \| 1.0033 \| 0.1044 \| 280 \| 1.0281 \| \| 0.5643 \| 0.1118 \| 300 \| 1.0264 \| \| 1.1797 \| 0.1193 \| 320 \| 1.0232 \| \| 0.8372 \| 0.1267 \| 340 \| 1.0210 \| \| 1.4117 \| 0.1342 \| 360 \| 1.0209 \| \| 1.0041 \| 0.1416 \| 380 \| 1.0193 \| \| 0.5658 \| 0.1491 \| 400 \| 1.0190 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.10.1 - Tokenizers 0.19.1
DownwardSpiral33/gpt2-imdb-pos-4c2-d6-reward-256_0_05-rewardprompts-2024.06.10.13.36	DownwardSpiral33	"2024-06-10T13:43:37Z"	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-10T13:43:22Z"	--- 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. 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]
Chinjuj/PT5-SignalIP-peft-lora	Chinjuj	"2024-06-10T13:44:07Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T13:43:59Z"	--- 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]
2nzi/videomae-surf-analytics-v2	2nzi	"2024-06-11T11:36:48Z"	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	"2024-06-10T13:46:53Z"	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: videomae-surf-analytics-sans-wandb 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. --> # videomae-surf-analytics-sans-wandb This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5544 - Accuracy: 0.8852 - F1: 0.8840 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1850 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.3096 \| 0.2005 \| 371 \| 1.3267 \| 0.4590 \| 0.2888 \| \| 1.0586 \| 1.2005 \| 742 \| 1.2866 \| 0.5820 \| 0.5035 \| \| 0.9781 \| 2.2005 \| 1113 \| 0.7952 \| 0.7459 \| 0.7466 \| \| 0.0034 \| 3.2005 \| 1484 \| 0.7218 \| 0.8361 \| 0.8343 \| \| 0.1895 \| 4.1978 \| 1850 \| 0.5544 \| 0.8852 \| 0.8840 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.0+cu118 - Datasets 2.19.2 - Tokenizers 0.19.1
samuelswandi/CodeBuddy-E-5-1K	samuelswandi	"2024-06-10T13:53:38Z"	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-10T13:48:37Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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BrokenSki8/gen_good	BrokenSki8	"2024-06-10T13:50:32Z"	0	null	[ "safetensors", "region:us" ]	null	"2024-06-10T13:50:28Z"	Entry not found
DownwardSpiral33/gpt2-imdb-pos-4c2-d6-reward-256_0_022-rewardprompts-2024.06.10.13.43	DownwardSpiral33	"2024-06-10T13:50:48Z"	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-10T13:50:33Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kyronale/blue	kyronale	"2024-06-10T13:50:38Z"	0	null	[ "region:us" ]	null	"2024-06-10T13:50:38Z"	Entry not found
Propicto/t2p-mbart-large-cc25-commonvoice	Propicto	"2024-06-10T14:22:37Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-10T13:51:42Z"	--- license: apache-2.0 ---
datakrems/trained-model	datakrems	"2024-06-20T10:10:53Z"	0	transformers	[ "transformers", "safetensors", "bloom", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-10T13:52:05Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Arash136360/Hamster	Arash136360	"2024-06-10T14:00:14Z"	0	null	[ "license:openrail++", "region:us" ]	null	"2024-06-10T13:54:37Z"	--- license: openrail++ ---
AskMyUncleSam/test	AskMyUncleSam	"2024-06-10T13:57:27Z"	0	null	[ "region:us" ]	null	"2024-06-10T13:57:27Z"	Entry not found
DownwardSpiral33/gpt2-imdb-pos-4c2-d6-reward-256_0_035-rewardprompts-2024.06.10.13.51	DownwardSpiral33	"2024-06-10T13:58:04Z"	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-10T13:57:49Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ic32k/llm	ic32k	"2024-06-10T13:58:01Z"	0	null	[ "region:us" ]	null	"2024-06-10T13:58:01Z"	Entry not found
tranthaihoa/llama2_evidence	tranthaihoa	"2024-06-10T13:59:03Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-10T13:58:41Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - Developed by: tranthaihoa - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-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)
DokiQueen/Tentacle-pit	DokiQueen	"2024-06-10T14:12:02Z"	0	null	[ "region:us" ]	null	"2024-06-10T13:58:51Z"	Entry not found
imdatta0/llama_2_13b_Magiccoder_evol_10k_reverse	imdatta0	"2024-06-10T17:34:07Z"	0	peft	[ "peft", "safetensors", "unsloth", "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-hf", "license:llama2", "region:us" ]	null	"2024-06-10T13:59:29Z"	--- license: llama2 library_name: peft tags: - unsloth - generated_from_trainer base_model: meta-llama/Llama-2-13b-hf model-index: - name: llama_2_13b_Magiccoder_evol_10k_reverse 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. --> # llama_2_13b_Magiccoder_evol_10k_reverse This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0887 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.02 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.173 \| 0.0262 \| 4 \| 1.1853 \| \| 1.1716 \| 0.0523 \| 8 \| 1.1587 \| \| 1.105 \| 0.0785 \| 12 \| 1.1410 \| \| 1.0534 \| 0.1047 \| 16 \| 1.1289 \| \| 1.0911 \| 0.1308 \| 20 \| 1.1239 \| \| 1.0565 \| 0.1570 \| 24 \| 1.1172 \| \| 1.0589 \| 0.1832 \| 28 \| 1.1140 \| \| 1.1027 \| 0.2093 \| 32 \| 1.1106 \| \| 1.0379 \| 0.2355 \| 36 \| 1.1096 \| \| 1.1134 \| 0.2617 \| 40 \| 1.1087 \| \| 1.0969 \| 0.2878 \| 44 \| 1.1049 \| \| 1.1361 \| 0.3140 \| 48 \| 1.1056 \| \| 1.1121 \| 0.3401 \| 52 \| 1.1023 \| \| 1.0828 \| 0.3663 \| 56 \| 1.1047 \| \| 1.1246 \| 0.3925 \| 60 \| 1.1027 \| \| 1.1285 \| 0.4186 \| 64 \| 1.0990 \| \| 1.0788 \| 0.4448 \| 68 \| 1.0998 \| \| 1.0917 \| 0.4710 \| 72 \| 1.0950 \| \| 1.0395 \| 0.4971 \| 76 \| 1.0977 \| \| 1.1267 \| 0.5233 \| 80 \| 1.0954 \| \| 1.1414 \| 0.5495 \| 84 \| 1.0955 \| \| 1.0821 \| 0.5756 \| 88 \| 1.0930 \| \| 1.0277 \| 0.6018 \| 92 \| 1.0908 \| \| 1.0303 \| 0.6280 \| 96 \| 1.0917 \| \| 1.0947 \| 0.6541 \| 100 \| 1.0905 \| \| 1.0824 \| 0.6803 \| 104 \| 1.0903 \| \| 1.0726 \| 0.7065 \| 108 \| 1.0912 \| \| 1.1064 \| 0.7326 \| 112 \| 1.0907 \| \| 1.0467 \| 0.7588 \| 116 \| 1.0892 \| \| 1.0725 \| 0.7850 \| 120 \| 1.0885 \| \| 1.09 \| 0.8111 \| 124 \| 1.0893 \| \| 1.0506 \| 0.8373 \| 128 \| 1.0900 \| \| 0.9951 \| 0.8635 \| 132 \| 1.0902 \| \| 1.1032 \| 0.8896 \| 136 \| 1.0895 \| \| 1.0116 \| 0.9158 \| 140 \| 1.0891 \| \| 1.0683 \| 0.9419 \| 144 \| 1.0889 \| \| 1.0902 \| 0.9681 \| 148 \| 1.0888 \| \| 1.0721 \| 0.9943 \| 152 \| 1.0887 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
justRyan/Private	justRyan	"2024-06-10T14:01:15Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:01:15Z"	Entry not found
TonyStarkD99/Unsloth_Mistral-7B-instruct-v0.2-Fine-tuned-FCN	TonyStarkD99	"2024-06-10T17:07:44Z"	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:03:02Z"	--- 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. 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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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casque/Cameltoe_FefaAIart	casque	"2024-06-10T14:06:09Z"	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	"2024-06-10T14:03:39Z"	--- license: creativeml-openrail-m ---
tranthaihoa/gemma_evidence	tranthaihoa	"2024-06-10T14:05:19Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:05:08Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-bnb-4bit --- # Uploaded model - Developed by: tranthaihoa - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-bnb-4bit This gemma 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)
kodefreezai/3d-icon-sdxl-lora	kodefreezai	"2024-06-10T14:07:21Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:07:21Z"	Entry not found
caya1015/wira.voice1	caya1015	"2024-06-10T14:07:48Z"	0	null	[ "license:other", "region:us" ]	null	"2024-06-10T14:07:48Z"	--- license: other license_name: wira license_link: LICENSE ---
xuanye/idea_summary_0610	xuanye	"2024-06-10T16:56:33Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-06-10T14:10:08Z"	Entry not found
DGurgurov/conceptnet_embeddings	DGurgurov	"2024-06-10T15:47:17Z"	0	null	[ "license:cc-by-4.0", "region:us" ]	null	"2024-06-10T14:11:38Z"	--- license: cc-by-4.0 --- # Clean ConceptNet Data for All Languages ## Data Details For our project on [Retrofitting Glove embeddings for Low Resource Languages](https://github.com/pyRis/retrofitting-embeddings-lrls/tree/main?tab=readme-ov-file), we extracted all data from the [ConceptNet](https://github.com/commonsense/conceptnet5/wiki/Downloads) database for 304 languages. The extraction process involved several steps to clean and analyze the data from the official ConceptNet dump available [here](https://s3.amazonaws.com/conceptnet/downloads/2019/edges/conceptnet-assertions-5.7.0.csv.gz). The final extracted dataset, available in another [HuggingFace repo](https://huggingface.co/datasets/DGurgurov/conceptnet_all), was used for training the graph embeddings using PPMI and consequently applying SVD on the co-occurence statistics of PPMI between the words. We generate graph embeddings for 72 languages present in both CC100 and ConceptNet. ### Dataset Structure Each file is a txt file with a word / phrase and corresponding embedding separated with a space. Use the following function to read in the embeddings: ```python def read_embeddings_from_text(file_path, embedding_size=300): """Function to read the embeddings from a txt file""" embeddings = {} with open(file_path, 'r', encoding='utf-8') as file: for line in file: parts = line.strip().split(' ') embedding_start_index = len(parts) - embedding_size phrase = ' '.join(parts[:embedding_start_index]) embedding = np.array([float(val) for val in parts[embedding_start_index:]]) embeddings[phrase] = embedding return embeddings ``` ### Language Details \| Language Code \| Language Name \| Vocabulary Size\| \| --- \| --- \| --- \| \| af \| Afrikaans \| 12973 \| \| sc \| Sardinian \| 573 \| \| yo \| Yoruba \| 2283 \| \| gn \| Guarani \| 131 \| \| qu \| Quechua \| 5156 \| \| li \| Limburgish \| 485 \| \| ln \| Lingala \| 4109 \| \| wo \| Wolof \| 1196 \| \| zu \| Zulu \| 2758 \| \| rm \| Romansh \| 3919 \| \| ht \| Haitian Creole \| 2699 \| \| su \| Sundanese \| 2514 \| \| br \| Breton \| 11665 \| \| gd \| Scottish Gaelic \| 14418 \| \| xh \| Xhosa \| 2504 \| \| mg \| Malagasy \| 26575 \| \| jv \| Javanese \| 4919 \| \| fy \| Frisian \| 7608 \| \| sa \| Sanskrit \| 5789 \| \| my \| Burmese \| 4875 \| \| ug \| Uyghur \| 998 \| \| yi \| Yiddish \| 8054 \| \| or \| Oriya \| 109 \| \| ha \| Hausa \| 802 \| \| la \| Latin \| 848943 \| \| sd \| Sindhi \| 143 \| \| so \| Somali \| 593 \| \| ku \| Kurdish \| 9737 \| \| pa \| Punjabi \| 4488 \| \| ps \| Pashto \| 1087 \| \| ga \| Irish \| 29459 \| \| am \| Amharic \| 1909 \| \| km \| Khmer \| 3466 \| \| uz \| Uzbek \| 5224 \| \| ky \| Kyrgyz \| 3574 \| \| cy \| Welsh \| 13243 \| \| gu \| Gujarati \| 4427 \| \| eo \| Esperanto \| 91074 \| \| sw \| Swahili \| 9131 \| \| mr \| Marathi \| 5545 \| \| kn \| Kannada \| 3415 \| \| ne \| Nepali \| 4224 \| \| mn \| Mongolian \| 6740 \| \| si \| Sinhala \| 2062 \| \| te \| Telugu \| 18707 \| \| be \| Belarusian \| 14871 \| \| mk \| Macedonian \| 28935 \| \| gl \| Galician \| 52824 \| \| hy \| Armenian \| 23434 \| \| is \| Icelandic \| 40287 \| \| ml \| Malayalam \| 6750 \| \| bn \| Bengali \| 7306 \| \| ur \| Urdu \| 8476 \| \| kk \| Kazakh \| 13700 \| \| ka \| Georgian \| 25014 \| \| az \| Azerbaijani \| 13277 \| \| sq \| Albanian \| 16262 \| \| ta \| Tamil \| 9064 \| \| et \| Estonian \| 20088 \| \| lv \| Latvian \| 30059 \| \| ms \| Malay \| 88416 \| \| sl \| Slovenian \| 89210 \| \| lt \| Lithuanian \| 21184 \| \| he \| Hebrew \| 27283 \| \| sk \| Slovak \| 21657 \| \| el \| Greek \| 39667 \| \| th \| Thai \| 94281 \| \| bg \| Bulgarian \| 171740 \| \| da \| Danish \| 46600 \| \| uk \| Ukrainian \| 27682 \| \| ro \| Romanian \| 36206 \| ### Licensing Information This work includes data from ConceptNet 5, which was compiled by the Commonsense Computing Initiative. ConceptNet 5 is freely available under the Creative Commons Attribution-ShareAlike license (CC BY SA 3.0) from http://conceptnet.io. ### Citation Information ``` @paper{speer2017conceptnet, author = {Robyn Speer and Joshua Chin and Catherine Havasi}, title = {ConceptNet 5.5: An Open Multilingual Graph of General Knowledge}, conference = {AAAI Conference on Artificial Intelligence}, year = {2017}, pages = {4444--4451}, keywords = {ConceptNet; knowledge graph; word embeddings}, url = {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972} } ```
Reihaneh/wav2vec2_fy_common_voice_35	Reihaneh	"2024-06-10T14:13:05Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:13:04Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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richie-ghost/llama3-8b-qlora-ultrachat	richie-ghost	"2024-06-10T14:30:52Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-06-10T14:13:40Z"	Entry not found
baf2b252097d46299a/loss_testing_9d7444b6ecea4be6a45bc1f3f06582ef	baf2b252097d46299a	"2024-06-10T14:14:45Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:14:19Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AnhDuc2507/model_weight_with_token_110_12	AnhDuc2507	"2024-06-10T14:15:39Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:15:37Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dadabu/testlora	dadabu	"2024-06-10T15:10:48Z"	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:15:55Z"	--- 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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(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]
thangduong0509/blip_vivqa_finetuned_200s	thangduong0509	"2024-06-10T16:08:09Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "blip", "visual-question-answering", "endpoints_compatible", "region:us" ]	visual-question-answering	"2024-06-10T14:16:20Z"	Entry not found
manbeast3b/KinoInferLord5	manbeast3b	"2024-06-10T14:17:26Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:17:18Z"	Entry not found
bckang/zephyr-7b-sft-full-gauss	bckang	"2024-06-10T14:20:14Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:20:14Z"	Entry not found
giacopara/multiview_test1	giacopara	"2024-06-10T14:20:17Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:20:17Z"	Entry not found
teste001/Narracao_nova	teste001	"2024-06-10T14:21:54Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-10T14:21:32Z"	--- license: openrail ---
Hitesh17/q-FrozenLake-v1-4x4-noSlippery	Hitesh17	"2024-06-10T14:22:15Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-10T14:22:12Z"	--- 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="Hitesh17/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"]) ```
yomilimi/Jeolla_model2	yomilimi	"2024-06-10T14:52:37Z"	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-06-10T14:23:12Z"	Entry not found
Propicto/t2p-mbart-large-cc25-orfeo	Propicto	"2024-06-10T14:54:27Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-10T14:23:37Z"	--- license: apache-2.0 ---
Avery-808/Whisper-checkpoints	Avery-808	"2024-06-10T14:25:01Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:25:01Z"	Entry not found
aleoaaaa/camembert2camembert_shared-finetuned-french-summarization_finetuned_10_06_14_25	aleoaaaa	"2024-06-10T14:25:51Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:25:51Z"	Entry not found
haidermasood99/openhermes-mistral-dpo-gptq	haidermasood99	"2024-06-10T17:07:58Z"	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TheBloke/OpenHermes-2-Mistral-7B-GPTQ", "license:apache-2.0", "region:us" ]	null	"2024-06-10T14:26:08Z"	--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: TheBloke/OpenHermes-2-Mistral-7B-GPTQ model-index: - name: openhermes-mistral-dpo-gptq results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # openhermes-mistral-dpo-gptq This model is a fine-tuned version of [TheBloke/OpenHermes-2-Mistral-7B-GPTQ](https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GPTQ) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7095 - Rewards/chosen: -0.1860 - Rewards/rejected: -0.3362 - Rewards/accuracies: 0.4904 - Rewards/margins: 0.1502 - Logps/rejected: -269.4139 - Logps/chosen: -269.0661 - Logits/rejected: -2.0876 - Logits/chosen: -2.1662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6952 \| 0.0002 \| 10 \| 0.6717 \| 0.1018 \| 0.0250 \| 0.5769 \| 0.0769 \| -265.8023 \| -266.1874 \| -2.1074 \| -2.1866 \| \| 0.7473 \| 0.0003 \| 20 \| 0.6787 \| 0.0390 \| -0.0403 \| 0.5192 \| 0.0793 \| -266.4547 \| -266.8159 \| -2.1064 \| -2.1840 \| \| 0.6557 \| 0.0005 \| 30 \| 0.7320 \| -0.2017 \| -0.2789 \| 0.4904 \| 0.0772 \| -268.8405 \| -269.2226 \| -2.0938 \| -2.1716 \| \| 0.8058 \| 0.0007 \| 40 \| 0.7174 \| -0.2018 \| -0.3209 \| 0.4808 \| 0.1192 \| -269.2612 \| -269.2236 \| -2.0878 \| -2.1663 \| \| 0.5939 \| 0.0009 \| 50 \| 0.7095 \| -0.1860 \| -0.3362 \| 0.4904 \| 0.1502 \| -269.4139 \| -269.0661 \| -2.0876 \| -2.1662 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.0.1+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1
Draxx32/Minimalist_Anime_Style	Draxx32	"2024-06-10T14:27:44Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:26:38Z"	Entry not found
Hitesh17/Taxi-v3	Hitesh17	"2024-06-10T14:26:56Z"	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-10T14:26:53Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Hitesh17/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ledigajobb/ledigajobb-matrix-hub	ledigajobb	"2024-06-10T14:27:35Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:27:02Z"	Entry not found
xionggz/test	xionggz	"2024-06-10T14:27:27Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:27:26Z"	Entry not found
baf2b252097d46299a/loss-testing_6c35ae7cde2544729eee0e79e1327e09	baf2b252097d46299a	"2024-06-10T14:28:55Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:28:22Z"	--- 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. (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]
mahmoud-hussein16/qwen1.5-llm	mahmoud-hussein16	"2024-06-10T14:30:22Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:30:22Z"	Entry not found
aspis/llama3_moq_generation_pt-br_v0.3	aspis	"2024-06-10T14:30:45Z"	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	"2024-06-10T14:30:31Z"	--- 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: aspis - 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)
mahmoud-hussein16/Llama-2-7b-chat-hf-SW2-test-fine-tuned	mahmoud-hussein16	"2024-06-10T14:31:16Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:31:16Z"	Entry not found
baf2b252097d46299a/loss_testing_256af9bec4934ab8ba893fc6c4eb68a3	baf2b252097d46299a	"2024-06-10T14:33:02Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:32:36Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
baf2b252097d46299a/loss_testing_eec4a65b5f534a3188b6c45d8cd6f2c8	baf2b252097d46299a	"2024-06-10T14:36:26Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:35:13Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
onizukal/Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2	onizukal	"2024-06-11T21:51:38Z"	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-10T14:36:58Z"	--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8322147651006712 --- <!-- 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. --> # Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.8357 - Accuracy: 0.8322 ## 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4181 \| 1.0 \| 631 \| 0.4865 \| 0.7722 \| \| 0.3391 \| 2.0 \| 1262 \| 0.4494 \| 0.8314 \| \| 0.1276 \| 3.0 \| 1893 \| 0.5148 \| 0.8393 \| \| 0.1436 \| 4.0 \| 2524 \| 0.7474 \| 0.8302 \| \| 0.1404 \| 5.0 \| 3155 \| 1.1243 \| 0.8287 \| \| 0.0742 \| 6.0 \| 3786 \| 1.4178 \| 0.8401 \| \| 0.0155 \| 7.0 \| 4417 \| 1.6465 \| 0.8247 \| \| 0.0 \| 8.0 \| 5048 \| 1.7427 \| 0.8239 \| \| 0.0 \| 9.0 \| 5679 \| 1.8000 \| 0.8346 \| \| 0.0 \| 10.0 \| 6310 \| 1.8357 \| 0.8322 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
Amber/spider-large-pretrain-2020	Amber	"2024-06-10T14:39:08Z"	0	transformers	[ "transformers", "safetensors", "dpr", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:37:32Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
richie-ghost/llama3-8b-qlora-customData	richie-ghost	"2024-06-10T14:40:03Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:40:03Z"	Entry not found
calisoul/arijit	calisoul	"2024-06-10T14:41:14Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:40:37Z"	Entry not found
z-z-anabioz/x-0	z-z-anabioz	"2024-06-14T15:45:08Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:42:37Z"	Entry not found
Sreedev11/olympics_prediction_model	Sreedev11	"2024-06-10T14:57:56Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:48:07Z"	# %% import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn import preprocessing from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report,accuracy_score from sklearn.model_selection import TimeSeriesSplit,train_test_split from sklearn.cluster import KMeans import matplotlib from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report,accuracy_score from sklearn.naive_bayes import GaussianNB from sklearn import metrics from sklearn.svm import LinearSVC import pylab as pl from sklearn.ensemble import RandomForestClassifier import warnings warnings.filterwarnings('ignore') df=pd.read_csv("athlete_events.csv") # %% df # %% df.head() # %% df.info() # %% df.describe() # %% df.dtypes # %% df.ndim # %% df.shape # %% df.isna().sum() # %% #DNW:Did Not win , missing values of medal are filled with DNW df['Medal'].fillna("DNW",inplace=True) # %% df_noc=pd.read_csv("noc_regions.csv") # %% df_noc # %% df_noc=df_noc.drop("notes",axis=1) # %% df_noc # %% df_noc.rename(columns={"region":"country"},inplace=True) # %% df_noc # %% df.sample(4) # %% #joining both dataset olympics_merge=df.merge(df_noc,left_on='NOC',right_on='NOC',how='left') # %% olympics_merge.sample() # %% print(olympics_merge.loc[olympics_merge['country'].isnull(),['NOC', 'Team']].drop_duplicates()) # %% # Replace missing Teams by the values 1. SGP - Singapore # 2. ROT - Refugee Olympic Athletes # 3. UNK - Unknown # 4. TUV - Tuvalu #olympics_merge.loc[olympics_merge['Country'].isnull(), ['Country']] = olympics_merge['Team'] # %% olympics_merge.loc[olympics_merge['country'].isnull(), ['country']] = olympics_merge['Team'] # %% olympics_merge # %% print(olympics_merge.loc[olympics_merge['country'].isnull(),['NOC', 'Team']].drop_duplicates()) # %% olympics_merge['country'] = np.where(olympics_merge['NOC']=='SGP', 'Singapore', olympics_merge['country']) olympics_merge['country'] = np.where(olympics_merge['NOC']=='ROT', 'Refugee Olympic Athletes', olympics_merge['country']) olympics_merge['country'] = np.where(olympics_merge['NOC']=='UNK', 'Unknown', olympics_merge['country']) olympics_merge['country'] = np.where(olympics_merge['NOC']=='TUV', 'Tuvalu', olympics_merge['country']) # %% olympics_merge # %% olympics_merge.drop("Team",axis=1,inplace=True) # %% olympics_merge.sample() # %% olympics_merge.rename(columns={'country':'Team'},inplace=True) # %% olympics_merge.head(2) # %% print(olympics_merge.loc[olympics_merge['Team'].isnull(),['NOC', 'Team']].drop_duplicates()) # %% olympics_merge.isnull().sum() # %% for i in ["Age","Height","Weight"]: sns.histplot(olympics_merge[i],kde=True) plt.show() # %% for i in ["Age","Weight",]: olympics_merge[i]=olympics_merge[i].fillna(olympics_merge[i].mean()) # %% olympics_merge["Height"]=olympics_merge["Height"].fillna(olympics_merge["Height"].mean()) # %% olympics_merge.isnull().sum() # %% olympics_merge.info() # %% olympics_merge['Sex']=np.where(olympics_merge['Sex']=='M',1,0) # %% olympics_merge.sample(2) # %% olympics_merge["Medal"].unique() # %% olympics_merge['Event'].unique() # %% olympics_merge['Sport'].unique() # %% olympics_merge1=olympics_merge # %% olympics_merge1 # %% from sklearn.preprocessing import LabelEncoder le=LabelEncoder() # %% olympics_merge1['Medal']=le.fit_transform(olympics_merge1['Medal']) # %% olympics_merge1 # %% olympics_merge1['Medal'].unique() # %% summer=olympics_merge1.loc[(olympics_merge1['Year']>1960)&(olympics_merge1['Season']=="Summer"), :] summer.head(5) # %% summer=summer.reset_index() summer.head(10) # %% summer.sample() # %% #extracting unique events in a new list # %% summerlistunique=summer.Event.unique() len(summerlistunique) # %% summerlistunique # %% summer.drop(['Season'],axis=1,inplace=True) summer.drop(['NOC'],axis=1,inplace=True) summer.drop(['Games'],axis=1,inplace=True) summer.drop(['City'],axis=1,inplace=True) summer.drop(['Year'],axis=1,inplace=True) summer.drop(['Sport'],axis=1,inplace=True) summer.drop(['ID'],axis=1,inplace=True) summer.drop(['Name'],axis=1,inplace=True) summer.drop(['index'],axis=1,inplace=True) # %% summer # %% #created a column for encoded team and encoded events in numerical form in original dataset summer['Team_encode']=le.fit_transform(summer['Team']) summer['Event_encode']=le.fit_transform(summer['Event']) # %% #storing the team names and corresponding encoded numerical values into a new csv file after sorting them according to team name TeamKeys=summer[['Team','Team_encode']].copy() TeamKeys.drop_duplicates(subset="Team",inplace=True) TeamKeys.to_csv("keystoteam.csv") # %% TeamKeys.head(4) # %% #storing event names and corresponding encoded numerical values into a new csv file after sorting them according to the event name EventKeys=summer[['Event','Event_encode']].copy() EventKeys.drop_duplicates(subset="Event",inplace=True) EventKeys.to_csv("keystoevent.csv") # %% EventKeys.head(4) # %% summer # %% summer.drop(['Event'],axis=1,inplace=True) summer.drop(['Team'],axis=1,inplace=True) # %% summer # %% y=summer['Medal'] # %% y # %% x=summer.drop("Medal",axis=1) # %% x # %% X_train, X_test, Y_train, Y_test = train_test_split(x,y,test_size=0.30, random_state=99) # %% x # %% y # %% X_test # %% Y_test # %% #ALGORITHM 1 LOGISTIC REGRESSION # %% lr=LogisticRegression() lr.fit(X_train,Y_train) Y_pred=lr.predict(X_test) sk_report=classification_report(digits=6,y_true=Y_test,y_pred=Y_pred) print("Accuracy",round(accuracy_score(Y_pred,Y_test)100,2)) print(sk_report) print(pd.crosstab(Y_test,Y_pred,rownames=['Actual'],colnames=['Predicted'],margins=True)) # %% #ALGORITHM 2 DECESSION TREE # %% decision_tree = DecisionTreeClassifier() decision_tree.fit(X_train, Y_train) Y_pred = decision_tree.predict(X_test) acc_decision_tree1 = round(decision_tree.score(X_test, Y_test) 100, 2) sk_report = classification_report(digits=6, y_true=Y_test, y_pred=Y_pred) print("Accuracy", acc_decision_tree1) print(sk_report) ### Confusion Matrix print(pd.crosstab(Y_test, Y_pred,rownames=['Actual'],colnames=['Predicted'],margins=True)) # %% #ALGORITHM 3 RANDOM FOREST # %% random_forest = RandomForestClassifier(n_estimators=200) random_forest.fit(X_train,Y_train) Y_pred = random_forest.predict(X_test) random_forest.score(X_test, Y_test) acc_random_forest1=round(random_forest.score(X_test, Y_test)*100,2) k_report = classification_report( digits=6, y_true=Y_test, y_pred=Y_pred) print("Accuracy" , acc_random_forest1) print(sk_report) pd.crosstab(Y_test, Y_pred,rownames=['Actual'],colnames=['Predicted'],margins=True) # %% x.sample(5) # %% y.sample(5) # %% summer.sample(4) # %% random_forest.predict([[1,19.0,173.0,70.0,87,163]]) # %% import pickle from joblib import dump,load dump(random_forest,'olympics_model.pkl') model_file = open(r"Projects\Olympics\olympics_model1.pkl","wb") pickle.dump(random_forest,model_file)
iloncka/exp_5_old_bg-subs_1_v_5_convnext_nano.in12k_ft_in1k_ep_60	iloncka	"2024-06-10T14:52:00Z"	0	fastai	[ "fastai", "region:us" ]	null	"2024-06-10T14:50:09Z"	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
imdatta0/qwen2_Magiccoder_evol_10k_reverse	imdatta0	"2024-06-10T18:45:26Z"	0	peft	[ "peft", "safetensors", "unsloth", "generated_from_trainer", "base_model:Qwen/Qwen2-7B", "license:apache-2.0", "region:us" ]	null	"2024-06-10T14:51:37Z"	--- license: apache-2.0 library_name: peft tags: - unsloth - generated_from_trainer base_model: Qwen/Qwen2-7B model-index: - name: qwen2_Magiccoder_evol_10k_reverse 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. --> # qwen2_Magiccoder_evol_10k_reverse This model is a fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8272 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.02 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8303 \| 0.0261 \| 4 \| 0.8571 \| \| 0.8267 \| 0.0522 \| 8 \| 0.8449 \| \| 0.8201 \| 0.0784 \| 12 \| 0.8389 \| \| 0.8002 \| 0.1045 \| 16 \| 0.8436 \| \| 0.8491 \| 0.1306 \| 20 \| 0.8414 \| \| 0.7448 \| 0.1567 \| 24 \| 0.8434 \| \| 0.7606 \| 0.1828 \| 28 \| 0.8459 \| \| 0.9214 \| 0.2089 \| 32 \| 0.8474 \| \| 0.8071 \| 0.2351 \| 36 \| 0.8466 \| \| 0.8353 \| 0.2612 \| 40 \| 0.8479 \| \| 0.8762 \| 0.2873 \| 44 \| 0.8473 \| \| 0.8544 \| 0.3134 \| 48 \| 0.8475 \| \| 0.7855 \| 0.3395 \| 52 \| 0.8482 \| \| 0.7725 \| 0.3656 \| 56 \| 0.8467 \| \| 0.8044 \| 0.3918 \| 60 \| 0.8470 \| \| 0.8282 \| 0.4179 \| 64 \| 0.8446 \| \| 0.853 \| 0.4440 \| 68 \| 0.8449 \| \| 0.8047 \| 0.4701 \| 72 \| 0.8439 \| \| 0.8145 \| 0.4962 \| 76 \| 0.8431 \| \| 0.8063 \| 0.5223 \| 80 \| 0.8411 \| \| 0.8782 \| 0.5485 \| 84 \| 0.8395 \| \| 0.7944 \| 0.5746 \| 88 \| 0.8395 \| \| 0.8728 \| 0.6007 \| 92 \| 0.8370 \| \| 0.7882 \| 0.6268 \| 96 \| 0.8363 \| \| 0.8999 \| 0.6529 \| 100 \| 0.8354 \| \| 0.7857 \| 0.6790 \| 104 \| 0.8341 \| \| 0.8258 \| 0.7052 \| 108 \| 0.8331 \| \| 0.7877 \| 0.7313 \| 112 \| 0.8317 \| \| 0.7686 \| 0.7574 \| 116 \| 0.8305 \| \| 0.7422 \| 0.7835 \| 120 \| 0.8299 \| \| 0.8229 \| 0.8096 \| 124 \| 0.8292 \| \| 0.7577 \| 0.8357 \| 128 \| 0.8285 \| \| 0.8811 \| 0.8619 \| 132 \| 0.8278 \| \| 0.8243 \| 0.8880 \| 136 \| 0.8277 \| \| 0.8243 \| 0.9141 \| 140 \| 0.8275 \| \| 0.8096 \| 0.9402 \| 144 \| 0.8275 \| \| 0.8476 \| 0.9663 \| 148 \| 0.8274 \| \| 0.8154 \| 0.9925 \| 152 \| 0.8272 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mahmoud-hussein16/Llama-2-7b-chat-hf-SW2-test-fine-tuned-cpu	mahmoud-hussein16	"2024-06-10T14:52:22Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:52:22Z"	Entry not found
DokiQueen/Tentacle-seedbed	DokiQueen	"2024-06-10T14:58:02Z"	0	null	[ "region:us" ]	null	"2024-06-10T14:55:27Z"	Entry not found
sounana/openai-whisper-large-v2-Lastversion	sounana	"2024-06-10T14:56:45Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:56:42Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
LarryAIDraw/raeDiffusionXL_v10	LarryAIDraw	"2024-06-10T21:59:34Z"	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	"2024-06-10T14:57:57Z"	--- license: creativeml-openrail-m --- https://civitai.com/models/506641/rae-diffusion-xl
baf2b252097d46299a/loss_testing_29c64edb08fc49cda6045b42216eb909	baf2b252097d46299a	"2024-06-10T14:59:43Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T14:59:18Z"	--- 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]
lcass00/Meta-Llama-3-8B-Instruct-pqa-10-merged-peft	lcass00	"2024-06-11T10:22:36Z"	0	peft	[ "peft", "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	"2024-06-10T14:59:20Z"	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - _load_in_8bit: False - _load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 - bnb_4bit_quant_storage: uint8 - load_in_4bit: True - load_in_8bit: False ### Framework versions - PEFT 0.5.0
kaylaisya/absa	kaylaisya	"2024-06-10T15:00:07Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-10T15:00:07Z"	--- license: apache-2.0 ---
onizukal/Boya2_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2	onizukal	"2024-06-11T22:38:21Z"	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-10T15:00:40Z"	--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Boya2_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8523783488244943 --- <!-- 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. --> # Boya2_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6527 - Accuracy: 0.8524 ## 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3607 \| 1.0 \| 913 \| 0.3589 \| 0.8565 \| \| 0.2545 \| 2.0 \| 1826 \| 0.3889 \| 0.8537 \| \| 0.1839 \| 3.0 \| 2739 \| 0.5447 \| 0.8455 \| \| 0.0174 \| 4.0 \| 3652 \| 0.8367 \| 0.8548 \| \| 0.0719 \| 5.0 \| 4565 \| 1.2411 \| 0.8428 \| \| 0.0044 \| 6.0 \| 5478 \| 1.3737 \| 0.8425 \| \| 0.0004 \| 7.0 \| 6391 \| 1.3329 \| 0.8529 \| \| 0.0003 \| 8.0 \| 7304 \| 1.6015 \| 0.8477 \| \| 0.0201 \| 9.0 \| 8217 \| 1.6119 \| 0.8491 \| \| 0.0002 \| 10.0 \| 9130 \| 1.6527 \| 0.8524 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
AriaRahmati1/2ghesmat9part1	AriaRahmati1	"2024-06-10T15:59:46Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-10T15:05:06Z"	--- license: openrail ---
StephenYX/Qwen2-7B-Instruct	StephenYX	"2024-06-10T15:06:23Z"	0	null	[ "region:us" ]	null	"2024-06-10T15:06:23Z"	Entry not found
ttangmo24/vit-base-classification-Eye-Diseases-New	ttangmo24	"2024-06-10T15:08:40Z"	0	null	[ "region:us" ]	null	"2024-06-10T15:08:40Z"	Entry not found
datek/google-gemma-2b-1718032125	datek	"2024-06-10T15:11:16Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-10T15:08:46Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
onizukal/Boya1_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold2	onizukal	"2024-06-12T20:57:23Z"	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-10T15:09:59Z"	--- license: apache-2.0 base_model: microsoft/beit-large-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: Boya1_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8421621621621621 --- <!-- 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. --> # Boya1_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0703 - Accuracy: 0.8422 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4897 \| 1.0 \| 923 \| 0.4183 \| 0.8332 \| \| 0.3508 \| 2.0 \| 1846 \| 0.4051 \| 0.8403 \| \| 0.248 \| 3.0 \| 2769 \| 0.6150 \| 0.8459 \| \| 0.1818 \| 4.0 \| 3692 \| 0.8410 \| 0.8446 \| \| 0.0386 \| 5.0 \| 4615 \| 1.0703 \| 0.8422 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
baf2b252097d46299a/loss_testing_727ce689a1594b9081c02678b92d46d6	baf2b252097d46299a	"2024-06-10T15:11:01Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T15:10:07Z"	--- 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. 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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]
hassanaliemon/bn_rag_llama3-8b	hassanaliemon	"2024-06-11T16:20:31Z"	1	transformers	[ "transformers", "safetensors", "question-answering", "bn", "dataset:BanglaLLM/bangla-alpaca", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	"2024-06-10T15:10:37Z"	--- license: apache-2.0 datasets: - BanglaLLM/bangla-alpaca language: - bn library_name: transformers pipeline_tag: question-answering --- # How to Use: You can use the model with a pipeline for a high-level helper or load the model directly. Here's how: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM # Determine the device to use (GPU if available, else CPU) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load pre-trained model and tokenizer tokenizer = AutoTokenizer.from_pretrained("hassanaliemon/bn_rag_llama3-8b") # model = AutoModelForCausalLM.from_pretrained("hassanaliemon/bn_rag_llama3-8b") model = AutoModelForCausalLM.from_pretrained( "hassanaliemon/bn_rag_llama3-8b", load_in_8bit=True, torch_dtype=torch.bfloat16, device_map="auto", ) # Define the prompt template prompt = """এখানে একটি নির্দেশনা দেওয়া হলো, যা একটি কাজ সম্পন্ন করার উপায় বর্ণনা করে, এবং এর সাথে একটি ইনপুট দেওয়া হলো যা আরও প্রেক্ষাপট প্রদান করে। একটি উত্তর লিখুন যা অনুরোধটি সঠিকভাবে পূরণ করে। প্রসঙ্গ থেকে সুনির্দিষ্ট উত্তর দিন. ### Instruction: {} ### Input: {} ### Response: {} """ def generate_response(question, context): # Tokenize the input and move tensors to the selected device inputs = tokenizer([prompt.format(question, context, "")], return_tensors="pt").to(device) # Generate the response outputs = model.generate(**inputs, max_new_tokens=1024, use_cache=True) # Decode the generated text responses = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] # Extract the response text response_start = responses.find("### Response:") + len("### Response:") response = responses[response_start:].strip() return response # Example Usage question = "ভারতীয় বাঙালি কথাসাহিত্যিক মহাশ্বেতা দেবীর মৃত্যু কবে হয় ?" context = "২০১৬ সালের ২৩ জুলাই হৃদরোগে আক্রান্ত হয়ে মহাশ্বেতা দেবী কলকাতার বেল ভিউ ক্লিনিকে ভর্তি হন। সেই বছরই ২৮ জুলাই একাধিক অঙ্গ বিকল হয়ে তাঁর মৃত্যু ঘটে। তিনি মধুমেহ, সেপ্টিসেমিয়া ও মূত্র সংক্রমণ রোগেও ভুগছিলেন।" answer = generate_response(question, context) print(answer) # মহাশ্বেতা দেবী ২০১৬ সালের ২৮ জুলাই মারা যান। ```
aleoaaaa/camembert2camembert_shared-finetuned-french-summarization_finetuned_10_06_15_10	aleoaaaa	"2024-06-10T15:10:48Z"	0	null	[ "region:us" ]	null	"2024-06-10T15:10:48Z"	Entry not found
moszis/MLChapterDemo	moszis	"2024-06-13T15:27:33Z"	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	"2024-06-10T15:11:25Z"	--- 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: moszis - 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)
aleoaaaa/camembert2camembert_shared-finetuned-french-summarization_finetuned_10_06_15_12	aleoaaaa	"2024-06-10T15:12:29Z"	0	null	[ "region:us" ]	null	"2024-06-10T15:12:29Z"	Entry not found
Adhi98ai/Movie_recommendation	Adhi98ai	"2024-06-10T15:13:27Z"	0	null	[ "region:us" ]	null	"2024-06-10T15:13:26Z"	Entry not found
ka05ar/DeepSeekMath-7B-Ins-Bn_Math_v2	ka05ar	"2024-06-10T15:17:23Z"	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T15:14:18Z"	--- 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]
aleoaaaa/camembert2camembert_shared-finetuned-french-summarization_finetuned_10_06_15_14	aleoaaaa	"2024-06-10T15:14:58Z"	0	null	[ "region:us" ]	null	"2024-06-10T15:14:58Z"	Entry not found
ismailpolas/0ba68f47-11a5-41ec-9748-4baddf2e830a	ismailpolas	"2024-06-10T15:17:53Z"	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-10T15:17:14Z"	Entry not found
Propicto/t2p-nmt-commonvoice	Propicto	"2024-06-10T15:17:39Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-10T15:17:39Z"	--- license: apache-2.0 ---
aleoaaaa/camembert2camembert_shared-finetuned-french-summarization_finetuned_10_06_15_17	aleoaaaa	"2024-06-10T15:22:39Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "encoder-decoder", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-06-10T15:17:49Z"	Entry not found
Propicto/t2p-nmt-orfeo	Propicto	"2024-06-10T15:17:55Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-10T15:17:55Z"	--- license: apache-2.0 ---
Litzy619/MIS0610NT	Litzy619	"2024-06-13T02:32:20Z"	0	null	[ "region:us" ]	null	"2024-06-10T15:20:08Z"	Entry not found
mnlp-2024/mcqa-gemma-lora	mnlp-2024	"2024-06-10T15:26:39Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-06-10T15:20:30Z"	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
longlivebigcat/hunheNew_qiwen7b_alp_lora3200_model	longlivebigcat	"2024-06-10T15:21:07Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2-7B-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-10T15:20:54Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl base_model: unsloth/Qwen2-7B-bnb-4bit --- # Uploaded model - Developed by: longlivebigcat - License: apache-2.0 - Finetuned from model : unsloth/Qwen2-7B-bnb-4bit This qwen2 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)
badrabdullah/xls-r-300-cv17-bulgarian-adap-ru	badrabdullah	"2024-06-10T21:18:19Z"	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_17_0", "base_model:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-06-10T15:21:24Z"	--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - common_voice_17_0 metrics: - wer model-index: - name: xls-r-300-cv17-bulgarian-adap-ru results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_17_0 type: common_voice_17_0 config: bg split: validation args: bg metrics: - name: Wer type: wer value: 0.3023246994576965 --- <!-- 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. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning-polish/runs/hevbjmzy) # xls-r-300-cv17-bulgarian-adap-ru This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3977 - Wer: 0.3023 - Cer: 0.0722 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\|:------:\| \| 3.1617 \| 0.6579 \| 100 \| 3.1554 \| 1.0 \| 1.0 \| \| 1.0032 \| 1.3158 \| 200 \| 1.0726 \| 0.8684 \| 0.2419 \| \| 0.5552 \| 1.9737 \| 300 \| 0.4924 \| 0.5297 \| 0.1303 \| \| 0.2763 \| 2.6316 \| 400 \| 0.3795 \| 0.4442 \| 0.1043 \| \| 0.2273 \| 3.2895 \| 500 \| 0.3769 \| 0.4222 \| 0.1014 \| \| 0.3216 \| 3.9474 \| 600 \| 0.3611 \| 0.3993 \| 0.0971 \| \| 0.1553 \| 4.6053 \| 700 \| 0.3566 \| 0.3927 \| 0.0936 \| \| 0.1414 \| 5.2632 \| 800 \| 0.3676 \| 0.3869 \| 0.0923 \| \| 0.1774 \| 5.9211 \| 900 \| 0.3680 \| 0.3758 \| 0.0901 \| \| 0.1256 \| 6.5789 \| 1000 \| 0.3637 \| 0.3775 \| 0.0916 \| \| 0.2416 \| 7.2368 \| 1100 \| 0.3893 \| 0.3963 \| 0.0951 \| \| 0.1213 \| 7.8947 \| 1200 \| 0.3677 \| 0.3596 \| 0.0864 \| \| 0.0911 \| 8.5526 \| 1300 \| 0.3850 \| 0.3739 \| 0.0891 \| \| 0.0859 \| 9.2105 \| 1400 \| 0.3962 \| 0.3658 \| 0.0883 \| \| 0.0998 \| 9.8684 \| 1500 \| 0.3608 \| 0.3530 \| 0.0846 \| \| 0.108 \| 10.5263 \| 1600 \| 0.3932 \| 0.3908 \| 0.0920 \| \| 0.0824 \| 11.1842 \| 1700 \| 0.4147 \| 0.3591 \| 0.0870 \| \| 0.0888 \| 11.8421 \| 1800 \| 0.4040 \| 0.3660 \| 0.0878 \| \| 0.0609 \| 12.5 \| 1900 \| 0.4097 \| 0.3542 \| 0.0857 \| \| 0.0692 \| 13.1579 \| 2000 \| 0.4127 \| 0.3639 \| 0.0874 \| \| 0.0513 \| 13.8158 \| 2100 \| 0.4118 \| 0.3560 \| 0.0870 \| \| 0.0752 \| 14.4737 \| 2200 \| 0.4044 \| 0.3591 \| 0.0888 \| \| 0.0833 \| 15.1316 \| 2300 \| 0.3956 \| 0.3374 \| 0.0812 \| \| 0.0826 \| 15.7895 \| 2400 \| 0.3953 \| 0.3356 \| 0.0811 \| \| 0.0934 \| 16.4474 \| 2500 \| 0.4053 \| 0.3394 \| 0.0819 \| \| 0.0562 \| 17.1053 \| 2600 \| 0.4243 \| 0.3534 \| 0.0843 \| \| 0.0661 \| 17.7632 \| 2700 \| 0.4021 \| 0.3340 \| 0.0791 \| \| 0.0496 \| 18.4211 \| 2800 \| 0.4052 \| 0.3387 \| 0.0818 \| \| 0.0599 \| 19.0789 \| 2900 \| 0.4101 \| 0.3385 \| 0.0806 \| \| 0.0446 \| 19.7368 \| 3000 \| 0.3990 \| 0.3362 \| 0.0810 \| \| 0.0482 \| 20.3947 \| 3100 \| 0.4077 \| 0.3274 \| 0.0781 \| \| 0.0309 \| 21.0526 \| 3200 \| 0.4343 \| 0.3397 \| 0.0817 \| \| 0.0757 \| 21.7105 \| 3300 \| 0.4154 \| 0.3252 \| 0.0781 \| \| 0.0377 \| 22.3684 \| 3400 \| 0.4273 \| 0.3206 \| 0.0770 \| \| 0.0282 \| 23.0263 \| 3500 \| 0.3998 \| 0.3159 \| 0.0751 \| \| 0.0676 \| 23.6842 \| 3600 \| 0.3960 \| 0.3111 \| 0.0745 \| \| 0.0673 \| 24.3421 \| 3700 \| 0.3997 \| 0.3100 \| 0.0741 \| \| 0.1793 \| 25.0 \| 3800 \| 0.4065 \| 0.3106 \| 0.0738 \| \| 0.0572 \| 25.6579 \| 3900 \| 0.3951 \| 0.3098 \| 0.0739 \| \| 0.0208 \| 26.3158 \| 4000 \| 0.4097 \| 0.3106 \| 0.0740 \| \| 0.0562 \| 26.9737 \| 4100 \| 0.4016 \| 0.3081 \| 0.0734 \| \| 0.0314 \| 27.6316 \| 4200 \| 0.3939 \| 0.3008 \| 0.0715 \| \| 0.0235 \| 28.2895 \| 4300 \| 0.4008 \| 0.3023 \| 0.0720 \| \| 0.0443 \| 28.9474 \| 4400 \| 0.3963 \| 0.3033 \| 0.0724 \| \| 0.027 \| 29.6053 \| 4500 \| 0.3977 \| 0.3023 \| 0.0722 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
DiversityDrive/G.O.A.T	DiversityDrive	"2024-06-10T15:22:30Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-10T15:22:30Z"	--- license: mit ---
CamillaMazzoleni01/sd-naruto-model	CamillaMazzoleni01	"2024-06-10T15:24:53Z"	0	null	[ "region:us" ]	null	"2024-06-10T15:24:53Z"	Entry not found
miguelpezo/prueba2modelo2	miguelpezo	"2024-06-10T15:25:00Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-10T15:24:58Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Vision-CAIR/VLV-Benchmark	Vision-CAIR	"2024-06-10T15:26:46Z"	0	null	[ "region:us" ]	null	"2024-06-10T15:26:46Z"	Entry not found
Daxuxu36/TinyBERT-4L-312D-SST-2	Daxuxu36	"2024-06-10T15:29:39Z"	0	transformers	[ "transformers", "pytorch", "bert", "endpoints_compatible", "region:us" ]	null	"2024-06-10T15:27:58Z"	Entry not found