Split (1)

train · 847k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-27 18:27:39	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 500 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-27 18:23:41	card stringlengths 11 1.01M
jeonsiyun/layoutlmv3-financial-document-classification4	jeonsiyun	2024-03-06T04:44:50Z	5	0	transformers	[ "transformers", "safetensors", "layoutlmv3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T04:44:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF	seyf1elislam	2024-03-06T04:33:26Z	0	0	null	[ "gguf", "GGUF", "base_model:seyf1elislam/WestKunai-Hermes-long-128k-test-7b", "base_model:quantized:seyf1elislam/WestKunai-Hermes-long-128k-test-7b", "endpoints_compatible", "region:us" ]	null	2024-03-06T02:14:44Z	--- tags: - GGUF base_model: - seyf1elislam/WestKunai-Hermes-long-128k-test-7b --- # WestKunai-Hermes-long-128k-test-7b - Model creator: [seyf1elislam](https://huggingface.co/seyf1elislam) - Original model: [WestKunai-Hermes-long-128k-test-7b](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b) <!-- description start --> ## Description This repo contains GGUF format model files for [seyf1elislam's WestKunai-Hermes-long-128k-test-7b ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b). ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [westkunai-hermes-long-128k-test-7b.Q2_K.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q2_K.gguf ) \| Q2_K \| 2 \| 2.72 GB\| 5.22 GB \| significant quality loss - not recommended for most purposes \| \| [westkunai-hermes-long-128k-test-7b.Q3_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q3_K_M.gguf ) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [WestKunai-Hermes-long-128k-test-7b.Q4_K_S.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/WestKunai-Hermes-long-128k-test-7b.Q4_K_S.gguf ) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [westkunai-hermes-long-128k-test-7b.Q4_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q4_K_M.gguf ) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [westkunai-hermes-long-128k-test-7b.Q5_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q5_K_M.gguf ) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [westkunai-hermes-long-128k-test-7b.Q6_K.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q6_K.gguf ) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [westkunai-hermes-long-128k-test-7b.Q8_0.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q8_0.gguf ) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \|
alinerodrigues/wav2vec2-xlsr-1b-mecita-portuguese-all-clean-09	alinerodrigues	2024-03-06T04:32:39Z	1	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-06T00:17:43Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xlsr-1b-mecita-portuguese-all-clean-09 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. --> # wav2vec2-xlsr-1b-mecita-portuguese-all-clean-09 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-xls-r-1b-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1091 - Wer: 0.0715 - Cer: 0.0201 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 28.1893 \| 1.0 \| 67 \| 5.5870 \| 0.9891 \| 0.9135 \| \| 8.4096 \| 2.0 \| 134 \| 3.3389 \| 0.9888 \| 0.9614 \| \| 4.6285 \| 3.0 \| 201 \| 3.5010 \| 0.9713 \| 0.9829 \| \| 4.6285 \| 4.0 \| 268 \| 3.1012 \| 0.9746 \| 0.9824 \| \| 4.1632 \| 5.0 \| 335 \| 3.0531 \| 0.9766 \| 0.9801 \| \| 3.6744 \| 6.0 \| 402 \| 3.0343 \| 0.9868 \| 0.9739 \| \| 3.6744 \| 7.0 \| 469 \| 2.8810 \| 1.0 \| 1.0 \| \| 3.0111 \| 8.0 \| 536 \| 2.4821 \| 0.9970 \| 0.9612 \| \| 2.0541 \| 9.0 \| 603 \| 0.4203 \| 0.5659 \| 0.1206 \| \| 2.0541 \| 10.0 \| 670 \| 0.1569 \| 0.1107 \| 0.0288 \| \| 0.4608 \| 11.0 \| 737 \| 0.1331 \| 0.0975 \| 0.0263 \| \| 0.2892 \| 12.0 \| 804 \| 0.1344 \| 0.0955 \| 0.0254 \| \| 0.2892 \| 13.0 \| 871 \| 0.1242 \| 0.0797 \| 0.0226 \| \| 0.2182 \| 14.0 \| 938 \| 0.1217 \| 0.0837 \| 0.0240 \| \| 0.2017 \| 15.0 \| 1005 \| 0.1147 \| 0.0728 \| 0.0208 \| \| 0.2017 \| 16.0 \| 1072 \| 0.1206 \| 0.0725 \| 0.0216 \| \| 0.1666 \| 17.0 \| 1139 \| 0.1155 \| 0.0744 \| 0.0215 \| \| 0.169 \| 18.0 \| 1206 \| 0.1175 \| 0.0744 \| 0.0213 \| \| 0.169 \| 19.0 \| 1273 \| 0.1187 \| 0.0787 \| 0.0218 \| \| 0.1678 \| 20.0 \| 1340 \| 0.1211 \| 0.0744 \| 0.0216 \| \| 0.148 \| 21.0 \| 1407 \| 0.1153 \| 0.0715 \| 0.0205 \| \| 0.148 \| 22.0 \| 1474 \| 0.1164 \| 0.0728 \| 0.0213 \| \| 0.1487 \| 23.0 \| 1541 \| 0.1091 \| 0.0715 \| 0.0201 \| \| 0.138 \| 24.0 \| 1608 \| 0.1204 \| 0.0705 \| 0.0202 \| \| 0.138 \| 25.0 \| 1675 \| 0.1114 \| 0.0698 \| 0.0201 \| \| 0.1251 \| 26.0 \| 1742 \| 0.1180 \| 0.0688 \| 0.0202 \| \| 0.1056 \| 27.0 \| 1809 \| 0.1188 \| 0.0675 \| 0.0199 \| \| 0.1056 \| 28.0 \| 1876 \| 0.1123 \| 0.0652 \| 0.0188 \| \| 0.1107 \| 29.0 \| 1943 \| 0.1226 \| 0.0728 \| 0.0215 \| \| 0.0972 \| 30.0 \| 2010 \| 0.1221 \| 0.0705 \| 0.0205 \| \| 0.0972 \| 31.0 \| 2077 \| 0.1226 \| 0.0702 \| 0.0207 \| \| 0.1032 \| 32.0 \| 2144 \| 0.1159 \| 0.0669 \| 0.0195 \| \| 0.1038 \| 33.0 \| 2211 \| 0.1205 \| 0.0711 \| 0.0204 \| \| 0.1038 \| 34.0 \| 2278 \| 0.1191 \| 0.0685 \| 0.0192 \| \| 0.1027 \| 35.0 \| 2345 \| 0.1170 \| 0.0688 \| 0.0198 \| \| 0.1 \| 36.0 \| 2412 \| 0.1189 \| 0.0659 \| 0.0198 \| \| 0.1 \| 37.0 \| 2479 \| 0.1102 \| 0.0649 \| 0.0187 \| \| 0.0989 \| 38.0 \| 2546 \| 0.1150 \| 0.0718 \| 0.0206 \| \| 0.089 \| 39.0 \| 2613 \| 0.1202 \| 0.0682 \| 0.0195 \| \| 0.089 \| 40.0 \| 2680 \| 0.1168 \| 0.0669 \| 0.0194 \| \| 0.0807 \| 41.0 \| 2747 \| 0.1161 \| 0.0669 \| 0.0190 \| \| 0.0812 \| 42.0 \| 2814 \| 0.1208 \| 0.0715 \| 0.0204 \| \| 0.0812 \| 43.0 \| 2881 \| 0.1260 \| 0.0629 \| 0.0193 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.2.1+cu121 - Datasets 2.17.0 - Tokenizers 0.13.3
bhaswata08/Llama-2-7b-chat-hf-function-calling-v3-AWQ	bhaswata08	2024-03-06T04:26:51Z	48	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-05T09:45:58Z	--- license: llama2 --- # Model Card for bhaswata08/Llama-2-7b-chat-hf-function-calling-v3-AWQ Model creator: Trelis Original model: Llama-2-7b-chat-hf-function-calling-v3 This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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] <!-- 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]
rachittshah/gemma-2b-Gujpaca	rachittshah	2024-03-06T04:24:53Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T04:21:24Z	--- 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]
DFJordan/binary-image-classifier	DFJordan	2024-03-06T04:23:02Z	8	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-06T04:03:43Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder model-index: - name: binary-image-classifier 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. --> # binary-image-classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1222 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1302 \| 1.0 \| 67 \| 0.1486 \| \| 0.0503 \| 2.0 \| 134 \| 0.1087 \| \| 0.0188 \| 3.0 \| 201 \| 0.1511 \| \| 0.0116 \| 4.0 \| 268 \| 0.1225 \| \| 0.0088 \| 5.0 \| 335 \| 0.1222 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
lcfrs/gemma-2b-it	lcfrs	2024-03-06T04:18:41Z	0	0	null	[ "gguf", "license:other", "endpoints_compatible", "region:us" ]	null	2024-03-06T03:55:44Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- Created by running `quantize` from `llama.cpp` on [gemma-2b-it.gguf](https://huggingface.co/google/gemma-2b-it-GGUF/blob/main/). ```sh llama.cpp $ ./quantize models/gemma-2b-it.gguf models/gemma-2b-it-Q4_K_M.gguf Q4_K_M main: build = 2351 (652ca2bd) main: built with Android (10552028, +pgo, +bolt, +lto, -mlgo, based on r487747d) clang version 17.0.2 (https://android.googlesource.com/toolchain/llvm-project d9f89f4d16663d5012e5c09495f3b30ece3d2362) for x86_64-apple-darwin22.5.0 main: quantizing 'models/gemma-2b-it.gguf' to 'models/gemma-2b-it-Q4_K_M.gguf' as Q4_K_M llama_model_loader: loaded meta data with 19 key-value pairs and 164 tensors from models/gemma-2b-it.gguf (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = gemma llama_model_loader: - kv 1: general.name str = gemma-2b-it llama_model_loader: - kv 2: gemma.context_length u32 = 8192 llama_model_loader: - kv 3: gemma.block_count u32 = 18 llama_model_loader: - kv 4: gemma.embedding_length u32 = 2048 llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 16384 llama_model_loader: - kv 6: gemma.attention.head_count u32 = 8 llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 1 llama_model_loader: - kv 8: gemma.attention.key_length u32 = 256 llama_model_loader: - kv 9: gemma.attention.value_length u32 = 256 llama_model_loader: - kv 10: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 11: tokenizer.ggml.model str = llama llama_model_loader: - kv 12: tokenizer.ggml.bos_token_id u32 = 2 llama_model_loader: - kv 13: tokenizer.ggml.eos_token_id u32 = 1 llama_model_loader: - kv 14: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 3 llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,256128] = ["<pad>", "<eos>", "<bos>", "<unk>", ... llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,256128] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,256128] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - type f32: 164 tensors llama_model_quantize_internal: meta size = 6042528 bytes [..snip..] llama_model_quantize_internal: model size = 9561.29 MB llama_model_quantize_internal: quant size = 1549.19 MB main: quantize time = 27285.22 ms main: total time = 27285.22 ms ```
uname-n/tiny-aquatic-llama.10k	uname-n	2024-03-06T04:16:13Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "conversational", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:uname-n/slim-orca-dedup-chat-10k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T02:30:10Z	--- license: apache-2.0 datasets: - Open-Orca/SlimOrca-Dedup - uname-n/slim-orca-dedup-chat-10k widget: - text: "<\|system\|>\nYou are a chatbot who can help code!</s>\n<\|user\|>\nWrite me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI.</s>\n<\|assistant\|>\n" --- <div align="center"> # Tiny Aquatic Llama </div> #### This Model This is a chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). The model was fine-tuned on a 10k sample from [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup). #### Note This model is deranged.
shg1421/t5_astrology_peft	shg1421	2024-03-06T04:01:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-06T01:20:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Sumail/Bubble_bee04_2b	Sumail	2024-03-06T03:55:03Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:tomaszki/gemma-28", "base_model:merge:tomaszki/gemma-28", "base_model:tomaszki/gemma-28-copy", "base_model:merge:tomaszki/gemma-28-copy", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T03:52:00Z	--- base_model: - tomaszki/gemma-28-copy - tomaszki/gemma-28 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [tomaszki/gemma-28-copy](https://huggingface.co/tomaszki/gemma-28-copy) * [tomaszki/gemma-28](https://huggingface.co/tomaszki/gemma-28) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: tomaszki/gemma-28 layer_range: [0, 18] - model: tomaszki/gemma-28-copy layer_range: [0, 18] merge_method: slerp base_model: tomaszki/gemma-28 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
OwOOwO/eacc_dc_4	OwOOwO	2024-03-06T03:48:57Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T03:46:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
dyang415/mixtral-fc-w-resp-new-format-4e-no-negative	dyang415	2024-03-06T03:47:37Z	7	0	peft	[ "peft", "safetensors", "mixtral", "generated_from_trainer", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2024-03-05T05:03:16Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 model-index: - name: mixtral-fc-w-resp-new-format-4e-no-negative 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/Mixtral-8x7B-Instruct-v0.1 model_type: AutoModelForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true load_in_8bit: false load_in_4bit: true strict: false chat_template: inst datasets: - path: ./data/with_function_response/function_not_used_training.jsonl type: sharegpt conversation: mistral # - path: ./data/with_function_response/no_function_training.jsonl # type: sharegpt # conversation: mistral - path: ./data/with_function_response/function_used_training.jsonl type: sharegpt conversation: mistral dataset_prepared_path: last_run_prepared val_set_size: 0.0 output_dir: ../mixtral-fc-w-resp-new-format-4e-no-negative model_config: output_router_logits: true adapter: qlora lora_model_dir: sequence_len: 16384 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 64 lora_dropout: 0.05 lora_target_modules: - q_proj - k_proj - v_proj - o_proj wandb_project: function-call wandb_name: mixtral-instruct-lora-no-negative wandb_log_model: end hub_model_id: dyang415/mixtral-fc-w-resp-new-format-4e-no-negative gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false gradient_checkpointing: true logging_steps: 1 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: weight_decay: 0.0 fsdp: fsdp_config: ``` </details><br> # mixtral-fc-w-resp-new-format-4e-no-negative This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-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 The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Framework versions - PEFT 0.7.0 - Transformers 4.37.0 - Pytorch 2.0.1+cu117 - Datasets 2.17.1 - Tokenizers 0.15.0
EarthnDusk/Dataset_Dumps_Zips	EarthnDusk	2024-03-06T03:44:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-20T00:39:51Z	--- license: creativeml-openrail-m --- Datasets Zipped. Please note these are for educational purposes and research purposes only. You are liable for any legal use of these zip files. Some of these are already made concepts, but may fit in concepts mixed with other datasets. Please do not use thse for illegal use, and if any of it is indeed E&D property - eg: Duskfall art, anything we've made in Second Life - don't claim the data as your own. We feel confident in sharing the datasets, and you will be clearly the trainer of your LORA or Full model, but you won't own the data that you use from this repository. Occasionally this repo may be privated, if you've gotten the link from us before - and want to access it - request access to be on the team. Join our Reddit: https://www.reddit.com/r/earthndusk/ WE ARE PROUDLY SPONSORED BY: https://www.piratediffusion.com/ Listen to the music that we've made that goes with our art: https://open.spotify.com/playlist/00R8x00YktB4u541imdSSf?si=b60d209385a74b38 any chance you can spare a coffee or three? https://ko-fi.com/DUSKFALLcrew [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/Z8Z8L4EO)
Commandante/german-party-sentiment-bert	Commandante	2024-03-06T03:43:59Z	18	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:oliverguhr/german-sentiment-bert", "base_model:finetune:oliverguhr/german-sentiment-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-22T09:34:10Z	--- license: mit base_model: oliverguhr/german-sentiment-bert tags: - generated_from_trainer model-index: - name: german-party-sentiment-bert-complete-gsbert 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. --> # German-Party-Sentiment-Bert This model is a fine-tuned version of [oliverguhr/german-sentiment-bert](https://huggingface.co/oliverguhr/german-sentiment-bert) on a dataset consisting of mentions of german political parties. It achieves the following results on the evaluation set: - Loss: 0.8912 ## 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: 20 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 120 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2844 \| 1.0 \| 65 \| 0.9382 \| \| 0.9704 \| 2.0 \| 130 \| 0.8912 \| \| 0.7394 \| 3.0 \| 195 \| 1.0455 \| \| 0.5401 \| 4.0 \| 260 \| 1.2711 \| \| 0.4274 \| 5.0 \| 325 \| 1.3578 \| \| 0.2289 \| 6.0 \| 390 \| 1.6143 \| \| 0.1949 \| 7.0 \| 455 \| 1.8376 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Tokenizers 0.15.1
OwOOwO/eacc_dc3	OwOOwO	2024-03-06T03:43:30Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T03:23:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
seyf1elislam/WestKunai-XS-7b-GGUF	seyf1elislam	2024-03-06T03:43:19Z	22	0	null	[ "gguf", "GGUF", "base_model:seyf1elislam/WestKunai-XS-7b", "base_model:quantized:seyf1elislam/WestKunai-XS-7b", "endpoints_compatible", "region:us" ]	null	2024-03-06T01:41:02Z	--- tags: - GGUF base_model: - seyf1elislam/WestKunai-X-7b --- # WestKunai-X-7b - Model creator: [seyf1elislam](https://huggingface.co/seyf1elislam) - Original model: [WestKunai-X-7b](https://huggingface.co/seyf1elislam/WestKunai-X-7b) <!-- description start --> ## Description This repo contains GGUF format model files for [seyf1elislam's WestKunai-X-7b ](https://huggingface.co/seyf1elislam/WestKunai-X-7b). ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [westkunai-x-7b.Q2_K.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q2_K.gguf ) \| Q2_K \| 2 \| 2.72 GB\| 5.22 GB \| significant quality loss - not recommended for most purposes \| \| [westkunai-x-7b.Q3_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q3_K_M.gguf ) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [WestKunai-X-7b.Q4_K_S.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/WestKunai-X-7b.Q4_K_S.gguf ) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [westkunai-x-7b.Q4_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q4_K_M.gguf ) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [westkunai-x-7b.Q5_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q5_K_M.gguf ) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [westkunai-x-7b.Q6_K.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q6_K.gguf ) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [westkunai-x-7b.Q8_0.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q8_0.gguf ) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \|
nitky/Superswallow-70b-NVE	nitky	2024-03-06T03:42:07Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "en", "ja", "arxiv:2311.10702", "arxiv:2203.05482", "base_model:allenai/tulu-2-dpo-70b", "base_model:merge:allenai/tulu-2-dpo-70b", "base_model:tokyotech-llm/Swallow-70b-instruct-hf", "base_model:merge:tokyotech-llm/Swallow-70b-instruct-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T02:45:45Z	--- base_model: - tokyotech-llm/Swallow-70b-instruct-hf - allenai/tulu-2-dpo-70b tags: - mergekit - merge language: - en - ja library_name: transformers pipeline_tag: text-generation license: llama2 model_type: llama --- # Superswallow-70b-NVE Important Notice: This model partially utilizes the parameters of Tulu V2 DPO finetuned based on Llama 2, so it may inherit the AI2 ImpACT license. Please use the model keeping in mind that there may be changes regarding the license if AI2 contacts me. The [AI2 ImpACT license](https://allenai.org/impact-license) includes information about data artifacts and model artifacts, but does not cover the case of directly applying parts of the LLM parameters of a model artifact to other models. However, I respect their research and great work, so I will change the license immediately if AI2 contacts me. ## Description This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). The model was created by injecting the ability to follow user intent from [Tulu 2 DPO](https://arxiv.org/abs/2311.10702) into the [Swallow](https://zenn.dev/tokyotech_lm/articles/d6cb3a8fdfc907) instract model. It was a proof of concept for merging LLMs trained in other languages, and paid close attention to preserving the linguistic capabilities of the merge-based model. As far as I know, Swallow is the full set Llama 2 model(7B, 13B, 70B) that can output the most beautiful Japanese. Therefore, I used it as the base model for merging this time. Thank you for their wonderful work. ## Test environment This model was tested using [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main). I use preset `simple-1` and `Null preset` for Generation. ### Recommendation Use `simple-1` settings: - temperature: 0.7 - top_p: 0.9 - repetition_penalty: 1.15 - top_k: 20 ### Tested `temperature` Range - temperature: 0.3 - 1.0 It works fine in most cases, but depending on the prompt, the output may become unstable at the temperature around 1.0. If the output does not follow the user intent, please lower the temperature to 0.5 or less. ### Tested `repetition_penalty` Range - repetition_penalty: 1.0 - 1.15 It works fine in most cases, but depending on the prompt, the output may become unstable at the repetition_penalty around 1.0. ## Prompt template All prompt templates are available as well. ### Tulu Style ``` <\|user\|> Your message here! <\|assistant\|> ``` For best results, format all inputs in this manner. Make sure to include a newline after `<\|assistant\|>`, this can affect generation quality quite a bit. ### Swallow Style (Alpaca format) ``` 以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。リクエストを適切に完了するための回答を記述してください。 ### 指示: {instruction} ### 応答: ``` ## Use the instruct model ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "nitky/Superswallow-70b-NVE" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto", load_in_4bit = True) PROMPT_DICT = { "prompt_input": ( "以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 入力:\n{input}\n\n### 応答:" ), "prompt_no_input": ( "以下に、あるタスクを説明する指示があります。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 応答:" ), } def create_prompt(instruction, input=None): """ Generates a prompt based on the given instruction and an optional input. If input is provided, it uses the 'prompt_input' template from PROMPT_DICT. If no input is provided, it uses the 'prompt_no_input' template. Args: instruction (str): The instruction describing the task. input (str, optional): Additional input providing context for the task. Default is None. Returns: str: The generated prompt. """ if input: # Use the 'prompt_input' template when additional input is provided return PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input) else: # Use the 'prompt_no_input' template when no additional input is provided return PROMPT_DICT["prompt_no_input"].format(instruction=instruction) # Example usage instruction_example = "以下のトピックに関する詳細な情報を提供してください。" input_example = "東京工業大学の主なキャンパスについて教えてください" prompt = create_prompt(instruction_example, input_example) input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=200, temperature=0.7, top_p=0.9, repetition_penalty=1.15, top_k=20, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [tokyotech-llm/Swallow-70b-NVE-instruct-hf](https://huggingface.co/tokyotech-llm/Swallow-70b-NVE-instruct-hf) * [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b) ### Configuration The command example: ```bash # please change the path and options according to your environment mergekit-mega --cuda Superswallow-70b-NVE.yml ~/text-generation-webui/models ``` The following YAML configuration was used to produce this model: ```yaml models: - model: tokyotech-llm/Swallow-70b-NVE-instruct-hf parameters: weight: 1.0 - model: allenai/tulu-2-dpo-70b parameters: weight: 1.0 merge_method: linear dtype: bfloat16 tokenizer_source: model:tokyotech-llm/Swallow-70b-NVE-instruct-hf name: Superswallow-70b-NVE ```
DingosGotMyBaby/uhn-twitch-chat	DingosGotMyBaby	2024-03-06T03:34:11Z	100	2	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-23T15:59:10Z	--- license: mit --- # A model based on UberHaxorNova's Twitch chat Trained on over 700 vods worth of chat and with some scuffed filtering it became a 300mb dataset. ## Dataset The dataset was created by downloading all the available vods at the time of creation as a json file and stripping out all the chat messages into a simple line-by-line text file. ## Training This was trained using [aitextgen](https://github.com/minimaxir/aitextgen), created by [Max Woolf](https://github.com/minimaxir), using the example notebook found [here](https://colab.research.google.com/drive/15qBZx5y9rdaQSyWpsreMDnTiZ5IlN0zD?usp=sharing). Using GPT-2's 124M model as the base, it was trained for 3000 steps and produces an output scuffed enough to look like a real Twitch chat user. ## Use This was created as a fun little project for the discord server and as such, should only be used for fun and not to harm people. This model must also follow the ethics guide of the tool that created it https://github.com/minimaxir/aitextgen/blob/master/docs/ethics.md
Ponce-01/DFEP-03	Ponce-01	2024-03-06T03:32:57Z	0	0	adapter-transformers	[ "adapter-transformers", "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T03:23:43Z	--- tags: - autotrain - text-generation widget: - text: 'I love AutoTrain because ' license: other library_name: adapter-transformers --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
agnedil/Mistral-7B-openassistant-guanaco-v2	agnedil	2024-03-06T03:20:17Z	0	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-03-05T08:55:35Z	Model [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) fine-tuned on the [openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset using the following [Colab notebook](https://colab.research.google.com/drive/19lYWzMvZAc2cWPojRiPnYIR5Ok62CgFQ?usp=drive_link).
julienkay/stable-diffusion-2-1	julienkay	2024-03-06T03:17:54Z	0	0	null	[ "onnx", "text-to-image", "license:openrail++", "region:us" ]	text-to-image	2024-02-15T20:47:45Z	--- license: openrail++ pipeline_tag: text-to-image --- The official [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) model converted to ONNX for usage with Unity Sentis. See [com.doji.diffusers](https://github.com/julienkay/com.doji.diffusers) for details.
ho1iday/pokemon-lora	ho1iday	2024-03-06T03:14:20Z	1	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-03-05T12:34:41Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - ho1iday/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Deepnoid/OPEN-SOLAR-KO-10.7B-v13	Deepnoid	2024-03-06T03:06:25Z	0	0	peft	[ "peft", "safetensors", "llama", "generated_from_trainer", "base_model:beomi/OPEN-SOLAR-KO-10.7B", "base_model:adapter:beomi/OPEN-SOLAR-KO-10.7B", "license:apache-2.0", "region:us" ]	null	2024-03-06T02:11:12Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: beomi/OPEN-SOLAR-KO-10.7B model-index: - name: data/Models/OPEN-SOLAR-KO-10.7B-v13 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) # data/Models/OPEN-SOLAR-KO-10.7B-v13 This model is a fine-tuned version of [beomi/OPEN-SOLAR-KO-10.7B](https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
Joyqiuyue/JoyFineTune	Joyqiuyue	2024-03-06T02:54:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-06T02:30:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mirfan899/kids_phoneme_sm_model	mirfan899	2024-03-06T02:54:21Z	41	1	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:mirfan899/kids_phoneme_sm", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-10T11:56:31Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - mirfan899/kids_phoneme_sm base_model: facebook/wav2vec2-large-xlsr-53 model-index: - name: kids_phoneme_sm_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kids_phoneme_sm_model This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the https://huggingface.co/datasets/mirfan899/kids_phoneme_sm dataset. It achieves the following results on the evaluation set: - Loss: 0.5405 - Cer: 0.2770 ## 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: 4e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.2595 \| 0.74 \| 500 \| 3.7094 \| 1.0 \| \| 2.8393 \| 1.48 \| 1000 \| 3.2563 \| 1.0 \| \| 2.7916 \| 2.22 \| 1500 \| 3.0450 \| 1.0 \| \| 1.9585 \| 2.96 \| 2000 \| 1.0280 \| 0.8428 \| \| 1.0099 \| 3.7 \| 2500 \| 0.6477 \| 0.5162 \| \| 0.7968 \| 4.44 \| 3000 \| 0.5551 \| 0.4592 \| \| 0.6977 \| 5.19 \| 3500 \| 0.5107 \| 0.4065 \| \| 0.609 \| 5.93 \| 4000 \| 0.4763 \| 0.3916 \| \| 0.5941 \| 6.67 \| 4500 \| 0.4817 \| 0.3850 \| \| 0.5411 \| 7.41 \| 5000 \| 0.4755 \| 0.3639 \| \| 0.5021 \| 8.15 \| 5500 \| 0.4649 \| 0.3622 \| \| 0.4884 \| 8.89 \| 6000 \| 0.4630 \| 0.3569 \| \| 0.4484 \| 9.63 \| 6500 \| 0.4675 \| 0.3420 \| \| 0.4432 \| 10.37 \| 7000 \| 0.4192 \| 0.3402 \| \| 0.399 \| 11.11 \| 7500 \| 0.4508 \| 0.3310 \| \| 0.4215 \| 11.85 \| 8000 \| 0.4406 \| 0.3345 \| \| 0.366 \| 12.59 \| 8500 \| 0.4620 \| 0.3248 \| \| 0.3708 \| 13.33 \| 9000 \| 0.4594 \| 0.3327 \| \| 0.3352 \| 14.07 \| 9500 \| 0.4649 \| 0.3121 \| \| 0.3468 \| 14.81 \| 10000 \| 0.4413 \| 0.3020 \| \| 0.3283 \| 15.56 \| 10500 \| 0.4948 \| 0.2915 \| \| 0.3222 \| 16.3 \| 11000 \| 0.4870 \| 0.3025 \| \| 0.3081 \| 17.04 \| 11500 \| 0.4779 \| 0.2919 \| \| 0.3099 \| 17.78 \| 12000 \| 0.4927 \| 0.2871 \| \| 0.2485 \| 18.52 \| 12500 \| 0.5013 \| 0.2831 \| \| 0.3163 \| 19.26 \| 13000 \| 0.4929 \| 0.2888 \| \| 0.2555 \| 20.0 \| 13500 \| 0.5234 \| 0.2888 \| \| 0.2705 \| 20.74 \| 14000 \| 0.5259 \| 0.2818 \| \| 0.2632 \| 21.48 \| 14500 \| 0.5105 \| 0.2831 \| \| 0.2374 \| 22.22 \| 15000 \| 0.5284 \| 0.2845 \| \| 0.2565 \| 22.96 \| 15500 \| 0.5237 \| 0.2875 \| \| 0.2394 \| 23.7 \| 16000 \| 0.5368 \| 0.2818 \| \| 0.2458 \| 24.44 \| 16500 \| 0.5386 \| 0.2814 \| \| 0.2383 \| 25.19 \| 17000 \| 0.5366 \| 0.2788 \| \| 0.2152 \| 25.93 \| 17500 \| 0.5320 \| 0.2770 \| \| 0.231 \| 26.67 \| 18000 \| 0.5441 \| 0.2779 \| \| 0.2061 \| 27.41 \| 18500 \| 0.5448 \| 0.2796 \| \| 0.245 \| 28.15 \| 19000 \| 0.5413 \| 0.2796 \| \| 0.2119 \| 28.89 \| 19500 \| 0.5379 \| 0.2774 \| \| 0.2155 \| 29.63 \| 20000 \| 0.5405 \| 0.2770 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.13.0 - Tokenizers 0.13.3
bartowski/Flora_7B-GGUF	bartowski	2024-03-06T02:48:22Z	17	2	transformers	[ "transformers", "gguf", "finetune", "text-generation", "en", "dataset:ResplendentAI/Synthetic_Soul_1k", "base_model:jeiku/FloraBase", "base_model:quantized:jeiku/FloraBase", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T02:34:06Z	--- base_model: - jeiku/FloraBase - jeiku/Synthetic_Soul_1k_Mistral_128 library_name: transformers tags: - finetune license: cc-by-sa-4.0 datasets: - ResplendentAI/Synthetic_Soul_1k language: - en quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp Quantizations of Flora_7B Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2334">b2334</a> for quantization. Original model: https://huggingface.co/ResplendentAI/Flora_7B Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Flora_7B-Q8_0.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q8_0.gguf) \| Q8_0 \| 7.69GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Flora_7B-Q6_K.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q6_K.gguf) \| Q6_K \| 5.94GB \| Very high quality, near perfect, recommended. \| \| [Flora_7B-Q5_K_M.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q5_K_M.gguf) \| Q5_K_M \| 5.13GB \| High quality, very usable. \| \| [Flora_7B-Q5_K_S.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q5_K_S.gguf) \| Q5_K_S \| 4.99GB \| High quality, very usable. \| \| [Flora_7B-Q5_0.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q5_0.gguf) \| Q5_0 \| 4.99GB \| High quality, older format, generally not recommended. \| \| [Flora_7B-Q4_K_M.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q4_K_M.gguf) \| Q4_K_M \| 4.36GB \| Good quality, similar to 4.25 bpw. \| \| [Flora_7B-Q4_K_S.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q4_K_S.gguf) \| Q4_K_S \| 4.14GB \| Slightly lower quality with small space savings. \| \| [Flora_7B-Q4_0.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q4_0.gguf) \| Q4_0 \| 4.10GB \| Decent quality, older format, generally not recommended. \| \| [Flora_7B-Q3_K_L.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q3_K_L.gguf) \| Q3_K_L \| 3.82GB \| Lower quality but usable, good for low RAM availability. \| \| [Flora_7B-Q3_K_M.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q3_K_M.gguf) \| Q3_K_M \| 3.51GB \| Even lower quality. \| \| [Flora_7B-Q3_K_S.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q3_K_S.gguf) \| Q3_K_S \| 3.16GB \| Low quality, not recommended. \| \| [Flora_7B-Q2_K.gguf](https://huggingface.co/bartowski/Flora_7B-GGUF/blob/main/Flora_7B-Q2_K.gguf) \| Q2_K \| 2.71GB \| Extremely low quality, not recommended. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
aditya11997/kandi2-decoder-3.2	aditya11997	2024-03-06T02:47:47Z	2	1	diffusers	[ "diffusers", "tensorboard", "safetensors", "kandinsky", "text-to-image", "diffusers-training", "dataset:kbharat7/DogChestXrayDatasetNew", "base_model:kandinsky-community/kandinsky-2-2-decoder", "base_model:finetune:kandinsky-community/kandinsky-2-2-decoder", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:KandinskyV22Pipeline", "region:us" ]	text-to-image	2024-03-05T20:26:13Z	--- license: creativeml-openrail-m base_model: kandinsky-community/kandinsky-2-2-decoder datasets: - kbharat7/DogChestXrayDatasetNew prior: - kandinsky-community/kandinsky-2-2-prior tags: - kandinsky - text-to-image - diffusers - diffusers-training inference: true --- # Finetuning - aditya11997/kandi2-decoder-3.2 This pipeline was finetuned from kandinsky-community/kandinsky-2-2-decoder on the kbharat7/DogChestXrayDatasetNew dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['photo of dogxraysmall']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = AutoPipelineForText2Image.from_pretrained("aditya11997/kandi2-decoder-3.2", torch_dtype=torch.float16) prompt = "photo of dogxraysmall" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 43 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 4 * Image resolution: 768 * Mixed-precision: None
kharato/opt-125m-gptq_4	kharato	2024-03-06T02:43:41Z	5	0	transformers	[ "transformers", "safetensors", "opt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-03-06T02:43:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lmh2011/whisper-small-vi	lmh2011	2024-03-06T02:42:59Z	60	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "vi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-04T07:35:59Z	--- language: - vi license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Vi 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 Small Vi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
kkimdev/llama-2-7b-bnb-4bit-3	kkimdev	2024-03-06T02:41:07Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-13b-bnb-4bit", "base_model:finetune:unsloth/llama-2-13b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-06T02:40:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-13b-bnb-4bit --- # Uploaded model - Developed by: kkimdev - License: apache-2.0 - Finetuned from model : unsloth/llama-2-13b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Verlocksss/q-FrozenLake-v1-4x4-noSlippery	Verlocksss	2024-03-06T02:39:09Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-06T02:39:05Z	--- 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="Verlocksss/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"]) ```
kharato/opt-125m-gptq	kharato	2024-03-06T02:38:24Z	5	0	transformers	[ "transformers", "safetensors", "opt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "gptq", "region:us" ]	text-generation	2024-03-06T02:38:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
OwOOwO/eacc_3_9	OwOOwO	2024-03-06T02:37:20Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T02:34:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
sampraxi/v5	sampraxi	2024-03-06T02:34:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-06T02:34:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
OwOOwO/eacc_bm2c10	OwOOwO	2024-03-06T02:33:56Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T02:31:30Z	--- 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]
regmisaugat59/phi-1_5-finetuned	regmisaugat59	2024-03-06T02:28:44Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-1_5", "base_model:adapter:microsoft/phi-1_5", "license:mit", "region:us" ]	null	2024-03-06T02:05:13Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-1_5 model-index: - name: phi-1_5-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-1_5-finetuned This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
kody0525/Open-platypus-Commercial-SOLAR-10.7B-v1.0	kody0525	2024-03-06T02:21:55Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "SOLAR-10.7B-v1.0", "Open-platypus-Commercial", "en", "dataset:kyujinpy/Open-platypus-Commercial", "base_model:upstage/SOLAR-10.7B-v1.0", "base_model:finetune:upstage/SOLAR-10.7B-v1.0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T01:59:23Z	--- license: apache-2.0 language: - en tags: - SOLAR-10.7B-v1.0 - Open-platypus-Commercial pipeline_tag: text-generation datasets: - kyujinpy/Open-platypus-Commercial base_model: upstage/SOLAR-10.7B-v1.0 model-index: - name: Open-platypus-Commercial-SOLAR-10.7B-v1.0 results: [] --- Update @ 2024.03.05 # Open-platypus-Commercial-SOLAR-10.7B-v1.0 This model is a fine-tuned version of upstage/SOLAR-10.7B-v1.0 ## Training hyperparameters The following hyperparameters were used during training: - batch_size = 16 - num_epochs = 1 - micro_batch = 1 - cutoff_len = 4096 - learning_rate = 4e-4 ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.7 - Tokenizers 0.14.1
aka7774/ECCV2022-RIFE	aka7774	2024-03-06T02:21:51Z	0	0	null	[ "region:us" ]	null	2023-03-29T08:30:39Z	# ECCV2022-RIFEと愉快なモデル - モチベーションが足りないので実装しない ## 何をするもの？ - むかし Stable Diffusion で動画作ろうと思って試してたフレーム補完ツールを動かすコード - 動画のフレーム補完をAIでいいかんじにするやつ - animatediffのような涙ぐましい動画生成技術で足りないfpsを補うために使えそうなツール - EasyPromptAnime に同梱されてるらしい - 4倍くらいに補完したいけど、ヌルヌルしてて気持ち悪いらしい？ ## インストール - 本家は https://github.com/megvii-research/ECCV2022-RIFE らしい - なぜかgoogle driveから(非公式の?)モデルをダウンロードしないと使えない - https://drive.google.com/file/d/1APIzVeI-4ZZCEuIRE1m6WYfSCaOsi_7_/view - 以前はモデルのダウンロードがなぜか baidu 経由しかなかったので苦労した - 本家からリンクされている様々な派生製品があり、モデルの改善が続いているように見える - どれを選べば最善かまでは調べてない - torch, torchvisionが古いバージョンに固定されていた問題は現状では解消されている ## 動作方法 - pythonで実装されているがコマンドライン版しかない - これを gradio や fastapi にするのがめんどくさい - Windows用のスクリプトとバッチを配布している事例がある - https://qiita.com/amaman/items/743c42d365a4e3bc155f - これもコマンドラインを経由するのでいじるのがめんどい - Super-SloMo という別のツールにも対応しているのが興味深い ## やりたかったこと - 動画を投げると指定のfpsに変換してくれる space を作りたかった - 上述の rife_video を移植するのが一番楽そうだけどかえってややこしくなりそうでもある自分で使う予定が無いので一旦保留。
josephloh/donut-receipts75	josephloh	2024-03-06T02:19:22Z	8	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-03-06T01:57:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
brescia/IndoBERT	brescia	2024-03-06T02:16:02Z	0	0	null	[ "code", "region:us" ]	null	2024-03-02T07:03:12Z	--- tags: - code --- # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="brescia/IndoBERT")
cookinai/Blitz-v0.1	cookinai	2024-03-06T02:15:08Z	103	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T00:58:54Z	--- license: cc-by-4.0 --- # Base finetune of [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on my [Kugelblitz Dataset](https://huggingface.co/datasets/cookinai/kugelblitz-alpha-v0.1) ![Kugelblitz](https://huggingface.co/cookinai/Blitz-v0.1/resolve/main/kugelblitz_black_hole.png) Trained on only 1 epoch V0.2 should be coming soon with some more epochs, if this one turns out well
Litzy619/V0305B2	Litzy619	2024-03-06T02:10:34Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:yahma/llama-7b-hf", "base_model:finetune:yahma/llama-7b-hf", "license:other", "region:us" ]	null	2024-03-05T21:30:12Z	--- license: other base_model: yahma/llama-7b-hf tags: - generated_from_trainer model-index: - name: V0305B2 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. --> # V0305B2 This model is a fine-tuned version of [yahma/llama-7b-hf](https://huggingface.co/yahma/llama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0894 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.352 \| 0.09 \| 10 \| 2.3256 \| \| 2.1754 \| 0.17 \| 20 \| 1.8064 \| \| 1.2745 \| 0.26 \| 30 \| 0.6844 \| \| 0.3789 \| 0.34 \| 40 \| 0.1687 \| \| 0.1587 \| 0.43 \| 50 \| 0.1487 \| \| 0.1563 \| 0.51 \| 60 \| 0.1506 \| \| 0.1505 \| 0.6 \| 70 \| 0.1502 \| \| 0.1525 \| 0.68 \| 80 \| 0.1487 \| \| 0.1481 \| 0.77 \| 90 \| 0.1492 \| \| 0.1504 \| 0.85 \| 100 \| 0.1441 \| \| 0.1501 \| 0.94 \| 110 \| 0.1436 \| \| 0.1439 \| 1.02 \| 120 \| 0.1360 \| \| 0.1411 \| 1.11 \| 130 \| 0.1276 \| \| 0.1349 \| 1.19 \| 140 \| 0.1259 \| \| 0.1345 \| 1.28 \| 150 \| 0.1190 \| \| 0.1299 \| 1.37 \| 160 \| 0.1114 \| \| 0.1275 \| 1.45 \| 170 \| 0.1058 \| \| 0.1159 \| 1.54 \| 180 \| 0.1013 \| \| 0.1189 \| 1.62 \| 190 \| 0.0997 \| \| 0.1203 \| 1.71 \| 200 \| 0.1012 \| \| 0.1177 \| 1.79 \| 210 \| 0.0973 \| \| 0.1144 \| 1.88 \| 220 \| 0.0932 \| \| 0.1128 \| 1.96 \| 230 \| 0.0933 \| \| 0.1084 \| 2.05 \| 240 \| 0.0952 \| \| 0.1081 \| 2.13 \| 250 \| 0.0930 \| \| 0.1037 \| 2.22 \| 260 \| 0.0921 \| \| 0.1011 \| 2.3 \| 270 \| 0.0923 \| \| 0.1072 \| 2.39 \| 280 \| 0.0912 \| \| 0.1058 \| 2.47 \| 290 \| 0.0902 \| \| 0.1107 \| 2.56 \| 300 \| 0.0899 \| \| 0.1066 \| 2.65 \| 310 \| 0.0897 \| \| 0.1091 \| 2.73 \| 320 \| 0.0895 \| \| 0.103 \| 2.82 \| 330 \| 0.0893 \| \| 0.1021 \| 2.9 \| 340 \| 0.0893 \| \| 0.103 \| 2.99 \| 350 \| 0.0894 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
CatBarks/t5_es100SEC2_2	CatBarks	2024-03-06T02:08:27Z	5	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T02:05:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jlbaker361/cyberpunk-lora-500-e10-s90-stable-minimal	jlbaker361	2024-03-06T02:04:00Z	0	0	null	[ "safetensors", "region:us" ]	null	2024-03-05T03:24:20Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - jlbaker361/cyberpunk-lora-500-e10-s90-stable-minimal These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the jlbaker361/cyberpunk-500-cropped dataset. Training epochs = 10 num_train_timesteps = 90 url: https://wandb.ai/jlbaker361/text2image-fine-tune/runs/mympnw8e lora scale: 1.0 tag_name: cyberpunk,anime You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ![img_4](./image_4.png) ![img_5](./image_5.png) ![img_6](./image_6.png) ![img_7](./image_7.png) ![img_8](./image_8.png) ![img_9](./image_9.png) ![img_10](./image_10.png) ![img_11](./image_11.png) ![img_12](./image_12.png) ![img_13](./image_13.png) ![img_14](./image_14.png) ![img_15](./image_15.png) ![img_16](./image_16.png) ![img_17](./image_17.png) ![img_18](./image_18.png) ![img_19](./image_19.png) ![img_20](./image_20.png) ![img_21](./image_21.png) ![img_22](./image_22.png) ![img_23](./image_23.png) ![img_24](./image_24.png) ![img_25](./image_25.png) ![img_26](./image_26.png) ![img_27](./image_27.png) ![img_28](./image_28.png) ![img_29](./image_29.png) ![img_30](./image_30.png) ![img_31](./image_31.png) ![img_32](./image_32.png) ![img_33](./image_33.png) ![img_34](./image_34.png) ![img_35](./image_35.png)
asn1814/openbookqa_bert-base-uncased_fact_retrieval_k_10	asn1814	2024-03-06T02:01:49Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "multiple-choice", "generated_from_trainer", "base_model:asn1814/openbookqa_bert-base-uncased", "base_model:finetune:asn1814/openbookqa_bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-06T01:21:46Z	--- license: apache-2.0 base_model: asn1814/openbookqa_bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: openbookqa_bert-base-uncased_fact_retrieval_k_10 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. --> # openbookqa_bert-base-uncased_fact_retrieval_k_10 This model is a fine-tuned version of [asn1814/openbookqa_bert-base-uncased](https://huggingface.co/asn1814/openbookqa_bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9155 - Accuracy: 0.59 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3035 \| 1.0 \| 310 \| 1.4148 \| 0.57 \| \| 0.1243 \| 2.0 \| 620 \| 1.9743 \| 0.57 \| \| 0.077 \| 3.0 \| 930 \| 2.4690 \| 0.584 \| \| 0.028 \| 4.0 \| 1240 \| 2.8887 \| 0.582 \| \| 0.0118 \| 5.0 \| 1550 \| 2.9155 \| 0.59 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
BlouseJury/Mistral-7B-Discord-0.1-DPO	BlouseJury	2024-03-06T01:58:51Z	9	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "base_model:BlouseJury/Mistral-7B-Discord-0.1", "base_model:finetune:BlouseJury/Mistral-7B-Discord-0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-29T18:22:59Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: BlouseJury/Mistral-7B-Discord-0.1 model-index: - name: Mistral-7B-Discord-0.1-DPO 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: BlouseJury/Mistral-7B-Discord-0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: Intel/orca_dpo_pairs type: system_prompt: "" field_system: system field_instruction: question field_output: rejected field_output: chosen format: "[INST] {instruction} [/INST]" no_input_format: "[INST] {instruction} [/INST]" dataset_prepared_path: val_set_size: 0.05 output_dir: ./out sequence_len: 8192 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 1 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000005 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 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_table_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details><br> # BlouseJury/Mistral-7B-Discord-0.1-DPO This model is a fine-tuned version of [BlouseJury/Mistral-7B-Discord-0.1](https://huggingface.co/BlouseJury/Mistral-7B-Discord-0.1) on the Intel/orca_dpo_pairs dataset. It achieves the following results on the evaluation set: - Loss: 0.7923 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1157 \| 0.01 \| 1 \| 1.1924 \| \| 1.0146 \| 0.26 \| 19 \| 0.8381 \| \| 0.9004 \| 0.51 \| 38 \| 0.8015 \| \| 0.8425 \| 0.77 \| 57 \| 0.7923 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BlouseJury__Mistral-7B-Discord-0.1-DPO) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|62.29\| \|AI2 Reasoning Challenge (25-Shot)\|63.23\| \|HellaSwag (10-Shot) \|83.27\| \|MMLU (5-Shot) \|62.62\| \|TruthfulQA (0-shot) \|55.28\| \|Winogrande (5-shot) \|78.93\| \|GSM8k (5-shot) \|30.40\|
AdithyanRS/my-pet-dog	AdithyanRS	2024-03-06T01:58:46Z	1	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T01:54:48Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by AdithyanRS following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/AdithyanRS/my-pet-dog/resolve/main/sample_images/images_(1).jpeg)
furrutiav/bert_qa_extractor_2022_ulra_by_question_ef_plus_nllf_v0_best_by_z_value_signal_it_136	furrutiav	2024-03-06T01:56:26Z	6	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-06T01:51:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
gokuls/hubert-base-ls960-finetuned-ic-slurp-wt_init-frz	gokuls	2024-03-06T01:54:27Z	19	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "base_model:facebook/hubert-base-ls960", "base_model:finetune:facebook/hubert-base-ls960", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-05T15:37:58Z	--- license: apache-2.0 base_model: facebook/hubert-base-ls960 tags: - generated_from_trainer metrics: - accuracy model-index: - name: hubert-base-ls960-finetuned-ic-slurp-wt_init-frz 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. --> # hubert-base-ls960-finetuned-ic-slurp-wt_init-frz This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0889 - Accuracy: 0.4598 ## 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: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 3.6605 \| 1.0 \| 527 \| 3.6385 \| 0.1020 \| \| 3.6135 \| 2.0 \| 1055 \| 3.5710 \| 0.1200 \| \| 3.4222 \| 3.0 \| 1582 \| 3.3394 \| 0.1738 \| \| 3.1948 \| 4.0 \| 2110 \| 3.2132 \| 0.2052 \| \| 2.8791 \| 5.0 \| 2637 \| 2.9508 \| 0.2581 \| \| 2.7807 \| 6.0 \| 3165 \| 2.7201 \| 0.3109 \| \| 2.4647 \| 7.0 \| 3692 \| 2.6056 \| 0.3393 \| \| 2.3009 \| 8.0 \| 4220 \| 2.4893 \| 0.3816 \| \| 2.0953 \| 9.0 \| 4747 \| 2.4874 \| 0.3902 \| \| 1.8074 \| 10.0 \| 5275 \| 2.4705 \| 0.4035 \| \| 1.8209 \| 11.0 \| 5802 \| 2.4465 \| 0.4177 \| \| 1.4822 \| 12.0 \| 6330 \| 2.5310 \| 0.4228 \| \| 1.426 \| 13.0 \| 6857 \| 2.5097 \| 0.4305 \| \| 1.2877 \| 14.0 \| 7385 \| 2.5365 \| 0.4368 \| \| 1.0833 \| 15.0 \| 7912 \| 2.5874 \| 0.4404 \| \| 1.0709 \| 16.0 \| 8440 \| 2.6478 \| 0.4373 \| \| 0.8176 \| 17.0 \| 8967 \| 2.7096 \| 0.4409 \| \| 0.803 \| 18.0 \| 9495 \| 2.7965 \| 0.4491 \| \| 0.6678 \| 19.0 \| 10022 \| 2.9335 \| 0.4470 \| \| 0.7066 \| 20.0 \| 10550 \| 3.0013 \| 0.4408 \| \| 0.5935 \| 21.0 \| 11077 \| 2.9613 \| 0.4544 \| \| 0.5703 \| 22.0 \| 11605 \| 2.9915 \| 0.4534 \| \| 0.5 \| 23.0 \| 12132 \| 3.0625 \| 0.4556 \| \| 0.55 \| 24.0 \| 12660 \| 3.0889 \| 0.4598 \| \| 0.3977 \| 25.0 \| 13187 \| 3.1962 \| 0.4551 \| \| 0.4578 \| 26.0 \| 13715 \| 3.2863 \| 0.4574 \| \| 0.3343 \| 27.0 \| 14242 \| 3.3401 \| 0.4531 \| \| 0.4414 \| 28.0 \| 14770 \| 3.3229 \| 0.4557 \| \| 0.2551 \| 29.0 \| 15297 \| 3.4294 \| 0.4567 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
kwchoi/DPO_mistral_7b_ultra_0124_v1	kwchoi	2024-03-06T01:45:13Z	49	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-25T00:21:32Z	--- language: - en license: apache-2.0 model-index: - name: DPO_mistral_7b_ultra_0124_v1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 66.13 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_7b_ultra_0124_v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 86.39 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_7b_ultra_0124_v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 59.78 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_7b_ultra_0124_v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 69.45 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_7b_ultra_0124_v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.48 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_7b_ultra_0124_v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 25.47 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_7b_ultra_0124_v1 name: Open LLM Leaderboard --- Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performanceTesting Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performanceTesting Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performanceTesting Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_kwchoi__DPO_mistral_7b_ultra_0124_v1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|64.45\| \|AI2 Reasoning Challenge (25-Shot)\|66.13\| \|HellaSwag (10-Shot) \|86.39\| \|MMLU (5-Shot) \|59.78\| \|TruthfulQA (0-shot) \|69.45\| \|Winogrande (5-shot) \|79.48\| \|GSM8k (5-shot) \|25.47\|
Adeptschneider/biomistral-finetuned-7b-v2.1-8-bit-gguf	Adeptschneider	2024-03-06T01:41:37Z	3	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:Adeptschneider/biomistralv2.0-fine-tuned-model", "base_model:quantized:Adeptschneider/biomistralv2.0-fine-tuned-model", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-06T01:37:24Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: Adeptschneider/biomistralv2.0-fine-tuned-model --- # Uploaded model - Developed by: Adeptschneider - License: apache-2.0 - Finetuned from model : Adeptschneider/biomistralv2.0-fine-tuned-model This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jungyuko/DAVinCI-42dot_LLM-PLM-1.3B-v1.2	jungyuko	2024-03-06T01:20:56Z	55	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-27T01:21:13Z	--- license: cc-by-nc-4.0 --- ## DAVinCI-42dot_LLM-PLM-1.3B-v1.2 This model is a fine-tuned version of [42dot/42dot_LLM-PLM-1.3B](https://huggingface.co/42dot/42dot_LLM-PLM-1.3B) on a custom dataset. ### Model description More information needed ### Intended uses & limitations More information needed ### Training and evaluation data More information needed ### Training procedure ### Training hyperparameters The following hyperparameters were used during training: * learning_rate: 2e-05 * train_batch_size: 24 * eval_batch_size: 8 * seed: 42 * gradient_accumulation_steps: 4 * total_train_batch_size: 96 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr_scheduler_type: linear * num_epochs: 1.0 * mixed_precision_training: Native AMP ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.1.2+cu121 * Datasets 2.0.0 * Tokenizers 0.15.0
asn1814/openbookqa_bert-base-uncased_fact_retrieval	asn1814	2024-03-06T01:20:19Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "multiple-choice", "generated_from_trainer", "base_model:asn1814/openbookqa_bert-base-uncased", "base_model:finetune:asn1814/openbookqa_bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-05T08:34:53Z	--- license: apache-2.0 base_model: asn1814/openbookqa_bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: openbookqa_bert-base-uncased_fact_retrieval 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. --> # openbookqa_bert-base-uncased_fact_retrieval This model is a fine-tuned version of [asn1814/openbookqa_bert-base-uncased](https://huggingface.co/asn1814/openbookqa_bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9008 - Accuracy: 0.57 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2123 \| 1.0 \| 155 \| 1.7825 \| 0.554 \| \| 0.096 \| 2.0 \| 310 \| 2.1296 \| 0.57 \| \| 0.0516 \| 3.0 \| 465 \| 2.4470 \| 0.566 \| \| 0.0206 \| 4.0 \| 620 \| 2.7527 \| 0.56 \| \| 0.0135 \| 5.0 \| 775 \| 2.9008 \| 0.57 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
farooqkhan2840503/gemma-Instruct-Finetune-simpleinput_20_0.001	farooqkhan2840503	2024-03-06T01:10:16Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T00:47:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
euser/KANN-I-0.1-7b-GGUF	euser	2024-03-06T01:08:50Z	23	0	null	[ "gguf", "GGUF", "base_model:euser/KANN-I-0.1-7b", "base_model:quantized:euser/KANN-I-0.1-7b", "endpoints_compatible", "region:us" ]	null	2024-02-18T22:27:16Z	--- tags: - GGUF base_model: - euser/KANN-I-0.1-7b --- # KANN-I-0.1-7b - Model creator: [euser](https://huggingface.co/euser) - Original model: [KANN-I-0.1-7b](https://huggingface.co/euser/KANN-I-0.1-7b) <!-- description start --> ## Description This repo contains GGUF format model files for [euser's KANN-I-0.1-7b ](https://huggingface.co/euser/KANN-I-0.1-7b). ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [kann-i-0.1-7b.Q2_K.gguf ](https://huggingface.co/euser/KANN-I-0.1-7b-GGUF/blob/main/kann-i-0.1-7b.Q2_K.gguf ) \| Q2_K \| 2 \| 2.72 GB\| 5.22 GB \| significant quality loss - not recommended for most purposes \| \| [kann-i-0.1-7b.Q3_K_M.gguf ](https://huggingface.co/euser/KANN-I-0.1-7b-GGUF/blob/main/kann-i-0.1-7b.Q3_K_M.gguf ) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [KANN-I-0.1-7b.Q4_K_S.gguf ](https://huggingface.co/euser/KANN-I-0.1-7b-GGUF/blob/main/KANN-I-0.1-7b.Q4_K_S.gguf ) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [kann-i-0.1-7b.Q4_K_M.gguf ](https://huggingface.co/euser/KANN-I-0.1-7b-GGUF/blob/main/kann-i-0.1-7b.Q4_K_M.gguf ) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [kann-i-0.1-7b.Q5_K_M.gguf ](https://huggingface.co/euser/KANN-I-0.1-7b-GGUF/blob/main/kann-i-0.1-7b.Q5_K_M.gguf ) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [kann-i-0.1-7b.Q6_K.gguf ](https://huggingface.co/euser/KANN-I-0.1-7b-GGUF/blob/main/kann-i-0.1-7b.Q6_K.gguf ) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [kann-i-0.1-7b.Q8_0.gguf ](https://huggingface.co/euser/KANN-I-0.1-7b-GGUF/blob/main/kann-i-0.1-7b.Q8_0.gguf ) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \|
euser/wKAN-7b-GGUF	euser	2024-03-06T01:08:06Z	75	0	null	[ "gguf", "GGUF", "base_model:euser/wKAN-7b", "base_model:quantized:euser/wKAN-7b", "endpoints_compatible", "region:us" ]	null	2024-02-19T01:42:12Z	--- tags: - GGUF base_model: - euser/wKAN-7b --- # wKAN-7b - Model creator: [euser](https://huggingface.co/euser) - Original model: [wKAN-7b](https://huggingface.co/euser/wKAN-7b) <!-- description start --> ## Description This repo contains GGUF format model files for [euser's wKAN-7b ](https://huggingface.co/euser/wKAN-7b). ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [wkan-7b.Q2_K.gguf ](https://huggingface.co/euser/wKAN-7b-GGUF/blob/main/wkan-7b.Q2_K.gguf ) \| Q2_K \| 2 \| 2.72 GB\| 5.22 GB \| significant quality loss - not recommended for most purposes \| \| [wkan-7b.Q3_K_M.gguf ](https://huggingface.co/euser/wKAN-7b-GGUF/blob/main/wkan-7b.Q3_K_M.gguf ) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [wKAN-7b.Q4_K_S.gguf ](https://huggingface.co/euser/wKAN-7b-GGUF/blob/main/wKAN-7b.Q4_K_S.gguf ) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [wkan-7b.Q4_K_M.gguf ](https://huggingface.co/euser/wKAN-7b-GGUF/blob/main/wkan-7b.Q4_K_M.gguf ) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [wkan-7b.Q5_K_M.gguf ](https://huggingface.co/euser/wKAN-7b-GGUF/blob/main/wkan-7b.Q5_K_M.gguf ) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [wkan-7b.Q6_K.gguf ](https://huggingface.co/euser/wKAN-7b-GGUF/blob/main/wkan-7b.Q6_K.gguf ) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [wkan-7b.Q8_0.gguf ](https://huggingface.co/euser/wKAN-7b-GGUF/blob/main/wkan-7b.Q8_0.gguf ) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \|
bartowski/Flora_7B-exl2	bartowski	2024-03-06T01:07:57Z	3	0	transformers	[ "transformers", "finetune", "text-generation", "en", "dataset:ResplendentAI/Synthetic_Soul_1k", "base_model:jeiku/FloraBase", "base_model:finetune:jeiku/FloraBase", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T00:55:58Z	--- base_model: - jeiku/FloraBase - jeiku/Synthetic_Soul_1k_Mistral_128 library_name: transformers tags: - finetune license: cc-by-sa-4.0 datasets: - ResplendentAI/Synthetic_Soul_1k language: - en quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Flora_7B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.14">turboderp's ExLlamaV2 v0.0.14</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/ResplendentAI/Flora_7B/ \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/Flora_7B-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.8 GB \| 11.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/Flora_7B-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.2 GB \| 8.6 GB \| 10.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/Flora_7B-exl2/tree/5_0) \| 5.0 \| 6.0 \| 6.0 GB \| 7.4 GB \| 9.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/Flora_7B-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.3 GB \| 6.7 GB \| 8.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/Flora_7B-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.7 GB \| 6.1 GB \| 8.1 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Flora_7B-exl2 Flora_7B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Flora_7B-exl2`: ```shell mkdir Flora_7B-exl2 huggingface-cli download bartowski/Flora_7B-exl2 --local-dir Flora_7B-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir Flora_7B-exl2-6_5 huggingface-cli download bartowski/Flora_7B-exl2 --revision 6_5 --local-dir Flora_7B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir Flora_7B-exl2-6.5 huggingface-cli download bartowski/Flora_7B-exl2 --revision 6_5 --local-dir Flora_7B-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
GIZ/SUBTARGET_multilabel_bge	GIZ	2024-03-06T00:55:17Z	6	0	setfit	[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "dataset:GIZ/policy_classification", "arxiv:2209.11055", "base_model:BAAI/bge-base-en-v1.5", "base_model:finetune:BAAI/bge-base-en-v1.5", "co2_eq_emissions", "region:us" ]	text-classification	2024-02-17T15:47:11Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: >- Unconditional Reduction The level of reduction planned unconditionally is expected to be up to 35% by 2030 as compared to the Business As Usual (BAU) scenario, taking 2005 as the reference year. Conditional Reduction In a conditional mitigation scenario Angola plans to reduce further its emissions. Therefore, the mitigation options identified in this scenario are expected to reduce an additional 15% below BAU emission levels by 2030. - text: >- Measure 300 MW total installed biomass power capacity in the country by Sector Energy GHG mitigation target 84 ktCO2e on average per year between 2020 and 2030 Monitoring procedures Newly added biomass capacity will be monitored on an annual basis by the Department of Climate Change of the Ministry of Natural Resources and Environment using data from the Ministry of Energy and Mines Comments - Installed capacity as of 2019 is around 40MW Measure 30% Electric Vehicles penetration for 2-wheelers and passengers cars in national vehicles mix Sector Transport GHG mitigation target 30 ktCO2e on average per year between 2020 and 2030 Monitoring procedures Share of Electric Vehicles in national vehicle mix will be monitored on an annual basis by the Department of Climate Change of the Ministry of Natural Resources and Environment using data from the Ministry of Public Works and Transport. - text: "� Australia adopts a target of net zero emissions by 2050. This is an economy-wide target,\_covering all sectors and gases included in Australia’s national inventory. � In order to achieve net zero by 2050, Australia commits to seven low emissions technology stretch goals - ambitious but realistic goals to bring priority low emissions technologies to economic parity with existing mature technologies." - text: >- The GoP has taken a series of major initiatives as outlined in chapters 4 and 5. Hence, Pakistan intends to set a cumulative ambitious conditional target of overall 50% reduction of its projected emissions by 2030, with 15% from the country’s own resources and 35% subject to provision of international grant ﬁnance that would require USD 101 billion just for energy transition. 7.1 HIGH PRIORITY ACTIONS Addressing the Global Climate Summit at the United Nations in December 2020, the Prime Minister of Pakistan made an announcement to reduce future GHG emissions on a high priority basis if international ﬁnancial and technical resources were made available: MITIGATION: 1. - text: >- This document enfolds Iceland’s first communication on its long-term strategy (LTS), to be updated when further analysis and policy documents are published on the matter. Iceland is committed to reducing its overall greenhouse gas emissions and reaching climate neutrality no later than 2040 and become fossil fuel free in 2050, which should set Iceland on a path to net negative emissions. pipeline_tag: text-classification inference: false co2_eq_emissions: emissions: 268.4261122496047 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: Intel(R) Xeon(R) CPU @ 2.20GHz ram_total_size: 12.674789428710938 hours_used: 2.03 hardware_used: 1 x Tesla V100-SXM2-16GB base_model: BAAI/bge-base-en-v1.5 datasets: - GIZ/policy_classification --- # SetFit with BAAI/bge-base-en-v1.5 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict 3 labels - GHGLabel, NetzeroLabel, NonGHGLabel- that are relevant to a particular task or application - GHGLabel: GHG targets refer to contributions framed as targeted \ outcomes in GHG terms - NetzeroLabel: Identifies if it contains Netzero Target or not. - NonGHGLabel: Target not in terms of GHG, like energy efficiency, expansion of Solar Energy production etc. ### Model Description - Model Type: SetFit - Sentence Transformer body: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - Classification head: a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 3 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("GIZ/SUBTARGET_multilabel_bge") # Run inference preds = model("This document enfolds Iceland’s first communication on its long-term strategy (LTS), to be updated when further analysis and policy documents are published on the matter. Iceland is committed to reducing its overall greenhouse gas emissions and reaching climate neutrality no later than 2040 and become fossil fuel free in 2050, which should set Iceland on a path to net negative emissions.") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 19 \| 78.5467 \| 173 \| - Training Dataset: 728 \| Class \| Positive Count of Class\| \|:-------------\|:--------\| \| GHGLabel \| 440 \| \| NetzeroLabel \| 120 \| \| NonGHGLabel \| 259\| - Validation Dataset: 80 \| Class \| Positive Count of Class\| \|:-------------\|:--------\| \| GHGLabel \| 49 \| \| NetzeroLabel \| 11 \| \| NonGHGLabel \| 30\| ### Training Hyperparameters - batch_size: (8, 2) - num_epochs: (1, 0) - max_steps: -1 - sampling_strategy: undersampling - body_learning_rate: (6.86e-06, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Embedding Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:-----:\|:-------------:\|:---------------:\| \| 0.0000 \| 1 \| 0.2227 \| - \| \| 0.1519 \| 5000 \| 0.015 \| 0.0831 \| \| 0.3038 \| 10000 \| 0.0146 \| 0.0924 \| \| 0.4557 \| 15000 \| 0.0197 \| 0.0827 \| \| 0.6076 \| 20000 \| 0.0031 \| 0.0883 \| \| 0.7595 \| 25000 \| 0.0439 \| 0.0865 \| \| 0.9114 \| 30000 \| 0.0029 \| 0.0914 \| \|label \| precision \|recall \|f1-score\| support\| \|:-------------:\|:---------:\|:-----:\|:------:\|:------:\| \|GHG \|0.884 \|0.938 \|0.910 \| 49.0 \| \|Netzero \|0.846 \|1.000 \|0.916 \| 11.0 \| \|NonGHG \|0.903 \|0.933 \|0.918 \| 30.0 \| ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Carbon Emitted: 0.268 kg of CO2 - Hours Used: 2.03 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x Tesla V100-SXM2-16GB - CPU Model: Intel(R) Xeon(R) CPU @ 2.20GHz - RAM Size: 12.67 GB ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.35.2 - PyTorch: 2.1.0+cu121 - Datasets: 2.17.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
DanielClough/Candle_MistralLite	DanielClough	2024-03-06T00:54:46Z	13	0	transformers	[ "transformers", "gguf", "mistral", "text-generation", "en", "dataset:amazon/MistralLite", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-12-21T00:48:47Z	--- datasets: - amazon/MistralLite language: - en pipeline_tag: text-generation license: apache-2.0 --- This repo includes `.gguf` built for HuggingFace/Candle. They will not work with `llama.cpp`. This model should be used with the `Config` [`config_chat_ml`]( https://github.com/huggingface/candle/blob/main/candle-transformers/src/models/mistral.rs). Refer to the [original repo](https://huggingface.co/amazon/MistralLite) for more details.
DanielClough/Candle_Mistral-7B-Instruct-v0.1	DanielClough	2024-03-06T00:50:57Z	120	3	transformers	[ "transformers", "gguf", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-12-10T08:50:30Z	--- license: apache-2.0 --- Here we have `.gguf` and `.safetensors` of `mistralai/Mistral-Instruct-v0.1` for use with `Huggingface/Candle`. You can try the models with [Candle Chat](https://github.com/danielclough/candle_chat), or make similar models with [Candle Tensor Tools](https://github.com/danielclough/Candle_Tensor-Tools). Refer to the main [model card](https://huggingface.co/mistralai/Mistral-Instruct-v0.1).
furrutiav/bert_qa_extractor_2022_ulra_by_kmeans_Q_nllf_ef_plus_nllf_v0_best_by_z_value_signal_it_150	furrutiav	2024-03-06T00:50:14Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-06T00:48:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
furrutiav/bert_qa_extractor_2022_ulra_by_kmeans_Q_nllf_ef_plus_nllf_best_by_z_value_signal_it_146	furrutiav	2024-03-06T00:50:03Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-06T00:48:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
state-spaces/mamba-2.8b-hf	state-spaces	2024-03-06T00:44:55Z	5,228	98	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T23:53:30Z	--- library_name: transformers tags: [] --- # Mamba <!-- Provide a quick summary of what the model is/does. --> This repository contains the `transfromers` compatible `mamba-2.8b`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo. # Usage You need to install `transformers` from `main` until `transformers=4.39.0` is released. ```bash pip install git+https://github.com/huggingface/transformers@main ``` We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: ```bash pip install causal-conv1d>=1.2.0 pip install mamba-ssm ``` If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used. ## Generation You can use the classic `generate` API: ```python >>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-2.8b-hf") >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-2.8b-hf") >>> input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"] >>> out = model.generate(input_ids, max_new_tokens=10) >>> print(tokenizer.batch_decode(out)) ["Hey how are you doing?\n\nI'm doing great.\n\nI"] ``` ## PEFT finetuning example In order to finetune using the `peft` library, we recommend keeping the model in float32! ```python from datasets import load_dataset from trl import SFTTrainer from peft import LoraConfig from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-2.8b-hf") model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-2.8b-hf") dataset = load_dataset("Abirate/english_quotes", split="train") training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=4, logging_dir='./logs', logging_steps=10, learning_rate=2e-3 ) lora_config = LoraConfig( r=8, target_modules=["x_proj", "embeddings", "in_proj", "out_proj"], task_type="CAUSAL_LM", bias="none" ) trainer = SFTTrainer( model=model, tokenizer=tokenizer, args=training_args, peft_config=lora_config, train_dataset=dataset, dataset_text_field="quote", ) trainer.train() ```
state-spaces/mamba-1.4b-hf	state-spaces	2024-03-06T00:44:32Z	3,130	10	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T23:56:34Z	--- library_name: transformers tags: [] --- # Mamba <!-- Provide a quick summary of what the model is/does. --> This repository contains the `transfromers` compatible `mamba-2.8b`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo. # Usage You need to install `transformers` from `main` until `transformers=4.39.0` is released. ```bash pip install git+https://github.com/huggingface/transformers@main ``` We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: ```bash pip install causal-conv1d>=1.2.0 pip install mamba-ssm ``` If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used. ## Generation You can use the classic `generate` API: ```python >>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-1.4b-hf") >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-1.4b-hf") >>> input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"] >>> out = model.generate(input_ids, max_new_tokens=10) >>> print(tokenizer.batch_decode(out)) ["Hey how are you doing?\n\nI'm doing great.\n\nI"] ``` ## PEFT finetuning example In order to finetune using the `peft` library, we recommend keeping the model in float32! ```python from datasets import load_dataset from trl import SFTTrainer from peft import LoraConfig from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-1.4b-hf") model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-1.4b-hf") dataset = load_dataset("Abirate/english_quotes", split="train") training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=4, logging_dir='./logs', logging_steps=10, learning_rate=2e-3 ) lora_config = LoraConfig( r=8, target_modules=["x_proj", "embeddings", "in_proj", "out_proj"], task_type="CAUSAL_LM", bias="none" ) trainer = SFTTrainer( model=model, tokenizer=tokenizer, args=training_args, peft_config=lora_config, train_dataset=dataset, dataset_text_field="quote", ) trainer.train() ```
state-spaces/mamba-790m-hf	state-spaces	2024-03-06T00:44:06Z	1,488	3	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T00:07:54Z	--- library_name: transformers tags: [] --- # Mamba <!-- Provide a quick summary of what the model is/does. --> This repository contains the `transfromers` compatible `mamba-2.8b`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo. # Usage You need to install `transformers` from `main` until `transformers=4.39.0` is released. ```bash pip install git+https://github.com/huggingface/transformers@main ``` We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: ```bash pip install causal-conv1d>=1.2.0 pip install mamba-ssm ``` If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used. ## Generation You can use the classic `generate` API: ```python >>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-790m-hf") >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-790m-hf") >>> input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"] >>> out = model.generate(input_ids, max_new_tokens=10) >>> print(tokenizer.batch_decode(out)) ["Hey how are you doing?\n\nI'm good.\n\nHow are"] ``` ## PEFT finetuning example In order to finetune using the `peft` library, we recommend keeping the model in float32! ```python from datasets import load_dataset from trl import SFTTrainer from peft import LoraConfig from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-790m-hf") model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-790m-hf") dataset = load_dataset("Abirate/english_quotes", split="train") training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=4, logging_dir='./logs', logging_steps=10, learning_rate=2e-3 ) lora_config = LoraConfig( r=8, target_modules=["x_proj", "embeddings", "in_proj", "out_proj"], task_type="CAUSAL_LM", bias="none" ) trainer = SFTTrainer( model=model, tokenizer=tokenizer, args=training_args, peft_config=lora_config, train_dataset=dataset, dataset_text_field="quote", ) trainer.train() ```
state-spaces/mamba-370m-hf	state-spaces	2024-03-06T00:40:36Z	2,486	13	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T00:08:03Z	--- library_name: transformers tags: [] --- # Mamba <!-- Provide a quick summary of what the model is/does. --> This repository contains the `transfromers` compatible `mamba-2.8b`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo. # Usage You need to install `transformers` from `main` until `transformers=4.39.0` is released. ```bash pip install git+https://github.com/huggingface/transformers@main ``` We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: ```bash pip install causal-conv1d>=1.2.0 pip install mamba-ssm ``` If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used. ## Generation You can use the classic `generate` API: ```python >>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-370m-hf") >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-370m-hf") >>> input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"] >>> out = model.generate(input_ids, max_new_tokens=10) >>> print(tokenizer.batch_decode(out)) ["Hey how are you doing?\n\nI'm doing great.\n\nI"] ``` ## PEFT finetuning example In order to finetune using the `peft` library, we recommend keeping the model in float32! ```python from datasets import load_dataset from trl import SFTTrainer from peft import LoraConfig from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-370m-hf") model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-370m-hf") dataset = load_dataset("Abirate/english_quotes", split="train") training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=4, logging_dir='./logs', logging_steps=10, learning_rate=2e-3 ) lora_config = LoraConfig( r=8, target_modules=["x_proj", "embeddings", "in_proj", "out_proj"], task_type="CAUSAL_LM", bias="none" ) trainer = SFTTrainer( model=model, tokenizer=tokenizer, args=training_args, peft_config=lora_config, train_dataset=dataset, dataset_text_field="quote", ) trainer.train() ```
Naoto0405/sd-class-butterflies-32	Naoto0405	2024-03-06T00:35:38Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-03-06T00:35:25Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Naoto0405/sd-class-butterflies-32') image = pipeline().images[0] image ```
gokuls/hubert-base-ls960-finetuned-ic-slurp-wt_init	gokuls	2024-03-06T00:25:20Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "base_model:facebook/hubert-base-ls960", "base_model:finetune:facebook/hubert-base-ls960", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-05T14:58:15Z	--- license: apache-2.0 base_model: facebook/hubert-base-ls960 tags: - generated_from_trainer metrics: - accuracy model-index: - name: hubert-base-ls960-finetuned-ic-slurp-wt_init 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. --> # hubert-base-ls960-finetuned-ic-slurp-wt_init This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1377 - Accuracy: 0.4604 ## 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: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 3.9613 \| 1.0 \| 527 \| 3.8944 \| 0.0803 \| \| 3.7817 \| 2.0 \| 1055 \| 3.7275 \| 0.0910 \| \| 3.6357 \| 3.0 \| 1582 \| 3.5410 \| 0.1308 \| \| 3.4527 \| 4.0 \| 2110 \| 3.3426 \| 0.1676 \| \| 3.0715 \| 5.0 \| 2637 \| 3.0751 \| 0.2331 \| \| 2.9153 \| 6.0 \| 3165 \| 2.8168 \| 0.2969 \| \| 2.5333 \| 7.0 \| 3692 \| 2.6229 \| 0.3375 \| \| 2.3807 \| 8.0 \| 4220 \| 2.5673 \| 0.3620 \| \| 2.181 \| 9.0 \| 4747 \| 2.4933 \| 0.3835 \| \| 1.9118 \| 10.0 \| 5275 \| 2.4411 \| 0.4046 \| \| 1.9015 \| 11.0 \| 5802 \| 2.4254 \| 0.4126 \| \| 1.5811 \| 12.0 \| 6330 \| 2.4216 \| 0.4275 \| \| 1.491 \| 13.0 \| 6857 \| 2.4833 \| 0.4284 \| \| 1.3697 \| 14.0 \| 7385 \| 2.5243 \| 0.4368 \| \| 1.1232 \| 15.0 \| 7912 \| 2.5944 \| 0.4309 \| \| 1.1071 \| 16.0 \| 8440 \| 2.6475 \| 0.4317 \| \| 0.9439 \| 17.0 \| 8967 \| 2.6379 \| 0.4449 \| \| 0.917 \| 18.0 \| 9495 \| 2.7438 \| 0.4468 \| \| 0.7628 \| 19.0 \| 10022 \| 2.7671 \| 0.4513 \| \| 0.7642 \| 20.0 \| 10550 \| 2.8993 \| 0.4418 \| \| 0.6716 \| 21.0 \| 11077 \| 2.9354 \| 0.4472 \| \| 0.6166 \| 22.0 \| 11605 \| 2.9961 \| 0.4510 \| \| 0.4819 \| 23.0 \| 12132 \| 3.0959 \| 0.4451 \| \| 0.5903 \| 24.0 \| 12660 \| 3.0542 \| 0.4557 \| \| 0.515 \| 25.0 \| 13187 \| 3.0723 \| 0.4589 \| \| 0.518 \| 26.0 \| 13715 \| 3.1377 \| 0.4604 \| \| 0.3902 \| 27.0 \| 14242 \| 3.2230 \| 0.4524 \| \| 0.4825 \| 28.0 \| 14770 \| 3.2925 \| 0.4583 \| \| 0.29 \| 29.0 \| 15297 \| 3.4027 \| 0.4498 \| \| 0.2789 \| 30.0 \| 15825 \| 3.3573 \| 0.4598 \| \| 0.3202 \| 31.0 \| 16352 \| 3.4381 \| 0.4542 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
bovision/distilgpt2-finetuned-wikitext2	bovision	2024-03-06T00:24:27Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T23:24:40Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 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. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3608 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 290 \| 3.3948 \| \| 3.5536 \| 2.0 \| 580 \| 3.3654 \| \| 3.5536 \| 3.0 \| 870 \| 3.3608 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1
Corianas/Neural-Mistral-7B	Corianas	2024-03-06T00:22:42Z	104	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:Intel/orca_dpo_pairs", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T15:22:01Z	--- library_name: transformers license: apache-2.0 datasets: - Intel/orca_dpo_pairs language: - en --- # Model Card for Model ID This is a DPO finetune of Mistral 7b-instruct0.2 following the article: https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac ## 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: Corianas - Model type: [More Information Needed] - License: Apache 2.0 - Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2 ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data Intel/orca_dpo_pairs ### Training Procedure https://medium.com/towards-data-science/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac #### Preprocessing [optional] def chatml_format(example): # Format system if len(example['system']) > 0: message = {"role": "user", "content": f"{example['system']}\n{example['question']}"} prompt = tokenizer.apply_chat_template([message], tokenize=False) else: # Format instruction message = {"role": "user", "content": example['question']} prompt = tokenizer.apply_chat_template([message], tokenize=False, add_generation_prompt=True) # Format chosen answer chosen = example['chosen'] + tokenizer.eos_token # Format rejected answer rejected = example['rejected'] + tokenizer.eos_token return { "prompt": prompt, "chosen": chosen, "rejected": rejected, } #### Training Hyperparameters training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=200, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) ## 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]
furrutiav/bert_qa_extractor_2022_ulra_by_question_type_ef_plus_nllf_v0_best_by_z_value_signal_it_142	furrutiav	2024-03-06T00:05:55Z	6	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-06T00:04:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
kkimdev/solar-10.7b-bnb-4bit-4	kkimdev	2024-03-06T00:00:51Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/solar-10.7b-bnb-4bit", "base_model:finetune:unsloth/solar-10.7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-05T23:59:17Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/solar-10.7b-bnb-4bit --- # Uploaded model - Developed by: kkimdev - License: apache-2.0 - Finetuned from model : unsloth/solar-10.7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
pinzhenchen/sft-lora-zh-pythia-12b	pinzhenchen	2024-03-05T23:54:19Z	0	0	null	[ "generation", "question answering", "instruction tuning", "zh", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:54:15Z	--- language: - zh tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-12b-deduped](https://huggingface.co/EleutherAI/pythia-12b-deduped) * Instruction tuning language: Chinese * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-es-pythia-12b	pinzhenchen	2024-03-05T23:54:04Z	0	0	null	[ "generation", "question answering", "instruction tuning", "es", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:54:01Z	--- language: - es tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-12b-deduped](https://huggingface.co/EleutherAI/pythia-12b-deduped) * Instruction tuning language: Spanish * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
turboderp/StarCoder2-7B-exl2	turboderp	2024-03-05T23:53:57Z	1	0	null	[ "region:us" ]	null	2024-03-05T23:51:09Z	EXL2 quants of [starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b). [3.00 bits per weight](https://huggingface.co/turboderp/StarCoder2-7B-exl2/tree/3.0bpw) [4.00 bits per weight](https://huggingface.co/turboderp/StarCoder2-7B-exl2/tree/4.0bpw) [5.00 bits per weight](https://huggingface.co/turboderp/StarCoder2-7B-exl2/tree/5.0bpw) [6.00 bits per weight](https://huggingface.co/turboderp/StarCoder2-7B-exl2/tree/6.0bpw) [measurement.json](https://huggingface.co/turboderp/StarCoder2-7B-exl2/blob/main/measurement.json)
pinzhenchen/sft-lora-bg-pythia-12b	pinzhenchen	2024-03-05T23:53:54Z	0	0	null	[ "generation", "question answering", "instruction tuning", "bg", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:53:50Z	--- language: - bg tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-12b-deduped](https://huggingface.co/EleutherAI/pythia-12b-deduped) * Instruction tuning language: Bulgarian * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-fr-pythia-6b9	pinzhenchen	2024-03-05T23:53:39Z	0	0	null	[ "generation", "question answering", "instruction tuning", "fr", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:53:36Z	--- language: - fr tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-6.9b-deduped](https://huggingface.co/EleutherAI/pythia-6.9b-deduped) * Instruction tuning language: French * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-es-pythia-6b9	pinzhenchen	2024-03-05T23:53:35Z	0	0	null	[ "generation", "question answering", "instruction tuning", "es", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:53:31Z	--- language: - es tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-6.9b-deduped](https://huggingface.co/EleutherAI/pythia-6.9b-deduped) * Instruction tuning language: Spanish * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-fi-pythia-2b8	pinzhenchen	2024-03-05T23:52:58Z	0	0	null	[ "generation", "question answering", "instruction tuning", "fi", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:52:55Z	--- language: - fi tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped) * Instruction tuning language: Finnish * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-cs-pythia-2b8	pinzhenchen	2024-03-05T23:52:41Z	0	0	null	[ "generation", "question answering", "instruction tuning", "cs", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:52:39Z	--- language: - cs tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped) * Instruction tuning language: Czech * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-bg-pythia-2b8	pinzhenchen	2024-03-05T23:52:37Z	0	0	null	[ "generation", "question answering", "instruction tuning", "bg", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:52:34Z	--- language: - bg tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped) * Instruction tuning language: Bulgarian * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-zh-pythia-1b4	pinzhenchen	2024-03-05T23:52:33Z	0	0	null	[ "generation", "question answering", "instruction tuning", "zh", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:52:29Z	--- language: - zh tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) * Instruction tuning language: Chinese * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-fr-pythia-1b4	pinzhenchen	2024-03-05T23:52:24Z	0	0	null	[ "generation", "question answering", "instruction tuning", "fr", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:52:21Z	--- language: - fr tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) * Instruction tuning language: French * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-fi-pythia-1b4	pinzhenchen	2024-03-05T23:52:20Z	0	0	null	[ "generation", "question answering", "instruction tuning", "fi", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:52:17Z	--- language: - fi tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) * Instruction tuning language: Finnish * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-es-pythia-1b4	pinzhenchen	2024-03-05T23:52:16Z	0	0	null	[ "generation", "question answering", "instruction tuning", "es", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:52:13Z	--- language: - es tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) * Instruction tuning language: Spanish * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-en-pythia-1b4	pinzhenchen	2024-03-05T23:52:11Z	0	0	null	[ "generation", "question answering", "instruction tuning", "en", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:52:08Z	--- language: - en tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) * Instruction tuning language: English * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-bg-pythia-1b4	pinzhenchen	2024-03-05T23:51:59Z	0	0	null	[ "generation", "question answering", "instruction tuning", "bg", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:51:56Z	--- language: - bg tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) * Instruction tuning language: Bulgarian * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-ru-pythia-1b	pinzhenchen	2024-03-05T23:51:51Z	0	0	null	[ "generation", "question answering", "instruction tuning", "ru", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:51:47Z	--- language: - ru tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) * Instruction tuning language: Russian * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-en-pythia-1b	pinzhenchen	2024-03-05T23:51:32Z	0	0	null	[ "generation", "question answering", "instruction tuning", "en", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:51:30Z	--- language: - en tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) * Instruction tuning language: English * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-bg-pythia-1b	pinzhenchen	2024-03-05T23:51:21Z	0	0	null	[ "generation", "question answering", "instruction tuning", "bg", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:51:18Z	--- language: - bg tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) * Instruction tuning language: Bulgarian * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-zh-pythia-410m	pinzhenchen	2024-03-05T23:51:17Z	0	0	null	[ "generation", "question answering", "instruction tuning", "zh", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:51:13Z	--- language: - zh tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) * Instruction tuning language: Chinese * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-en-pythia-410m	pinzhenchen	2024-03-05T23:50:56Z	0	0	null	[ "generation", "question answering", "instruction tuning", "en", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:50:53Z	--- language: - en tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) * Instruction tuning language: English * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-zh-pythia-160m	pinzhenchen	2024-03-05T23:50:40Z	0	0	null	[ "generation", "question answering", "instruction tuning", "zh", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:50:36Z	--- language: - zh tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) * Instruction tuning language: Chinese * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-ru-pythia-160m	pinzhenchen	2024-03-05T23:50:35Z	0	0	null	[ "generation", "question answering", "instruction tuning", "ru", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:50:31Z	--- language: - ru tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) * Instruction tuning language: Russian * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
gokuls/wav2vec2-base-finetuned-ic-slurp-wt_init-frz	gokuls	2024-03-05T23:50:33Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-05T15:42:54Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ic-slurp-wt_init-frz 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. --> # wav2vec2-base-finetuned-ic-slurp-wt_init-frz This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8656 - Accuracy: 0.0665 ## 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: 0.001 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.7043 \| 1.0 \| 527 \| 3.9874 \| 0.0430 \| \| 3.6973 \| 2.0 \| 1055 \| 3.8656 \| 0.0665 \| \| 3.6275 \| 3.0 \| 1582 \| 4.3487 \| 0.0104 \| \| 3.4852 \| 4.0 \| 2110 \| 4.1588 \| 0.0525 \| \| 3.8932 \| 5.0 \| 2637 \| 3.8819 \| 0.0627 \| \| 3.9246 \| 6.0 \| 3165 \| 3.8627 \| 0.0627 \| \| 3.8914 \| 7.0 \| 3692 \| 3.8517 \| 0.0627 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
pinzhenchen/sft-lora-fr-pythia-160m	pinzhenchen	2024-03-05T23:50:30Z	0	0	null	[ "generation", "question answering", "instruction tuning", "fr", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:50:27Z	--- language: - fr tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) * Instruction tuning language: French * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-es-pythia-160m	pinzhenchen	2024-03-05T23:50:22Z	0	0	null	[ "generation", "question answering", "instruction tuning", "es", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:50:19Z	--- language: - es tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) * Instruction tuning language: Spanish * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-en-pythia-160m	pinzhenchen	2024-03-05T23:50:18Z	0	0	null	[ "generation", "question answering", "instruction tuning", "en", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:50:15Z	--- language: - en tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) * Instruction tuning language: English * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-de-pythia-160m	pinzhenchen	2024-03-05T23:50:14Z	0	0	null	[ "generation", "question answering", "instruction tuning", "de", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:50:11Z	--- language: - de tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) * Instruction tuning language: German * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
pinzhenchen/sft-lora-bg-pythia-160m	pinzhenchen	2024-03-05T23:50:06Z	0	0	null	[ "generation", "question answering", "instruction tuning", "bg", "arxiv:2309.08958", "license:cc-by-nc-4.0", "region:us" ]	null	2024-03-05T23:50:03Z	--- language: - bg tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) * Instruction tuning language: Bulgarian * Training method: LoRA. * LoRA details: rank=8, alpha=16, target modules={key, query, value}. * Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```

Subsets and Splits