librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 122	author stringlengths 2 42	last_modified unknown	likes int64 0 6.52k	library_name stringclasses 345 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 51 values	createdAt unknown	card stringlengths 1 913k
SandeepBoddu/BigBull	SandeepBoddu	"2024-04-06T06:27:51Z"	0	null	[ "region:us" ]	null	"2024-04-06T06:27:50Z"	Entry not found
teragron/canv3	teragron	"2024-04-06T06:33:48Z"	0	transformers	[ "transformers", "tensorboard", "endpoints_compatible", "region:us" ]	null	"2024-04-06T06:28:25Z"	Entry not found
Inishds/code-llama-7b-text-to-sql	Inishds	"2024-04-06T06:28:30Z"	0	null	[ "region:us" ]	null	"2024-04-06T06:28:30Z"	Entry not found
oreeenn/jennie	oreeenn	"2024-08-11T10:25:13Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-04-06T06:41:13Z"	--- license: openrail ---
Jinwoo870/Llama2_Finetuned_train_textdata_prompt_Instruction_Set	Jinwoo870	"2024-04-08T07:22:40Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-06T06:46:00Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
thanhtuit96/ssd	thanhtuit96	"2024-04-14T18:00:01Z"	0	null	[ "tensorboard", "region:us" ]	null	"2024-04-06T06:51:03Z"	Entry not found
lmms-lab/PG_Video_LLaVA-projector	lmms-lab	"2024-04-06T06:52:42Z"	1	null	[ "region:us" ]	null	"2024-04-06T06:51:33Z"	projector checkpoints from ``` @article{munasinghe2023PGVideoLLaVA, title={PG-Video-LLaVA: Pixel Grounding Large Video-Language Models}, author={Shehan Munasinghe and Rusiru Thushara and Muhammad Maaz and Hanoona Abdul Rasheed and Salman Khan and Mubarak Shah and Fahad Khan}, journal={ArXiv 2311.13435}, year={2023} } ``` for you to more easily to download with
BhushanP-01/AdGenerator	BhushanP-01	"2024-04-06T06:53:15Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-06T06:53:10Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Inishds/deepseekcoder1.3B-text-to-sql	Inishds	"2024-04-06T08:12:16Z"	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:deepseek-ai/deepseek-coder-1.3b-base", "base_model:adapter:deepseek-ai/deepseek-coder-1.3b-base", "license:other", "region:us" ]	null	"2024-04-06T06:54:37Z"	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: deepseek-ai/deepseek-coder-1.3b-base model-index: - name: deepseekcoder1.3B-text-to-sql 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. --> # deepseekcoder1.3B-text-to-sql This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
tkwon4/whisper-large-v3-finetuned-6	tkwon4	"2024-04-06T06:57:43Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-04-06T06:55:35Z"	--- license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-large-v3-finetuned-6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v3-finetuned-6 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1070 - Wer: 115.1997 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-07 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3572 \| 1.0 \| 7532 \| 0.2604 \| 105.7528 \| \| 0.0008 \| 2.0 \| 15064 \| 0.1888 \| 99.7556 \| \| 0.0001 \| 3.0 \| 22596 \| 0.1495 \| 106.2064 \| \| 0.0 \| 4.0 \| 30128 \| 0.1288 \| 97.6591 \| \| 0.0025 \| 5.0 \| 37660 \| 0.1170 \| 124.9313 \| \| 0.0001 \| 6.0 \| 45192 \| 0.1071 \| 122.8575 \| \| 0.0001 \| 7.0 \| 52724 \| 0.1053 \| 117.5784 \| \| 0.0009 \| 8.0 \| 60256 \| 0.1045 \| 117.7447 \| \| 0.0 \| 9.0 \| 67788 \| 0.1058 \| 118.8636 \| \| 0.0003 \| 10.0 \| 75320 \| 0.1070 \| 115.1997 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
wfdsdfsdfwer/FL	wfdsdfsdfwer	"2024-05-08T19:35:50Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:04:52Z"	Entry not found
Aditadot23/ultramen.bksi	Aditadot23	"2024-04-06T07:10:28Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:06:06Z"	Entry not found
devesh1496/prompt_recover	devesh1496	"2024-04-06T07:07:14Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T07:07:14Z"	--- license: apache-2.0 ---
bei0209/test_0406	bei0209	"2024-04-07T07:15:49Z"	0	null	[ "code", "graph-ml", "en", "dataset:imagenet-1k", "doi:10.57967/hf/2041", "license:apache-2.0", "region:us" ]	graph-ml	"2024-04-06T07:15:38Z"	--- license: apache-2.0 datasets: - imagenet-1k language: - en metrics: - accuracy pipeline_tag: graph-ml tags: - code ---
viber1/Llama-2-7b-chat-finetune	viber1	"2024-04-06T07:16:32Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:16:32Z"	Entry not found
MinhMinh09/gpt2-vietnamese-finetuned-ner	MinhMinh09	"2024-04-06T07:19:15Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:19:14Z"	Entry not found
Simonk97/LUNAS	Simonk97	"2024-04-06T10:57:05Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:19:38Z"	Entry not found
leptonai/UNA-SimpleSmaug-34b-v1beta-4heads	leptonai	"2024-04-06T07:20:55Z"	0	transformers	[ "transformers", "safetensors", "endpoints_compatible", "region:us" ]	null	"2024-04-06T07:20:20Z"	Entry not found
silencer107/bobik03	silencer107	"2024-04-06T07:28:36Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:25:58Z"	Entry not found
yohanchu/tripleS	yohanchu	"2024-04-06T07:28:22Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:26:22Z"	Entry not found
tistak/sn3_12	tistak	"2024-06-03T07:14:47Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:26:56Z"	Entry not found
tistak/sn3_8	tistak	"2024-06-03T07:14:08Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:27:00Z"	Entry not found
tistak/sn3_7	tistak	"2024-06-03T07:14:13Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:27:00Z"	Entry not found
tistak/sn3_6	tistak	"2024-06-03T07:14:49Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:27:01Z"	Entry not found
tistak/sn3_11	tistak	"2024-06-03T07:14:10Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:27:01Z"	Entry not found
tistak/sn3_9	tistak	"2024-06-03T07:14:48Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:27:01Z"	Entry not found
tistak/sn3_10	tistak	"2024-06-03T07:14:13Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:27:01Z"	Entry not found
xjyplayer/cyberpunk-goggles	xjyplayer	"2024-04-06T07:40:46Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T07:27:23Z"	--- license: apache-2.0 ---
yraziel/edengolan	yraziel	"2024-04-06T07:32:03Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:28:02Z"	Entry not found
UlutSoftLLC/kyrgyz-tts	UlutSoftLLC	"2024-04-12T09:26:44Z"	1	null	[ "Text-to-speech", "region:us" ]	null	"2024-04-06T07:29:36Z"	--- tags: - Text-to-speech --- Кыргыз Республикасынын Президентине караштуу Мамлекеттик тил боюнча улуттук комиссиясы Кыргызча текстти аудиого айландыруу Kyrgyz Text-To-Speech Models trained by Ulutsoft LLC. Эркек: checkpoint_epoch=279.ckpt Аял: checkpoint_epoch=479.ckpt GitHub: https://github.com/UlutSoftLLC/MamtilTTS
silencer107/bobik04	silencer107	"2024-04-06T07:31:43Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:31:32Z"	Entry not found
aa1223/MTCNN	aa1223	"2024-04-06T07:39:29Z"	0	espnet	[ "espnet", "deepfake", "en", "dataset:Cohere/wikipedia-2023-11-embed-multilingual-v3", "license:mit", "region:us" ]	null	"2024-04-06T07:34:16Z"	--- license: mit datasets: - Cohere/wikipedia-2023-11-embed-multilingual-v3 language: - en metrics: - bertscore library_name: espnet tags: - deepfake ---
oneandahalfcats/halfcatpooplol	oneandahalfcats	"2024-04-06T07:36:13Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:35:16Z"	Entry not found
silencer107/bobik05	silencer107	"2024-04-06T07:37:18Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:37:10Z"	Entry not found
oneandahalfcats/notenoughcats	oneandahalfcats	"2024-04-06T07:38:32Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:38:26Z"	Entry not found
smahjouri/falcon-40b-formai	smahjouri	"2024-04-06T07:40:34Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-06T07:40:20Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
oneandahalfcats/valstillbrokeniprofit	oneandahalfcats	"2024-04-06T07:41:32Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:41:27Z"	Entry not found
AMKAK/REALSTATEBUZZ	AMKAK	"2024-04-06T07:50:22Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-04-06T07:50:22Z"	--- license: mit ---
pisad/f	pisad	"2024-04-06T07:52:17Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:52:17Z"	Entry not found
oneandahalfcats/valbrokeiprofithehe	oneandahalfcats	"2024-04-06T08:08:45Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:55:52Z"	Entry not found
Or4cl3-1/SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp	Or4cl3-1	"2024-04-06T07:58:02Z"	0	null	[ "merge", "mergekit", "lazymergekit", "SuperAGI/SAM", "GoogleAI/Gemini", "bigscience/bloom", "openai/opt-175b", "deepmind/gopher", "microsoft/megatron-turing-nlg", "base_model:SuperAGI/SAM", "base_model:merge:SuperAGI/SAM", "base_model:bigscience/bloom", "base_model:merge:bigscience/bloom", "region:us" ]	null	"2024-04-06T07:58:02Z"	--- tags: - merge - mergekit - lazymergekit - SuperAGI/SAM - GoogleAI/Gemini - bigscience/bloom - openai/opt-175b - deepmind/gopher - microsoft/megatron-turing-nlg base_model: - SuperAGI/SAM - GoogleAI/Gemini - bigscience/bloom - openai/opt-175b - deepmind/gopher - microsoft/megatron-turing-nlg --- # SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [SuperAGI/SAM](https://huggingface.co/SuperAGI/SAM) * [GoogleAI/Gemini](https://huggingface.co/GoogleAI/Gemini) * [bigscience/bloom](https://huggingface.co/bigscience/bloom) * [openai/opt-175b](https://huggingface.co/openai/opt-175b) * [deepmind/gopher](https://huggingface.co/deepmind/gopher) * [microsoft/megatron-turing-nlg](https://huggingface.co/microsoft/megatron-turing-nlg) ## 🧩 Configuration ```yaml slices: - sources: - model: SuperAGI/SAM layer_range: [0, 32] - model: GoogleAI/Gemini layer_range: [0, 32] - model: bigscience/bloom layer_range: [0, 32] - model: openai/opt-175b layer_range: [0, 32] - model: deepmind/gopher layer_range: [0, 32] - model: microsoft/megatron-turing-nlg layer_range: [0, 32] merge_method: slerp base_model: SuperAGI/SAM parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat1 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Or4cl3-1/SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
lijingnian/gollie-7B	lijingnian	"2024-04-06T07:59:46Z"	0	null	[ "region:us" ]	null	"2024-04-06T07:59:46Z"	Entry not found
StandardCAS-NSTID/RAIDP-LICENSES	StandardCAS-NSTID	"2024-04-06T08:12:47Z"	0	null	[ "license:other", "region:us" ]	null	"2024-04-06T08:04:49Z"	--- license: other license_name: raidp license_link: LICENSE ---
axs2k/anubis	axs2k	"2024-04-06T11:09:59Z"	1	transformers	[ "transformers", "dataset:zefang-liu/secqa", "dataset:zefang-liu/phishing-email-dataset", "dataset:zefang-liu/cve-and-cwe-mapping-dataset", "dataset:morpheuslord/cve-llm-training", "dataset:EddieChen372/python_vul_cvefix_small", "dataset:icantiemyshoe/cve-to-metasploit-module", "dataset:lambdasec/cve-single-line-fixes", "dataset:lambdasec/gh-top-1000-projects-vulns", "dataset:hackaprompt/hackaprompt-dataset", "dataset:pentest-org-2/dataset-2", "dataset:Isamu136/penetration_testing_scraped_dataset", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:Vezora/Tested-143k-Python-Alpaca", "dataset:neuralsentry/bigvul_devign_cvefixes_neuralsentry_commits", "dataset:hotal/linux_commands", "dataset:Nexa4AI/android_data_test", "dataset:SamsungSAILMontreal/deepnets1m", "license:gpl-3.0", "endpoints_compatible", "region:us" ]	null	"2024-04-06T08:07:12Z"	--- license: gpl-3.0 datasets: - zefang-liu/secqa - zefang-liu/phishing-email-dataset - zefang-liu/cve-and-cwe-mapping-dataset - morpheuslord/cve-llm-training - EddieChen372/python_vul_cvefix_small - icantiemyshoe/cve-to-metasploit-module - lambdasec/cve-single-line-fixes - lambdasec/gh-top-1000-projects-vulns - hackaprompt/hackaprompt-dataset - pentest-org-2/dataset-2 - Isamu136/penetration_testing_scraped_dataset - iamtarun/python_code_instructions_18k_alpaca - Vezora/Tested-143k-Python-Alpaca - neuralsentry/bigvul_devign_cvefixes_neuralsentry_commits - hotal/linux_commands - Nexa4AI/android_data_test - SamsungSAILMontreal/deepnets1m metrics: - accuracy - precision - bleurt - rouge - code_eval - f1 - bertscore - bleu library_name: transformers ---
Reyouf/speecht5_tts_Ar	Reyouf	"2024-04-06T08:42:01Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "endpoints_compatible", "region:us" ]	text-to-audio	"2024-04-06T08:07:32Z"	Entry not found
TechWezz/Q_A	TechWezz	"2024-04-06T08:12:23Z"	0	null	[ "license:llama2", "region:us" ]	null	"2024-04-06T08:12:23Z"	--- license: llama2 ---
Alphaiborkano/Alphaschool	Alphaiborkano	"2024-04-06T08:14:56Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T08:14:56Z"	--- license: apache-2.0 ---
haryoaw/scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_delta	haryoaw	"2024-04-06T08:16:41Z"	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:massive", "base_model:haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base", "base_model:finetune:haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	null	"2024-04-06T08:15:55Z"	--- license: mit base_model: haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base tags: - generated_from_trainer datasets: - massive metrics: - accuracy - f1 model-index: - name: scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_delta 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. --> # scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_delta This model is a fine-tuned version of [haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base](https://huggingface.co/haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 1.0311 - Accuracy: 0.8606 - F1: 0.8342 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 11213 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:------:\| \| 0.9866 \| 0.27 \| 5000 \| 1.5404 \| 0.8352 \| 0.8118 \| \| 0.8243 \| 0.53 \| 10000 \| 1.4475 \| 0.8405 \| 0.8176 \| \| 0.7347 \| 0.8 \| 15000 \| 1.4195 \| 0.8413 \| 0.8225 \| \| 0.5835 \| 1.07 \| 20000 \| 1.3804 \| 0.8457 \| 0.8210 \| \| 0.5829 \| 1.34 \| 25000 \| 1.3623 \| 0.8460 \| 0.8221 \| \| 0.569 \| 1.6 \| 30000 \| 1.3527 \| 0.8474 \| 0.8266 \| \| 0.5566 \| 1.87 \| 35000 \| 1.3316 \| 0.8463 \| 0.8242 \| \| 0.478 \| 2.14 \| 40000 \| 1.3076 \| 0.8492 \| 0.8250 \| \| 0.4712 \| 2.41 \| 45000 \| 1.2920 \| 0.8507 \| 0.8270 \| \| 0.4646 \| 2.67 \| 50000 \| 1.2986 \| 0.8497 \| 0.8256 \| \| 0.4534 \| 2.94 \| 55000 \| 1.2796 \| 0.8503 \| 0.8284 \| \| 0.4099 \| 3.21 \| 60000 \| 1.2625 \| 0.8520 \| 0.8297 \| \| 0.4002 \| 3.47 \| 65000 \| 1.2672 \| 0.8503 \| 0.8290 \| \| 0.4116 \| 3.74 \| 70000 \| 1.2519 \| 0.8494 \| 0.8259 \| \| 0.4068 \| 4.01 \| 75000 \| 1.2484 \| 0.8494 \| 0.8238 \| \| 0.3706 \| 4.28 \| 80000 \| 1.2264 \| 0.8519 \| 0.8280 \| \| 0.3724 \| 4.54 \| 85000 \| 1.2330 \| 0.8525 \| 0.8278 \| \| 0.3804 \| 4.81 \| 90000 \| 1.2312 \| 0.8483 \| 0.8251 \| \| 0.34 \| 5.08 \| 95000 \| 1.2227 \| 0.8508 \| 0.8257 \| \| 0.3606 \| 5.34 \| 100000 \| 1.1968 \| 0.8534 \| 0.8278 \| \| 0.3473 \| 5.61 \| 105000 \| 1.2000 \| 0.8551 \| 0.8311 \| \| 0.3554 \| 5.88 \| 110000 \| 1.2086 \| 0.8536 \| 0.8299 \| \| 0.3234 \| 6.15 \| 115000 \| 1.1836 \| 0.8553 \| 0.8313 \| \| 0.325 \| 6.41 \| 120000 \| 1.1953 \| 0.8536 \| 0.8303 \| \| 0.3227 \| 6.68 \| 125000 \| 1.1653 \| 0.8559 \| 0.8305 \| \| 0.3213 \| 6.95 \| 130000 \| 1.1733 \| 0.8550 \| 0.8305 \| \| 0.3042 \| 7.22 \| 135000 \| 1.1637 \| 0.8550 \| 0.8297 \| \| 0.3099 \| 7.48 \| 140000 \| 1.1601 \| 0.8563 \| 0.8314 \| \| 0.3064 \| 7.75 \| 145000 \| 1.1568 \| 0.8567 \| 0.8296 \| \| 0.2928 \| 8.02 \| 150000 \| 1.1507 \| 0.8559 \| 0.8322 \| \| 0.298 \| 8.28 \| 155000 \| 1.1578 \| 0.8556 \| 0.8316 \| \| 0.2896 \| 8.55 \| 160000 \| 1.1591 \| 0.8574 \| 0.8349 \| \| 0.2895 \| 8.82 \| 165000 \| 1.1504 \| 0.8558 \| 0.8299 \| \| 0.2827 \| 9.09 \| 170000 \| 1.1552 \| 0.8551 \| 0.8286 \| \| 0.2864 \| 9.35 \| 175000 \| 1.1277 \| 0.8563 \| 0.8328 \| \| 0.2796 \| 9.62 \| 180000 \| 1.1345 \| 0.8564 \| 0.8317 \| \| 0.28 \| 9.89 \| 185000 \| 1.1409 \| 0.8562 \| 0.8297 \| \| 0.2753 \| 10.15 \| 190000 \| 1.1290 \| 0.8557 \| 0.8307 \| \| 0.2766 \| 10.42 \| 195000 \| 1.1143 \| 0.8580 \| 0.8321 \| \| 0.2652 \| 10.69 \| 200000 \| 1.1150 \| 0.8560 \| 0.8306 \| \| 0.2741 \| 10.96 \| 205000 \| 1.1275 \| 0.8568 \| 0.8296 \| \| 0.2668 \| 11.22 \| 210000 \| 1.1069 \| 0.8574 \| 0.8333 \| \| 0.2629 \| 11.49 \| 215000 \| 1.1168 \| 0.8580 \| 0.8315 \| \| 0.2607 \| 11.76 \| 220000 \| 1.1173 \| 0.8584 \| 0.8344 \| \| 0.2546 \| 12.03 \| 225000 \| 1.1096 \| 0.8574 \| 0.8315 \| \| 0.2583 \| 12.29 \| 230000 \| 1.1244 \| 0.8554 \| 0.8303 \| \| 0.2557 \| 12.56 \| 235000 \| 1.1080 \| 0.8572 \| 0.8339 \| \| 0.2571 \| 12.83 \| 240000 \| 1.1008 \| 0.8578 \| 0.8344 \| \| 0.2471 \| 13.09 \| 245000 \| 1.0980 \| 0.8578 \| 0.8323 \| \| 0.2528 \| 13.36 \| 250000 \| 1.0975 \| 0.8576 \| 0.8311 \| \| 0.2478 \| 13.63 \| 255000 \| 1.0921 \| 0.8581 \| 0.8322 \| \| 0.2548 \| 13.9 \| 260000 \| 1.0826 \| 0.8604 \| 0.8352 \| \| 0.2429 \| 14.16 \| 265000 \| 1.0903 \| 0.8578 \| 0.8332 \| \| 0.2416 \| 14.43 \| 270000 \| 1.0892 \| 0.8593 \| 0.8344 \| \| 0.2331 \| 14.7 \| 275000 \| 1.0807 \| 0.8601 \| 0.8339 \| \| 0.2449 \| 14.96 \| 280000 \| 1.0792 \| 0.8579 \| 0.8312 \| \| 0.237 \| 15.23 \| 285000 \| 1.0863 \| 0.8600 \| 0.8348 \| \| 0.2338 \| 15.5 \| 290000 \| 1.0805 \| 0.8575 \| 0.8310 \| \| 0.2378 \| 15.77 \| 295000 \| 1.0822 \| 0.8579 \| 0.8321 \| \| 0.2346 \| 16.03 \| 300000 \| 1.0757 \| 0.8583 \| 0.8309 \| \| 0.2274 \| 16.3 \| 305000 \| 1.0847 \| 0.8579 \| 0.8312 \| \| 0.2414 \| 16.57 \| 310000 \| 1.0734 \| 0.8602 \| 0.8353 \| \| 0.2314 \| 16.84 \| 315000 \| 1.0814 \| 0.8584 \| 0.8333 \| \| 0.2291 \| 17.1 \| 320000 \| 1.0677 \| 0.8604 \| 0.8340 \| \| 0.223 \| 17.37 \| 325000 \| 1.0731 \| 0.8590 \| 0.8340 \| \| 0.2224 \| 17.64 \| 330000 \| 1.0648 \| 0.8601 \| 0.8338 \| \| 0.2264 \| 17.9 \| 335000 \| 1.0603 \| 0.8593 \| 0.8333 \| \| 0.2252 \| 18.17 \| 340000 \| 1.0639 \| 0.8590 \| 0.8323 \| \| 0.2164 \| 18.44 \| 345000 \| 1.0607 \| 0.8595 \| 0.8336 \| \| 0.2231 \| 18.71 \| 350000 \| 1.0614 \| 0.8597 \| 0.8338 \| \| 0.2204 \| 18.97 \| 355000 \| 1.0702 \| 0.8582 \| 0.8323 \| \| 0.2155 \| 19.24 \| 360000 \| 1.0539 \| 0.8583 \| 0.8321 \| \| 0.2107 \| 19.51 \| 365000 \| 1.0579 \| 0.8594 \| 0.8335 \| \| 0.2163 \| 19.77 \| 370000 \| 1.0608 \| 0.8584 \| 0.8323 \| \| 0.2136 \| 20.04 \| 375000 \| 1.0552 \| 0.8593 \| 0.8330 \| \| 0.2062 \| 20.31 \| 380000 \| 1.0577 \| 0.8587 \| 0.8345 \| \| 0.212 \| 20.58 \| 385000 \| 1.0594 \| 0.8592 \| 0.8321 \| \| 0.2116 \| 20.84 \| 390000 \| 1.0561 \| 0.8593 \| 0.8348 \| \| 0.2058 \| 21.11 \| 395000 \| 1.0490 \| 0.8597 \| 0.8358 \| \| 0.2106 \| 21.38 \| 400000 \| 1.0557 \| 0.8593 \| 0.8346 \| \| 0.2061 \| 21.65 \| 405000 \| 1.0508 \| 0.8601 \| 0.8349 \| \| 0.2119 \| 21.91 \| 410000 \| 1.0496 \| 0.8591 \| 0.8343 \| \| 0.2057 \| 22.18 \| 415000 \| 1.0447 \| 0.8602 \| 0.8345 \| \| 0.2017 \| 22.45 \| 420000 \| 1.0486 \| 0.8597 \| 0.8338 \| \| 0.1997 \| 22.71 \| 425000 \| 1.0346 \| 0.8612 \| 0.8377 \| \| 0.1959 \| 22.98 \| 430000 \| 1.0478 \| 0.8586 \| 0.8323 \| \| 0.1989 \| 23.25 \| 435000 \| 1.0436 \| 0.8599 \| 0.8327 \| \| 0.1996 \| 23.52 \| 440000 \| 1.0459 \| 0.8603 \| 0.8342 \| \| 0.1982 \| 23.78 \| 445000 \| 1.0410 \| 0.8602 \| 0.8348 \| \| 0.1955 \| 24.05 \| 450000 \| 1.0416 \| 0.8596 \| 0.8344 \| \| 0.1977 \| 24.32 \| 455000 \| 1.0383 \| 0.8594 \| 0.8329 \| \| 0.1951 \| 24.58 \| 460000 \| 1.0413 \| 0.8599 \| 0.8351 \| \| 0.2014 \| 24.85 \| 465000 \| 1.0332 \| 0.8606 \| 0.8357 \| \| 0.1948 \| 25.12 \| 470000 \| 1.0370 \| 0.8599 \| 0.8351 \| \| 0.1933 \| 25.39 \| 475000 \| 1.0393 \| 0.8598 \| 0.8342 \| \| 0.1937 \| 25.65 \| 480000 \| 1.0334 \| 0.8607 \| 0.8346 \| \| 0.1923 \| 25.92 \| 485000 \| 1.0358 \| 0.8607 \| 0.8358 \| \| 0.1911 \| 26.19 \| 490000 \| 1.0323 \| 0.8604 \| 0.8345 \| \| 0.1921 \| 26.46 \| 495000 \| 1.0347 \| 0.8600 \| 0.8337 \| \| 0.1917 \| 26.72 \| 500000 \| 1.0327 \| 0.8602 \| 0.8346 \| \| 0.1882 \| 26.99 \| 505000 \| 1.0293 \| 0.8606 \| 0.8340 \| \| 0.1876 \| 27.26 \| 510000 \| 1.0324 \| 0.8603 \| 0.8349 \| \| 0.189 \| 27.52 \| 515000 \| 1.0310 \| 0.8613 \| 0.8355 \| \| 0.1891 \| 27.79 \| 520000 \| 1.0309 \| 0.8606 \| 0.8341 \| \| 0.1847 \| 28.06 \| 525000 \| 1.0290 \| 0.8609 \| 0.8352 \| \| 0.1827 \| 28.33 \| 530000 \| 1.0272 \| 0.8601 \| 0.8333 \| \| 0.1913 \| 28.59 \| 535000 \| 1.0279 \| 0.8609 \| 0.8352 \| \| 0.1872 \| 28.86 \| 540000 \| 1.0291 \| 0.8606 \| 0.8341 \| \| 0.1875 \| 29.13 \| 545000 \| 1.0266 \| 0.8606 \| 0.8339 \| \| 0.1856 \| 29.39 \| 550000 \| 1.0283 \| 0.8609 \| 0.8363 \| \| 0.1823 \| 29.66 \| 555000 \| 1.0282 \| 0.8602 \| 0.8339 \| \| 0.1823 \| 29.93 \| 560000 \| 1.0311 \| 0.8606 \| 0.8342 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3
yujia23/axolotl-mistral-cold-3e-4-lora	yujia23	"2024-04-06T08:17:54Z"	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	"2024-04-06T08:17:08Z"	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: home/yujia/home/CN_Hateful/trained_models/mistral/cold/3e-4/ results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: true load_in_4bit: false strict: false datasets: # - path: mhenrichsen/alpaca_2k_test # - path: /home/yujia/home/CN_Hateful/train_toxiCN.json # - path: /home/yujia/home/CN_Hateful/train_toxiCN_cn.json - path: /home/yujia/home/CN_Hateful/train.json ds_type: json type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.1 # output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/toxi/1e-5/ # output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/CN/toxi/1e-5/ output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/cold/3e-4/ adapter: lora lora_model_dir: sequence_len: 256 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 4 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0003 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # home/yujia/home/CN_Hateful/trained_models/mistral/cold/3e-4/ This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0406 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.9655 \| 0.01 \| 1 \| 2.9710 \| \| 0.0451 \| 0.25 \| 46 \| 0.0619 \| \| 0.0541 \| 0.5 \| 92 \| 0.0392 \| \| 0.0353 \| 0.75 \| 138 \| 0.0345 \| \| 0.0249 \| 1.0 \| 184 \| 0.0315 \| \| 0.0259 \| 1.23 \| 230 \| 0.0329 \| \| 0.0238 \| 1.48 \| 276 \| 0.0309 \| \| 0.019 \| 1.73 \| 322 \| 0.0305 \| \| 0.0173 \| 1.97 \| 368 \| 0.0313 \| \| 0.0051 \| 2.21 \| 414 \| 0.0369 \| \| 0.0093 \| 2.46 \| 460 \| 0.0436 \| \| 0.0023 \| 2.71 \| 506 \| 0.0407 \| \| 0.003 \| 2.95 \| 552 \| 0.0406 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0
haryoaw/scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_beta	haryoaw	"2024-04-06T08:18:53Z"	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:massive", "base_model:haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base", "base_model:finetune:haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	null	"2024-04-06T08:18:06Z"	--- license: mit base_model: haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base tags: - generated_from_trainer datasets: - massive metrics: - accuracy - f1 model-index: - name: scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_beta 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. --> # scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_beta This model is a fine-tuned version of [haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base](https://huggingface.co/haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 1.0301 - Accuracy: 0.8597 - F1: 0.8341 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 112233 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:------:\| \| 1.0109 \| 0.27 \| 5000 \| 1.5142 \| 0.8386 \| 0.8137 \| \| 0.8133 \| 0.53 \| 10000 \| 1.4503 \| 0.8411 \| 0.8191 \| \| 0.7197 \| 0.8 \| 15000 \| 1.4028 \| 0.8438 \| 0.8204 \| \| 0.5839 \| 1.07 \| 20000 \| 1.3897 \| 0.8463 \| 0.8223 \| \| 0.5816 \| 1.34 \| 25000 \| 1.3593 \| 0.8455 \| 0.8186 \| \| 0.5482 \| 1.6 \| 30000 \| 1.3400 \| 0.8465 \| 0.8225 \| \| 0.5493 \| 1.87 \| 35000 \| 1.3227 \| 0.8449 \| 0.8199 \| \| 0.4571 \| 2.14 \| 40000 \| 1.3476 \| 0.8449 \| 0.8215 \| \| 0.4612 \| 2.41 \| 45000 \| 1.2853 \| 0.8515 \| 0.8298 \| \| 0.4589 \| 2.67 \| 50000 \| 1.3029 \| 0.8493 \| 0.8283 \| \| 0.4417 \| 2.94 \| 55000 \| 1.2670 \| 0.8515 \| 0.8281 \| \| 0.4113 \| 3.21 \| 60000 \| 1.2688 \| 0.8496 \| 0.8251 \| \| 0.402 \| 3.47 \| 65000 \| 1.2629 \| 0.8521 \| 0.8286 \| \| 0.4041 \| 3.74 \| 70000 \| 1.2528 \| 0.8501 \| 0.8246 \| \| 0.3878 \| 4.01 \| 75000 \| 1.2265 \| 0.8523 \| 0.8316 \| \| 0.3715 \| 4.28 \| 80000 \| 1.2460 \| 0.8501 \| 0.8256 \| \| 0.3702 \| 4.54 \| 85000 \| 1.2228 \| 0.8538 \| 0.8321 \| \| 0.373 \| 4.81 \| 90000 \| 1.2171 \| 0.8533 \| 0.8271 \| \| 0.3494 \| 5.08 \| 95000 \| 1.2042 \| 0.8537 \| 0.8312 \| \| 0.3509 \| 5.34 \| 100000 \| 1.2069 \| 0.8544 \| 0.8280 \| \| 0.3421 \| 5.61 \| 105000 \| 1.2048 \| 0.8535 \| 0.8286 \| \| 0.3448 \| 5.88 \| 110000 \| 1.1911 \| 0.8542 \| 0.8296 \| \| 0.3249 \| 6.15 \| 115000 \| 1.1902 \| 0.8539 \| 0.8245 \| \| 0.3252 \| 6.41 \| 120000 \| 1.1825 \| 0.8564 \| 0.8288 \| \| 0.3176 \| 6.68 \| 125000 \| 1.1763 \| 0.8546 \| 0.8313 \| \| 0.3123 \| 6.95 \| 130000 \| 1.1913 \| 0.8530 \| 0.8307 \| \| 0.3059 \| 7.22 \| 135000 \| 1.1681 \| 0.8548 \| 0.8284 \| \| 0.316 \| 7.48 \| 140000 \| 1.1605 \| 0.8557 \| 0.8292 \| \| 0.3067 \| 7.75 \| 145000 \| 1.1802 \| 0.8541 \| 0.8302 \| \| 0.3022 \| 8.02 \| 150000 \| 1.1536 \| 0.8548 \| 0.8306 \| \| 0.2985 \| 8.28 \| 155000 \| 1.1672 \| 0.8528 \| 0.8265 \| \| 0.3004 \| 8.55 \| 160000 \| 1.1480 \| 0.8556 \| 0.8318 \| \| 0.2942 \| 8.82 \| 165000 \| 1.1470 \| 0.8549 \| 0.8291 \| \| 0.2854 \| 9.09 \| 170000 \| 1.1378 \| 0.8558 \| 0.8314 \| \| 0.2967 \| 9.35 \| 175000 \| 1.1340 \| 0.8553 \| 0.8307 \| \| 0.2901 \| 9.62 \| 180000 \| 1.1443 \| 0.8557 \| 0.8313 \| \| 0.2844 \| 9.89 \| 185000 \| 1.1419 \| 0.8551 \| 0.8312 \| \| 0.2742 \| 10.15 \| 190000 \| 1.1258 \| 0.8564 \| 0.8334 \| \| 0.2763 \| 10.42 \| 195000 \| 1.1267 \| 0.8572 \| 0.8323 \| \| 0.2757 \| 10.69 \| 200000 \| 1.1275 \| 0.8558 \| 0.8295 \| \| 0.2728 \| 10.96 \| 205000 \| 1.1238 \| 0.8578 \| 0.8348 \| \| 0.2739 \| 11.22 \| 210000 \| 1.1029 \| 0.8578 \| 0.8319 \| \| 0.2579 \| 11.49 \| 215000 \| 1.1155 \| 0.8575 \| 0.8350 \| \| 0.2604 \| 11.76 \| 220000 \| 1.1021 \| 0.8583 \| 0.8375 \| \| 0.2605 \| 12.03 \| 225000 \| 1.1134 \| 0.8567 \| 0.8301 \| \| 0.2496 \| 12.29 \| 230000 \| 1.0996 \| 0.8565 \| 0.8291 \| \| 0.2518 \| 12.56 \| 235000 \| 1.1162 \| 0.8567 \| 0.8302 \| \| 0.2574 \| 12.83 \| 240000 \| 1.1053 \| 0.8576 \| 0.8327 \| \| 0.2488 \| 13.09 \| 245000 \| 1.1105 \| 0.8574 \| 0.8346 \| \| 0.2423 \| 13.36 \| 250000 \| 1.1050 \| 0.8580 \| 0.8326 \| \| 0.2473 \| 13.63 \| 255000 \| 1.0882 \| 0.8584 \| 0.8348 \| \| 0.2479 \| 13.9 \| 260000 \| 1.0926 \| 0.8589 \| 0.8366 \| \| 0.254 \| 14.16 \| 265000 \| 1.0919 \| 0.8576 \| 0.8312 \| \| 0.24 \| 14.43 \| 270000 \| 1.0941 \| 0.8576 \| 0.8339 \| \| 0.2415 \| 14.7 \| 275000 \| 1.0881 \| 0.8584 \| 0.8336 \| \| 0.2421 \| 14.96 \| 280000 \| 1.0816 \| 0.8580 \| 0.8326 \| \| 0.2395 \| 15.23 \| 285000 \| 1.0832 \| 0.8574 \| 0.8323 \| \| 0.2336 \| 15.5 \| 290000 \| 1.0822 \| 0.8580 \| 0.8341 \| \| 0.2324 \| 15.77 \| 295000 \| 1.0873 \| 0.8586 \| 0.8344 \| \| 0.2274 \| 16.03 \| 300000 \| 1.0795 \| 0.8583 \| 0.8336 \| \| 0.233 \| 16.3 \| 305000 \| 1.0857 \| 0.8581 \| 0.8331 \| \| 0.2281 \| 16.57 \| 310000 \| 1.0852 \| 0.8578 \| 0.8341 \| \| 0.2268 \| 16.84 \| 315000 \| 1.0803 \| 0.8583 \| 0.8338 \| \| 0.2253 \| 17.1 \| 320000 \| 1.0756 \| 0.8590 \| 0.8345 \| \| 0.2247 \| 17.37 \| 325000 \| 1.0650 \| 0.8589 \| 0.8328 \| \| 0.2182 \| 17.64 \| 330000 \| 1.0696 \| 0.8582 \| 0.8336 \| \| 0.2187 \| 17.9 \| 335000 \| 1.0694 \| 0.8578 \| 0.8324 \| \| 0.2202 \| 18.17 \| 340000 \| 1.0621 \| 0.8584 \| 0.8329 \| \| 0.2214 \| 18.44 \| 345000 \| 1.0737 \| 0.8578 \| 0.8328 \| \| 0.2186 \| 18.71 \| 350000 \| 1.0606 \| 0.8586 \| 0.8346 \| \| 0.2171 \| 18.97 \| 355000 \| 1.0623 \| 0.8583 \| 0.8345 \| \| 0.2123 \| 19.24 \| 360000 \| 1.0641 \| 0.8589 \| 0.8352 \| \| 0.2141 \| 19.51 \| 365000 \| 1.0627 \| 0.8586 \| 0.8349 \| \| 0.2145 \| 19.77 \| 370000 \| 1.0616 \| 0.8585 \| 0.8336 \| \| 0.2087 \| 20.04 \| 375000 \| 1.0570 \| 0.8593 \| 0.8332 \| \| 0.2098 \| 20.31 \| 380000 \| 1.0639 \| 0.8585 \| 0.8330 \| \| 0.2122 \| 20.58 \| 385000 \| 1.0501 \| 0.8602 \| 0.8347 \| \| 0.2071 \| 20.84 \| 390000 \| 1.0583 \| 0.8592 \| 0.8328 \| \| 0.2083 \| 21.11 \| 395000 \| 1.0554 \| 0.8589 \| 0.8337 \| \| 0.2041 \| 21.38 \| 400000 \| 1.0604 \| 0.8586 \| 0.8331 \| \| 0.2035 \| 21.65 \| 405000 \| 1.0560 \| 0.8581 \| 0.8333 \| \| 0.2099 \| 21.91 \| 410000 \| 1.0557 \| 0.8586 \| 0.8346 \| \| 0.2031 \| 22.18 \| 415000 \| 1.0511 \| 0.8593 \| 0.8348 \| \| 0.2024 \| 22.45 \| 420000 \| 1.0534 \| 0.8586 \| 0.8340 \| \| 0.2024 \| 22.71 \| 425000 \| 1.0447 \| 0.8604 \| 0.8343 \| \| 0.2025 \| 22.98 \| 430000 \| 1.0465 \| 0.8593 \| 0.8344 \| \| 0.2029 \| 23.25 \| 435000 \| 1.0472 \| 0.8592 \| 0.8346 \| \| 0.197 \| 23.52 \| 440000 \| 1.0467 \| 0.8595 \| 0.8345 \| \| 0.1996 \| 23.78 \| 445000 \| 1.0428 \| 0.8592 \| 0.8346 \| \| 0.1938 \| 24.05 \| 450000 \| 1.0428 \| 0.8593 \| 0.8342 \| \| 0.1949 \| 24.32 \| 455000 \| 1.0390 \| 0.8605 \| 0.8352 \| \| 0.1976 \| 24.58 \| 460000 \| 1.0453 \| 0.8586 \| 0.8325 \| \| 0.1957 \| 24.85 \| 465000 \| 1.0346 \| 0.8605 \| 0.8350 \| \| 0.1946 \| 25.12 \| 470000 \| 1.0370 \| 0.8592 \| 0.8328 \| \| 0.1954 \| 25.39 \| 475000 \| 1.0385 \| 0.8600 \| 0.8348 \| \| 0.1877 \| 25.65 \| 480000 \| 1.0358 \| 0.8604 \| 0.8351 \| \| 0.1911 \| 25.92 \| 485000 \| 1.0385 \| 0.8597 \| 0.8357 \| \| 0.1919 \| 26.19 \| 490000 \| 1.0400 \| 0.8593 \| 0.8340 \| \| 0.19 \| 26.46 \| 495000 \| 1.0357 \| 0.8596 \| 0.8345 \| \| 0.1923 \| 26.72 \| 500000 \| 1.0386 \| 0.8600 \| 0.8356 \| \| 0.1942 \| 26.99 \| 505000 \| 1.0407 \| 0.8590 \| 0.8337 \| \| 0.189 \| 27.26 \| 510000 \| 1.0333 \| 0.8604 \| 0.8359 \| \| 0.1888 \| 27.52 \| 515000 \| 1.0352 \| 0.8590 \| 0.8334 \| \| 0.1899 \| 27.79 \| 520000 \| 1.0330 \| 0.8595 \| 0.8347 \| \| 0.1934 \| 28.06 \| 525000 \| 1.0310 \| 0.8593 \| 0.8342 \| \| 0.1873 \| 28.33 \| 530000 \| 1.0342 \| 0.8600 \| 0.8350 \| \| 0.1869 \| 28.59 \| 535000 \| 1.0305 \| 0.8601 \| 0.8358 \| \| 0.1799 \| 28.86 \| 540000 \| 1.0326 \| 0.8590 \| 0.8337 \| \| 0.1832 \| 29.13 \| 545000 \| 1.0331 \| 0.8597 \| 0.8350 \| \| 0.1842 \| 29.39 \| 550000 \| 1.0323 \| 0.8599 \| 0.8349 \| \| 0.1818 \| 29.66 \| 555000 \| 1.0313 \| 0.8602 \| 0.8360 \| \| 0.1819 \| 29.93 \| 560000 \| 1.0301 \| 0.8597 \| 0.8341 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3
paularusti78/q-FrozenLake-v1-4x4-noSlippery	paularusti78	"2024-04-06T08:18:20Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-04-06T08:18:17Z"	--- 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="paularusti78/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"]) ```
Esmaeilkiani/Zali	Esmaeilkiani	"2024-04-06T08:22:57Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T08:22:57Z"	--- license: apache-2.0 ---
Qin56/text2imagev1	Qin56	"2024-04-07T07:49:25Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T08:23:24Z"	--- license: apache-2.0 ---
LongBabin/babin	LongBabin	"2024-04-06T08:25:54Z"	0	null	[ "region:us" ]	null	"2024-04-06T08:25:54Z"	Entry not found
paularusti78/tax1-v3-1	paularusti78	"2024-04-06T08:29:02Z"	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-04-06T08:29:01Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: tax1-v3-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 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="paularusti78/tax1-v3-1", 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"]) ```
harupurito/whisper-small-hi-cv	harupurito	"2024-04-06T11:55:37Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-04-06T08:32:24Z"	Entry not found
spraja08/fine-bitsy	spraja08	"2024-04-07T08:18:20Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "license:mit", "endpoints_compatible", "region:us" ]	null	"2024-04-06T08:34:12Z"	--- license: mit library_name: transformers --- # Fine-Bitsy Fine-Bitsy is a model (that has an attempted) specialty on US federsal reserves faq. This is my experimental attempt to create a specialist model that can be trained within a few dollars but can produce surprising good results. Phi-2 seems to have "seen" most commercially available content datasets. To convincingly test the "specialisation effect", I had to hunt for a less common dataset. 🤗 came handy with such a variety of datasets that can be readily used without much dataprep... ## Model Details Base Model : Phi-2 FineTuned with US Federal Reserves Q&A ### Model Description - Developed by: SP Raja - License: MIT - Finetuned from model Phi-2: Supervised Fine Tuning using custom dataset ## Uses This is an experiment on finetuning. Use the code below to get started with the model. ``` #load the model from the hub from peft import PeftConfig peft_model_id_from_hub = "spraja08/fine-bitsy" config = PeftConfig.from_pretrained(peft_model_id_from_hub) model_from_hub = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, return_dict=True, load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) #try the inference model_from_hub.eval() eval_prompt = """Instruction:What should I do if I have damaged or mutilated currency? Assistant:""" #eval_prompt = "Who is on the Federal Open Market Committee?" #eval_prompt = """What does the Federal Reserve mean when it says monetary policy remains "accommodative"?""" model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda") with torch.no_grad(): print(tokenizer.decode(model_from_hub.generate(model_input, max_new_tokens=80)[0], skip_special_tokens=True)) ``` ### Training Data [us-federal-reserve-qa](https://huggingface.co/datasets/clement-cvll/us-federal-reserve-qa) ### Training Procedure [As in the notebook here](https://github.com/spraja08/supervised_fine_tuning/tree/main) #### 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]
SJTU-TES/Everybody_Dance_Now	SJTU-TES	"2024-04-06T08:34:55Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T08:34:55Z"	--- license: apache-2.0 ---
csAugust/Llama-2-7b-chat-hf-q0f16-MLC	csAugust	"2024-04-07T02:44:01Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T08:35:00Z"	--- license: apache-2.0 ---
zgcr654321/human_matting_training	zgcr654321	"2024-04-06T08:59:10Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-04-06T08:35:03Z"	--- license: mit ---
zgcr654321/salient_object_detection_training	zgcr654321	"2024-04-27T06:50:40Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-04-06T08:35:36Z"	--- license: mit ---
zgcr654321/interactive_segmentation_training	zgcr654321	"2024-05-17T23:56:08Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-04-06T08:36:16Z"	--- license: mit ---
Or4cl3-1/code-slerp	Or4cl3-1	"2024-04-06T08:36:56Z"	0	null	[ "merge", "mergekit", "lazymergekit", "microsoft/codebert-base", "EleutherAI/gpt-neo-x-20b", "openai/codex", "bigscience/bloom", "google/jurassic-1-jumbo", "google/t5-v1_1-large", "facebook/bart-large", "base_model:bigscience/bloom", "base_model:merge:bigscience/bloom", "base_model:facebook/bart-large", "base_model:merge:facebook/bart-large", "base_model:google/t5-v1_1-large", "base_model:merge:google/t5-v1_1-large", "base_model:microsoft/codebert-base", "base_model:merge:microsoft/codebert-base", "region:us" ]	null	"2024-04-06T08:36:54Z"	--- tags: - merge - mergekit - lazymergekit - microsoft/codebert-base - EleutherAI/gpt-neo-x-20b - openai/codex - bigscience/bloom - google/jurassic-1-jumbo - google/t5-v1_1-large - facebook/bart-large base_model: - microsoft/codebert-base - EleutherAI/gpt-neo-x-20b - openai/codex - bigscience/bloom - google/jurassic-1-jumbo - google/t5-v1_1-large - facebook/bart-large --- # code-slerp code-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) * [EleutherAI/gpt-neo-x-20b](https://huggingface.co/EleutherAI/gpt-neo-x-20b) * [openai/codex](https://huggingface.co/openai/codex) * [bigscience/bloom](https://huggingface.co/bigscience/bloom) * [google/jurassic-1-jumbo](https://huggingface.co/google/jurassic-1-jumbo) * [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) * [facebook/bart-large](https://huggingface.co/facebook/bart-large) ## 🧩 Configuration ```yaml slices: - sources: - model: microsoft/codebert-base layer_range: [0, 32] - model: EleutherAI/gpt-neo-x-20b layer_range: [0, 32] - model: openai/codex layer_range: [0, 32] - model: bigscience/bloom layer_range: [0, 32] - model: google/jurassic-1-jumbo layer_range: [0, 32] - model: google/t5-v1_1-large layer_range: [0, 32] - model: facebook/bart-large layer_range: [0, 32] merge_method: slerp base_model: microsoft/codebert-base parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat1 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Or4cl3-1/code-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
faiimea/openpose	faiimea	"2024-04-06T08:38:11Z"	0	null	[ "region:us" ]	null	"2024-04-06T08:38:10Z"	Entry not found
SJTU-TES/RobustVideoMatting	SJTU-TES	"2024-04-06T09:04:27Z"	0	null	[ "region:us" ]	null	"2024-04-06T08:41:20Z"	Entry not found
ajeya-op/mistral	ajeya-op	"2024-04-06T08:42:52Z"	0	null	[ "region:us" ]	null	"2024-04-06T08:42:52Z"	Entry not found
LuckyMan123/sushi_style	LuckyMan123	"2024-04-06T09:04:53Z"	0	null	[ "region:us" ]	null	"2024-04-06T08:43:06Z"	Entry not found
nuratamton/test3	nuratamton	"2024-04-06T09:14:52Z"	0	peft	[ "peft", "pytorch", "safetensors", "mistral", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "4-bit", "bitsandbytes", "region:us" ]	null	"2024-04-06T08:48:35Z"	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2
faiimea/uap	faiimea	"2024-04-06T08:50:42Z"	0	null	[ "region:us" ]	null	"2024-04-06T08:50:42Z"	Entry not found
shrimalrishika/cnn-dm	shrimalrishika	"2024-04-06T08:51:26Z"	0	null	[ "region:us" ]	null	"2024-04-06T08:51:26Z"	Entry not found
HeydarS/flant5_xl_EQ_peft_v2	HeydarS	"2024-04-06T08:51:41Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/flan-t5-xl", "base_model:adapter:google/flan-t5-xl", "region:us" ]	null	"2024-04-06T08:51:35Z"	--- library_name: peft base_model: google/flan-t5-xl --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0
faiimea/wav2com	faiimea	"2024-04-06T08:52:52Z"	0	null	[ "region:us" ]	null	"2024-04-06T08:52:52Z"	Entry not found
Gopal2002/zehpyr-gemma-dpo-finetune	Gopal2002	"2024-04-23T09:50:07Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-06T08:54:57Z"	--- 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]
Shashashasha/Serial_Designation_N_GPT_SoVITS	Shashashasha	"2024-04-06T08:57:38Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-04-06T08:55:07Z"	--- license: openrail ---
dianamihalache27/Sarcasm.detector.bert_base_LSTM_FE_recall_CE_loss_Imbalance_datasampler	dianamihalache27	"2024-04-06T08:57:02Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-04-06T08:57:02Z"	--- license: mit ---
dianamihalache27/Sarcasm.detector.bert_tweet_kim_cnn_recall_CE_loss_Imbalance_datasampler	dianamihalache27	"2024-04-06T08:59:42Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-04-06T08:59:08Z"	--- license: mit ---
giuly97/finetuning-sentiment-model-3000-samples	giuly97	"2024-04-06T09:00:21Z"	0	null	[ "region:us" ]	null	"2024-04-06T09:00:20Z"	Entry not found
tumanggors/Jamba-v0.1	tumanggors	"2024-04-06T09:12:50Z"	0	null	[ "region:us" ]	null	"2024-04-06T09:12:50Z"	Entry not found
abhijitstat/my_awesome_billsum_model	abhijitstat	"2024-04-06T09:15:40Z"	0	null	[ "region:us" ]	null	"2024-04-06T09:15:39Z"	Entry not found
Caska26/bert-base-multilingual-uncased-sentiment	Caska26	"2024-04-06T09:24:20Z"	0	null	[ "region:us" ]	null	"2024-04-06T09:24:20Z"	Entry not found
mrsarthakgupta/onnx-classifier	mrsarthakgupta	"2024-04-08T21:01:36Z"	0	transformers	[ "transformers", "onnx", "vit", "endpoints_compatible", "region:us" ]	null	"2024-04-06T09:24:23Z"	Entry not found
24abhimanyu10/api-classification	24abhimanyu10	"2024-04-06T09:27:06Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T09:27:06Z"	--- license: apache-2.0 ---
shrimalrishika/medical_summarization	shrimalrishika	"2024-04-06T09:30:07Z"	0	null	[ "region:us" ]	null	"2024-04-06T09:30:05Z"	Entry not found
kenchiayy/wav2vec2-xls-r-1b-atcosim_corpus-google-colab	kenchiayy	"2024-04-06T09:31:26Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-06T09:31:24Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
giuly97/bert-base-multilingual-uncased-sentiment	giuly97	"2024-04-06T09:32:24Z"	0	null	[ "region:us" ]	null	"2024-04-06T09:32:23Z"	Entry not found
huybopbi/vpnfast	huybopbi	"2024-04-06T09:35:19Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T09:35:19Z"	--- license: apache-2.0 ---
John-Yakuza/markymooolora	John-Yakuza	"2024-05-15T22:11:16Z"	0	null	[ "region:us" ]	null	"2024-04-06T09:40:58Z"	Entry not found
TroyHow/Helsinki-NLP	TroyHow	"2024-05-18T18:28:42Z"	0	null	[ "region:us" ]	null	"2024-04-06T09:43:27Z"	# These are the models required for running OFFLINE Translation You can either download all the files or just the files that you need. Please make sure it is in a format like ./Helsinki-NLP/XXX
TropikOyuncu/Baldiback	TropikOyuncu	"2024-04-06T09:51:43Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-04-06T09:50:06Z"	--- license: openrail ---
ShahlaDnshi96/mobile_mistral_2	ShahlaDnshi96	"2024-04-07T07:54:10Z"	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	"2024-04-06T09:52:03Z"	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mobile_mistral_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mobile_mistral_2 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
OpenNLG/OpenBA-V2-Vocab-Pruning	OpenNLG	"2024-04-06T10:02:20Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-06T10:02:20Z"	--- license: apache-2.0 ---
swsn21/forrxl	swsn21	"2024-10-01T06:36:37Z"	0	null	[ "region:us" ]	null	"2024-04-06T10:02:37Z"	Entry not found
devin-ai/devin	devin-ai	"2024-04-06T10:02:45Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-04-06T10:02:45Z"	--- license: mit ---
Jakolo121/Sappho_V0.0.1-GGUF	Jakolo121	"2024-04-06T10:06:27Z"	0	null	[ "region:us" ]	null	"2024-04-06T10:06:27Z"	Entry not found
abhinandom42/test_sum	abhinandom42	"2024-04-06T10:12:08Z"	0	null	[ "region:us" ]	null	"2024-04-06T10:12:08Z"	Entry not found
HeydarS/flant5_bs_popQA_peft_v6	HeydarS	"2024-04-06T10:16:48Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/flan-t5-base", "base_model:adapter:google/flan-t5-base", "region:us" ]	null	"2024-04-06T10:16:45Z"	--- library_name: peft base_model: google/flan-t5-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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SwimChoi/villama2-7b-Denmark-lora	SwimChoi	"2024-04-09T10:56:34Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	"2024-04-06T10:22:44Z"	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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SwimChoi/villama2-7b-Bulgaria-lora	SwimChoi	"2024-04-09T10:57:51Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	"2024-04-06T10:22:55Z"	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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SwimChoi/villama2-7b-Belgium-lora	SwimChoi	"2024-04-09T10:59:11Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	"2024-04-06T10:23:06Z"	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0
SwimChoi/villama2-7b-France-lora	SwimChoi	"2024-04-09T11:00:36Z"	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	"2024-04-06T10:23:18Z"	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

SandeepBoddu/BigBull

SandeepBoddu

"2024-04-06T06:27:51Z"

0

null

[ "region:us" ]

null

"2024-04-06T06:27:50Z"

Entry not found

teragron/canv3

teragron

"2024-04-06T06:33:48Z"

0

transformers

[ "transformers", "tensorboard", "endpoints_compatible", "region:us" ]

null

"2024-04-06T06:28:25Z"

Entry not found

Inishds/code-llama-7b-text-to-sql

Inishds

"2024-04-06T06:28:30Z"

0

null

[ "region:us" ]

null

"2024-04-06T06:28:30Z"

Entry not found

oreeenn/jennie

oreeenn

"2024-08-11T10:25:13Z"

0

null

[ "license:openrail", "region:us" ]

null

"2024-04-06T06:41:13Z"

--- license: openrail ---

Jinwoo870/Llama2_Finetuned_train_textdata_prompt_Instruction_Set

Jinwoo870

"2024-04-08T07:22:40Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-04-06T06:46:00Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

thanhtuit96/ssd

thanhtuit96

"2024-04-14T18:00:01Z"

0

null

[ "tensorboard", "region:us" ]

null

"2024-04-06T06:51:03Z"

Entry not found

lmms-lab/PG_Video_LLaVA-projector

lmms-lab

"2024-04-06T06:52:42Z"

0

1

null

[ "region:us" ]

null

"2024-04-06T06:51:33Z"

projector checkpoints from ``` @article{munasinghe2023PGVideoLLaVA, title={PG-Video-LLaVA: Pixel Grounding Large Video-Language Models}, author={Shehan Munasinghe and Rusiru Thushara and Muhammad Maaz and Hanoona Abdul Rasheed and Salman Khan and Mubarak Shah and Fahad Khan}, journal={ArXiv 2311.13435}, year={2023} } ``` for you to more easily to download with

BhushanP-01/AdGenerator

BhushanP-01

"2024-04-06T06:53:15Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-04-06T06:53:10Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Inishds/deepseekcoder1.3B-text-to-sql

Inishds

"2024-04-06T08:12:16Z"

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:deepseek-ai/deepseek-coder-1.3b-base", "base_model:adapter:deepseek-ai/deepseek-coder-1.3b-base", "license:other", "region:us" ]

null

"2024-04-06T06:54:37Z"

--- license: other library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: deepseek-ai/deepseek-coder-1.3b-base model-index: - name: deepseekcoder1.3B-text-to-sql results: [] ---  # deepseekcoder1.3B-text-to-sql This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2

tkwon4/whisper-large-v3-finetuned-6

tkwon4

"2024-04-06T06:57:43Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2024-04-06T06:55:35Z"

--- license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-large-v3-finetuned-6 results: [] ---  # whisper-large-v3-finetuned-6 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1070 - Wer: 115.1997 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-07 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3572 | 1.0 | 7532 | 0.2604 | 105.7528 | | 0.0008 | 2.0 | 15064 | 0.1888 | 99.7556 | | 0.0001 | 3.0 | 22596 | 0.1495 | 106.2064 | | 0.0 | 4.0 | 30128 | 0.1288 | 97.6591 | | 0.0025 | 5.0 | 37660 | 0.1170 | 124.9313 | | 0.0001 | 6.0 | 45192 | 0.1071 | 122.8575 | | 0.0001 | 7.0 | 52724 | 0.1053 | 117.5784 | | 0.0009 | 8.0 | 60256 | 0.1045 | 117.7447 | | 0.0 | 9.0 | 67788 | 0.1058 | 118.8636 | | 0.0003 | 10.0 | 75320 | 0.1070 | 115.1997 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

wfdsdfsdfwer/FL

wfdsdfsdfwer

"2024-05-08T19:35:50Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:04:52Z"

Entry not found

Aditadot23/ultramen.bksi

Aditadot23

"2024-04-06T07:10:28Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:06:06Z"

Entry not found

devesh1496/prompt_recover

devesh1496

"2024-04-06T07:07:14Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T07:07:14Z"

--- license: apache-2.0 ---

bei0209/test_0406

bei0209

"2024-04-07T07:15:49Z"

0

null

[ "code", "graph-ml", "en", "dataset:imagenet-1k", "doi:10.57967/hf/2041", "license:apache-2.0", "region:us" ]

graph-ml

"2024-04-06T07:15:38Z"

--- license: apache-2.0 datasets: - imagenet-1k language: - en metrics: - accuracy pipeline_tag: graph-ml tags: - code ---

viber1/Llama-2-7b-chat-finetune

viber1

"2024-04-06T07:16:32Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:16:32Z"

Entry not found

MinhMinh09/gpt2-vietnamese-finetuned-ner

MinhMinh09

"2024-04-06T07:19:15Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:19:14Z"

Entry not found

Simonk97/LUNAS

Simonk97

"2024-04-06T10:57:05Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:19:38Z"

Entry not found

leptonai/UNA-SimpleSmaug-34b-v1beta-4heads

leptonai

"2024-04-06T07:20:55Z"

0

transformers

[ "transformers", "safetensors", "endpoints_compatible", "region:us" ]

null

"2024-04-06T07:20:20Z"

Entry not found

silencer107/bobik03

silencer107

"2024-04-06T07:28:36Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:25:58Z"

Entry not found

yohanchu/tripleS

yohanchu

"2024-04-06T07:28:22Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:26:22Z"

Entry not found

tistak/sn3_12

tistak

"2024-06-03T07:14:47Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:26:56Z"

Entry not found

tistak/sn3_8

tistak

"2024-06-03T07:14:08Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:27:00Z"

Entry not found

tistak/sn3_7

tistak

"2024-06-03T07:14:13Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:27:00Z"

Entry not found

tistak/sn3_6

tistak

"2024-06-03T07:14:49Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:27:01Z"

Entry not found

tistak/sn3_11

tistak

"2024-06-03T07:14:10Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:27:01Z"

Entry not found

tistak/sn3_9

tistak

"2024-06-03T07:14:48Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:27:01Z"

Entry not found

tistak/sn3_10

tistak

"2024-06-03T07:14:13Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:27:01Z"

Entry not found

xjyplayer/cyberpunk-goggles

xjyplayer

"2024-04-06T07:40:46Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T07:27:23Z"

--- license: apache-2.0 ---

yraziel/edengolan

yraziel

"2024-04-06T07:32:03Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:28:02Z"

Entry not found

UlutSoftLLC/kyrgyz-tts

UlutSoftLLC

"2024-04-12T09:26:44Z"

0

1

null

[ "Text-to-speech", "region:us" ]

null

"2024-04-06T07:29:36Z"

--- tags: - Text-to-speech --- Кыргыз Республикасынын Президентине караштуу Мамлекеттик тил боюнча улуттук комиссиясы Кыргызча текстти аудиого айландыруу Kyrgyz Text-To-Speech Models trained by Ulutsoft LLC. Эркек: checkpoint_epoch=279.ckpt Аял: checkpoint_epoch=479.ckpt GitHub: https://github.com/UlutSoftLLC/MamtilTTS

silencer107/bobik04

silencer107

"2024-04-06T07:31:43Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:31:32Z"

Entry not found

aa1223/MTCNN

aa1223

"2024-04-06T07:39:29Z"

0

espnet

[ "espnet", "deepfake", "en", "dataset:Cohere/wikipedia-2023-11-embed-multilingual-v3", "license:mit", "region:us" ]

null

"2024-04-06T07:34:16Z"

--- license: mit datasets: - Cohere/wikipedia-2023-11-embed-multilingual-v3 language: - en metrics: - bertscore library_name: espnet tags: - deepfake ---

oneandahalfcats/halfcatpooplol

oneandahalfcats

"2024-04-06T07:36:13Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:35:16Z"

Entry not found

silencer107/bobik05

silencer107

"2024-04-06T07:37:18Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:37:10Z"

Entry not found

oneandahalfcats/notenoughcats

oneandahalfcats

"2024-04-06T07:38:32Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:38:26Z"

Entry not found

smahjouri/falcon-40b-formai

smahjouri

"2024-04-06T07:40:34Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-04-06T07:40:20Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

oneandahalfcats/valstillbrokeniprofit

oneandahalfcats

"2024-04-06T07:41:32Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:41:27Z"

Entry not found

AMKAK/REALSTATEBUZZ

AMKAK

"2024-04-06T07:50:22Z"

0

null

[ "license:mit", "region:us" ]

null

"2024-04-06T07:50:22Z"

--- license: mit ---

pisad/f

pisad

"2024-04-06T07:52:17Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:52:17Z"

Entry not found

oneandahalfcats/valbrokeiprofithehe

oneandahalfcats

"2024-04-06T08:08:45Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:55:52Z"

Entry not found

Or4cl3-1/SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp

Or4cl3-1

"2024-04-06T07:58:02Z"

0

null

[ "merge", "mergekit", "lazymergekit", "SuperAGI/SAM", "GoogleAI/Gemini", "bigscience/bloom", "openai/opt-175b", "deepmind/gopher", "microsoft/megatron-turing-nlg", "base_model:SuperAGI/SAM", "base_model:merge:SuperAGI/SAM", "base_model:bigscience/bloom", "base_model:merge:bigscience/bloom", "region:us" ]

null

"2024-04-06T07:58:02Z"

--- tags: - merge - mergekit - lazymergekit - SuperAGI/SAM - GoogleAI/Gemini - bigscience/bloom - openai/opt-175b - deepmind/gopher - microsoft/megatron-turing-nlg base_model: - SuperAGI/SAM - GoogleAI/Gemini - bigscience/bloom - openai/opt-175b - deepmind/gopher - microsoft/megatron-turing-nlg --- # SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [SuperAGI/SAM](https://huggingface.co/SuperAGI/SAM) * [GoogleAI/Gemini](https://huggingface.co/GoogleAI/Gemini) * [bigscience/bloom](https://huggingface.co/bigscience/bloom) * [openai/opt-175b](https://huggingface.co/openai/opt-175b) * [deepmind/gopher](https://huggingface.co/deepmind/gopher) * [microsoft/megatron-turing-nlg](https://huggingface.co/microsoft/megatron-turing-nlg) ## 🧩 Configuration ```yaml slices: - sources: - model: SuperAGI/SAM layer_range: [0, 32] - model: GoogleAI/Gemini layer_range: [0, 32] - model: bigscience/bloom layer_range: [0, 32] - model: openai/opt-175b layer_range: [0, 32] - model: deepmind/gopher layer_range: [0, 32] - model: microsoft/megatron-turing-nlg layer_range: [0, 32] merge_method: slerp base_model: SuperAGI/SAM parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat1 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Or4cl3-1/SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

lijingnian/gollie-7B

lijingnian

"2024-04-06T07:59:46Z"

0

null

[ "region:us" ]

null

"2024-04-06T07:59:46Z"

Entry not found

StandardCAS-NSTID/RAIDP-LICENSES

StandardCAS-NSTID

"2024-04-06T08:12:47Z"

0

null

[ "license:other", "region:us" ]

null

"2024-04-06T08:04:49Z"

--- license: other license_name: raidp license_link: LICENSE ---

axs2k/anubis

axs2k

"2024-04-06T11:09:59Z"

0

1

transformers

[ "transformers", "dataset:zefang-liu/secqa", "dataset:zefang-liu/phishing-email-dataset", "dataset:zefang-liu/cve-and-cwe-mapping-dataset", "dataset:morpheuslord/cve-llm-training", "dataset:EddieChen372/python_vul_cvefix_small", "dataset:icantiemyshoe/cve-to-metasploit-module", "dataset:lambdasec/cve-single-line-fixes", "dataset:lambdasec/gh-top-1000-projects-vulns", "dataset:hackaprompt/hackaprompt-dataset", "dataset:pentest-org-2/dataset-2", "dataset:Isamu136/penetration_testing_scraped_dataset", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:Vezora/Tested-143k-Python-Alpaca", "dataset:neuralsentry/bigvul_devign_cvefixes_neuralsentry_commits", "dataset:hotal/linux_commands", "dataset:Nexa4AI/android_data_test", "dataset:SamsungSAILMontreal/deepnets1m", "license:gpl-3.0", "endpoints_compatible", "region:us" ]

null

"2024-04-06T08:07:12Z"

--- license: gpl-3.0 datasets: - zefang-liu/secqa - zefang-liu/phishing-email-dataset - zefang-liu/cve-and-cwe-mapping-dataset - morpheuslord/cve-llm-training - EddieChen372/python_vul_cvefix_small - icantiemyshoe/cve-to-metasploit-module - lambdasec/cve-single-line-fixes - lambdasec/gh-top-1000-projects-vulns - hackaprompt/hackaprompt-dataset - pentest-org-2/dataset-2 - Isamu136/penetration_testing_scraped_dataset - iamtarun/python_code_instructions_18k_alpaca - Vezora/Tested-143k-Python-Alpaca - neuralsentry/bigvul_devign_cvefixes_neuralsentry_commits - hotal/linux_commands - Nexa4AI/android_data_test - SamsungSAILMontreal/deepnets1m metrics: - accuracy - precision - bleurt - rouge - code_eval - f1 - bertscore - bleu library_name: transformers ---

Reyouf/speecht5_tts_Ar

Reyouf

"2024-04-06T08:42:01Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "endpoints_compatible", "region:us" ]

text-to-audio

"2024-04-06T08:07:32Z"

Entry not found

TechWezz/Q_A

TechWezz

"2024-04-06T08:12:23Z"

0

null

[ "license:llama2", "region:us" ]

null

"2024-04-06T08:12:23Z"

--- license: llama2 ---

Alphaiborkano/Alphaschool

Alphaiborkano

"2024-04-06T08:14:56Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T08:14:56Z"

--- license: apache-2.0 ---

haryoaw/scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_delta

haryoaw

"2024-04-06T08:16:41Z"

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:massive", "base_model:haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base", "base_model:finetune:haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]

null

"2024-04-06T08:15:55Z"

--- license: mit base_model: haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base tags: - generated_from_trainer datasets: - massive metrics: - accuracy - f1 model-index: - name: scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_delta results: [] ---  # scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_delta This model is a fine-tuned version of [haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base](https://huggingface.co/haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 1.0311 - Accuracy: 0.8606 - F1: 0.8342 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 11213 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 0.9866 | 0.27 | 5000 | 1.5404 | 0.8352 | 0.8118 | | 0.8243 | 0.53 | 10000 | 1.4475 | 0.8405 | 0.8176 | | 0.7347 | 0.8 | 15000 | 1.4195 | 0.8413 | 0.8225 | | 0.5835 | 1.07 | 20000 | 1.3804 | 0.8457 | 0.8210 | | 0.5829 | 1.34 | 25000 | 1.3623 | 0.8460 | 0.8221 | | 0.569 | 1.6 | 30000 | 1.3527 | 0.8474 | 0.8266 | | 0.5566 | 1.87 | 35000 | 1.3316 | 0.8463 | 0.8242 | | 0.478 | 2.14 | 40000 | 1.3076 | 0.8492 | 0.8250 | | 0.4712 | 2.41 | 45000 | 1.2920 | 0.8507 | 0.8270 | | 0.4646 | 2.67 | 50000 | 1.2986 | 0.8497 | 0.8256 | | 0.4534 | 2.94 | 55000 | 1.2796 | 0.8503 | 0.8284 | | 0.4099 | 3.21 | 60000 | 1.2625 | 0.8520 | 0.8297 | | 0.4002 | 3.47 | 65000 | 1.2672 | 0.8503 | 0.8290 | | 0.4116 | 3.74 | 70000 | 1.2519 | 0.8494 | 0.8259 | | 0.4068 | 4.01 | 75000 | 1.2484 | 0.8494 | 0.8238 | | 0.3706 | 4.28 | 80000 | 1.2264 | 0.8519 | 0.8280 | | 0.3724 | 4.54 | 85000 | 1.2330 | 0.8525 | 0.8278 | | 0.3804 | 4.81 | 90000 | 1.2312 | 0.8483 | 0.8251 | | 0.34 | 5.08 | 95000 | 1.2227 | 0.8508 | 0.8257 | | 0.3606 | 5.34 | 100000 | 1.1968 | 0.8534 | 0.8278 | | 0.3473 | 5.61 | 105000 | 1.2000 | 0.8551 | 0.8311 | | 0.3554 | 5.88 | 110000 | 1.2086 | 0.8536 | 0.8299 | | 0.3234 | 6.15 | 115000 | 1.1836 | 0.8553 | 0.8313 | | 0.325 | 6.41 | 120000 | 1.1953 | 0.8536 | 0.8303 | | 0.3227 | 6.68 | 125000 | 1.1653 | 0.8559 | 0.8305 | | 0.3213 | 6.95 | 130000 | 1.1733 | 0.8550 | 0.8305 | | 0.3042 | 7.22 | 135000 | 1.1637 | 0.8550 | 0.8297 | | 0.3099 | 7.48 | 140000 | 1.1601 | 0.8563 | 0.8314 | | 0.3064 | 7.75 | 145000 | 1.1568 | 0.8567 | 0.8296 | | 0.2928 | 8.02 | 150000 | 1.1507 | 0.8559 | 0.8322 | | 0.298 | 8.28 | 155000 | 1.1578 | 0.8556 | 0.8316 | | 0.2896 | 8.55 | 160000 | 1.1591 | 0.8574 | 0.8349 | | 0.2895 | 8.82 | 165000 | 1.1504 | 0.8558 | 0.8299 | | 0.2827 | 9.09 | 170000 | 1.1552 | 0.8551 | 0.8286 | | 0.2864 | 9.35 | 175000 | 1.1277 | 0.8563 | 0.8328 | | 0.2796 | 9.62 | 180000 | 1.1345 | 0.8564 | 0.8317 | | 0.28 | 9.89 | 185000 | 1.1409 | 0.8562 | 0.8297 | | 0.2753 | 10.15 | 190000 | 1.1290 | 0.8557 | 0.8307 | | 0.2766 | 10.42 | 195000 | 1.1143 | 0.8580 | 0.8321 | | 0.2652 | 10.69 | 200000 | 1.1150 | 0.8560 | 0.8306 | | 0.2741 | 10.96 | 205000 | 1.1275 | 0.8568 | 0.8296 | | 0.2668 | 11.22 | 210000 | 1.1069 | 0.8574 | 0.8333 | | 0.2629 | 11.49 | 215000 | 1.1168 | 0.8580 | 0.8315 | | 0.2607 | 11.76 | 220000 | 1.1173 | 0.8584 | 0.8344 | | 0.2546 | 12.03 | 225000 | 1.1096 | 0.8574 | 0.8315 | | 0.2583 | 12.29 | 230000 | 1.1244 | 0.8554 | 0.8303 | | 0.2557 | 12.56 | 235000 | 1.1080 | 0.8572 | 0.8339 | | 0.2571 | 12.83 | 240000 | 1.1008 | 0.8578 | 0.8344 | | 0.2471 | 13.09 | 245000 | 1.0980 | 0.8578 | 0.8323 | | 0.2528 | 13.36 | 250000 | 1.0975 | 0.8576 | 0.8311 | | 0.2478 | 13.63 | 255000 | 1.0921 | 0.8581 | 0.8322 | | 0.2548 | 13.9 | 260000 | 1.0826 | 0.8604 | 0.8352 | | 0.2429 | 14.16 | 265000 | 1.0903 | 0.8578 | 0.8332 | | 0.2416 | 14.43 | 270000 | 1.0892 | 0.8593 | 0.8344 | | 0.2331 | 14.7 | 275000 | 1.0807 | 0.8601 | 0.8339 | | 0.2449 | 14.96 | 280000 | 1.0792 | 0.8579 | 0.8312 | | 0.237 | 15.23 | 285000 | 1.0863 | 0.8600 | 0.8348 | | 0.2338 | 15.5 | 290000 | 1.0805 | 0.8575 | 0.8310 | | 0.2378 | 15.77 | 295000 | 1.0822 | 0.8579 | 0.8321 | | 0.2346 | 16.03 | 300000 | 1.0757 | 0.8583 | 0.8309 | | 0.2274 | 16.3 | 305000 | 1.0847 | 0.8579 | 0.8312 | | 0.2414 | 16.57 | 310000 | 1.0734 | 0.8602 | 0.8353 | | 0.2314 | 16.84 | 315000 | 1.0814 | 0.8584 | 0.8333 | | 0.2291 | 17.1 | 320000 | 1.0677 | 0.8604 | 0.8340 | | 0.223 | 17.37 | 325000 | 1.0731 | 0.8590 | 0.8340 | | 0.2224 | 17.64 | 330000 | 1.0648 | 0.8601 | 0.8338 | | 0.2264 | 17.9 | 335000 | 1.0603 | 0.8593 | 0.8333 | | 0.2252 | 18.17 | 340000 | 1.0639 | 0.8590 | 0.8323 | | 0.2164 | 18.44 | 345000 | 1.0607 | 0.8595 | 0.8336 | | 0.2231 | 18.71 | 350000 | 1.0614 | 0.8597 | 0.8338 | | 0.2204 | 18.97 | 355000 | 1.0702 | 0.8582 | 0.8323 | | 0.2155 | 19.24 | 360000 | 1.0539 | 0.8583 | 0.8321 | | 0.2107 | 19.51 | 365000 | 1.0579 | 0.8594 | 0.8335 | | 0.2163 | 19.77 | 370000 | 1.0608 | 0.8584 | 0.8323 | | 0.2136 | 20.04 | 375000 | 1.0552 | 0.8593 | 0.8330 | | 0.2062 | 20.31 | 380000 | 1.0577 | 0.8587 | 0.8345 | | 0.212 | 20.58 | 385000 | 1.0594 | 0.8592 | 0.8321 | | 0.2116 | 20.84 | 390000 | 1.0561 | 0.8593 | 0.8348 | | 0.2058 | 21.11 | 395000 | 1.0490 | 0.8597 | 0.8358 | | 0.2106 | 21.38 | 400000 | 1.0557 | 0.8593 | 0.8346 | | 0.2061 | 21.65 | 405000 | 1.0508 | 0.8601 | 0.8349 | | 0.2119 | 21.91 | 410000 | 1.0496 | 0.8591 | 0.8343 | | 0.2057 | 22.18 | 415000 | 1.0447 | 0.8602 | 0.8345 | | 0.2017 | 22.45 | 420000 | 1.0486 | 0.8597 | 0.8338 | | 0.1997 | 22.71 | 425000 | 1.0346 | 0.8612 | 0.8377 | | 0.1959 | 22.98 | 430000 | 1.0478 | 0.8586 | 0.8323 | | 0.1989 | 23.25 | 435000 | 1.0436 | 0.8599 | 0.8327 | | 0.1996 | 23.52 | 440000 | 1.0459 | 0.8603 | 0.8342 | | 0.1982 | 23.78 | 445000 | 1.0410 | 0.8602 | 0.8348 | | 0.1955 | 24.05 | 450000 | 1.0416 | 0.8596 | 0.8344 | | 0.1977 | 24.32 | 455000 | 1.0383 | 0.8594 | 0.8329 | | 0.1951 | 24.58 | 460000 | 1.0413 | 0.8599 | 0.8351 | | 0.2014 | 24.85 | 465000 | 1.0332 | 0.8606 | 0.8357 | | 0.1948 | 25.12 | 470000 | 1.0370 | 0.8599 | 0.8351 | | 0.1933 | 25.39 | 475000 | 1.0393 | 0.8598 | 0.8342 | | 0.1937 | 25.65 | 480000 | 1.0334 | 0.8607 | 0.8346 | | 0.1923 | 25.92 | 485000 | 1.0358 | 0.8607 | 0.8358 | | 0.1911 | 26.19 | 490000 | 1.0323 | 0.8604 | 0.8345 | | 0.1921 | 26.46 | 495000 | 1.0347 | 0.8600 | 0.8337 | | 0.1917 | 26.72 | 500000 | 1.0327 | 0.8602 | 0.8346 | | 0.1882 | 26.99 | 505000 | 1.0293 | 0.8606 | 0.8340 | | 0.1876 | 27.26 | 510000 | 1.0324 | 0.8603 | 0.8349 | | 0.189 | 27.52 | 515000 | 1.0310 | 0.8613 | 0.8355 | | 0.1891 | 27.79 | 520000 | 1.0309 | 0.8606 | 0.8341 | | 0.1847 | 28.06 | 525000 | 1.0290 | 0.8609 | 0.8352 | | 0.1827 | 28.33 | 530000 | 1.0272 | 0.8601 | 0.8333 | | 0.1913 | 28.59 | 535000 | 1.0279 | 0.8609 | 0.8352 | | 0.1872 | 28.86 | 540000 | 1.0291 | 0.8606 | 0.8341 | | 0.1875 | 29.13 | 545000 | 1.0266 | 0.8606 | 0.8339 | | 0.1856 | 29.39 | 550000 | 1.0283 | 0.8609 | 0.8363 | | 0.1823 | 29.66 | 555000 | 1.0282 | 0.8602 | 0.8339 | | 0.1823 | 29.93 | 560000 | 1.0311 | 0.8606 | 0.8342 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3

yujia23/axolotl-mistral-cold-3e-4-lora

yujia23

"2024-04-06T08:17:54Z"

0

peft

[ "peft", "tensorboard", "safetensors", "mistral", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]

null

"2024-04-06T08:17:08Z"

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: home/yujia/home/CN_Hateful/trained_models/mistral/cold/3e-4/ results: [] ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: true load_in_4bit: false strict: false datasets: # - path: mhenrichsen/alpaca_2k_test # - path: /home/yujia/home/CN_Hateful/train_toxiCN.json # - path: /home/yujia/home/CN_Hateful/train_toxiCN_cn.json - path: /home/yujia/home/CN_Hateful/train.json ds_type: json type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.1 # output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/toxi/1e-5/ # output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/CN/toxi/1e-5/ output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/cold/3e-4/ adapter: lora lora_model_dir: sequence_len: 256 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 4 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0003 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # home/yujia/home/CN_Hateful/trained_models/mistral/cold/3e-4/ This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0406 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9655 | 0.01 | 1 | 2.9710 | | 0.0451 | 0.25 | 46 | 0.0619 | | 0.0541 | 0.5 | 92 | 0.0392 | | 0.0353 | 0.75 | 138 | 0.0345 | | 0.0249 | 1.0 | 184 | 0.0315 | | 0.0259 | 1.23 | 230 | 0.0329 | | 0.0238 | 1.48 | 276 | 0.0309 | | 0.019 | 1.73 | 322 | 0.0305 | | 0.0173 | 1.97 | 368 | 0.0313 | | 0.0051 | 2.21 | 414 | 0.0369 | | 0.0093 | 2.46 | 460 | 0.0436 | | 0.0023 | 2.71 | 506 | 0.0407 | | 0.003 | 2.95 | 552 | 0.0406 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0

haryoaw/scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_beta

haryoaw

"2024-04-06T08:18:53Z"

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:massive", "base_model:haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base", "base_model:finetune:haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]

null

"2024-04-06T08:18:06Z"

--- license: mit base_model: haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base tags: - generated_from_trainer datasets: - massive metrics: - accuracy - f1 model-index: - name: scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_beta results: [] ---  # scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_1_beta This model is a fine-tuned version of [haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base](https://huggingface.co/haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 1.0301 - Accuracy: 0.8597 - F1: 0.8341 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 112233 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 1.0109 | 0.27 | 5000 | 1.5142 | 0.8386 | 0.8137 | | 0.8133 | 0.53 | 10000 | 1.4503 | 0.8411 | 0.8191 | | 0.7197 | 0.8 | 15000 | 1.4028 | 0.8438 | 0.8204 | | 0.5839 | 1.07 | 20000 | 1.3897 | 0.8463 | 0.8223 | | 0.5816 | 1.34 | 25000 | 1.3593 | 0.8455 | 0.8186 | | 0.5482 | 1.6 | 30000 | 1.3400 | 0.8465 | 0.8225 | | 0.5493 | 1.87 | 35000 | 1.3227 | 0.8449 | 0.8199 | | 0.4571 | 2.14 | 40000 | 1.3476 | 0.8449 | 0.8215 | | 0.4612 | 2.41 | 45000 | 1.2853 | 0.8515 | 0.8298 | | 0.4589 | 2.67 | 50000 | 1.3029 | 0.8493 | 0.8283 | | 0.4417 | 2.94 | 55000 | 1.2670 | 0.8515 | 0.8281 | | 0.4113 | 3.21 | 60000 | 1.2688 | 0.8496 | 0.8251 | | 0.402 | 3.47 | 65000 | 1.2629 | 0.8521 | 0.8286 | | 0.4041 | 3.74 | 70000 | 1.2528 | 0.8501 | 0.8246 | | 0.3878 | 4.01 | 75000 | 1.2265 | 0.8523 | 0.8316 | | 0.3715 | 4.28 | 80000 | 1.2460 | 0.8501 | 0.8256 | | 0.3702 | 4.54 | 85000 | 1.2228 | 0.8538 | 0.8321 | | 0.373 | 4.81 | 90000 | 1.2171 | 0.8533 | 0.8271 | | 0.3494 | 5.08 | 95000 | 1.2042 | 0.8537 | 0.8312 | | 0.3509 | 5.34 | 100000 | 1.2069 | 0.8544 | 0.8280 | | 0.3421 | 5.61 | 105000 | 1.2048 | 0.8535 | 0.8286 | | 0.3448 | 5.88 | 110000 | 1.1911 | 0.8542 | 0.8296 | | 0.3249 | 6.15 | 115000 | 1.1902 | 0.8539 | 0.8245 | | 0.3252 | 6.41 | 120000 | 1.1825 | 0.8564 | 0.8288 | | 0.3176 | 6.68 | 125000 | 1.1763 | 0.8546 | 0.8313 | | 0.3123 | 6.95 | 130000 | 1.1913 | 0.8530 | 0.8307 | | 0.3059 | 7.22 | 135000 | 1.1681 | 0.8548 | 0.8284 | | 0.316 | 7.48 | 140000 | 1.1605 | 0.8557 | 0.8292 | | 0.3067 | 7.75 | 145000 | 1.1802 | 0.8541 | 0.8302 | | 0.3022 | 8.02 | 150000 | 1.1536 | 0.8548 | 0.8306 | | 0.2985 | 8.28 | 155000 | 1.1672 | 0.8528 | 0.8265 | | 0.3004 | 8.55 | 160000 | 1.1480 | 0.8556 | 0.8318 | | 0.2942 | 8.82 | 165000 | 1.1470 | 0.8549 | 0.8291 | | 0.2854 | 9.09 | 170000 | 1.1378 | 0.8558 | 0.8314 | | 0.2967 | 9.35 | 175000 | 1.1340 | 0.8553 | 0.8307 | | 0.2901 | 9.62 | 180000 | 1.1443 | 0.8557 | 0.8313 | | 0.2844 | 9.89 | 185000 | 1.1419 | 0.8551 | 0.8312 | | 0.2742 | 10.15 | 190000 | 1.1258 | 0.8564 | 0.8334 | | 0.2763 | 10.42 | 195000 | 1.1267 | 0.8572 | 0.8323 | | 0.2757 | 10.69 | 200000 | 1.1275 | 0.8558 | 0.8295 | | 0.2728 | 10.96 | 205000 | 1.1238 | 0.8578 | 0.8348 | | 0.2739 | 11.22 | 210000 | 1.1029 | 0.8578 | 0.8319 | | 0.2579 | 11.49 | 215000 | 1.1155 | 0.8575 | 0.8350 | | 0.2604 | 11.76 | 220000 | 1.1021 | 0.8583 | 0.8375 | | 0.2605 | 12.03 | 225000 | 1.1134 | 0.8567 | 0.8301 | | 0.2496 | 12.29 | 230000 | 1.0996 | 0.8565 | 0.8291 | | 0.2518 | 12.56 | 235000 | 1.1162 | 0.8567 | 0.8302 | | 0.2574 | 12.83 | 240000 | 1.1053 | 0.8576 | 0.8327 | | 0.2488 | 13.09 | 245000 | 1.1105 | 0.8574 | 0.8346 | | 0.2423 | 13.36 | 250000 | 1.1050 | 0.8580 | 0.8326 | | 0.2473 | 13.63 | 255000 | 1.0882 | 0.8584 | 0.8348 | | 0.2479 | 13.9 | 260000 | 1.0926 | 0.8589 | 0.8366 | | 0.254 | 14.16 | 265000 | 1.0919 | 0.8576 | 0.8312 | | 0.24 | 14.43 | 270000 | 1.0941 | 0.8576 | 0.8339 | | 0.2415 | 14.7 | 275000 | 1.0881 | 0.8584 | 0.8336 | | 0.2421 | 14.96 | 280000 | 1.0816 | 0.8580 | 0.8326 | | 0.2395 | 15.23 | 285000 | 1.0832 | 0.8574 | 0.8323 | | 0.2336 | 15.5 | 290000 | 1.0822 | 0.8580 | 0.8341 | | 0.2324 | 15.77 | 295000 | 1.0873 | 0.8586 | 0.8344 | | 0.2274 | 16.03 | 300000 | 1.0795 | 0.8583 | 0.8336 | | 0.233 | 16.3 | 305000 | 1.0857 | 0.8581 | 0.8331 | | 0.2281 | 16.57 | 310000 | 1.0852 | 0.8578 | 0.8341 | | 0.2268 | 16.84 | 315000 | 1.0803 | 0.8583 | 0.8338 | | 0.2253 | 17.1 | 320000 | 1.0756 | 0.8590 | 0.8345 | | 0.2247 | 17.37 | 325000 | 1.0650 | 0.8589 | 0.8328 | | 0.2182 | 17.64 | 330000 | 1.0696 | 0.8582 | 0.8336 | | 0.2187 | 17.9 | 335000 | 1.0694 | 0.8578 | 0.8324 | | 0.2202 | 18.17 | 340000 | 1.0621 | 0.8584 | 0.8329 | | 0.2214 | 18.44 | 345000 | 1.0737 | 0.8578 | 0.8328 | | 0.2186 | 18.71 | 350000 | 1.0606 | 0.8586 | 0.8346 | | 0.2171 | 18.97 | 355000 | 1.0623 | 0.8583 | 0.8345 | | 0.2123 | 19.24 | 360000 | 1.0641 | 0.8589 | 0.8352 | | 0.2141 | 19.51 | 365000 | 1.0627 | 0.8586 | 0.8349 | | 0.2145 | 19.77 | 370000 | 1.0616 | 0.8585 | 0.8336 | | 0.2087 | 20.04 | 375000 | 1.0570 | 0.8593 | 0.8332 | | 0.2098 | 20.31 | 380000 | 1.0639 | 0.8585 | 0.8330 | | 0.2122 | 20.58 | 385000 | 1.0501 | 0.8602 | 0.8347 | | 0.2071 | 20.84 | 390000 | 1.0583 | 0.8592 | 0.8328 | | 0.2083 | 21.11 | 395000 | 1.0554 | 0.8589 | 0.8337 | | 0.2041 | 21.38 | 400000 | 1.0604 | 0.8586 | 0.8331 | | 0.2035 | 21.65 | 405000 | 1.0560 | 0.8581 | 0.8333 | | 0.2099 | 21.91 | 410000 | 1.0557 | 0.8586 | 0.8346 | | 0.2031 | 22.18 | 415000 | 1.0511 | 0.8593 | 0.8348 | | 0.2024 | 22.45 | 420000 | 1.0534 | 0.8586 | 0.8340 | | 0.2024 | 22.71 | 425000 | 1.0447 | 0.8604 | 0.8343 | | 0.2025 | 22.98 | 430000 | 1.0465 | 0.8593 | 0.8344 | | 0.2029 | 23.25 | 435000 | 1.0472 | 0.8592 | 0.8346 | | 0.197 | 23.52 | 440000 | 1.0467 | 0.8595 | 0.8345 | | 0.1996 | 23.78 | 445000 | 1.0428 | 0.8592 | 0.8346 | | 0.1938 | 24.05 | 450000 | 1.0428 | 0.8593 | 0.8342 | | 0.1949 | 24.32 | 455000 | 1.0390 | 0.8605 | 0.8352 | | 0.1976 | 24.58 | 460000 | 1.0453 | 0.8586 | 0.8325 | | 0.1957 | 24.85 | 465000 | 1.0346 | 0.8605 | 0.8350 | | 0.1946 | 25.12 | 470000 | 1.0370 | 0.8592 | 0.8328 | | 0.1954 | 25.39 | 475000 | 1.0385 | 0.8600 | 0.8348 | | 0.1877 | 25.65 | 480000 | 1.0358 | 0.8604 | 0.8351 | | 0.1911 | 25.92 | 485000 | 1.0385 | 0.8597 | 0.8357 | | 0.1919 | 26.19 | 490000 | 1.0400 | 0.8593 | 0.8340 | | 0.19 | 26.46 | 495000 | 1.0357 | 0.8596 | 0.8345 | | 0.1923 | 26.72 | 500000 | 1.0386 | 0.8600 | 0.8356 | | 0.1942 | 26.99 | 505000 | 1.0407 | 0.8590 | 0.8337 | | 0.189 | 27.26 | 510000 | 1.0333 | 0.8604 | 0.8359 | | 0.1888 | 27.52 | 515000 | 1.0352 | 0.8590 | 0.8334 | | 0.1899 | 27.79 | 520000 | 1.0330 | 0.8595 | 0.8347 | | 0.1934 | 28.06 | 525000 | 1.0310 | 0.8593 | 0.8342 | | 0.1873 | 28.33 | 530000 | 1.0342 | 0.8600 | 0.8350 | | 0.1869 | 28.59 | 535000 | 1.0305 | 0.8601 | 0.8358 | | 0.1799 | 28.86 | 540000 | 1.0326 | 0.8590 | 0.8337 | | 0.1832 | 29.13 | 545000 | 1.0331 | 0.8597 | 0.8350 | | 0.1842 | 29.39 | 550000 | 1.0323 | 0.8599 | 0.8349 | | 0.1818 | 29.66 | 555000 | 1.0313 | 0.8602 | 0.8360 | | 0.1819 | 29.93 | 560000 | 1.0301 | 0.8597 | 0.8341 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3

paularusti78/q-FrozenLake-v1-4x4-noSlippery

paularusti78

"2024-04-06T08:18:20Z"

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2024-04-06T08:18:17Z"

--- 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="paularusti78/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"]) ```

Esmaeilkiani/Zali

Esmaeilkiani

"2024-04-06T08:22:57Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T08:22:57Z"

--- license: apache-2.0 ---

Qin56/text2imagev1

Qin56

"2024-04-07T07:49:25Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T08:23:24Z"

--- license: apache-2.0 ---

LongBabin/babin

LongBabin

"2024-04-06T08:25:54Z"

0

null

[ "region:us" ]

null

"2024-04-06T08:25:54Z"

Entry not found

paularusti78/tax1-v3-1

paularusti78

"2024-04-06T08:29:02Z"

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2024-04-06T08:29:01Z"

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: tax1-v3-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 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="paularusti78/tax1-v3-1", 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"]) ```

harupurito/whisper-small-hi-cv

harupurito

"2024-04-06T11:55:37Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2024-04-06T08:32:24Z"

Entry not found

spraja08/fine-bitsy

spraja08

"2024-04-07T08:18:20Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "license:mit", "endpoints_compatible", "region:us" ]

null

"2024-04-06T08:34:12Z"

--- license: mit library_name: transformers --- # Fine-Bitsy Fine-Bitsy is a model (that has an attempted) specialty on US federsal reserves faq. This is my experimental attempt to create a specialist model that can be trained within a few dollars but can produce surprising good results. Phi-2 seems to have "seen" most commercially available content datasets. To convincingly test the "specialisation effect", I had to hunt for a less common dataset. 🤗 came handy with such a variety of datasets that can be readily used without much dataprep... ## Model Details Base Model : Phi-2 FineTuned with US Federal Reserves Q&A ### Model Description - **Developed by:** SP Raja - **License:** MIT - **Finetuned from model Phi-2:** Supervised Fine Tuning using custom dataset ## Uses This is an experiment on finetuning. Use the code below to get started with the model. ``` #load the model from the hub from peft import PeftConfig peft_model_id_from_hub = "spraja08/fine-bitsy" config = PeftConfig.from_pretrained(peft_model_id_from_hub) model_from_hub = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, return_dict=True, load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) #try the inference model_from_hub.eval() eval_prompt = """Instruction:What should I do if I have damaged or mutilated currency? Assistant:""" #eval_prompt = "Who is on the Federal Open Market Committee?" #eval_prompt = """What does the Federal Reserve mean when it says monetary policy remains "accommodative"?""" model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda") with torch.no_grad(): print(tokenizer.decode(model_from_hub.generate(**model_input, max_new_tokens=80)[0], skip_special_tokens=True)) ``` ### Training Data [us-federal-reserve-qa](https://huggingface.co/datasets/clement-cvll/us-federal-reserve-qa) ### Training Procedure [As in the notebook here](https://github.com/spraja08/supervised_fine_tuning/tree/main) #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

SJTU-TES/Everybody_Dance_Now

SJTU-TES

"2024-04-06T08:34:55Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T08:34:55Z"

--- license: apache-2.0 ---

csAugust/Llama-2-7b-chat-hf-q0f16-MLC

csAugust

"2024-04-07T02:44:01Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T08:35:00Z"

--- license: apache-2.0 ---

zgcr654321/human_matting_training

zgcr654321

"2024-04-06T08:59:10Z"

0

null

[ "license:mit", "region:us" ]

null

"2024-04-06T08:35:03Z"

--- license: mit ---

zgcr654321/salient_object_detection_training

zgcr654321

"2024-04-27T06:50:40Z"

0

null

[ "license:mit", "region:us" ]

null

"2024-04-06T08:35:36Z"

--- license: mit ---

zgcr654321/interactive_segmentation_training

zgcr654321

"2024-05-17T23:56:08Z"

0

null

[ "license:mit", "region:us" ]

null

"2024-04-06T08:36:16Z"

--- license: mit ---

Or4cl3-1/code-slerp

Or4cl3-1

"2024-04-06T08:36:56Z"

0

null

[ "merge", "mergekit", "lazymergekit", "microsoft/codebert-base", "EleutherAI/gpt-neo-x-20b", "openai/codex", "bigscience/bloom", "google/jurassic-1-jumbo", "google/t5-v1_1-large", "facebook/bart-large", "base_model:bigscience/bloom", "base_model:merge:bigscience/bloom", "base_model:facebook/bart-large", "base_model:merge:facebook/bart-large", "base_model:google/t5-v1_1-large", "base_model:merge:google/t5-v1_1-large", "base_model:microsoft/codebert-base", "base_model:merge:microsoft/codebert-base", "region:us" ]

null

"2024-04-06T08:36:54Z"

--- tags: - merge - mergekit - lazymergekit - microsoft/codebert-base - EleutherAI/gpt-neo-x-20b - openai/codex - bigscience/bloom - google/jurassic-1-jumbo - google/t5-v1_1-large - facebook/bart-large base_model: - microsoft/codebert-base - EleutherAI/gpt-neo-x-20b - openai/codex - bigscience/bloom - google/jurassic-1-jumbo - google/t5-v1_1-large - facebook/bart-large --- # code-slerp code-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) * [EleutherAI/gpt-neo-x-20b](https://huggingface.co/EleutherAI/gpt-neo-x-20b) * [openai/codex](https://huggingface.co/openai/codex) * [bigscience/bloom](https://huggingface.co/bigscience/bloom) * [google/jurassic-1-jumbo](https://huggingface.co/google/jurassic-1-jumbo) * [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) * [facebook/bart-large](https://huggingface.co/facebook/bart-large) ## 🧩 Configuration ```yaml slices: - sources: - model: microsoft/codebert-base layer_range: [0, 32] - model: EleutherAI/gpt-neo-x-20b layer_range: [0, 32] - model: openai/codex layer_range: [0, 32] - model: bigscience/bloom layer_range: [0, 32] - model: google/jurassic-1-jumbo layer_range: [0, 32] - model: google/t5-v1_1-large layer_range: [0, 32] - model: facebook/bart-large layer_range: [0, 32] merge_method: slerp base_model: microsoft/codebert-base parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat1 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Or4cl3-1/code-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

faiimea/openpose

faiimea

"2024-04-06T08:38:11Z"

0

null

[ "region:us" ]

null

"2024-04-06T08:38:10Z"

Entry not found

SJTU-TES/RobustVideoMatting

SJTU-TES

"2024-04-06T09:04:27Z"

0

null

[ "region:us" ]

null

"2024-04-06T08:41:20Z"

Entry not found

ajeya-op/mistral

ajeya-op

"2024-04-06T08:42:52Z"

0

null

[ "region:us" ]

null

"2024-04-06T08:42:52Z"

Entry not found

LuckyMan123/sushi_style

LuckyMan123

"2024-04-06T09:04:53Z"

0

null

[ "region:us" ]

null

"2024-04-06T08:43:06Z"

Entry not found

nuratamton/test3

nuratamton

"2024-04-06T09:14:52Z"

0

peft

[ "peft", "pytorch", "safetensors", "mistral", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "4-bit", "bitsandbytes", "region:us" ]

null

"2024-04-06T08:48:35Z"

--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2

faiimea/uap

faiimea

"2024-04-06T08:50:42Z"

0

null

[ "region:us" ]

null

"2024-04-06T08:50:42Z"

Entry not found

shrimalrishika/cnn-dm

shrimalrishika

"2024-04-06T08:51:26Z"

0

null

[ "region:us" ]

null

"2024-04-06T08:51:26Z"

Entry not found

HeydarS/flant5_xl_EQ_peft_v2

HeydarS

"2024-04-06T08:51:41Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/flan-t5-xl", "base_model:adapter:google/flan-t5-xl", "region:us" ]

null

"2024-04-06T08:51:35Z"

--- library_name: peft base_model: google/flan-t5-xl --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0

faiimea/wav2com

faiimea

"2024-04-06T08:52:52Z"

0

null

[ "region:us" ]

null

"2024-04-06T08:52:52Z"

Entry not found

Gopal2002/zehpyr-gemma-dpo-finetune

Gopal2002

"2024-04-23T09:50:07Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-04-06T08:54:57Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Shashashasha/Serial_Designation_N_GPT_SoVITS

Shashashasha

"2024-04-06T08:57:38Z"

0

null

[ "license:openrail", "region:us" ]

null

"2024-04-06T08:55:07Z"

--- license: openrail ---

dianamihalache27/Sarcasm.detector.bert_base_LSTM_FE_recall_CE_loss_Imbalance_datasampler

dianamihalache27

"2024-04-06T08:57:02Z"

0

null

[ "license:mit", "region:us" ]

null

"2024-04-06T08:57:02Z"

--- license: mit ---

dianamihalache27/Sarcasm.detector.bert_tweet_kim_cnn_recall_CE_loss_Imbalance_datasampler

dianamihalache27

"2024-04-06T08:59:42Z"

0

null

[ "license:mit", "region:us" ]

null

"2024-04-06T08:59:08Z"

--- license: mit ---

giuly97/finetuning-sentiment-model-3000-samples

giuly97

"2024-04-06T09:00:21Z"

0

null

[ "region:us" ]

null

"2024-04-06T09:00:20Z"

Entry not found

tumanggors/Jamba-v0.1

tumanggors

"2024-04-06T09:12:50Z"

0

null

[ "region:us" ]

null

"2024-04-06T09:12:50Z"

Entry not found

abhijitstat/my_awesome_billsum_model

abhijitstat

"2024-04-06T09:15:40Z"

0

null

[ "region:us" ]

null

"2024-04-06T09:15:39Z"

Entry not found

Caska26/bert-base-multilingual-uncased-sentiment

Caska26

"2024-04-06T09:24:20Z"

0

null

[ "region:us" ]

null

"2024-04-06T09:24:20Z"

Entry not found

mrsarthakgupta/onnx-classifier

mrsarthakgupta

"2024-04-08T21:01:36Z"

0

transformers

[ "transformers", "onnx", "vit", "endpoints_compatible", "region:us" ]

null

"2024-04-06T09:24:23Z"

Entry not found

24abhimanyu10/api-classification

24abhimanyu10

"2024-04-06T09:27:06Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T09:27:06Z"

--- license: apache-2.0 ---

shrimalrishika/medical_summarization

shrimalrishika

"2024-04-06T09:30:07Z"

0

null

[ "region:us" ]

null

"2024-04-06T09:30:05Z"

Entry not found

kenchiayy/wav2vec2-xls-r-1b-atcosim_corpus-google-colab

kenchiayy

"2024-04-06T09:31:26Z"

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-04-06T09:31:24Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

giuly97/bert-base-multilingual-uncased-sentiment

giuly97

"2024-04-06T09:32:24Z"

0

null

[ "region:us" ]

null

"2024-04-06T09:32:23Z"

Entry not found

huybopbi/vpnfast

huybopbi

"2024-04-06T09:35:19Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T09:35:19Z"

--- license: apache-2.0 ---

John-Yakuza/markymooolora

John-Yakuza

"2024-05-15T22:11:16Z"

0

null

[ "region:us" ]

null

"2024-04-06T09:40:58Z"

Entry not found

TroyHow/Helsinki-NLP

TroyHow

"2024-05-18T18:28:42Z"

0

null

[ "region:us" ]

null

"2024-04-06T09:43:27Z"

# These are the models required for running OFFLINE Translation You can either download all the files or just the files that you need. Please make sure it is in a format like ./Helsinki-NLP/XXX

TropikOyuncu/Baldiback

TropikOyuncu

"2024-04-06T09:51:43Z"

0

null

[ "license:openrail", "region:us" ]

null

"2024-04-06T09:50:06Z"

--- license: openrail ---

ShahlaDnshi96/mobile_mistral_2

ShahlaDnshi96

"2024-04-07T07:54:10Z"

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

"2024-04-06T09:52:03Z"

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mobile_mistral_2 results: [] ---  # mobile_mistral_2 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2

OpenNLG/OpenBA-V2-Vocab-Pruning

OpenNLG

"2024-04-06T10:02:20Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2024-04-06T10:02:20Z"

--- license: apache-2.0 ---

swsn21/forrxl

swsn21

"2024-10-01T06:36:37Z"

0

null

[ "region:us" ]

null

"2024-04-06T10:02:37Z"

Entry not found

devin-ai/devin

devin-ai

"2024-04-06T10:02:45Z"

0

null

[ "license:mit", "region:us" ]

null

"2024-04-06T10:02:45Z"

--- license: mit ---

Jakolo121/Sappho_V0.0.1-GGUF

Jakolo121

"2024-04-06T10:06:27Z"

0

null

[ "region:us" ]

null

"2024-04-06T10:06:27Z"

Entry not found

abhinandom42/test_sum

abhinandom42

"2024-04-06T10:12:08Z"

0

null

[ "region:us" ]

null

"2024-04-06T10:12:08Z"

Entry not found

HeydarS/flant5_bs_popQA_peft_v6

HeydarS

"2024-04-06T10:16:48Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/flan-t5-base", "base_model:adapter:google/flan-t5-base", "region:us" ]

null

"2024-04-06T10:16:45Z"

--- library_name: peft base_model: google/flan-t5-base --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0

SwimChoi/villama2-7b-Denmark-lora

SwimChoi

"2024-04-09T10:56:34Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

"2024-04-06T10:22:44Z"

--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

SwimChoi/villama2-7b-Bulgaria-lora

SwimChoi

"2024-04-09T10:57:51Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

"2024-04-06T10:22:55Z"

--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

SwimChoi/villama2-7b-Belgium-lora

SwimChoi

"2024-04-09T10:59:11Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

"2024-04-06T10:23:06Z"

--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

SwimChoi/villama2-7b-France-lora

SwimChoi

"2024-04-09T11:00:36Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]

null

"2024-04-06T10:23:18Z"

--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0