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pipeline_tag stringclasses 48 values	library_name stringclasses 205 values	text stringlengths 0 18.3M	metadata stringlengths 2 1.07B	id stringlengths 5 122	last_modified null	tags sequencelengths 1 1.84k	sha null	created_at stringlengths 25 25
null	peft	<!-- 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. --> # GUE_prom_prom_300_all-seqsight_16384_512_34M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.2161 - F1 Score: 0.9122 - Accuracy: 0.9122 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4298 \| 0.54 \| 200 \| 0.3156 \| 0.8804 \| 0.8806 \| \| 0.3062 \| 1.08 \| 400 \| 0.2651 \| 0.8976 \| 0.8976 \| \| 0.2825 \| 1.62 \| 600 \| 0.2513 \| 0.8980 \| 0.8980 \| \| 0.2626 \| 2.16 \| 800 \| 0.2415 \| 0.9006 \| 0.9007 \| \| 0.2555 \| 2.7 \| 1000 \| 0.2399 \| 0.9015 \| 0.9015 \| \| 0.2461 \| 3.24 \| 1200 \| 0.2334 \| 0.9073 \| 0.9073 \| \| 0.247 \| 3.78 \| 1400 \| 0.2271 \| 0.9081 \| 0.9081 \| \| 0.2428 \| 4.32 \| 1600 \| 0.2244 \| 0.9098 \| 0.9098 \| \| 0.2331 \| 4.86 \| 1800 \| 0.2285 \| 0.9090 \| 0.9090 \| \| 0.2364 \| 5.41 \| 2000 \| 0.2229 \| 0.9108 \| 0.9108 \| \| 0.2315 \| 5.95 \| 2200 \| 0.2170 \| 0.9128 \| 0.9128 \| \| 0.2308 \| 6.49 \| 2400 \| 0.2153 \| 0.9128 \| 0.9128 \| \| 0.2314 \| 7.03 \| 2600 \| 0.2169 \| 0.9113 \| 0.9113 \| \| 0.2254 \| 7.57 \| 2800 \| 0.2162 \| 0.9118 \| 0.9118 \| \| 0.2245 \| 8.11 \| 3000 \| 0.2194 \| 0.9105 \| 0.9105 \| \| 0.2262 \| 8.65 \| 3200 \| 0.2221 \| 0.9082 \| 0.9083 \| \| 0.2168 \| 9.19 \| 3400 \| 0.2145 \| 0.9113 \| 0.9113 \| \| 0.2161 \| 9.73 \| 3600 \| 0.2171 \| 0.9103 \| 0.9103 \| \| 0.222 \| 10.27 \| 3800 \| 0.2090 \| 0.9123 \| 0.9123 \| \| 0.2151 \| 10.81 \| 4000 \| 0.2075 \| 0.9132 \| 0.9132 \| \| 0.2189 \| 11.35 \| 4200 \| 0.2056 \| 0.9130 \| 0.9130 \| \| 0.2134 \| 11.89 \| 4400 \| 0.2111 \| 0.9142 \| 0.9142 \| \| 0.2142 \| 12.43 \| 4600 \| 0.2061 \| 0.9130 \| 0.9130 \| \| 0.2152 \| 12.97 \| 4800 \| 0.2049 \| 0.9130 \| 0.9130 \| \| 0.2127 \| 13.51 \| 5000 \| 0.2060 \| 0.9130 \| 0.9130 \| \| 0.2161 \| 14.05 \| 5200 \| 0.2043 \| 0.9139 \| 0.9139 \| \| 0.2086 \| 14.59 \| 5400 \| 0.2026 \| 0.9132 \| 0.9132 \| \| 0.2084 \| 15.14 \| 5600 \| 0.2016 \| 0.9135 \| 0.9135 \| \| 0.2067 \| 15.68 \| 5800 \| 0.2036 \| 0.9132 \| 0.9132 \| \| 0.2126 \| 16.22 \| 6000 \| 0.2016 \| 0.9132 \| 0.9132 \| \| 0.206 \| 16.76 \| 6200 \| 0.2040 \| 0.9145 \| 0.9145 \| \| 0.207 \| 17.3 \| 6400 \| 0.2054 \| 0.9145 \| 0.9145 \| \| 0.2105 \| 17.84 \| 6600 \| 0.2028 \| 0.9139 \| 0.9139 \| \| 0.2019 \| 18.38 \| 6800 \| 0.2037 \| 0.9155 \| 0.9155 \| \| 0.211 \| 18.92 \| 7000 \| 0.2019 \| 0.9164 \| 0.9164 \| \| 0.2065 \| 19.46 \| 7200 \| 0.2086 \| 0.9164 \| 0.9164 \| \| 0.205 \| 20.0 \| 7400 \| 0.2034 \| 0.9155 \| 0.9155 \| \| 0.2077 \| 20.54 \| 7600 \| 0.2042 \| 0.9164 \| 0.9164 \| \| 0.2018 \| 21.08 \| 7800 \| 0.2008 \| 0.9160 \| 0.9160 \| \| 0.2052 \| 21.62 \| 8000 \| 0.2012 \| 0.9169 \| 0.9169 \| \| 0.2025 \| 22.16 \| 8200 \| 0.2027 \| 0.9150 \| 0.9150 \| \| 0.1994 \| 22.7 \| 8400 \| 0.2017 \| 0.9162 \| 0.9162 \| \| 0.205 \| 23.24 \| 8600 \| 0.2006 \| 0.9171 \| 0.9171 \| \| 0.2002 \| 23.78 \| 8800 \| 0.2010 \| 0.9155 \| 0.9155 \| \| 0.2055 \| 24.32 \| 9000 \| 0.2049 \| 0.9162 \| 0.9162 \| \| 0.1998 \| 24.86 \| 9200 \| 0.2002 \| 0.9172 \| 0.9172 \| \| 0.2026 \| 25.41 \| 9400 \| 0.2016 \| 0.9154 \| 0.9154 \| \| 0.2016 \| 25.95 \| 9600 \| 0.2027 \| 0.9159 \| 0.9159 \| \| 0.2014 \| 26.49 \| 9800 \| 0.2010 \| 0.9162 \| 0.9162 \| \| 0.2011 \| 27.03 \| 10000 \| 0.2012 \| 0.9162 \| 0.9162 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_16384_512_34M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_16384_512_34M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:18:30+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_all-seqsight_16384_512_34M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1870 - F1 Score: 0.9291 - Accuracy: 0.9291 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3435 \| 0.54 \| 200 \| 0.2473 \| 0.9016 \| 0.9019 \| \| 0.247 \| 1.08 \| 400 \| 0.2192 \| 0.9140 \| 0.9140 \| \| 0.2308 \| 1.62 \| 600 \| 0.2072 \| 0.9162 \| 0.9162 \| \| 0.2141 \| 2.16 \| 800 \| 0.2036 \| 0.9180 \| 0.9181 \| \| 0.2109 \| 2.7 \| 1000 \| 0.2012 \| 0.9187 \| 0.9187 \| \| 0.2025 \| 3.24 \| 1200 \| 0.1952 \| 0.9187 \| 0.9187 \| \| 0.1996 \| 3.78 \| 1400 \| 0.1881 \| 0.9211 \| 0.9211 \| \| 0.1974 \| 4.32 \| 1600 \| 0.1857 \| 0.9226 \| 0.9226 \| \| 0.1864 \| 4.86 \| 1800 \| 0.1960 \| 0.9194 \| 0.9194 \| \| 0.1848 \| 5.41 \| 2000 \| 0.1838 \| 0.9243 \| 0.9243 \| \| 0.1852 \| 5.95 \| 2200 \| 0.1821 \| 0.9255 \| 0.9255 \| \| 0.1803 \| 6.49 \| 2400 \| 0.1968 \| 0.9198 \| 0.9199 \| \| 0.1795 \| 7.03 \| 2600 \| 0.1761 \| 0.9274 \| 0.9274 \| \| 0.168 \| 7.57 \| 2800 \| 0.1754 \| 0.9279 \| 0.9279 \| \| 0.1713 \| 8.11 \| 3000 \| 0.1829 \| 0.9287 \| 0.9287 \| \| 0.1685 \| 8.65 \| 3200 \| 0.1777 \| 0.9282 \| 0.9282 \| \| 0.16 \| 9.19 \| 3400 \| 0.1812 \| 0.9284 \| 0.9284 \| \| 0.1587 \| 9.73 \| 3600 \| 0.1747 \| 0.9282 \| 0.9282 \| \| 0.1637 \| 10.27 \| 3800 \| 0.1736 \| 0.9287 \| 0.9287 \| \| 0.1557 \| 10.81 \| 4000 \| 0.1735 \| 0.9296 \| 0.9296 \| \| 0.1571 \| 11.35 \| 4200 \| 0.1745 \| 0.9289 \| 0.9289 \| \| 0.1499 \| 11.89 \| 4400 \| 0.1769 \| 0.9292 \| 0.9292 \| \| 0.1527 \| 12.43 \| 4600 \| 0.1737 \| 0.9331 \| 0.9331 \| \| 0.1488 \| 12.97 \| 4800 \| 0.1712 \| 0.9314 \| 0.9314 \| \| 0.1442 \| 13.51 \| 5000 \| 0.1780 \| 0.9299 \| 0.9299 \| \| 0.1468 \| 14.05 \| 5200 \| 0.1775 \| 0.9289 \| 0.9289 \| \| 0.1385 \| 14.59 \| 5400 \| 0.1741 \| 0.9312 \| 0.9313 \| \| 0.1387 \| 15.14 \| 5600 \| 0.1760 \| 0.9333 \| 0.9333 \| \| 0.1373 \| 15.68 \| 5800 \| 0.1818 \| 0.9297 \| 0.9297 \| \| 0.1397 \| 16.22 \| 6000 \| 0.1723 \| 0.9324 \| 0.9324 \| \| 0.1317 \| 16.76 \| 6200 \| 0.1917 \| 0.9275 \| 0.9275 \| \| 0.1361 \| 17.3 \| 6400 \| 0.1733 \| 0.9297 \| 0.9297 \| \| 0.1352 \| 17.84 \| 6600 \| 0.1756 \| 0.9302 \| 0.9302 \| \| 0.1309 \| 18.38 \| 6800 \| 0.1762 \| 0.9321 \| 0.9321 \| \| 0.1312 \| 18.92 \| 7000 \| 0.1753 \| 0.9326 \| 0.9326 \| \| 0.1292 \| 19.46 \| 7200 \| 0.1870 \| 0.9316 \| 0.9316 \| \| 0.1264 \| 20.0 \| 7400 \| 0.1821 \| 0.9326 \| 0.9326 \| \| 0.1271 \| 20.54 \| 7600 \| 0.1814 \| 0.9321 \| 0.9321 \| \| 0.1236 \| 21.08 \| 7800 \| 0.1732 \| 0.9329 \| 0.9329 \| \| 0.1231 \| 21.62 \| 8000 \| 0.1771 \| 0.9329 \| 0.9329 \| \| 0.1208 \| 22.16 \| 8200 \| 0.1779 \| 0.9299 \| 0.9299 \| \| 0.1192 \| 22.7 \| 8400 \| 0.1814 \| 0.9297 \| 0.9297 \| \| 0.1191 \| 23.24 \| 8600 \| 0.1829 \| 0.9326 \| 0.9326 \| \| 0.121 \| 23.78 \| 8800 \| 0.1793 \| 0.9319 \| 0.9319 \| \| 0.1192 \| 24.32 \| 9000 \| 0.1845 \| 0.9314 \| 0.9314 \| \| 0.1158 \| 24.86 \| 9200 \| 0.1805 \| 0.9304 \| 0.9304 \| \| 0.1181 \| 25.41 \| 9400 \| 0.1857 \| 0.9309 \| 0.9309 \| \| 0.1148 \| 25.95 \| 9600 \| 0.1834 \| 0.9311 \| 0.9311 \| \| 0.1132 \| 26.49 \| 9800 \| 0.1836 \| 0.9324 \| 0.9324 \| \| 0.1159 \| 27.03 \| 10000 \| 0.1824 \| 0.9319 \| 0.9319 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_16384_512_34M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_16384_512_34M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:18:59+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_all-seqsight_16384_512_34M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1966 - F1 Score: 0.9223 - Accuracy: 0.9223 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3873 \| 0.54 \| 200 \| 0.2597 \| 0.8966 \| 0.8966 \| \| 0.2654 \| 1.08 \| 400 \| 0.2347 \| 0.9106 \| 0.9106 \| \| 0.2484 \| 1.62 \| 600 \| 0.2208 \| 0.9103 \| 0.9103 \| \| 0.2292 \| 2.16 \| 800 \| 0.2135 \| 0.9163 \| 0.9164 \| \| 0.2254 \| 2.7 \| 1000 \| 0.2098 \| 0.9128 \| 0.9128 \| \| 0.2163 \| 3.24 \| 1200 \| 0.2076 \| 0.9135 \| 0.9135 \| \| 0.2157 \| 3.78 \| 1400 \| 0.2016 \| 0.9172 \| 0.9172 \| \| 0.2135 \| 4.32 \| 1600 \| 0.1970 \| 0.9193 \| 0.9193 \| \| 0.2032 \| 4.86 \| 1800 \| 0.2089 \| 0.9186 \| 0.9186 \| \| 0.2047 \| 5.41 \| 2000 \| 0.1957 \| 0.9199 \| 0.9199 \| \| 0.2034 \| 5.95 \| 2200 \| 0.1903 \| 0.9209 \| 0.9209 \| \| 0.2005 \| 6.49 \| 2400 \| 0.1952 \| 0.9219 \| 0.9220 \| \| 0.2004 \| 7.03 \| 2600 \| 0.1875 \| 0.9208 \| 0.9208 \| \| 0.1906 \| 7.57 \| 2800 \| 0.1850 \| 0.9199 \| 0.9199 \| \| 0.1944 \| 8.11 \| 3000 \| 0.1916 \| 0.9233 \| 0.9233 \| \| 0.1929 \| 8.65 \| 3200 \| 0.1880 \| 0.9226 \| 0.9226 \| \| 0.1847 \| 9.19 \| 3400 \| 0.1898 \| 0.9226 \| 0.9226 \| \| 0.1832 \| 9.73 \| 3600 \| 0.1894 \| 0.9212 \| 0.9213 \| \| 0.1894 \| 10.27 \| 3800 \| 0.1800 \| 0.9250 \| 0.925 \| \| 0.1823 \| 10.81 \| 4000 \| 0.1835 \| 0.9219 \| 0.9220 \| \| 0.1858 \| 11.35 \| 4200 \| 0.1802 \| 0.9253 \| 0.9253 \| \| 0.1787 \| 11.89 \| 4400 \| 0.1839 \| 0.9258 \| 0.9258 \| \| 0.1831 \| 12.43 \| 4600 \| 0.1804 \| 0.9265 \| 0.9265 \| \| 0.1807 \| 12.97 \| 4800 \| 0.1748 \| 0.9275 \| 0.9275 \| \| 0.1754 \| 13.51 \| 5000 \| 0.1804 \| 0.9270 \| 0.9270 \| \| 0.1785 \| 14.05 \| 5200 \| 0.1808 \| 0.9255 \| 0.9255 \| \| 0.1714 \| 14.59 \| 5400 \| 0.1773 \| 0.9267 \| 0.9267 \| \| 0.1719 \| 15.14 \| 5600 \| 0.1750 \| 0.9267 \| 0.9267 \| \| 0.1715 \| 15.68 \| 5800 \| 0.1792 \| 0.9284 \| 0.9284 \| \| 0.1753 \| 16.22 \| 6000 \| 0.1738 \| 0.9275 \| 0.9275 \| \| 0.1694 \| 16.76 \| 6200 \| 0.1880 \| 0.9271 \| 0.9272 \| \| 0.1711 \| 17.3 \| 6400 \| 0.1769 \| 0.9290 \| 0.9291 \| \| 0.1723 \| 17.84 \| 6600 \| 0.1778 \| 0.9289 \| 0.9289 \| \| 0.1668 \| 18.38 \| 6800 \| 0.1817 \| 0.9273 \| 0.9274 \| \| 0.1714 \| 18.92 \| 7000 \| 0.1780 \| 0.9283 \| 0.9284 \| \| 0.1682 \| 19.46 \| 7200 \| 0.1826 \| 0.9272 \| 0.9272 \| \| 0.1651 \| 20.0 \| 7400 \| 0.1807 \| 0.9295 \| 0.9296 \| \| 0.1677 \| 20.54 \| 7600 \| 0.1801 \| 0.9297 \| 0.9297 \| \| 0.1638 \| 21.08 \| 7800 \| 0.1737 \| 0.9307 \| 0.9307 \| \| 0.1645 \| 21.62 \| 8000 \| 0.1757 \| 0.9277 \| 0.9277 \| \| 0.1646 \| 22.16 \| 8200 \| 0.1764 \| 0.9307 \| 0.9307 \| \| 0.1605 \| 22.7 \| 8400 \| 0.1779 \| 0.9309 \| 0.9309 \| \| 0.1625 \| 23.24 \| 8600 \| 0.1776 \| 0.9299 \| 0.9299 \| \| 0.1622 \| 23.78 \| 8800 \| 0.1772 \| 0.9306 \| 0.9306 \| \| 0.1643 \| 24.32 \| 9000 \| 0.1809 \| 0.9299 \| 0.9299 \| \| 0.1604 \| 24.86 \| 9200 \| 0.1760 \| 0.9306 \| 0.9306 \| \| 0.1614 \| 25.41 \| 9400 \| 0.1797 \| 0.9304 \| 0.9304 \| \| 0.1588 \| 25.95 \| 9600 \| 0.1792 \| 0.9305 \| 0.9306 \| \| 0.1586 \| 26.49 \| 9800 \| 0.1784 \| 0.9304 \| 0.9304 \| \| 0.1602 \| 27.03 \| 10000 \| 0.1774 \| 0.9306 \| 0.9306 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_16384_512_34M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_16384_512_34M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:20:05+00:00
sentence-similarity	sentence-transformers	# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 113 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 500, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->	{"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}	Mihaiii/test13	null	[ "sentence-transformers", "onnx", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:21:03+00:00
null	null	LoRA extraction from Gradient AI's https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k model. LoRA extraction only targeted from the self_attn modules. Rank: 1024	{}	winglian/llama-3-1m-context-gradient-lora	null	[ "safetensors", "region:us" ]	null	2024-04-29T21:22:04+00:00
null	transformers	## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/giux78/llama3-8B-usenet-merged <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/llama3-8B-usenet-merged-GGUF/resolve/main/llama3-8B-usenet-merged.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "library_name": "transformers", "tags": [], "base_model": "giux78/llama3-8B-usenet-merged", "quantized_by": "mradermacher"}	mradermacher/llama3-8B-usenet-merged-GGUF	null	[ "transformers", "gguf", "en", "base_model:giux78/llama3-8B-usenet-merged", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:22:07+00:00
text-to-image	diffusers	<!-- 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. --> # SDXL LoRA DreamBooth - embracellm/sushi04_LoRA <Gallery /> ## Model description These are embracellm/sushi04_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sushi to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](embracellm/sushi04_LoRA/tree/main) them in the Files & versions tab. ## 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]	{"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of sushi", "widget": []}	embracellm/sushi04_LoRA	null	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	null	2024-04-29T21:22:15+00:00
fill-mask	transformers		{}	turalizada/AzBERTaContextualizedWordEmbeddingsinAzerbaijaniLanguage	null	[ "transformers", "safetensors", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:22:16+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	lunarsylph/mooncell_v32	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:22:18+00:00
null	peft	<!-- 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-model This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2126 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 16 - 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: linear - lr_scheduler_warmup_steps: 1 - training_steps: 250 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7168 \| 0.0 \| 25 \| 2.6101 \| \| 2.652 \| 0.0 \| 50 \| 2.5280 \| \| 2.5867 \| 0.0 \| 75 \| 2.4430 \| \| 2.5081 \| 0.0 \| 100 \| 2.3748 \| \| 2.4728 \| 0.0 \| 125 \| 2.3105 \| \| 2.4563 \| 0.0 \| 150 \| 2.2719 \| \| 2.3669 \| 0.01 \| 175 \| 2.2473 \| \| 2.3839 \| 0.01 \| 200 \| 2.2292 \| \| 2.3617 \| 0.01 \| 225 \| 2.2150 \| \| 2.3729 \| 0.01 \| 250 \| 2.2126 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 1.13.1 - Datasets 2.17.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "distilbert/distilgpt2", "model-index": [{"name": "DistilGPT2-model", "results": []}]}	anushkat/DistilGPT2-model	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:distilbert/distilgpt2", "license:apache-2.0", "region:us" ]	null	2024-04-29T21:24:14+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_16384_512_34M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4929 - F1 Score: 0.7736 - Accuracy: 0.7725 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6003 \| 0.97 \| 200 \| 0.5799 \| 0.7133 \| 0.7123 \| \| 0.5508 \| 1.93 \| 400 \| 0.5234 \| 0.7485 \| 0.7467 \| \| 0.5336 \| 2.9 \| 600 \| 0.5558 \| 0.7286 \| 0.7283 \| \| 0.529 \| 3.86 \| 800 \| 0.5103 \| 0.7628 \| 0.7616 \| \| 0.5206 \| 4.83 \| 1000 \| 0.5345 \| 0.7415 \| 0.7404 \| \| 0.5172 \| 5.8 \| 1200 \| 0.5288 \| 0.7463 \| 0.7449 \| \| 0.5157 \| 6.76 \| 1400 \| 0.5146 \| 0.7525 \| 0.7510 \| \| 0.5111 \| 7.73 \| 1600 \| 0.5075 \| 0.7644 \| 0.7628 \| \| 0.5058 \| 8.7 \| 1800 \| 0.5124 \| 0.7580 \| 0.7564 \| \| 0.505 \| 9.66 \| 2000 \| 0.5182 \| 0.7543 \| 0.7528 \| \| 0.5068 \| 10.63 \| 2200 \| 0.5384 \| 0.7428 \| 0.7419 \| \| 0.498 \| 11.59 \| 2400 \| 0.4985 \| 0.7659 \| 0.7643 \| \| 0.501 \| 12.56 \| 2600 \| 0.5268 \| 0.7529 \| 0.7516 \| \| 0.5001 \| 13.53 \| 2800 \| 0.5198 \| 0.7514 \| 0.7501 \| \| 0.4972 \| 14.49 \| 3000 \| 0.5324 \| 0.7465 \| 0.7455 \| \| 0.4903 \| 15.46 \| 3200 \| 0.5011 \| 0.7650 \| 0.7634 \| \| 0.4951 \| 16.43 \| 3400 \| 0.5306 \| 0.7449 \| 0.7440 \| \| 0.4942 \| 17.39 \| 3600 \| 0.5056 \| 0.7617 \| 0.7601 \| \| 0.4914 \| 18.36 \| 3800 \| 0.4964 \| 0.7671 \| 0.7655 \| \| 0.4918 \| 19.32 \| 4000 \| 0.5075 \| 0.7632 \| 0.7616 \| \| 0.4884 \| 20.29 \| 4200 \| 0.5106 \| 0.7641 \| 0.7625 \| \| 0.4896 \| 21.26 \| 4400 \| 0.5118 \| 0.7625 \| 0.7610 \| \| 0.4875 \| 22.22 \| 4600 \| 0.5338 \| 0.7459 \| 0.7449 \| \| 0.4889 \| 23.19 \| 4800 \| 0.4999 \| 0.7653 \| 0.7637 \| \| 0.4888 \| 24.15 \| 5000 \| 0.5070 \| 0.7619 \| 0.7604 \| \| 0.4859 \| 25.12 \| 5200 \| 0.5240 \| 0.7540 \| 0.7528 \| \| 0.4844 \| 26.09 \| 5400 \| 0.5120 \| 0.7634 \| 0.7619 \| \| 0.4849 \| 27.05 \| 5600 \| 0.5322 \| 0.7502 \| 0.7492 \| \| 0.4836 \| 28.02 \| 5800 \| 0.4956 \| 0.7701 \| 0.7685 \| \| 0.4845 \| 28.99 \| 6000 \| 0.5183 \| 0.7553 \| 0.7540 \| \| 0.4823 \| 29.95 \| 6200 \| 0.5245 \| 0.7559 \| 0.7546 \| \| 0.482 \| 30.92 \| 6400 \| 0.4980 \| 0.7683 \| 0.7667 \| \| 0.4823 \| 31.88 \| 6600 \| 0.5047 \| 0.7632 \| 0.7616 \| \| 0.4778 \| 32.85 \| 6800 \| 0.5137 \| 0.7606 \| 0.7592 \| \| 0.4828 \| 33.82 \| 7000 \| 0.5245 \| 0.7562 \| 0.7549 \| \| 0.4793 \| 34.78 \| 7200 \| 0.5183 \| 0.7566 \| 0.7552 \| \| 0.4822 \| 35.75 \| 7400 \| 0.5119 \| 0.7607 \| 0.7592 \| \| 0.4747 \| 36.71 \| 7600 \| 0.5138 \| 0.7637 \| 0.7622 \| \| 0.4789 \| 37.68 \| 7800 \| 0.5127 \| 0.7619 \| 0.7604 \| \| 0.4761 \| 38.65 \| 8000 \| 0.5030 \| 0.7647 \| 0.7631 \| \| 0.4837 \| 39.61 \| 8200 \| 0.5079 \| 0.7622 \| 0.7607 \| \| 0.4717 \| 40.58 \| 8400 \| 0.5143 \| 0.7628 \| 0.7613 \| \| 0.4763 \| 41.55 \| 8600 \| 0.5099 \| 0.7640 \| 0.7625 \| \| 0.4758 \| 42.51 \| 8800 \| 0.5121 \| 0.7628 \| 0.7613 \| \| 0.4781 \| 43.48 \| 9000 \| 0.5206 \| 0.7609 \| 0.7595 \| \| 0.4753 \| 44.44 \| 9200 \| 0.5192 \| 0.7609 \| 0.7595 \| \| 0.4803 \| 45.41 \| 9400 \| 0.5114 \| 0.7628 \| 0.7613 \| \| 0.4717 \| 46.38 \| 9600 \| 0.5167 \| 0.7609 \| 0.7595 \| \| 0.4784 \| 47.34 \| 9800 \| 0.5133 \| 0.7613 \| 0.7598 \| \| 0.4757 \| 48.31 \| 10000 \| 0.5110 \| 0.7634 \| 0.7619 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_16384_512_34M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_16384_512_34M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:24:41+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_16384_512_34M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4869 - F1 Score: 0.7726 - Accuracy: 0.7719 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5802 \| 0.97 \| 200 \| 0.5322 \| 0.7430 \| 0.7413 \| \| 0.5301 \| 1.93 \| 400 \| 0.5063 \| 0.7623 \| 0.7607 \| \| 0.5157 \| 2.9 \| 600 \| 0.5404 \| 0.7333 \| 0.7328 \| \| 0.5125 \| 3.86 \| 800 \| 0.4997 \| 0.7661 \| 0.7646 \| \| 0.5031 \| 4.83 \| 1000 \| 0.5276 \| 0.7509 \| 0.7498 \| \| 0.4993 \| 5.8 \| 1200 \| 0.5180 \| 0.7515 \| 0.7501 \| \| 0.4962 \| 6.76 \| 1400 \| 0.5067 \| 0.7599 \| 0.7582 \| \| 0.4915 \| 7.73 \| 1600 \| 0.5040 \| 0.7614 \| 0.7598 \| \| 0.4852 \| 8.7 \| 1800 \| 0.5192 \| 0.7555 \| 0.7543 \| \| 0.4838 \| 9.66 \| 2000 \| 0.5179 \| 0.7569 \| 0.7555 \| \| 0.4845 \| 10.63 \| 2200 \| 0.5250 \| 0.7577 \| 0.7564 \| \| 0.4754 \| 11.59 \| 2400 \| 0.4952 \| 0.7677 \| 0.7661 \| \| 0.4772 \| 12.56 \| 2600 \| 0.5157 \| 0.7615 \| 0.7601 \| \| 0.474 \| 13.53 \| 2800 \| 0.5158 \| 0.7566 \| 0.7552 \| \| 0.4708 \| 14.49 \| 3000 \| 0.5174 \| 0.7575 \| 0.7561 \| \| 0.4626 \| 15.46 \| 3200 \| 0.4984 \| 0.7713 \| 0.7697 \| \| 0.4662 \| 16.43 \| 3400 \| 0.5138 \| 0.7568 \| 0.7555 \| \| 0.4641 \| 17.39 \| 3600 \| 0.5002 \| 0.7683 \| 0.7667 \| \| 0.4604 \| 18.36 \| 3800 \| 0.4880 \| 0.7748 \| 0.7737 \| \| 0.4573 \| 19.32 \| 4000 \| 0.5014 \| 0.7668 \| 0.7652 \| \| 0.4547 \| 20.29 \| 4200 \| 0.5045 \| 0.7740 \| 0.7725 \| \| 0.4551 \| 21.26 \| 4400 \| 0.5086 \| 0.7649 \| 0.7634 \| \| 0.4503 \| 22.22 \| 4600 \| 0.5307 \| 0.7519 \| 0.7507 \| \| 0.4507 \| 23.19 \| 4800 \| 0.4967 \| 0.7718 \| 0.7703 \| \| 0.4524 \| 24.15 \| 5000 \| 0.5058 \| 0.7623 \| 0.7607 \| \| 0.4457 \| 25.12 \| 5200 \| 0.5223 \| 0.7605 \| 0.7592 \| \| 0.4432 \| 26.09 \| 5400 \| 0.5108 \| 0.7610 \| 0.7595 \| \| 0.4431 \| 27.05 \| 5600 \| 0.5375 \| 0.7516 \| 0.7507 \| \| 0.4419 \| 28.02 \| 5800 \| 0.5027 \| 0.7715 \| 0.7700 \| \| 0.441 \| 28.99 \| 6000 \| 0.5024 \| 0.7707 \| 0.7691 \| \| 0.4382 \| 29.95 \| 6200 \| 0.5183 \| 0.7611 \| 0.7595 \| \| 0.4354 \| 30.92 \| 6400 \| 0.4986 \| 0.7736 \| 0.7725 \| \| 0.4364 \| 31.88 \| 6600 \| 0.4992 \| 0.7685 \| 0.7670 \| \| 0.43 \| 32.85 \| 6800 \| 0.5202 \| 0.7652 \| 0.7637 \| \| 0.4349 \| 33.82 \| 7000 \| 0.5296 \| 0.7566 \| 0.7552 \| \| 0.4316 \| 34.78 \| 7200 \| 0.5211 \| 0.7610 \| 0.7595 \| \| 0.4321 \| 35.75 \| 7400 \| 0.5167 \| 0.7662 \| 0.7646 \| \| 0.4247 \| 36.71 \| 7600 \| 0.5167 \| 0.7668 \| 0.7652 \| \| 0.4264 \| 37.68 \| 7800 \| 0.5181 \| 0.7635 \| 0.7619 \| \| 0.4264 \| 38.65 \| 8000 \| 0.5162 \| 0.7638 \| 0.7622 \| \| 0.4329 \| 39.61 \| 8200 \| 0.5062 \| 0.7635 \| 0.7619 \| \| 0.419 \| 40.58 \| 8400 \| 0.5248 \| 0.7665 \| 0.7649 \| \| 0.4225 \| 41.55 \| 8600 \| 0.5232 \| 0.7671 \| 0.7655 \| \| 0.4246 \| 42.51 \| 8800 \| 0.5165 \| 0.7656 \| 0.7640 \| \| 0.4256 \| 43.48 \| 9000 \| 0.5269 \| 0.7634 \| 0.7619 \| \| 0.4205 \| 44.44 \| 9200 \| 0.5279 \| 0.7616 \| 0.7601 \| \| 0.4274 \| 45.41 \| 9400 \| 0.5198 \| 0.7671 \| 0.7655 \| \| 0.416 \| 46.38 \| 9600 \| 0.5247 \| 0.7644 \| 0.7628 \| \| 0.4222 \| 47.34 \| 9800 \| 0.5202 \| 0.7650 \| 0.7634 \| \| 0.419 \| 48.31 \| 10000 \| 0.5183 \| 0.7638 \| 0.7622 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_16384_512_34M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_16384_512_34M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:24:41+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_16384_512_34M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.6031 - F1 Score: 0.6777 - Accuracy: 0.6794 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6611 \| 1.04 \| 200 \| 0.6414 \| 0.5857 \| 0.6289 \| \| 0.629 \| 2.08 \| 400 \| 0.6326 \| 0.6468 \| 0.6461 \| \| 0.6218 \| 3.12 \| 600 \| 0.6257 \| 0.6309 \| 0.6615 \| \| 0.6174 \| 4.17 \| 800 \| 0.6176 \| 0.6572 \| 0.6644 \| \| 0.6137 \| 5.21 \| 1000 \| 0.6141 \| 0.6684 \| 0.6742 \| \| 0.6109 \| 6.25 \| 1200 \| 0.6113 \| 0.6620 \| 0.6719 \| \| 0.6048 \| 7.29 \| 1400 \| 0.6166 \| 0.6670 \| 0.6663 \| \| 0.6048 \| 8.33 \| 1600 \| 0.6118 \| 0.6682 \| 0.6716 \| \| 0.6022 \| 9.38 \| 1800 \| 0.6260 \| 0.6504 \| 0.6478 \| \| 0.5994 \| 10.42 \| 2000 \| 0.6097 \| 0.6664 \| 0.6670 \| \| 0.6025 \| 11.46 \| 2200 \| 0.6034 \| 0.6673 \| 0.6768 \| \| 0.5925 \| 12.5 \| 2400 \| 0.6056 \| 0.6680 \| 0.6729 \| \| 0.5911 \| 13.54 \| 2600 \| 0.6031 \| 0.6667 \| 0.6738 \| \| 0.5936 \| 14.58 \| 2800 \| 0.6039 \| 0.6635 \| 0.6732 \| \| 0.5978 \| 15.62 \| 3000 \| 0.6047 \| 0.6707 \| 0.6729 \| \| 0.5887 \| 16.67 \| 3200 \| 0.6062 \| 0.6709 \| 0.6712 \| \| 0.5891 \| 17.71 \| 3400 \| 0.6048 \| 0.6678 \| 0.6689 \| \| 0.5876 \| 18.75 \| 3600 \| 0.6001 \| 0.6700 \| 0.6777 \| \| 0.5893 \| 19.79 \| 3800 \| 0.6006 \| 0.6729 \| 0.6764 \| \| 0.5843 \| 20.83 \| 4000 \| 0.6032 \| 0.6707 \| 0.6716 \| \| 0.5862 \| 21.88 \| 4200 \| 0.6095 \| 0.6715 \| 0.6706 \| \| 0.5846 \| 22.92 \| 4400 \| 0.6021 \| 0.6707 \| 0.6738 \| \| 0.5846 \| 23.96 \| 4600 \| 0.6090 \| 0.6660 \| 0.6650 \| \| 0.5814 \| 25.0 \| 4800 \| 0.6015 \| 0.6717 \| 0.6742 \| \| 0.5817 \| 26.04 \| 5000 \| 0.6023 \| 0.6750 \| 0.6774 \| \| 0.5796 \| 27.08 \| 5200 \| 0.6028 \| 0.6736 \| 0.6751 \| \| 0.5811 \| 28.12 \| 5400 \| 0.6036 \| 0.6720 \| 0.6725 \| \| 0.5786 \| 29.17 \| 5600 \| 0.6008 \| 0.6704 \| 0.6729 \| \| 0.5778 \| 30.21 \| 5800 \| 0.6033 \| 0.6743 \| 0.6755 \| \| 0.5785 \| 31.25 \| 6000 \| 0.6062 \| 0.6709 \| 0.6709 \| \| 0.5778 \| 32.29 \| 6200 \| 0.5980 \| 0.6708 \| 0.6745 \| \| 0.5779 \| 33.33 \| 6400 \| 0.5994 \| 0.6712 \| 0.6742 \| \| 0.5761 \| 34.38 \| 6600 \| 0.5987 \| 0.6738 \| 0.6784 \| \| 0.574 \| 35.42 \| 6800 \| 0.6013 \| 0.6683 \| 0.6696 \| \| 0.5721 \| 36.46 \| 7000 \| 0.5987 \| 0.6735 \| 0.6774 \| \| 0.5722 \| 37.5 \| 7200 \| 0.6022 \| 0.6707 \| 0.6722 \| \| 0.5719 \| 38.54 \| 7400 \| 0.6009 \| 0.6740 \| 0.6764 \| \| 0.5783 \| 39.58 \| 7600 \| 0.5976 \| 0.6745 \| 0.6794 \| \| 0.5755 \| 40.62 \| 7800 \| 0.6029 \| 0.6671 \| 0.6673 \| \| 0.5732 \| 41.67 \| 8000 \| 0.6016 \| 0.6695 \| 0.6706 \| \| 0.569 \| 42.71 \| 8200 \| 0.6009 \| 0.6748 \| 0.6797 \| \| 0.5734 \| 43.75 \| 8400 \| 0.6010 \| 0.6709 \| 0.6738 \| \| 0.5713 \| 44.79 \| 8600 \| 0.6038 \| 0.6668 \| 0.6673 \| \| 0.5687 \| 45.83 \| 8800 \| 0.6008 \| 0.6722 \| 0.6755 \| \| 0.5734 \| 46.88 \| 9000 \| 0.6042 \| 0.6665 \| 0.6670 \| \| 0.5705 \| 47.92 \| 9200 \| 0.6031 \| 0.6675 \| 0.6686 \| \| 0.5721 \| 48.96 \| 9400 \| 0.6010 \| 0.6715 \| 0.6745 \| \| 0.5721 \| 50.0 \| 9600 \| 0.6029 \| 0.6687 \| 0.6699 \| \| 0.5694 \| 51.04 \| 9800 \| 0.6021 \| 0.6702 \| 0.6722 \| \| 0.5691 \| 52.08 \| 10000 \| 0.6021 \| 0.6703 \| 0.6722 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_16384_512_34M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_16384_512_34M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:24:41+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_16384_512_34M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4977 - F1 Score: 0.7731 - Accuracy: 0.7719 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5714 \| 0.97 \| 200 \| 0.5270 \| 0.7505 \| 0.7489 \| \| 0.5196 \| 1.93 \| 400 \| 0.5001 \| 0.7686 \| 0.7670 \| \| 0.5058 \| 2.9 \| 600 \| 0.5232 \| 0.7505 \| 0.7492 \| \| 0.5005 \| 3.86 \| 800 \| 0.4948 \| 0.7662 \| 0.7649 \| \| 0.4896 \| 4.83 \| 1000 \| 0.5262 \| 0.7573 \| 0.7561 \| \| 0.4836 \| 5.8 \| 1200 \| 0.5105 \| 0.7562 \| 0.7546 \| \| 0.4775 \| 6.76 \| 1400 \| 0.4992 \| 0.7722 \| 0.7707 \| \| 0.4696 \| 7.73 \| 1600 \| 0.5042 \| 0.7656 \| 0.7640 \| \| 0.4617 \| 8.7 \| 1800 \| 0.5191 \| 0.7545 \| 0.7534 \| \| 0.4576 \| 9.66 \| 2000 \| 0.5178 \| 0.7540 \| 0.7528 \| \| 0.4552 \| 10.63 \| 2200 \| 0.5097 \| 0.7637 \| 0.7622 \| \| 0.4434 \| 11.59 \| 2400 \| 0.4976 \| 0.7709 \| 0.7694 \| \| 0.4409 \| 12.56 \| 2600 \| 0.5074 \| 0.7661 \| 0.7646 \| \| 0.4363 \| 13.53 \| 2800 \| 0.5158 \| 0.7586 \| 0.7570 \| \| 0.4262 \| 14.49 \| 3000 \| 0.5163 \| 0.7602 \| 0.7585 \| \| 0.4161 \| 15.46 \| 3200 \| 0.5112 \| 0.7625 \| 0.7610 \| \| 0.4164 \| 16.43 \| 3400 \| 0.5108 \| 0.7659 \| 0.7643 \| \| 0.4087 \| 17.39 \| 3600 \| 0.5204 \| 0.7587 \| 0.7570 \| \| 0.4034 \| 18.36 \| 3800 \| 0.5061 \| 0.7568 \| 0.7567 \| \| 0.395 \| 19.32 \| 4000 \| 0.5132 \| 0.7656 \| 0.7643 \| \| 0.3895 \| 20.29 \| 4200 \| 0.5399 \| 0.7583 \| 0.7576 \| \| 0.3889 \| 21.26 \| 4400 \| 0.5212 \| 0.7662 \| 0.7646 \| \| 0.3775 \| 22.22 \| 4600 \| 0.5523 \| 0.7523 \| 0.7507 \| \| 0.374 \| 23.19 \| 4800 \| 0.5437 \| 0.7598 \| 0.7585 \| \| 0.3713 \| 24.15 \| 5000 \| 0.5454 \| 0.7596 \| 0.7579 \| \| 0.3603 \| 25.12 \| 5200 \| 0.5542 \| 0.7632 \| 0.7616 \| \| 0.3573 \| 26.09 \| 5400 \| 0.5515 \| 0.7550 \| 0.7534 \| \| 0.3526 \| 27.05 \| 5600 \| 0.5675 \| 0.7599 \| 0.7582 \| \| 0.3482 \| 28.02 \| 5800 \| 0.5677 \| 0.7609 \| 0.7595 \| \| 0.3464 \| 28.99 \| 6000 \| 0.5469 \| 0.7673 \| 0.7658 \| \| 0.337 \| 29.95 \| 6200 \| 0.5943 \| 0.7553 \| 0.7537 \| \| 0.3308 \| 30.92 \| 6400 \| 0.5690 \| 0.7651 \| 0.7643 \| \| 0.3334 \| 31.88 \| 6600 \| 0.5501 \| 0.7568 \| 0.7552 \| \| 0.3241 \| 32.85 \| 6800 \| 0.5957 \| 0.7518 \| 0.7501 \| \| 0.3243 \| 33.82 \| 7000 \| 0.5794 \| 0.7578 \| 0.7561 \| \| 0.3179 \| 34.78 \| 7200 \| 0.5894 \| 0.7491 \| 0.7474 \| \| 0.3202 \| 35.75 \| 7400 \| 0.5888 \| 0.7497 \| 0.7480 \| \| 0.3096 \| 36.71 \| 7600 \| 0.5861 \| 0.7554 \| 0.7540 \| \| 0.3084 \| 37.68 \| 7800 \| 0.5927 \| 0.7609 \| 0.7595 \| \| 0.307 \| 38.65 \| 8000 \| 0.5960 \| 0.7588 \| 0.7573 \| \| 0.308 \| 39.61 \| 8200 \| 0.5936 \| 0.7563 \| 0.7549 \| \| 0.2982 \| 40.58 \| 8400 \| 0.6147 \| 0.7575 \| 0.7558 \| \| 0.297 \| 41.55 \| 8600 \| 0.6329 \| 0.7572 \| 0.7555 \| \| 0.2997 \| 42.51 \| 8800 \| 0.6017 \| 0.7577 \| 0.7561 \| \| 0.2959 \| 43.48 \| 9000 \| 0.6147 \| 0.7596 \| 0.7579 \| \| 0.2887 \| 44.44 \| 9200 \| 0.6209 \| 0.7548 \| 0.7531 \| \| 0.2994 \| 45.41 \| 9400 \| 0.6124 \| 0.7572 \| 0.7555 \| \| 0.2885 \| 46.38 \| 9600 \| 0.6118 \| 0.7611 \| 0.7595 \| \| 0.2913 \| 47.34 \| 9800 \| 0.6095 \| 0.7634 \| 0.7619 \| \| 0.285 \| 48.31 \| 10000 \| 0.6096 \| 0.7616 \| 0.7601 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_16384_512_34M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_16384_512_34M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:24:41+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	armaniii/llama-3-8b-claim-topic-extraction	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-04-29T21:24:56+00:00
null	transformers		{}	SlimCognito/wonkamodel2	null	[ "transformers", "gguf", "llama", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:24:57+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_16384_512_34M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.6299 - F1 Score: 0.6726 - Accuracy: 0.6722 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.654 \| 1.04 \| 200 \| 0.6328 \| 0.6135 \| 0.6471 \| \| 0.6208 \| 2.08 \| 400 \| 0.6257 \| 0.6519 \| 0.6497 \| \| 0.6133 \| 3.12 \| 600 \| 0.6080 \| 0.6592 \| 0.6729 \| \| 0.6076 \| 4.17 \| 800 \| 0.6232 \| 0.6535 \| 0.6510 \| \| 0.6016 \| 5.21 \| 1000 \| 0.6033 \| 0.6699 \| 0.6794 \| \| 0.5982 \| 6.25 \| 1200 \| 0.6011 \| 0.6725 \| 0.6768 \| \| 0.5902 \| 7.29 \| 1400 \| 0.6046 \| 0.6715 \| 0.6716 \| \| 0.5885 \| 8.33 \| 1600 \| 0.6143 \| 0.6687 \| 0.6670 \| \| 0.5841 \| 9.38 \| 1800 \| 0.6135 \| 0.6585 \| 0.6562 \| \| 0.5787 \| 10.42 \| 2000 \| 0.5974 \| 0.6767 \| 0.6797 \| \| 0.5825 \| 11.46 \| 2200 \| 0.5925 \| 0.6785 \| 0.6830 \| \| 0.5687 \| 12.5 \| 2400 \| 0.6006 \| 0.6667 \| 0.6696 \| \| 0.5664 \| 13.54 \| 2600 \| 0.6117 \| 0.6743 \| 0.6738 \| \| 0.5677 \| 14.58 \| 2800 \| 0.6029 \| 0.6686 \| 0.6725 \| \| 0.5707 \| 15.62 \| 3000 \| 0.6106 \| 0.6649 \| 0.6637 \| \| 0.5603 \| 16.67 \| 3200 \| 0.5992 \| 0.6736 \| 0.6755 \| \| 0.5613 \| 17.71 \| 3400 \| 0.6178 \| 0.6634 \| 0.6611 \| \| 0.5568 \| 18.75 \| 3600 \| 0.6036 \| 0.6754 \| 0.6758 \| \| 0.5571 \| 19.79 \| 3800 \| 0.6165 \| 0.6696 \| 0.6676 \| \| 0.5513 \| 20.83 \| 4000 \| 0.6045 \| 0.6737 \| 0.6742 \| \| 0.5524 \| 21.88 \| 4200 \| 0.6270 \| 0.6641 \| 0.6618 \| \| 0.5478 \| 22.92 \| 4400 \| 0.6197 \| 0.6765 \| 0.6751 \| \| 0.5481 \| 23.96 \| 4600 \| 0.6126 \| 0.6715 \| 0.6699 \| \| 0.545 \| 25.0 \| 4800 \| 0.6300 \| 0.6655 \| 0.6631 \| \| 0.5414 \| 26.04 \| 5000 \| 0.6193 \| 0.6771 \| 0.6771 \| \| 0.5396 \| 27.08 \| 5200 \| 0.6249 \| 0.6714 \| 0.6693 \| \| 0.5384 \| 28.12 \| 5400 \| 0.6173 \| 0.6703 \| 0.6686 \| \| 0.5352 \| 29.17 \| 5600 \| 0.6192 \| 0.6758 \| 0.6742 \| \| 0.5326 \| 30.21 \| 5800 \| 0.6355 \| 0.6697 \| 0.6676 \| \| 0.5328 \| 31.25 \| 6000 \| 0.6439 \| 0.6691 \| 0.6667 \| \| 0.5325 \| 32.29 \| 6200 \| 0.6185 \| 0.6743 \| 0.6729 \| \| 0.5327 \| 33.33 \| 6400 \| 0.6235 \| 0.6713 \| 0.6696 \| \| 0.5219 \| 34.38 \| 6600 \| 0.6232 \| 0.6799 \| 0.6797 \| \| 0.5279 \| 35.42 \| 6800 \| 0.6274 \| 0.6715 \| 0.6696 \| \| 0.522 \| 36.46 \| 7000 \| 0.6249 \| 0.6759 \| 0.6742 \| \| 0.522 \| 37.5 \| 7200 \| 0.6346 \| 0.6712 \| 0.6689 \| \| 0.5193 \| 38.54 \| 7400 \| 0.6308 \| 0.6760 \| 0.6742 \| \| 0.5258 \| 39.58 \| 7600 \| 0.6189 \| 0.6798 \| 0.6797 \| \| 0.5223 \| 40.62 \| 7800 \| 0.6384 \| 0.6707 \| 0.6683 \| \| 0.5189 \| 41.67 \| 8000 \| 0.6271 \| 0.6747 \| 0.6729 \| \| 0.5133 \| 42.71 \| 8200 \| 0.6318 \| 0.6759 \| 0.6745 \| \| 0.5179 \| 43.75 \| 8400 \| 0.6220 \| 0.6749 \| 0.6735 \| \| 0.5161 \| 44.79 \| 8600 \| 0.6297 \| 0.6727 \| 0.6706 \| \| 0.5111 \| 45.83 \| 8800 \| 0.6307 \| 0.6773 \| 0.6758 \| \| 0.515 \| 46.88 \| 9000 \| 0.6398 \| 0.6719 \| 0.6696 \| \| 0.5129 \| 47.92 \| 9200 \| 0.6354 \| 0.6730 \| 0.6709 \| \| 0.5153 \| 48.96 \| 9400 \| 0.6314 \| 0.6756 \| 0.6738 \| \| 0.5135 \| 50.0 \| 9600 \| 0.6364 \| 0.6724 \| 0.6703 \| \| 0.5101 \| 51.04 \| 9800 \| 0.6373 \| 0.6737 \| 0.6716 \| \| 0.5064 \| 52.08 \| 10000 \| 0.6376 \| 0.6743 \| 0.6722 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_16384_512_34M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_16384_512_34M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:25:02+00:00
feature-extraction	transformers	# 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]	{"library_name": "transformers", "tags": []}	andersonbcdefg/tiny-emb-2024-04-29_21-26-53	null	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:26:53+00:00
text-generation	transformers		{}	alexred7/vulnerabilty-classification-18-llama-2-7b_dataset-2	null	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:26:58+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	efeno/llama3_RAFT	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:27:46+00:00
unconditional-image-generation	diffusers	# 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('Joanton/sd-class-butterflies-32') image = pipeline().images[0] image ```	{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]}	Joanton/sd-class-butterflies-32	null	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	2024-04-29T21:28:05+00:00
text-generation	transformers		{}	amoldwalunj/Mixtral-pretrain_32k_mixtral_04_05-Mar_16k_04_29_legal_finetuned	null	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:29:23+00:00
null	null		{}	edwardnakamoto/love	null	[ "region:us" ]	null	2024-04-29T21:29:24+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	cilantro9246/cavwnn7	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:30:13+00:00
null	null		{}	nabeeeha/kk	null	[ "region:us" ]	null	2024-04-29T21:30:24+00:00
sentence-similarity	sentence-transformers	# Venusaur This is a distill of [Bulbasaur](https://huggingface.co/Mihaiii/Bulbasaur) using [qa-assistant](https://huggingface.co/datasets/Mihaiii/qa-assistant). ## Intended purpose <span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span> ## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny)) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('Mihaiii/Venusaur') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny)) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('Mihaiii/Venusaur') model = AutoModel.from_pretrained('Mihaiii/Venusaur') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small)) This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.	{"license": "mit", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "gte", "mteb"], "datasets": ["Mihaiii/qa-assistant"], "base_model": "Mihaiii/Bulbasaur", "pipeline_tag": "sentence-similarity", "model-index": [{"name": "Venusaur", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 73.17910447761194}, {"type": "ap", "value": 35.29994612283548}, {"type": "f1", "value": 66.87845205993153}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 79.993525}, {"type": "ap", "value": 74.7042261687233}, {"type": "f1", "value": 79.9004149386498}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (en)", "type": "mteb/amazon_reviews_multi", "config": "en", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 39.656000000000006}, {"type": "f1", "value": 39.287139345446256}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB ArguAna", "type": "mteb/arguana", "config": "default", "split": "test", "revision": "c22ab2a51041ffd869aaddef7af8d8215647e41a"}, "metrics": [{"type": "map_at_1", 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83.29254385989515}, {"type": "manhattan_f1", "value": 75.70975618644992}, {"type": "manhattan_precision", "value": 71.8773787281157}, {"type": "manhattan_recall", "value": 79.97382198952879}, {"type": "max_accuracy", "value": 87.81387045445726}, {"type": "max_ap", "value": 83.33289584854239}, {"type": "max_f1", "value": 75.85641331494391}]}]}]}	Mihaiii/Venusaur	null	[ "sentence-transformers", "onnx", "safetensors", "bert", "feature-extraction", "sentence-similarity", "gte", "mteb", "dataset:Mihaiii/qa-assistant", "base_model:Mihaiii/Bulbasaur", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:30:53+00:00
null	null		{"license": "openrail"}	Excelsus/LilDoyl	null	[ "license:openrail", "region:us" ]	null	2024-04-29T21:31:23+00:00
null	fastai	# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed	{"tags": ["fastai"]}	adperem/entregable2	null	[ "fastai", "region:us", "has_space" ]	null	2024-04-29T21:31:31+00:00
null	null		{}	Joanton/sd-class-butterflies-64	null	[ "region:us" ]	null	2024-04-29T21:32:48+00:00
null	null		{"license": "mit"}	scspinney/mental-health-text-classifier	null	[ "license:mit", "region:us" ]	null	2024-04-29T21:34:03+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	tennant/llava-llama-3-8b-hqedit	null	[ "transformers", "safetensors", "llava_llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:34:24+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]	{"library_name": "transformers", "tags": []}	whizzzzkid/nose_gemma_ft91	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:34:36+00:00
null	transformers	## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q2_K.gguf) \| Q2_K \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.IQ3_S.gguf) \| IQ3_S \| 7.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.IQ3_M.gguf) \| IQ3_M \| 7.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 8.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 9.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 10.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 11.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 11.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q6_K.gguf) \| Q6_K \| 13.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/resolve/main/starcoder2-15b-instruct-v0.1.Q8_0.gguf) \| Q8_0 \| 17.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "license": "bigcode-openrail-m", "library_name": "transformers", "tags": ["code"], "datasets": ["bigcode/self-oss-instruct-sc2-exec-filter-50k"], "base_model": "bigcode/starcoder2-15b-instruct-v0.1", "quantized_by": "mradermacher"}	mradermacher/starcoder2-15b-instruct-v0.1-GGUF	null	[ "transformers", "gguf", "code", "en", "dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k", "base_model:bigcode/starcoder2-15b-instruct-v0.1", "license:bigcode-openrail-m", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:35:14+00:00
null	null		{}	Gaphilly/gpt2_shakespeare	null	[ "region:us" ]	null	2024-04-29T21:35:19+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	whizzzzkid/nous_sevens71	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:35:58+00:00
text-generation	transformers		{"language": ["en"], "license": "mit", "library_name": "transformers", "pipeline_tag": "text-generation", "widget": [{"text": "Is this review positive or negative? Review: Best cast iron skillet you will ever buy.", "example_title": "Sentiment analysis"}, {"text": "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ...", "example_title": "Coreference resolution"}, {"text": "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book ...", "example_title": "Logic puzzles"}, {"text": "The two men running to become New York City's next mayor will face off in their first debate Wednesday night ...", "example_title": "Reading comprehension"}]}	Gaphilly/gpt2_shakespeare_cp4350	null	[ "transformers", "safetensors", "gpt2", "text-generation", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:37:23+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	shallow6414/v32k1no	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:38:38+00:00
null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0	{"library_name": "peft", "base_model": "Mistral-7B-Instruct-v0.2"}	NandGate1110/mistral_7b_bakery	null	[ "peft", "safetensors", "mistral", "arxiv:1910.09700", "base_model:Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-04-29T21:38:53+00:00
null	null		{"license": "apache-2.0"}	kekcheburek/rugpt3small_uni_qa	null	[ "license:apache-2.0", "region:us" ]	null	2024-04-29T21:39:06+00:00
null	null		{"license": "openrail"}	Agus144/jovani	null	[ "license:openrail", "region:us" ]	null	2024-04-29T21:40:10+00:00
text-generation	transformers	# Uploaded model - Developed by: 1024m - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	1024m/LLAMA3-SMM4H-Task5-16bit	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:41:20+00:00
null	null		{"license": "openrail"}	Coolwowsocoolwow/Informaniac	null	[ "license:openrail", "region:us" ]	null	2024-04-29T21:41:28+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_16384_512_34M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4780 - F1 Score: 0.7817 - Accuracy: 0.7812 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5872 \| 1.15 \| 200 \| 0.5703 \| 0.7144 \| 0.7146 \| \| 0.5223 \| 2.3 \| 400 \| 0.5733 \| 0.7104 \| 0.7132 \| \| 0.4978 \| 3.45 \| 600 \| 0.5385 \| 0.7347 \| 0.7352 \| \| 0.4909 \| 4.6 \| 800 \| 0.5267 \| 0.7363 \| 0.7362 \| \| 0.487 \| 5.75 \| 1000 \| 0.5179 \| 0.7496 \| 0.7492 \| \| 0.4813 \| 6.9 \| 1200 \| 0.5370 \| 0.7364 \| 0.7370 \| \| 0.4761 \| 8.05 \| 1400 \| 0.5318 \| 0.7392 \| 0.7395 \| \| 0.4738 \| 9.2 \| 1600 \| 0.5360 \| 0.7373 \| 0.7391 \| \| 0.4677 \| 10.34 \| 1800 \| 0.5085 \| 0.7597 \| 0.7593 \| \| 0.467 \| 11.49 \| 2000 \| 0.5052 \| 0.7579 \| 0.7575 \| \| 0.4564 \| 12.64 \| 2200 \| 0.5208 \| 0.7503 \| 0.7503 \| \| 0.46 \| 13.79 \| 2400 \| 0.5162 \| 0.7492 \| 0.7496 \| \| 0.4533 \| 14.94 \| 2600 \| 0.5106 \| 0.7557 \| 0.7553 \| \| 0.4493 \| 16.09 \| 2800 \| 0.5288 \| 0.7430 \| 0.7445 \| \| 0.4492 \| 17.24 \| 3000 \| 0.5114 \| 0.7639 \| 0.7636 \| \| 0.4466 \| 18.39 \| 3200 \| 0.5253 \| 0.7506 \| 0.7510 \| \| 0.4455 \| 19.54 \| 3400 \| 0.5026 \| 0.7600 \| 0.7596 \| \| 0.4373 \| 20.69 \| 3600 \| 0.5018 \| 0.7744 \| 0.7740 \| \| 0.4387 \| 21.84 \| 3800 \| 0.5171 \| 0.7490 \| 0.7492 \| \| 0.4334 \| 22.99 \| 4000 \| 0.5341 \| 0.7396 \| 0.7413 \| \| 0.435 \| 24.14 \| 4200 \| 0.5029 \| 0.7640 \| 0.7636 \| \| 0.4234 \| 25.29 \| 4400 \| 0.5208 \| 0.7662 \| 0.7657 \| \| 0.4302 \| 26.44 \| 4600 \| 0.5060 \| 0.7673 \| 0.7668 \| \| 0.4251 \| 27.59 \| 4800 \| 0.5092 \| 0.7616 \| 0.7614 \| \| 0.4184 \| 28.74 \| 5000 \| 0.5090 \| 0.7577 \| 0.7575 \| \| 0.4232 \| 29.89 \| 5200 \| 0.5160 \| 0.7624 \| 0.7621 \| \| 0.4169 \| 31.03 \| 5400 \| 0.5197 \| 0.7559 \| 0.7560 \| \| 0.4169 \| 32.18 \| 5600 \| 0.5021 \| 0.7670 \| 0.7665 \| \| 0.4082 \| 33.33 \| 5800 \| 0.5084 \| 0.7709 \| 0.7704 \| \| 0.4175 \| 34.48 \| 6000 \| 0.5024 \| 0.7669 \| 0.7665 \| \| 0.4084 \| 35.63 \| 6200 \| 0.5067 \| 0.7716 \| 0.7711 \| \| 0.4142 \| 36.78 \| 6400 \| 0.5075 \| 0.7605 \| 0.7603 \| \| 0.4066 \| 37.93 \| 6600 \| 0.5221 \| 0.7577 \| 0.7575 \| \| 0.4066 \| 39.08 \| 6800 \| 0.5066 \| 0.7673 \| 0.7668 \| \| 0.4035 \| 40.23 \| 7000 \| 0.5195 \| 0.7620 \| 0.7618 \| \| 0.405 \| 41.38 \| 7200 \| 0.5203 \| 0.7615 \| 0.7614 \| \| 0.4023 \| 42.53 \| 7400 \| 0.5128 \| 0.7643 \| 0.7639 \| \| 0.3976 \| 43.68 \| 7600 \| 0.5121 \| 0.7652 \| 0.7647 \| \| 0.3954 \| 44.83 \| 7800 \| 0.5249 \| 0.7604 \| 0.7603 \| \| 0.3974 \| 45.98 \| 8000 \| 0.5046 \| 0.7684 \| 0.7679 \| \| 0.3973 \| 47.13 \| 8200 \| 0.5210 \| 0.7635 \| 0.7632 \| \| 0.3929 \| 48.28 \| 8400 \| 0.5216 \| 0.7635 \| 0.7632 \| \| 0.394 \| 49.43 \| 8600 \| 0.5217 \| 0.7629 \| 0.7625 \| \| 0.397 \| 50.57 \| 8800 \| 0.5262 \| 0.7598 \| 0.7596 \| \| 0.3931 \| 51.72 \| 9000 \| 0.5239 \| 0.7632 \| 0.7629 \| \| 0.3905 \| 52.87 \| 9200 \| 0.5309 \| 0.7576 \| 0.7575 \| \| 0.3913 \| 54.02 \| 9400 \| 0.5252 \| 0.7660 \| 0.7657 \| \| 0.3908 \| 55.17 \| 9600 \| 0.5271 \| 0.7617 \| 0.7614 \| \| 0.3882 \| 56.32 \| 9800 \| 0.5204 \| 0.7672 \| 0.7668 \| \| 0.3917 \| 57.47 \| 10000 \| 0.5215 \| 0.7657 \| 0.7654 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_16384_512_34M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_16384_512_34M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:42:11+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_16384_512_34M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4837 - F1 Score: 0.7768 - Accuracy: 0.7762 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6099 \| 1.15 \| 200 \| 0.5647 \| 0.7192 \| 0.7186 \| \| 0.5488 \| 2.3 \| 400 \| 0.5828 \| 0.7025 \| 0.7049 \| \| 0.5232 \| 3.45 \| 600 \| 0.5743 \| 0.7113 \| 0.7132 \| \| 0.5169 \| 4.6 \| 800 \| 0.5584 \| 0.7160 \| 0.7175 \| \| 0.5113 \| 5.75 \| 1000 \| 0.5361 \| 0.7367 \| 0.7362 \| \| 0.5042 \| 6.9 \| 1200 \| 0.5543 \| 0.7305 \| 0.7308 \| \| 0.4981 \| 8.05 \| 1400 \| 0.5393 \| 0.7337 \| 0.7334 \| \| 0.4974 \| 9.2 \| 1600 \| 0.5702 \| 0.7102 \| 0.7143 \| \| 0.4912 \| 10.34 \| 1800 \| 0.5368 \| 0.7413 \| 0.7409 \| \| 0.4939 \| 11.49 \| 2000 \| 0.5188 \| 0.7432 \| 0.7427 \| \| 0.4822 \| 12.64 \| 2200 \| 0.5570 \| 0.7267 \| 0.7287 \| \| 0.488 \| 13.79 \| 2400 \| 0.5235 \| 0.7432 \| 0.7431 \| \| 0.4828 \| 14.94 \| 2600 \| 0.5317 \| 0.7383 \| 0.7384 \| \| 0.4798 \| 16.09 \| 2800 \| 0.5325 \| 0.7381 \| 0.7384 \| \| 0.4808 \| 17.24 \| 3000 \| 0.5377 \| 0.7382 \| 0.7388 \| \| 0.4778 \| 18.39 \| 3200 \| 0.5397 \| 0.7331 \| 0.7337 \| \| 0.48 \| 19.54 \| 3400 \| 0.5249 \| 0.7419 \| 0.7420 \| \| 0.4742 \| 20.69 \| 3600 \| 0.5159 \| 0.7458 \| 0.7452 \| \| 0.4754 \| 21.84 \| 3800 \| 0.5422 \| 0.7243 \| 0.7262 \| \| 0.4727 \| 22.99 \| 4000 \| 0.5297 \| 0.7391 \| 0.7395 \| \| 0.475 \| 24.14 \| 4200 \| 0.5157 \| 0.7474 \| 0.7470 \| \| 0.4657 \| 25.29 \| 4400 \| 0.5343 \| 0.7431 \| 0.7431 \| \| 0.4727 \| 26.44 \| 4600 \| 0.5235 \| 0.7459 \| 0.7456 \| \| 0.4689 \| 27.59 \| 4800 \| 0.5315 \| 0.7406 \| 0.7409 \| \| 0.4651 \| 28.74 \| 5000 \| 0.5302 \| 0.7363 \| 0.7370 \| \| 0.4719 \| 29.89 \| 5200 \| 0.5327 \| 0.7414 \| 0.7416 \| \| 0.4657 \| 31.03 \| 5400 \| 0.5328 \| 0.7354 \| 0.7359 \| \| 0.4675 \| 32.18 \| 5600 \| 0.5118 \| 0.7537 \| 0.7531 \| \| 0.4594 \| 33.33 \| 5800 \| 0.5160 \| 0.7569 \| 0.7564 \| \| 0.4719 \| 34.48 \| 6000 \| 0.5219 \| 0.7448 \| 0.7449 \| \| 0.46 \| 35.63 \| 6200 \| 0.5166 \| 0.7515 \| 0.7510 \| \| 0.4672 \| 36.78 \| 6400 \| 0.5241 \| 0.7410 \| 0.7413 \| \| 0.4639 \| 37.93 \| 6600 \| 0.5342 \| 0.7427 \| 0.7431 \| \| 0.4647 \| 39.08 \| 6800 \| 0.5155 \| 0.7499 \| 0.7496 \| \| 0.4606 \| 40.23 \| 7000 \| 0.5210 \| 0.7490 \| 0.7488 \| \| 0.4643 \| 41.38 \| 7200 \| 0.5263 \| 0.7433 \| 0.7434 \| \| 0.4615 \| 42.53 \| 7400 \| 0.5212 \| 0.7479 \| 0.7478 \| \| 0.4591 \| 43.68 \| 7600 \| 0.5212 \| 0.7476 \| 0.7474 \| \| 0.459 \| 44.83 \| 7800 \| 0.5350 \| 0.7406 \| 0.7413 \| \| 0.4595 \| 45.98 \| 8000 \| 0.5190 \| 0.7480 \| 0.7478 \| \| 0.4613 \| 47.13 \| 8200 \| 0.5250 \| 0.7452 \| 0.7452 \| \| 0.4573 \| 48.28 \| 8400 \| 0.5238 \| 0.7442 \| 0.7442 \| \| 0.4586 \| 49.43 \| 8600 \| 0.5227 \| 0.7468 \| 0.7467 \| \| 0.46 \| 50.57 \| 8800 \| 0.5243 \| 0.7441 \| 0.7442 \| \| 0.457 \| 51.72 \| 9000 \| 0.5287 \| 0.7433 \| 0.7434 \| \| 0.4557 \| 52.87 \| 9200 \| 0.5293 \| 0.7428 \| 0.7431 \| \| 0.4587 \| 54.02 \| 9400 \| 0.5250 \| 0.7449 \| 0.7449 \| \| 0.4583 \| 55.17 \| 9600 \| 0.5292 \| 0.7439 \| 0.7442 \| \| 0.4532 \| 56.32 \| 9800 \| 0.5225 \| 0.7483 \| 0.7481 \| \| 0.4593 \| 57.47 \| 10000 \| 0.5251 \| 0.7431 \| 0.7431 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_16384_512_34M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_16384_512_34M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:42:11+00:00
null	transformers	# Uploaded model - Developed by: bincoder - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	bincoder/lora_model-PFG	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:42:39+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	tennant/llava-llama-3-8b-vanilla	null	[ "transformers", "safetensors", "llava_llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:43:59+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_16384_512_34M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4951 - F1 Score: 0.7893 - Accuracy: 0.7891 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5664 \| 1.15 \| 200 \| 0.5584 \| 0.7301 \| 0.7301 \| \| 0.5068 \| 2.3 \| 400 \| 0.5501 \| 0.7201 \| 0.7229 \| \| 0.4854 \| 3.45 \| 600 \| 0.5285 \| 0.7409 \| 0.7413 \| \| 0.4765 \| 4.6 \| 800 \| 0.5183 \| 0.7457 \| 0.7456 \| \| 0.4718 \| 5.75 \| 1000 \| 0.5121 \| 0.7646 \| 0.7643 \| \| 0.4616 \| 6.9 \| 1200 \| 0.5281 \| 0.7499 \| 0.7503 \| \| 0.4532 \| 8.05 \| 1400 \| 0.5211 \| 0.7538 \| 0.7535 \| \| 0.4465 \| 9.2 \| 1600 \| 0.5214 \| 0.7544 \| 0.7549 \| \| 0.4393 \| 10.34 \| 1800 \| 0.5120 \| 0.7680 \| 0.7675 \| \| 0.4319 \| 11.49 \| 2000 \| 0.5135 \| 0.7580 \| 0.7575 \| \| 0.421 \| 12.64 \| 2200 \| 0.5113 \| 0.7641 \| 0.7636 \| \| 0.4171 \| 13.79 \| 2400 \| 0.5466 \| 0.7411 \| 0.7416 \| \| 0.4077 \| 14.94 \| 2600 \| 0.5058 \| 0.7704 \| 0.7701 \| \| 0.3998 \| 16.09 \| 2800 \| 0.5542 \| 0.7347 \| 0.7362 \| \| 0.391 \| 17.24 \| 3000 \| 0.5264 \| 0.7647 \| 0.7643 \| \| 0.3875 \| 18.39 \| 3200 \| 0.5596 \| 0.7490 \| 0.7496 \| \| 0.3863 \| 19.54 \| 3400 \| 0.5334 \| 0.7626 \| 0.7621 \| \| 0.3685 \| 20.69 \| 3600 \| 0.5326 \| 0.7707 \| 0.7708 \| \| 0.3684 \| 21.84 \| 3800 \| 0.5444 \| 0.7629 \| 0.7625 \| \| 0.3587 \| 22.99 \| 4000 \| 0.5514 \| 0.7628 \| 0.7629 \| \| 0.3533 \| 24.14 \| 4200 \| 0.5588 \| 0.7637 \| 0.7632 \| \| 0.3422 \| 25.29 \| 4400 \| 0.5704 \| 0.7670 \| 0.7665 \| \| 0.3396 \| 26.44 \| 4600 \| 0.6107 \| 0.7536 \| 0.7539 \| \| 0.3404 \| 27.59 \| 4800 \| 0.5826 \| 0.7579 \| 0.7582 \| \| 0.3255 \| 28.74 \| 5000 \| 0.5754 \| 0.7532 \| 0.7531 \| \| 0.3225 \| 29.89 \| 5200 \| 0.6105 \| 0.7562 \| 0.7560 \| \| 0.3145 \| 31.03 \| 5400 \| 0.5976 \| 0.7564 \| 0.7564 \| \| 0.3115 \| 32.18 \| 5600 \| 0.6186 \| 0.7557 \| 0.7557 \| \| 0.3022 \| 33.33 \| 5800 \| 0.6102 \| 0.7687 \| 0.7683 \| \| 0.3016 \| 34.48 \| 6000 \| 0.6241 \| 0.7619 \| 0.7614 \| \| 0.2958 \| 35.63 \| 6200 \| 0.6375 \| 0.7587 \| 0.7582 \| \| 0.2941 \| 36.78 \| 6400 \| 0.6043 \| 0.7590 \| 0.7585 \| \| 0.2856 \| 37.93 \| 6600 \| 0.6269 \| 0.7619 \| 0.7614 \| \| 0.2801 \| 39.08 \| 6800 \| 0.6485 \| 0.7530 \| 0.7524 \| \| 0.2776 \| 40.23 \| 7000 \| 0.6492 \| 0.7572 \| 0.7567 \| \| 0.275 \| 41.38 \| 7200 \| 0.6604 \| 0.7546 \| 0.7542 \| \| 0.2653 \| 42.53 \| 7400 \| 0.6950 \| 0.7559 \| 0.7557 \| \| 0.2638 \| 43.68 \| 7600 \| 0.6751 \| 0.7572 \| 0.7567 \| \| 0.2604 \| 44.83 \| 7800 \| 0.6750 \| 0.7568 \| 0.7564 \| \| 0.2571 \| 45.98 \| 8000 \| 0.6835 \| 0.7594 \| 0.7589 \| \| 0.2561 \| 47.13 \| 8200 \| 0.6873 \| 0.7568 \| 0.7564 \| \| 0.2521 \| 48.28 \| 8400 \| 0.7091 \| 0.7506 \| 0.7503 \| \| 0.2529 \| 49.43 \| 8600 \| 0.7059 \| 0.7471 \| 0.7467 \| \| 0.2467 \| 50.57 \| 8800 \| 0.7208 \| 0.7552 \| 0.7549 \| \| 0.2453 \| 51.72 \| 9000 \| 0.7161 \| 0.7553 \| 0.7549 \| \| 0.244 \| 52.87 \| 9200 \| 0.7334 \| 0.7461 \| 0.7460 \| \| 0.2447 \| 54.02 \| 9400 \| 0.7296 \| 0.7505 \| 0.7503 \| \| 0.2415 \| 55.17 \| 9600 \| 0.7271 \| 0.7512 \| 0.7510 \| \| 0.2364 \| 56.32 \| 9800 \| 0.7258 \| 0.7507 \| 0.7503 \| \| 0.2393 \| 57.47 \| 10000 \| 0.7288 \| 0.7528 \| 0.7524 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_16384_512_34M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_16384_512_34M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:44:13+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	hugozanini/fine-tunning-tutorial	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:45:04+00:00
null	null		{}	bisumasu/newlongzhu	null	[ "region:us" ]	null	2024-04-29T21:45:26+00:00
text-generation	transformers		{}	w32zhong/s3d-phi3_fft_layer724	null	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:46:48+00:00
text-classification	transformers	<!-- 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. --> # Sentimiento-appmovilesPG This model is a fine-tuned version of [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3058 - Accuracy: 0.9367 - F1: 0.8364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 332 \| 0.3105 \| 0.9217 \| 0.8297 \| \| 0.3353 \| 2.0 \| 664 \| 0.3109 \| 0.9367 \| 0.8362 \| \| 0.3353 \| 3.0 \| 996 \| 0.3058 \| 0.9367 \| 0.8364 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "pysentimiento/robertuito-sentiment-analysis", "model-index": [{"name": "Sentimiento-appmovilesPG", "results": []}]}	misaza/Sentimiento-appmovilesPG	null	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:pysentimiento/robertuito-sentiment-analysis", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:47:18+00:00
text-generation	null	# Llama-3-Open-Ko-8B-GGUF - Original model: [Llama-3-Open-Ko-8B](https://huggingface.co/beomi/Llama-3-Open-Ko-8B) <!-- description start --> ## Description This repo contains GGUF format model files for [Llama-3-Open-Ko-8B](https://huggingface.co/beomi/Llama-3-Open-Ko-8B). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/Llama-3-Open-Ko-8B-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/Llama-3-Open-Ko-8B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/Llama-3-Open-Ko-8B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Llama-3-Open-Ko-8B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: Llama-3-Open-Ko-8B > Update @ 2024.04.24: Release Llama-3-Open-Ko-8B model & [Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview) ## Model Details Llama-3-Open-Ko-8B Llama-3-Open-Ko-8B model is continued pretrained language model based on Llama-3-8B. This model is trained fully with publicily available resource, with 60GB+ of deduplicated texts. With the new Llama-3 tokenizer, the pretraining conducted with 17.7B+ tokens, which slightly more than Korean tokenizer(Llama-2-Ko tokenizer). The train was done on TPUv5e-256, with the warm support from TRC program by Google. Note for [Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview) With applying the idea from [Chat Vector paper](https://arxiv.org/abs/2310.04799), I released Instruction model named [Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview). Since it is NOT finetuned with any Korean instruction set(indeed `preview`), but it would be great starting point for creating new Chat/Instruct models. Meta Llama-3 Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Junbum Lee (Beomi) Variations Llama-3-Open-Ko comes in one size — 8B. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama-3-Open-Ko </td> <td rowspan="2" >Same as Open-Solar-Ko Dataset </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >17.7B+ </td> <td>Jun, 2023 </td> </tr> </table> You can find dataset list here: https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B/tree/main/corpus Model Release Date 2024.04.24. Status This is a static model trained on an offline dataset. License Llama3 License: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use TBD ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions Llama-3-Open-Ko ``` @article{llama3openko, title={Llama-3-Open-Ko}, author={L, Junbum}, year={2024}, url={https://huggingface.co/beomi/Llama-3-Open-Ko-8B} } ``` Original Llama-3 ``` @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ``` <!-- original-model-card end -->	{"language": ["en", "ko"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko", "GGUF"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "quantized_by": "andrijdavid"}	LiteLLMs/Llama-3-Open-Ko-8B-GGUF	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko", "GGUF", "text-generation", "en", "ko", "arxiv:2310.04799", "license:other", "region:us" ]	null	2024-04-29T21:47:20+00:00
null	transformers	# Uploaded model - Developed by: 1024m - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	1024m/LLAMA3-SMM4H-Task5-LoRA	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:47:33+00:00
text-generation	transformers		{}	AhmadShapiro/tcv-phi3	null	[ "transformers", "safetensors", "TCVForCausalLM", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:47:45+00:00
text-generation	transformers	# Uploaded model - Developed by: 1024m - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	1024m/LLAMA3-SMM4H-Task5-4bit	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	null	2024-04-29T21:49:44+00:00
null	null		{}	Lagartijo/cruz_gaudiniana	null	[ "region:us" ]	null	2024-04-29T21:49:58+00:00
null	transformers	# Uploaded model - Developed by: bincoder - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	bincoder/lora_model-PFG-003	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:52:42+00:00
null	null		{}	Lagartijo/cruz	null	[ "region:us" ]	null	2024-04-29T21:53:28+00:00
null	transformers	# Uploaded model - Developed by: tingting - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	tingting/llama3_lora_model_Data_50	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:53:46+00:00
text-generation	transformers	# merged 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Azazelle/Llama-3-8B-contaminated-roleplay](https://huggingface.co/Azazelle/Llama-3-8B-contaminated-roleplay) as a base. ### Models Merged The following models were included in the merge: * [ResplendentAI/Aura_Uncensored_l3_8B](https://huggingface.co/ResplendentAI/Aura_Uncensored_l3_8B) * [Undi95/Llama-3-LewdPlay-8B-evo](https://huggingface.co/Undi95/Llama-3-LewdPlay-8B-evo) * [ajibawa-2023/Scarlett-Llama-3-8B](https://huggingface.co/ajibawa-2023/Scarlett-Llama-3-8B) * [MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Undi95/Llama-3-LewdPlay-8B-evo - model: ResplendentAI/Aura_Uncensored_l3_8B - model: MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3 - model: ajibawa-2023/Scarlett-Llama-3-8B - model: Azazelle/Llama-3-8B-contaminated-roleplay merge_method: model_stock base_model: Azazelle/Llama-3-8B-contaminated-roleplay dtype: bfloat16 ```	{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Azazelle/Llama-3-8B-contaminated-roleplay", "ResplendentAI/Aura_Uncensored_l3_8B", "Undi95/Llama-3-LewdPlay-8B-evo", "ajibawa-2023/Scarlett-Llama-3-8B", "MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3"]}	Azazelle/Llama-3-8B-Help-Me	null	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Azazelle/Llama-3-8B-contaminated-roleplay", "base_model:ResplendentAI/Aura_Uncensored_l3_8B", "base_model:Undi95/Llama-3-LewdPlay-8B-evo", "base_model:ajibawa-2023/Scarlett-Llama-3-8B", "base_model:MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:54:49+00:00
null	transformers	# Uploaded model - Developed by: tingting - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	tingting/llama3_lora_model_Data_100	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T21:57:32+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_16384_512_34M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5819 - F1 Score: 0.6983 - Accuracy: 0.6986 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6551 \| 0.87 \| 200 \| 0.6283 \| 0.6521 \| 0.6524 \| \| 0.6182 \| 1.74 \| 400 \| 0.6165 \| 0.6560 \| 0.6587 \| \| 0.6042 \| 2.61 \| 600 \| 0.6067 \| 0.6657 \| 0.6658 \| \| 0.5973 \| 3.48 \| 800 \| 0.5989 \| 0.6755 \| 0.6753 \| \| 0.5914 \| 4.35 \| 1000 \| 0.5966 \| 0.6758 \| 0.6755 \| \| 0.5877 \| 5.22 \| 1200 \| 0.6048 \| 0.6680 \| 0.6720 \| \| 0.5826 \| 6.09 \| 1400 \| 0.6240 \| 0.6507 \| 0.6598 \| \| 0.5755 \| 6.96 \| 1600 \| 0.6038 \| 0.6724 \| 0.6742 \| \| 0.5733 \| 7.83 \| 1800 \| 0.5992 \| 0.6881 \| 0.6878 \| \| 0.5711 \| 8.7 \| 2000 \| 0.6064 \| 0.6786 \| 0.6804 \| \| 0.5639 \| 9.57 \| 2200 \| 0.5861 \| 0.6850 \| 0.6851 \| \| 0.5648 \| 10.43 \| 2400 \| 0.5967 \| 0.6872 \| 0.6880 \| \| 0.5586 \| 11.3 \| 2600 \| 0.5932 \| 0.6792 \| 0.6818 \| \| 0.558 \| 12.17 \| 2800 \| 0.5872 \| 0.6921 \| 0.6918 \| \| 0.5541 \| 13.04 \| 3000 \| 0.5917 \| 0.6873 \| 0.6878 \| \| 0.5522 \| 13.91 \| 3200 \| 0.5870 \| 0.6937 \| 0.6943 \| \| 0.5481 \| 14.78 \| 3400 \| 0.5937 \| 0.6850 \| 0.6875 \| \| 0.5467 \| 15.65 \| 3600 \| 0.5885 \| 0.6913 \| 0.6918 \| \| 0.5431 \| 16.52 \| 3800 \| 0.5891 \| 0.6965 \| 0.6967 \| \| 0.5406 \| 17.39 \| 4000 \| 0.6020 \| 0.6856 \| 0.6872 \| \| 0.5407 \| 18.26 \| 4200 \| 0.6029 \| 0.6868 \| 0.6889 \| \| 0.5387 \| 19.13 \| 4400 \| 0.6015 \| 0.6905 \| 0.6905 \| \| 0.5356 \| 20.0 \| 4600 \| 0.5960 \| 0.6829 \| 0.6853 \| \| 0.5343 \| 20.87 \| 4800 \| 0.5975 \| 0.6876 \| 0.6883 \| \| 0.5303 \| 21.74 \| 5000 \| 0.5994 \| 0.6910 \| 0.6916 \| \| 0.5302 \| 22.61 \| 5200 \| 0.6004 \| 0.6833 \| 0.6845 \| \| 0.5296 \| 23.48 \| 5400 \| 0.6135 \| 0.6803 \| 0.6840 \| \| 0.5247 \| 24.35 \| 5600 \| 0.6058 \| 0.6865 \| 0.6886 \| \| 0.5255 \| 25.22 \| 5800 \| 0.6063 \| 0.6839 \| 0.6861 \| \| 0.5174 \| 26.09 \| 6000 \| 0.6189 \| 0.6815 \| 0.6837 \| \| 0.5211 \| 26.96 \| 6200 \| 0.6138 \| 0.6831 \| 0.6861 \| \| 0.5188 \| 27.83 \| 6400 \| 0.6256 \| 0.6738 \| 0.6780 \| \| 0.5174 \| 28.7 \| 6600 \| 0.6064 \| 0.6847 \| 0.6851 \| \| 0.5157 \| 29.57 \| 6800 \| 0.6028 \| 0.6843 \| 0.6859 \| \| 0.515 \| 30.43 \| 7000 \| 0.6059 \| 0.6860 \| 0.6872 \| \| 0.5163 \| 31.3 \| 7200 \| 0.6121 \| 0.6886 \| 0.6894 \| \| 0.5115 \| 32.17 \| 7400 \| 0.6099 \| 0.6876 \| 0.6883 \| \| 0.5093 \| 33.04 \| 7600 \| 0.6122 \| 0.6846 \| 0.6853 \| \| 0.511 \| 33.91 \| 7800 \| 0.6117 \| 0.6849 \| 0.6856 \| \| 0.5073 \| 34.78 \| 8000 \| 0.6187 \| 0.6896 \| 0.6902 \| \| 0.506 \| 35.65 \| 8200 \| 0.6203 \| 0.6833 \| 0.6834 \| \| 0.5061 \| 36.52 \| 8400 \| 0.6176 \| 0.6811 \| 0.6826 \| \| 0.5048 \| 37.39 \| 8600 \| 0.6159 \| 0.6867 \| 0.6872 \| \| 0.499 \| 38.26 \| 8800 \| 0.6343 \| 0.6813 \| 0.6834 \| \| 0.5114 \| 39.13 \| 9000 \| 0.6115 \| 0.6826 \| 0.6837 \| \| 0.502 \| 40.0 \| 9200 \| 0.6190 \| 0.6856 \| 0.6861 \| \| 0.5001 \| 40.87 \| 9400 \| 0.6190 \| 0.6855 \| 0.6861 \| \| 0.4999 \| 41.74 \| 9600 \| 0.6202 \| 0.6834 \| 0.6842 \| \| 0.5061 \| 42.61 \| 9800 \| 0.6173 \| 0.6835 \| 0.6842 \| \| 0.497 \| 43.48 \| 10000 \| 0.6196 \| 0.6840 \| 0.6848 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_16384_512_34M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_16384_512_34M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:59:01+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_16384_512_34M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5858 - F1 Score: 0.6914 - Accuracy: 0.6916 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6653 \| 0.87 \| 200 \| 0.6495 \| 0.6260 \| 0.6261 \| \| 0.633 \| 1.74 \| 400 \| 0.6257 \| 0.6558 \| 0.6557 \| \| 0.6211 \| 2.61 \| 600 \| 0.6195 \| 0.6554 \| 0.6552 \| \| 0.613 \| 3.48 \| 800 \| 0.6121 \| 0.6596 \| 0.6592 \| \| 0.61 \| 4.35 \| 1000 \| 0.6107 \| 0.6612 \| 0.6611 \| \| 0.6053 \| 5.22 \| 1200 \| 0.6242 \| 0.6454 \| 0.6533 \| \| 0.6026 \| 6.09 \| 1400 \| 0.6216 \| 0.6491 \| 0.6543 \| \| 0.5984 \| 6.96 \| 1600 \| 0.6152 \| 0.6590 \| 0.6609 \| \| 0.5974 \| 7.83 \| 1800 \| 0.6044 \| 0.6673 \| 0.6674 \| \| 0.5964 \| 8.7 \| 2000 \| 0.6079 \| 0.6627 \| 0.6639 \| \| 0.5918 \| 9.57 \| 2200 \| 0.5993 \| 0.6692 \| 0.6693 \| \| 0.5935 \| 10.43 \| 2400 \| 0.6037 \| 0.6678 \| 0.6685 \| \| 0.5894 \| 11.3 \| 2600 \| 0.6027 \| 0.6647 \| 0.6663 \| \| 0.5892 \| 12.17 \| 2800 \| 0.6000 \| 0.6665 \| 0.6663 \| \| 0.5883 \| 13.04 \| 3000 \| 0.5988 \| 0.6679 \| 0.6685 \| \| 0.5856 \| 13.91 \| 3200 \| 0.5957 \| 0.6668 \| 0.6671 \| \| 0.5825 \| 14.78 \| 3400 \| 0.5979 \| 0.6666 \| 0.6682 \| \| 0.5836 \| 15.65 \| 3600 \| 0.6020 \| 0.6667 \| 0.6679 \| \| 0.5812 \| 16.52 \| 3800 \| 0.6004 \| 0.6703 \| 0.6704 \| \| 0.581 \| 17.39 \| 4000 \| 0.5971 \| 0.6723 \| 0.6728 \| \| 0.5817 \| 18.26 \| 4200 \| 0.5978 \| 0.6707 \| 0.6712 \| \| 0.5781 \| 19.13 \| 4400 \| 0.6007 \| 0.6746 \| 0.675 \| \| 0.5787 \| 20.0 \| 4600 \| 0.5975 \| 0.6654 \| 0.6674 \| \| 0.5777 \| 20.87 \| 4800 \| 0.5988 \| 0.6738 \| 0.6742 \| \| 0.5771 \| 21.74 \| 5000 \| 0.6004 \| 0.6685 \| 0.6698 \| \| 0.5747 \| 22.61 \| 5200 \| 0.5958 \| 0.6721 \| 0.6726 \| \| 0.5766 \| 23.48 \| 5400 \| 0.6138 \| 0.6573 \| 0.6622 \| \| 0.575 \| 24.35 \| 5600 \| 0.5975 \| 0.6733 \| 0.6739 \| \| 0.5755 \| 25.22 \| 5800 \| 0.6044 \| 0.6649 \| 0.6685 \| \| 0.5694 \| 26.09 \| 6000 \| 0.6082 \| 0.6642 \| 0.6671 \| \| 0.5737 \| 26.96 \| 6200 \| 0.6049 \| 0.6629 \| 0.6663 \| \| 0.5718 \| 27.83 \| 6400 \| 0.6122 \| 0.6624 \| 0.6679 \| \| 0.5707 \| 28.7 \| 6600 \| 0.5995 \| 0.6700 \| 0.6712 \| \| 0.5714 \| 29.57 \| 6800 \| 0.5950 \| 0.6730 \| 0.6742 \| \| 0.569 \| 30.43 \| 7000 \| 0.6007 \| 0.6701 \| 0.6728 \| \| 0.5724 \| 31.3 \| 7200 \| 0.5998 \| 0.6704 \| 0.6720 \| \| 0.5705 \| 32.17 \| 7400 \| 0.5969 \| 0.6702 \| 0.6717 \| \| 0.5668 \| 33.04 \| 7600 \| 0.5937 \| 0.6723 \| 0.6728 \| \| 0.5691 \| 33.91 \| 7800 \| 0.5966 \| 0.6711 \| 0.6723 \| \| 0.5674 \| 34.78 \| 8000 \| 0.5970 \| 0.6733 \| 0.6736 \| \| 0.5692 \| 35.65 \| 8200 \| 0.5958 \| 0.6741 \| 0.6747 \| \| 0.5669 \| 36.52 \| 8400 \| 0.6005 \| 0.6704 \| 0.6723 \| \| 0.5656 \| 37.39 \| 8600 \| 0.5978 \| 0.6715 \| 0.6720 \| \| 0.5614 \| 38.26 \| 8800 \| 0.6058 \| 0.6686 \| 0.6709 \| \| 0.5733 \| 39.13 \| 9000 \| 0.5958 \| 0.6699 \| 0.6715 \| \| 0.5649 \| 40.0 \| 9200 \| 0.5973 \| 0.6715 \| 0.6726 \| \| 0.5641 \| 40.87 \| 9400 \| 0.5970 \| 0.6758 \| 0.6761 \| \| 0.5639 \| 41.74 \| 9600 \| 0.5976 \| 0.6709 \| 0.6717 \| \| 0.572 \| 42.61 \| 9800 \| 0.5959 \| 0.6722 \| 0.6731 \| \| 0.5616 \| 43.48 \| 10000 \| 0.5965 \| 0.6718 \| 0.6726 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_16384_512_34M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_16384_512_34M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:59:01+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	cilantro9246/6hb0u7i	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T21:59:25+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_16384_512_34M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6927 - F1 Score: 0.7080 - Accuracy: 0.7079 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6462 \| 0.87 \| 200 \| 0.6227 \| 0.6578 \| 0.6582 \| \| 0.6094 \| 1.74 \| 400 \| 0.6174 \| 0.6612 \| 0.6655 \| \| 0.5947 \| 2.61 \| 600 \| 0.5993 \| 0.6768 \| 0.6774 \| \| 0.5867 \| 3.48 \| 800 \| 0.5905 \| 0.6868 \| 0.6870 \| \| 0.5771 \| 4.35 \| 1000 \| 0.5897 \| 0.6896 \| 0.6894 \| \| 0.572 \| 5.22 \| 1200 \| 0.5901 \| 0.6865 \| 0.6870 \| \| 0.5641 \| 6.09 \| 1400 \| 0.6154 \| 0.6769 \| 0.6813 \| \| 0.5544 \| 6.96 \| 1600 \| 0.5937 \| 0.6904 \| 0.6905 \| \| 0.548 \| 7.83 \| 1800 \| 0.5979 \| 0.6925 \| 0.6924 \| \| 0.5449 \| 8.7 \| 2000 \| 0.5921 \| 0.6892 \| 0.6899 \| \| 0.5342 \| 9.57 \| 2200 \| 0.5918 \| 0.6879 \| 0.6883 \| \| 0.5318 \| 10.43 \| 2400 \| 0.6269 \| 0.6954 \| 0.6959 \| \| 0.5219 \| 11.3 \| 2600 \| 0.6109 \| 0.6856 \| 0.6883 \| \| 0.5213 \| 12.17 \| 2800 \| 0.6120 \| 0.6786 \| 0.6796 \| \| 0.5126 \| 13.04 \| 3000 \| 0.6063 \| 0.6857 \| 0.6872 \| \| 0.5068 \| 13.91 \| 3200 \| 0.6074 \| 0.6934 \| 0.6946 \| \| 0.4991 \| 14.78 \| 3400 \| 0.6265 \| 0.6800 \| 0.6834 \| \| 0.4941 \| 15.65 \| 3600 \| 0.6156 \| 0.6880 \| 0.6894 \| \| 0.4875 \| 16.52 \| 3800 \| 0.6119 \| 0.6933 \| 0.6935 \| \| 0.4783 \| 17.39 \| 4000 \| 0.6453 \| 0.6957 \| 0.6973 \| \| 0.4788 \| 18.26 \| 4200 \| 0.6418 \| 0.6868 \| 0.6886 \| \| 0.4708 \| 19.13 \| 4400 \| 0.6275 \| 0.6914 \| 0.6913 \| \| 0.4617 \| 20.0 \| 4600 \| 0.6468 \| 0.6906 \| 0.6932 \| \| 0.4568 \| 20.87 \| 4800 \| 0.6477 \| 0.6895 \| 0.6894 \| \| 0.4529 \| 21.74 \| 5000 \| 0.6592 \| 0.6905 \| 0.6902 \| \| 0.45 \| 22.61 \| 5200 \| 0.6671 \| 0.6859 \| 0.6883 \| \| 0.444 \| 23.48 \| 5400 \| 0.6539 \| 0.6904 \| 0.6916 \| \| 0.4347 \| 24.35 \| 5600 \| 0.6802 \| 0.6871 \| 0.6886 \| \| 0.4298 \| 25.22 \| 5800 \| 0.6856 \| 0.6883 \| 0.6880 \| \| 0.4255 \| 26.09 \| 6000 \| 0.6934 \| 0.6918 \| 0.6918 \| \| 0.4212 \| 26.96 \| 6200 \| 0.6919 \| 0.6810 \| 0.6840 \| \| 0.4166 \| 27.83 \| 6400 \| 0.6909 \| 0.6931 \| 0.6935 \| \| 0.4144 \| 28.7 \| 6600 \| 0.6866 \| 0.6872 \| 0.6870 \| \| 0.4112 \| 29.57 \| 6800 \| 0.6787 \| 0.6891 \| 0.6894 \| \| 0.4069 \| 30.43 \| 7000 \| 0.7013 \| 0.6979 \| 0.6981 \| \| 0.4094 \| 31.3 \| 7200 \| 0.6948 \| 0.6953 \| 0.6951 \| \| 0.3965 \| 32.17 \| 7400 \| 0.7125 \| 0.6909 \| 0.6913 \| \| 0.3935 \| 33.04 \| 7600 \| 0.7157 \| 0.6901 \| 0.6902 \| \| 0.3937 \| 33.91 \| 7800 \| 0.7264 \| 0.6889 \| 0.6897 \| \| 0.3865 \| 34.78 \| 8000 \| 0.7227 \| 0.6926 \| 0.6927 \| \| 0.3849 \| 35.65 \| 8200 \| 0.7225 \| 0.6954 \| 0.6951 \| \| 0.3846 \| 36.52 \| 8400 \| 0.7241 \| 0.6933 \| 0.6932 \| \| 0.3809 \| 37.39 \| 8600 \| 0.7149 \| 0.6971 \| 0.6970 \| \| 0.3773 \| 38.26 \| 8800 \| 0.7407 \| 0.6949 \| 0.6957 \| \| 0.3835 \| 39.13 \| 9000 \| 0.7206 \| 0.6984 \| 0.6984 \| \| 0.3743 \| 40.0 \| 9200 \| 0.7252 \| 0.6946 \| 0.6943 \| \| 0.3755 \| 40.87 \| 9400 \| 0.7238 \| 0.6929 \| 0.6929 \| \| 0.3717 \| 41.74 \| 9600 \| 0.7310 \| 0.6943 \| 0.6943 \| \| 0.3771 \| 42.61 \| 9800 \| 0.7312 \| 0.6940 \| 0.6940 \| \| 0.3643 \| 43.48 \| 10000 \| 0.7356 \| 0.6961 \| 0.6962 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_16384_512_34M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_16384_512_34M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T21:59:47+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_16384_512_34M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2635 - F1 Score: 0.8955 - Accuracy: 0.8953 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3651 \| 2.17 \| 200 \| 0.2899 \| 0.8877 \| 0.8877 \| \| 0.2892 \| 4.35 \| 400 \| 0.2807 \| 0.8918 \| 0.8919 \| \| 0.274 \| 6.52 \| 600 \| 0.2787 \| 0.8929 \| 0.8932 \| \| 0.2657 \| 8.7 \| 800 \| 0.2817 \| 0.8915 \| 0.8912 \| \| 0.2477 \| 10.87 \| 1000 \| 0.2652 \| 0.8919 \| 0.8919 \| \| 0.2397 \| 13.04 \| 1200 \| 0.2706 \| 0.8947 \| 0.8946 \| \| 0.2289 \| 15.22 \| 1400 \| 0.2694 \| 0.8907 \| 0.8905 \| \| 0.2212 \| 17.39 \| 1600 \| 0.2765 \| 0.8874 \| 0.8871 \| \| 0.2167 \| 19.57 \| 1800 \| 0.2653 \| 0.8994 \| 0.8994 \| \| 0.2076 \| 21.74 \| 2000 \| 0.2781 \| 0.8962 \| 0.8960 \| \| 0.201 \| 23.91 \| 2200 \| 0.2734 \| 0.9009 \| 0.9008 \| \| 0.1944 \| 26.09 \| 2400 \| 0.2822 \| 0.8914 \| 0.8912 \| \| 0.1891 \| 28.26 \| 2600 \| 0.2806 \| 0.8974 \| 0.8973 \| \| 0.1865 \| 30.43 \| 2800 \| 0.2796 \| 0.8920 \| 0.8919 \| \| 0.1778 \| 32.61 \| 3000 \| 0.2935 \| 0.8933 \| 0.8932 \| \| 0.1711 \| 34.78 \| 3200 \| 0.2977 \| 0.8892 \| 0.8891 \| \| 0.1698 \| 36.96 \| 3400 \| 0.3048 \| 0.8941 \| 0.8939 \| \| 0.1647 \| 39.13 \| 3600 \| 0.3102 \| 0.8865 \| 0.8864 \| \| 0.157 \| 41.3 \| 3800 \| 0.3083 \| 0.8877 \| 0.8877 \| \| 0.1564 \| 43.48 \| 4000 \| 0.3216 \| 0.8877 \| 0.8877 \| \| 0.1559 \| 45.65 \| 4200 \| 0.3104 \| 0.8931 \| 0.8932 \| \| 0.1484 \| 47.83 \| 4400 \| 0.3172 \| 0.8841 \| 0.8843 \| \| 0.1443 \| 50.0 \| 4600 \| 0.3275 \| 0.8840 \| 0.8843 \| \| 0.1426 \| 52.17 \| 4800 \| 0.3386 \| 0.8918 \| 0.8919 \| \| 0.1368 \| 54.35 \| 5000 \| 0.3372 \| 0.8912 \| 0.8912 \| \| 0.1363 \| 56.52 \| 5200 \| 0.3469 \| 0.8792 \| 0.8789 \| \| 0.1313 \| 58.7 \| 5400 \| 0.3454 \| 0.8926 \| 0.8925 \| \| 0.1293 \| 60.87 \| 5600 \| 0.3442 \| 0.8843 \| 0.8843 \| \| 0.1237 \| 63.04 \| 5800 \| 0.3646 \| 0.8830 \| 0.8830 \| \| 0.124 \| 65.22 \| 6000 \| 0.3682 \| 0.8862 \| 0.8864 \| \| 0.1211 \| 67.39 \| 6200 \| 0.3671 \| 0.8845 \| 0.8843 \| \| 0.1216 \| 69.57 \| 6400 \| 0.3674 \| 0.8851 \| 0.8850 \| \| 0.1177 \| 71.74 \| 6600 \| 0.3694 \| 0.8829 \| 0.8830 \| \| 0.1119 \| 73.91 \| 6800 \| 0.3831 \| 0.8898 \| 0.8898 \| \| 0.1082 \| 76.09 \| 7000 \| 0.3965 \| 0.8784 \| 0.8782 \| \| 0.1099 \| 78.26 \| 7200 \| 0.3829 \| 0.8856 \| 0.8857 \| \| 0.1116 \| 80.43 \| 7400 \| 0.3763 \| 0.8856 \| 0.8857 \| \| 0.1049 \| 82.61 \| 7600 \| 0.3920 \| 0.8848 \| 0.8850 \| \| 0.1031 \| 84.78 \| 7800 \| 0.3968 \| 0.8898 \| 0.8898 \| \| 0.1021 \| 86.96 \| 8000 \| 0.3980 \| 0.8811 \| 0.8809 \| \| 0.1006 \| 89.13 \| 8200 \| 0.4058 \| 0.8796 \| 0.8795 \| \| 0.1041 \| 91.3 \| 8400 \| 0.4011 \| 0.8856 \| 0.8857 \| \| 0.0957 \| 93.48 \| 8600 \| 0.4051 \| 0.8883 \| 0.8884 \| \| 0.0977 \| 95.65 \| 8800 \| 0.4055 \| 0.8869 \| 0.8871 \| \| 0.0971 \| 97.83 \| 9000 \| 0.4080 \| 0.8849 \| 0.8850 \| \| 0.0987 \| 100.0 \| 9200 \| 0.4098 \| 0.8769 \| 0.8768 \| \| 0.0971 \| 102.17 \| 9400 \| 0.4083 \| 0.8789 \| 0.8789 \| \| 0.093 \| 104.35 \| 9600 \| 0.4140 \| 0.8762 \| 0.8761 \| \| 0.0943 \| 106.52 \| 9800 \| 0.4120 \| 0.8809 \| 0.8809 \| \| 0.0941 \| 108.7 \| 10000 \| 0.4137 \| 0.8788 \| 0.8789 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H4-seqsight_16384_512_34M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_16384_512_34M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T22:00:20+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_16384_512_34M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2515 - F1 Score: 0.9028 - Accuracy: 0.9028 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3868 \| 2.17 \| 200 \| 0.3011 \| 0.8823 \| 0.8823 \| \| 0.2993 \| 4.35 \| 400 \| 0.2907 \| 0.8894 \| 0.8891 \| \| 0.2877 \| 6.52 \| 600 \| 0.2826 \| 0.8912 \| 0.8912 \| \| 0.287 \| 8.7 \| 800 \| 0.2883 \| 0.8894 \| 0.8891 \| \| 0.2754 \| 10.87 \| 1000 \| 0.2780 \| 0.8910 \| 0.8912 \| \| 0.2721 \| 13.04 \| 1200 \| 0.2798 \| 0.8871 \| 0.8871 \| \| 0.2666 \| 15.22 \| 1400 \| 0.2746 \| 0.8931 \| 0.8932 \| \| 0.262 \| 17.39 \| 1600 \| 0.2767 \| 0.8949 \| 0.8946 \| \| 0.2587 \| 19.57 \| 1800 \| 0.2665 \| 0.8973 \| 0.8973 \| \| 0.253 \| 21.74 \| 2000 \| 0.2742 \| 0.8901 \| 0.8898 \| \| 0.2494 \| 23.91 \| 2200 \| 0.2736 \| 0.8928 \| 0.8925 \| \| 0.2434 \| 26.09 \| 2400 \| 0.2743 \| 0.8942 \| 0.8939 \| \| 0.2422 \| 28.26 \| 2600 \| 0.2640 \| 0.9009 \| 0.9008 \| \| 0.2374 \| 30.43 \| 2800 \| 0.2688 \| 0.8949 \| 0.8946 \| \| 0.2335 \| 32.61 \| 3000 \| 0.2659 \| 0.9002 \| 0.9001 \| \| 0.2307 \| 34.78 \| 3200 \| 0.2655 \| 0.8989 \| 0.8987 \| \| 0.2289 \| 36.96 \| 3400 \| 0.2659 \| 0.8955 \| 0.8953 \| \| 0.2296 \| 39.13 \| 3600 \| 0.2718 \| 0.8948 \| 0.8946 \| \| 0.223 \| 41.3 \| 3800 \| 0.2675 \| 0.8968 \| 0.8966 \| \| 0.2227 \| 43.48 \| 4000 \| 0.2666 \| 0.8946 \| 0.8946 \| \| 0.228 \| 45.65 \| 4200 \| 0.2627 \| 0.8974 \| 0.8973 \| \| 0.219 \| 47.83 \| 4400 \| 0.2644 \| 0.8954 \| 0.8953 \| \| 0.2212 \| 50.0 \| 4600 \| 0.2621 \| 0.8979 \| 0.8980 \| \| 0.215 \| 52.17 \| 4800 \| 0.2688 \| 0.8975 \| 0.8973 \| \| 0.2184 \| 54.35 \| 5000 \| 0.2825 \| 0.8922 \| 0.8919 \| \| 0.215 \| 56.52 \| 5200 \| 0.2808 \| 0.8908 \| 0.8905 \| \| 0.2121 \| 58.7 \| 5400 \| 0.2696 \| 0.8954 \| 0.8953 \| \| 0.2122 \| 60.87 \| 5600 \| 0.2761 \| 0.8921 \| 0.8919 \| \| 0.2099 \| 63.04 \| 5800 \| 0.2787 \| 0.8955 \| 0.8953 \| \| 0.2108 \| 65.22 \| 6000 \| 0.2759 \| 0.8955 \| 0.8953 \| \| 0.2095 \| 67.39 \| 6200 \| 0.2716 \| 0.8982 \| 0.8980 \| \| 0.2062 \| 69.57 \| 6400 \| 0.2734 \| 0.8968 \| 0.8966 \| \| 0.2086 \| 71.74 \| 6600 \| 0.2719 \| 0.8960 \| 0.8960 \| \| 0.2066 \| 73.91 \| 6800 \| 0.2780 \| 0.8955 \| 0.8953 \| \| 0.2013 \| 76.09 \| 7000 \| 0.2794 \| 0.8969 \| 0.8966 \| \| 0.2047 \| 78.26 \| 7200 \| 0.2741 \| 0.8975 \| 0.8973 \| \| 0.2037 \| 80.43 \| 7400 \| 0.2738 \| 0.8961 \| 0.8960 \| \| 0.2025 \| 82.61 \| 7600 \| 0.2738 \| 0.8946 \| 0.8946 \| \| 0.2033 \| 84.78 \| 7800 \| 0.2809 \| 0.8941 \| 0.8939 \| \| 0.1993 \| 86.96 \| 8000 \| 0.2781 \| 0.8927 \| 0.8925 \| \| 0.2017 \| 89.13 \| 8200 \| 0.2771 \| 0.8940 \| 0.8939 \| \| 0.2013 \| 91.3 \| 8400 \| 0.2766 \| 0.8975 \| 0.8973 \| \| 0.1967 \| 93.48 \| 8600 \| 0.2777 \| 0.8946 \| 0.8946 \| \| 0.2013 \| 95.65 \| 8800 \| 0.2757 \| 0.8974 \| 0.8973 \| \| 0.1978 \| 97.83 \| 9000 \| 0.2772 \| 0.8967 \| 0.8966 \| \| 0.2019 \| 100.0 \| 9200 \| 0.2784 \| 0.8975 \| 0.8973 \| \| 0.1974 \| 102.17 \| 9400 \| 0.2795 \| 0.8968 \| 0.8966 \| \| 0.1993 \| 104.35 \| 9600 \| 0.2788 \| 0.8975 \| 0.8973 \| \| 0.1989 \| 106.52 \| 9800 \| 0.2771 \| 0.8954 \| 0.8953 \| \| 0.1979 \| 108.7 \| 10000 \| 0.2780 \| 0.8947 \| 0.8946 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H4-seqsight_16384_512_34M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_16384_512_34M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T22:00:22+00:00
text-classification	transformers	<!-- 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. --> # emotion-classifier This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2192 - Accuracy: 0.9343 ## 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2362 \| 1.0 \| 670 \| 0.2192 \| 0.9343 \| \| 0.1782 \| 2.0 \| 1340 \| 0.2249 \| 0.9241 \| \| 0.0811 \| 3.0 \| 2010 \| 0.2288 \| 0.9444 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "emotion-classifier", "results": []}]}	scspinney/emotion-classifier	null	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:00:23+00:00
text2text-generation	transformers	<!-- 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. --> # t5-base_te This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3917 - Bleu: 0.0241 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| 2.1859 \| 1.0 \| 2420 \| 2.0410 \| 0.0101 \| 19.0 \| \| 3.7976 \| 2.0 \| 4840 \| 3.3917 \| 0.0241 \| 19.0 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "google-t5/t5-base", "model-index": [{"name": "t5-base_te", "results": []}]}	lesha-grishchenko/t5-base_te	null	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T22:02:13+00:00
text-generation	transformers	<!-- 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. --> # 0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_2 This model is a fine-tuned version of [ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_1](https://huggingface.co/ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_1) on the updated and the original datasets. ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_1", "model-index": [{"name": "0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_2", "results": []}]}	ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_2	null	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T22:03:14+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	shallow6414/6h8psvj	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T22:04:27+00:00
null	null		{}	omalcolm/ExactApptModel	null	[ "region:us" ]	null	2024-04-29T22:04:33+00:00
text-generation	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) ```	{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	aniketarahane/autotrain-omkul-hydox	null	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:04:36+00:00
null	transformers	## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF/resolve/main/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2", "quantized_by": "mradermacher"}	mradermacher/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2-GGUF	null	[ "transformers", "gguf", "trl", "sft", "generated_from_trainer", "en", "dataset:generator", "base_model:yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_German_v2", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:04:41+00:00
null	transformers	# Uploaded model - Developed by: tingting - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	tingting/llama3_lora_model_Data_400	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:04:46+00:00
question-answering	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # omarSorour123/sorour_qa_model This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6044 - Validation Loss: 1.6929 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 435, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.6956 \| 1.5308 \| 0 \| \| 1.1261 \| 1.5328 \| 1 \| \| 0.8398 \| 1.6445 \| 2 \| \| 0.6846 \| 1.6727 \| 3 \| \| 0.6044 \| 1.6929 \| 4 \| ### Framework versions - Transformers 4.40.0 - TensorFlow 2.15.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"language": ["ar"], "license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "timpal0l/mdeberta-v3-base-squad2", "model-index": [{"name": "omarSorour123/sorour_qa_model", "results": []}]}	gp-tar4/QA_FineTuned_mdeberta-v3-base-squad2	null	[ "transformers", "tf", "deberta-v2", "question-answering", "generated_from_keras_callback", "ar", "base_model:timpal0l/mdeberta-v3-base-squad2", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:04:51+00:00
null	transformers	# Uploaded model - Developed by: tingting - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	tingting/llama3_lora_model_Data_40	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:06:38+00:00
automatic-speech-recognition	transformers		{}	ymoslem/whisper-small-ga2en-v5.3	null	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:07:41+00:00
text-to-image	diffusers	<!-- 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. --> # SDXL LoRA DreamBooth - embracellm/sushi05_LoRA <Gallery /> ## Model description These are embracellm/sushi05_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sushi to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](embracellm/sushi05_LoRA/tree/main) them in the Files & versions tab. ## 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]	{"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of sushi", "widget": []}	embracellm/sushi05_LoRA	null	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	null	2024-04-29T22:08:49+00:00
null	transformers	# Uploaded model - Developed by: tingting - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	tingting/llama3_lora_model_Data_80	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:09:14+00:00
text-to-image	diffusers	# kisaragi_mix API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/2260467291714428523.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "kisaragimix" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/kisaragimix) Model link: [View model](https://modelslab.com/models/kisaragimix) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "kisaragimix", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN	{"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true}	stablediffusionapi/kisaragimix	null	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	null	2024-04-29T22:09:47+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	axel-rda/ARIA-70B-V3-qlora-sft-adapters	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:10:52+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	abc88767/model11	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:10:59+00:00
null	null	# MeliodasNeuralsynthesis-7B MeliodasNeuralsynthesis-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 - model: AurelPx/Meliodas-7b-dare - model: Kukedlc/NeuralSynthesis-7B-v0.1 merge_method: model_stock base_model: mistralai/Mistral-7B-v0.1 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/MeliodasNeuralsynthesis-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]}	automerger/MeliodasNeuralsynthesis-7B	null	[ "merge", "mergekit", "lazymergekit", "automerger", "license:apache-2.0", "region:us" ]	null	2024-04-29T22:11:03+00:00
text-generation	null	# Llama-3-8b-64k-PoSE-GGUF - Original model: [Llama-3-8b-64k-PoSE](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE) <!-- description start --> ## Description This repo contains GGUF format model files for [Llama-3-8b-64k-PoSE](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/Llama-3-8b-64k-PoSE-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/Llama-3-8b-64k-PoSE-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/Llama-3-8b-64k-PoSE-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Llama-3-8b-64k-PoSE-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: Llama-3-8b-64k-PoSE ## Llama 3 8B 64K [<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) <img src="https://huggingface.co/winglian/Llama-3-8b-64k-PoSE/resolve/main/output.png" /> This model uses [PoSE](https://huggingface.co/papers/2309.10400) to extend Llama's context length from 8k to 64k @ rope_theta: 500000.0. We used PoSE with continued pretraining on 300M tokens from the RedPajama V1 dataset using data between 6k-8k tokens. We have further set rope_theta to 2M after continued pre-training to potentially further extend the context past 64k. This was trained on a subset of the RedPajama v1 dataset with text between 6k-8k context. We trained a rank stabilized LoRA of rank 256. [WandB](https://wandb.ai/oaaic/llama-3-64k/runs/tkcyjt37) ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Meta Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date April 18, 2024. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python >>> import transformers >>> import torch >>> model_id = "meta-llama/Meta-Llama-3-8B" >>> pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto" ) >>> pipeline("Hey how are you doing today?") ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/" --local-dir Meta-Llama-3-8B ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software Training Factors* We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos <!-- original-model-card end -->	{"language": ["en"], "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "axolotl", "GGUF"], "pipeline_tag": "text-generation", "quantized_by": "andrijdavid"}	LiteLLMs/Llama-3-8b-64k-PoSE-GGUF	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "axolotl", "GGUF", "text-generation", "en", "arxiv:2309.10400", "region:us" ]	null	2024-04-29T22:11:12+00:00
null	transformers	# Uploaded model - Developed by: tingting - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-bnb-4bit"}	tingting/mistral_lora_model_Data_50	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:13:42+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_16384_512_34M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2865 - F1 Score: 0.8966 - Accuracy: 0.8966 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3485 \| 2.17 \| 200 \| 0.2828 \| 0.8885 \| 0.8884 \| \| 0.2772 \| 4.35 \| 400 \| 0.2785 \| 0.8915 \| 0.8919 \| \| 0.2571 \| 6.52 \| 600 \| 0.2730 \| 0.8925 \| 0.8925 \| \| 0.2422 \| 8.7 \| 800 \| 0.2825 \| 0.8847 \| 0.8843 \| \| 0.2183 \| 10.87 \| 1000 \| 0.2719 \| 0.8936 \| 0.8939 \| \| 0.2035 \| 13.04 \| 1200 \| 0.3005 \| 0.8854 \| 0.8850 \| \| 0.1875 \| 15.22 \| 1400 \| 0.3016 \| 0.8894 \| 0.8891 \| \| 0.1773 \| 17.39 \| 1600 \| 0.3037 \| 0.8888 \| 0.8884 \| \| 0.1625 \| 19.57 \| 1800 \| 0.2925 \| 0.9007 \| 0.9008 \| \| 0.1508 \| 21.74 \| 2000 \| 0.3000 \| 0.8878 \| 0.8877 \| \| 0.1367 \| 23.91 \| 2200 \| 0.3192 \| 0.8899 \| 0.8898 \| \| 0.1265 \| 26.09 \| 2400 \| 0.3402 \| 0.8860 \| 0.8857 \| \| 0.1192 \| 28.26 \| 2600 \| 0.3521 \| 0.8860 \| 0.8857 \| \| 0.1069 \| 30.43 \| 2800 \| 0.3616 \| 0.8758 \| 0.8754 \| \| 0.0956 \| 32.61 \| 3000 \| 0.3884 \| 0.8843 \| 0.8850 \| \| 0.0866 \| 34.78 \| 3200 \| 0.4128 \| 0.8803 \| 0.8802 \| \| 0.083 \| 36.96 \| 3400 \| 0.3861 \| 0.8926 \| 0.8925 \| \| 0.0765 \| 39.13 \| 3600 \| 0.4141 \| 0.8900 \| 0.8898 \| \| 0.0693 \| 41.3 \| 3800 \| 0.4428 \| 0.8848 \| 0.8850 \| \| 0.0585 \| 43.48 \| 4000 \| 0.5073 \| 0.8870 \| 0.8871 \| \| 0.0621 \| 45.65 \| 4200 \| 0.4515 \| 0.8923 \| 0.8925 \| \| 0.057 \| 47.83 \| 4400 \| 0.4664 \| 0.8769 \| 0.8768 \| \| 0.0542 \| 50.0 \| 4600 \| 0.4722 \| 0.8880 \| 0.8884 \| \| 0.0495 \| 52.17 \| 4800 \| 0.5062 \| 0.8948 \| 0.8946 \| \| 0.0422 \| 54.35 \| 5000 \| 0.5186 \| 0.8796 \| 0.8795 \| \| 0.0417 \| 56.52 \| 5200 \| 0.5115 \| 0.8865 \| 0.8864 \| \| 0.0387 \| 58.7 \| 5400 \| 0.5247 \| 0.8813 \| 0.8816 \| \| 0.0382 \| 60.87 \| 5600 \| 0.5120 \| 0.8833 \| 0.8836 \| \| 0.0364 \| 63.04 \| 5800 \| 0.5497 \| 0.8823 \| 0.8823 \| \| 0.0359 \| 65.22 \| 6000 \| 0.5503 \| 0.8852 \| 0.8850 \| \| 0.0343 \| 67.39 \| 6200 \| 0.5308 \| 0.8823 \| 0.8823 \| \| 0.0289 \| 69.57 \| 6400 \| 0.5874 \| 0.8816 \| 0.8816 \| \| 0.0269 \| 71.74 \| 6600 \| 0.6000 \| 0.8815 \| 0.8816 \| \| 0.0269 \| 73.91 \| 6800 \| 0.5910 \| 0.8834 \| 0.8836 \| \| 0.0267 \| 76.09 \| 7000 \| 0.5808 \| 0.8796 \| 0.8795 \| \| 0.0245 \| 78.26 \| 7200 \| 0.5922 \| 0.8797 \| 0.8795 \| \| 0.0226 \| 80.43 \| 7400 \| 0.6174 \| 0.8823 \| 0.8823 \| \| 0.0235 \| 82.61 \| 7600 \| 0.5692 \| 0.8807 \| 0.8809 \| \| 0.0222 \| 84.78 \| 7800 \| 0.6206 \| 0.8844 \| 0.8843 \| \| 0.0188 \| 86.96 \| 8000 \| 0.6282 \| 0.8794 \| 0.8795 \| \| 0.0196 \| 89.13 \| 8200 \| 0.6483 \| 0.8865 \| 0.8864 \| \| 0.02 \| 91.3 \| 8400 \| 0.6444 \| 0.8873 \| 0.8871 \| \| 0.0178 \| 93.48 \| 8600 \| 0.6574 \| 0.8771 \| 0.8775 \| \| 0.0184 \| 95.65 \| 8800 \| 0.6285 \| 0.8829 \| 0.8830 \| \| 0.0171 \| 97.83 \| 9000 \| 0.6424 \| 0.8807 \| 0.8809 \| \| 0.0163 \| 100.0 \| 9200 \| 0.6469 \| 0.8836 \| 0.8836 \| \| 0.0155 \| 102.17 \| 9400 \| 0.6474 \| 0.8796 \| 0.8795 \| \| 0.0137 \| 104.35 \| 9600 \| 0.6637 \| 0.8803 \| 0.8802 \| \| 0.0164 \| 106.52 \| 9800 \| 0.6561 \| 0.8795 \| 0.8795 \| \| 0.0147 \| 108.7 \| 10000 \| 0.6597 \| 0.8809 \| 0.8809 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H4-seqsight_16384_512_34M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_16384_512_34M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T22:16:00+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3-seqsight_16384_512_34M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3038 - F1 Score: 0.8818 - Accuracy: 0.8818 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4984 \| 2.13 \| 200 \| 0.4333 \| 0.8089 \| 0.8103 \| \| 0.3828 \| 4.26 \| 400 \| 0.3892 \| 0.8443 \| 0.8444 \| \| 0.3575 \| 6.38 \| 600 \| 0.3858 \| 0.8416 \| 0.8417 \| \| 0.3385 \| 8.51 \| 800 \| 0.3699 \| 0.8414 \| 0.8417 \| \| 0.3244 \| 10.64 \| 1000 \| 0.3534 \| 0.8537 \| 0.8537 \| \| 0.3045 \| 12.77 \| 1200 \| 0.3511 \| 0.8550 \| 0.8550 \| \| 0.2885 \| 14.89 \| 1400 \| 0.3410 \| 0.8617 \| 0.8617 \| \| 0.2796 \| 17.02 \| 1600 \| 0.3388 \| 0.8611 \| 0.8611 \| \| 0.2736 \| 19.15 \| 1800 \| 0.3393 \| 0.8604 \| 0.8604 \| \| 0.2686 \| 21.28 \| 2000 \| 0.3312 \| 0.8651 \| 0.8651 \| \| 0.2668 \| 23.4 \| 2200 \| 0.3295 \| 0.8651 \| 0.8651 \| \| 0.2548 \| 25.53 \| 2400 \| 0.3429 \| 0.8550 \| 0.8550 \| \| 0.259 \| 27.66 \| 2600 \| 0.3268 \| 0.8631 \| 0.8631 \| \| 0.2548 \| 29.79 \| 2800 \| 0.3290 \| 0.8677 \| 0.8677 \| \| 0.2521 \| 31.91 \| 3000 \| 0.3282 \| 0.8664 \| 0.8664 \| \| 0.2463 \| 34.04 \| 3200 \| 0.3263 \| 0.8684 \| 0.8684 \| \| 0.2448 \| 36.17 \| 3400 \| 0.3356 \| 0.8610 \| 0.8611 \| \| 0.2426 \| 38.3 \| 3600 \| 0.3293 \| 0.8637 \| 0.8637 \| \| 0.2423 \| 40.43 \| 3800 \| 0.3233 \| 0.8637 \| 0.8637 \| \| 0.2396 \| 42.55 \| 4000 \| 0.3312 \| 0.8604 \| 0.8604 \| \| 0.2364 \| 44.68 \| 4200 \| 0.3270 \| 0.8683 \| 0.8684 \| \| 0.2374 \| 46.81 \| 4400 \| 0.3393 \| 0.8581 \| 0.8584 \| \| 0.2361 \| 48.94 \| 4600 \| 0.3261 \| 0.8610 \| 0.8611 \| \| 0.2328 \| 51.06 \| 4800 \| 0.3275 \| 0.8637 \| 0.8637 \| \| 0.2318 \| 53.19 \| 5000 \| 0.3447 \| 0.8622 \| 0.8624 \| \| 0.2321 \| 55.32 \| 5200 \| 0.3176 \| 0.8697 \| 0.8697 \| \| 0.2259 \| 57.45 \| 5400 \| 0.3429 \| 0.8610 \| 0.8611 \| \| 0.2247 \| 59.57 \| 5600 \| 0.3228 \| 0.8671 \| 0.8671 \| \| 0.2282 \| 61.7 \| 5800 \| 0.3192 \| 0.8717 \| 0.8717 \| \| 0.2251 \| 63.83 \| 6000 \| 0.3467 \| 0.8656 \| 0.8657 \| \| 0.2258 \| 65.96 \| 6200 \| 0.3281 \| 0.8717 \| 0.8717 \| \| 0.2239 \| 68.09 \| 6400 \| 0.3272 \| 0.8737 \| 0.8737 \| \| 0.2245 \| 70.21 \| 6600 \| 0.3270 \| 0.8697 \| 0.8697 \| \| 0.2211 \| 72.34 \| 6800 \| 0.3255 \| 0.8697 \| 0.8697 \| \| 0.2217 \| 74.47 \| 7000 \| 0.3470 \| 0.8682 \| 0.8684 \| \| 0.2182 \| 76.6 \| 7200 \| 0.3315 \| 0.8697 \| 0.8697 \| \| 0.2227 \| 78.72 \| 7400 \| 0.3282 \| 0.8697 \| 0.8697 \| \| 0.2169 \| 80.85 \| 7600 \| 0.3308 \| 0.8651 \| 0.8651 \| \| 0.2143 \| 82.98 \| 7800 \| 0.3299 \| 0.8704 \| 0.8704 \| \| 0.2194 \| 85.11 \| 8000 \| 0.3289 \| 0.8730 \| 0.8731 \| \| 0.2157 \| 87.23 \| 8200 \| 0.3287 \| 0.8677 \| 0.8677 \| \| 0.2142 \| 89.36 \| 8400 \| 0.3332 \| 0.8704 \| 0.8704 \| \| 0.2126 \| 91.49 \| 8600 \| 0.3368 \| 0.8704 \| 0.8704 \| \| 0.2161 \| 93.62 \| 8800 \| 0.3326 \| 0.8704 \| 0.8704 \| \| 0.2156 \| 95.74 \| 9000 \| 0.3324 \| 0.8704 \| 0.8704 \| \| 0.2103 \| 97.87 \| 9200 \| 0.3302 \| 0.8684 \| 0.8684 \| \| 0.2132 \| 100.0 \| 9400 \| 0.3322 \| 0.8677 \| 0.8677 \| \| 0.2124 \| 102.13 \| 9600 \| 0.3320 \| 0.8677 \| 0.8677 \| \| 0.2116 \| 104.26 \| 9800 \| 0.3320 \| 0.8664 \| 0.8664 \| \| 0.2125 \| 106.38 \| 10000 \| 0.3320 \| 0.8671 \| 0.8671 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3-seqsight_16384_512_34M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3-seqsight_16384_512_34M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T22:16:00+00:00
text-generation	transformers	## Llamacpp imatrix Quantizations of starcoder2-15b-instruct-v0.1 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2756">b2756</a> for quantization. Original model: https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) ## Prompt format ``` <\|endoftext\|>You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions. ### Instruction {prompt} ### Response <\|endoftext\|> ``` Note that this model does not support a System prompt. ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [starcoder2-15b-instruct-v0.1-Q8_0.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q8_0.gguf) \| Q8_0 \| 16.96GB \| Extremely high quality, generally unneeded but max available quant. \| \| [starcoder2-15b-instruct-v0.1-Q6_K.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q6_K.gguf) \| Q6_K \| 13.10GB \| Very high quality, near perfect, recommended. \| \| [starcoder2-15b-instruct-v0.1-Q5_K_M.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q5_K_M.gguf) \| Q5_K_M \| 11.43GB \| High quality, recommended. \| \| [starcoder2-15b-instruct-v0.1-Q5_K_S.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q5_K_S.gguf) \| Q5_K_S \| 11.02GB \| High quality, recommended. \| \| [starcoder2-15b-instruct-v0.1-Q4_K_M.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q4_K_M.gguf) \| Q4_K_M \| 9.86GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [starcoder2-15b-instruct-v0.1-Q4_K_S.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q4_K_S.gguf) \| Q4_K_S \| 9.16GB \| Slightly lower quality with more space savings, recommended. \| \| [starcoder2-15b-instruct-v0.1-IQ4_NL.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ4_NL.gguf) \| IQ4_NL \| 9.08GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [starcoder2-15b-instruct-v0.1-IQ4_XS.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ4_XS.gguf) \| IQ4_XS \| 8.59GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [starcoder2-15b-instruct-v0.1-Q3_K_L.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q3_K_L.gguf) \| Q3_K_L \| 8.96GB \| Lower quality but usable, good for low RAM availability. \| \| [starcoder2-15b-instruct-v0.1-Q3_K_M.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q3_K_M.gguf) \| Q3_K_M \| 8.04GB \| Even lower quality. \| \| [starcoder2-15b-instruct-v0.1-IQ3_M.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ3_M.gguf) \| IQ3_M \| 7.30GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [starcoder2-15b-instruct-v0.1-IQ3_S.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ3_S.gguf) \| IQ3_S \| 7.00GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [starcoder2-15b-instruct-v0.1-Q3_K_S.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q3_K_S.gguf) \| Q3_K_S \| 6.98GB \| Low quality, not recommended. \| \| [starcoder2-15b-instruct-v0.1-IQ3_XS.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ3_XS.gguf) \| IQ3_XS \| 6.71GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [starcoder2-15b-instruct-v0.1-IQ3_XXS.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ3_XXS.gguf) \| IQ3_XXS \| 6.21GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [starcoder2-15b-instruct-v0.1-Q2_K.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-Q2_K.gguf) \| Q2_K \| 6.19GB \| Very low quality but surprisingly usable. \| \| [starcoder2-15b-instruct-v0.1-IQ2_M.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ2_M.gguf) \| IQ2_M \| 5.54GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [starcoder2-15b-instruct-v0.1-IQ2_S.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ2_S.gguf) \| IQ2_S \| 5.14GB \| Very low quality, uses SOTA techniques to be usable. \| \| [starcoder2-15b-instruct-v0.1-IQ2_XS.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ2_XS.gguf) \| IQ2_XS \| 4.82GB \| Very low quality, uses SOTA techniques to be usable. \| \| [starcoder2-15b-instruct-v0.1-IQ2_XXS.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ2_XXS.gguf) \| IQ2_XXS \| 4.36GB \| Lower quality, uses SOTA techniques to be usable. \| \| [starcoder2-15b-instruct-v0.1-IQ1_M.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ1_M.gguf) \| IQ1_M \| 3.86GB \| Extremely low quality, not recommended. \| \| [starcoder2-15b-instruct-v0.1-IQ1_S.gguf](https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-GGUF/blob/main/starcoder2-15b-instruct-v0.1-IQ1_S.gguf) \| IQ1_S \| 3.55GB \| Extremely low quality, not recommended. \| ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski	{"license": "bigcode-openrail-m", "library_name": "transformers", "tags": ["code"], "datasets": ["bigcode/self-oss-instruct-sc2-exec-filter-50k"], "pipeline_tag": "text-generation", "base_model": "bigcode/starcoder2-15b", "quantized_by": "bartowski", "model-index": [{"name": "starcoder2-15b-instruct-v0.1", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (code generation)", "type": "livecodebench-codegeneration"}, "metrics": [{"type": "pass@1", "value": 20.4}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (self repair)", "type": "livecodebench-selfrepair"}, "metrics": [{"type": "pass@1", "value": 20.9}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (test output prediction)", "type": "livecodebench-testoutputprediction"}, "metrics": [{"type": "pass@1", "value": 29.8}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (code execution)", "type": "livecodebench-codeexecution"}, "metrics": [{"type": "pass@1", "value": 28.1}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval", "type": "humaneval"}, "metrics": [{"type": "pass@1", "value": 72.6}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval+", "type": "humanevalplus"}, "metrics": [{"type": "pass@1", "value": 63.4}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "MBPP", "type": "mbpp"}, "metrics": [{"type": "pass@1", "value": 75.2}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "MBPP+", "type": "mbppplus"}, "metrics": [{"type": "pass@1", "value": 61.2}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "DS-1000", "type": "ds-1000"}, "metrics": [{"type": "pass@1", "value": 40.6}]}]}]}	bartowski/starcoder2-15b-instruct-v0.1-GGUF	null	[ "transformers", "gguf", "code", "text-generation", "dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k", "base_model:bigcode/starcoder2-15b", "license:bigcode-openrail-m", "model-index", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:16:56+00:00
text-generation	transformers	# Uploaded model - Developed by: axel-rda - License: apache-2.0 - Finetuned from model : Faradaylab/ARIA-70B-V3 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "Faradaylab/ARIA-70B-V3"}	axel-rda/ARIA-70B-V3-qlora-sft	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:Faradaylab/ARIA-70B-V3", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	null	2024-04-29T22:18:44+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3-seqsight_16384_512_34M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_34M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_34M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3629 - F1 Score: 0.8684 - Accuracy: 0.8684 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4585 \| 2.13 \| 200 \| 0.3940 \| 0.8409 \| 0.8410 \| \| 0.333 \| 4.26 \| 400 \| 0.3430 \| 0.8630 \| 0.8631 \| \| 0.2899 \| 6.38 \| 600 \| 0.3729 \| 0.8493 \| 0.8497 \| \| 0.2754 \| 8.51 \| 800 \| 0.3195 \| 0.8724 \| 0.8724 \| \| 0.2631 \| 10.64 \| 1000 \| 0.3234 \| 0.8684 \| 0.8684 \| \| 0.2546 \| 12.77 \| 1200 \| 0.3287 \| 0.8664 \| 0.8664 \| \| 0.2443 \| 14.89 \| 1400 \| 0.3515 \| 0.8594 \| 0.8597 \| \| 0.2375 \| 17.02 \| 1600 \| 0.3163 \| 0.8751 \| 0.8751 \| \| 0.2288 \| 19.15 \| 1800 \| 0.3348 \| 0.8684 \| 0.8684 \| \| 0.2243 \| 21.28 \| 2000 \| 0.3513 \| 0.8676 \| 0.8677 \| \| 0.2227 \| 23.4 \| 2200 \| 0.3344 \| 0.8656 \| 0.8657 \| \| 0.2085 \| 25.53 \| 2400 \| 0.3422 \| 0.8697 \| 0.8697 \| \| 0.2133 \| 27.66 \| 2600 \| 0.3310 \| 0.8744 \| 0.8744 \| \| 0.2036 \| 29.79 \| 2800 \| 0.3745 \| 0.8633 \| 0.8637 \| \| 0.1974 \| 31.91 \| 3000 \| 0.3421 \| 0.8664 \| 0.8664 \| \| 0.1933 \| 34.04 \| 3200 \| 0.3459 \| 0.8784 \| 0.8784 \| \| 0.1895 \| 36.17 \| 3400 \| 0.3762 \| 0.8667 \| 0.8671 \| \| 0.1828 \| 38.3 \| 3600 \| 0.3801 \| 0.8622 \| 0.8624 \| \| 0.1804 \| 40.43 \| 3800 \| 0.3669 \| 0.8682 \| 0.8684 \| \| 0.1743 \| 42.55 \| 4000 \| 0.4119 \| 0.8606 \| 0.8611 \| \| 0.1694 \| 44.68 \| 4200 \| 0.3770 \| 0.8704 \| 0.8704 \| \| 0.1669 \| 46.81 \| 4400 \| 0.3873 \| 0.8648 \| 0.8651 \| \| 0.1685 \| 48.94 \| 4600 \| 0.3926 \| 0.8667 \| 0.8671 \| \| 0.1619 \| 51.06 \| 4800 \| 0.3690 \| 0.8744 \| 0.8744 \| \| 0.1612 \| 53.19 \| 5000 \| 0.4081 \| 0.8634 \| 0.8637 \| \| 0.1555 \| 55.32 \| 5200 \| 0.3844 \| 0.8791 \| 0.8791 \| \| 0.1526 \| 57.45 \| 5400 \| 0.4042 \| 0.8717 \| 0.8717 \| \| 0.1483 \| 59.57 \| 5600 \| 0.4244 \| 0.8622 \| 0.8624 \| \| 0.1484 \| 61.7 \| 5800 \| 0.3813 \| 0.8744 \| 0.8744 \| \| 0.1465 \| 63.83 \| 6000 \| 0.4256 \| 0.8695 \| 0.8697 \| \| 0.1434 \| 65.96 \| 6200 \| 0.4202 \| 0.8675 \| 0.8677 \| \| 0.1389 \| 68.09 \| 6400 \| 0.4033 \| 0.8764 \| 0.8764 \| \| 0.1388 \| 70.21 \| 6600 \| 0.4336 \| 0.8724 \| 0.8724 \| \| 0.135 \| 72.34 \| 6800 \| 0.4049 \| 0.8764 \| 0.8764 \| \| 0.135 \| 74.47 \| 7000 \| 0.4618 \| 0.8552 \| 0.8557 \| \| 0.13 \| 76.6 \| 7200 \| 0.4369 \| 0.8663 \| 0.8664 \| \| 0.1348 \| 78.72 \| 7400 \| 0.4264 \| 0.8757 \| 0.8758 \| \| 0.129 \| 80.85 \| 7600 \| 0.4316 \| 0.8677 \| 0.8677 \| \| 0.1231 \| 82.98 \| 7800 \| 0.4316 \| 0.8717 \| 0.8717 \| \| 0.1257 \| 85.11 \| 8000 \| 0.4365 \| 0.8744 \| 0.8744 \| \| 0.1228 \| 87.23 \| 8200 \| 0.4485 \| 0.8703 \| 0.8704 \| \| 0.1195 \| 89.36 \| 8400 \| 0.4391 \| 0.8763 \| 0.8764 \| \| 0.1201 \| 91.49 \| 8600 \| 0.4615 \| 0.8689 \| 0.8691 \| \| 0.1189 \| 93.62 \| 8800 \| 0.4506 \| 0.8763 \| 0.8764 \| \| 0.1203 \| 95.74 \| 9000 \| 0.4538 \| 0.8716 \| 0.8717 \| \| 0.1166 \| 97.87 \| 9200 \| 0.4507 \| 0.8737 \| 0.8737 \| \| 0.1178 \| 100.0 \| 9400 \| 0.4551 \| 0.8737 \| 0.8737 \| \| 0.1174 \| 102.13 \| 9600 \| 0.4543 \| 0.8730 \| 0.8731 \| \| 0.116 \| 104.26 \| 9800 \| 0.4593 \| 0.8696 \| 0.8697 \| \| 0.1141 \| 106.38 \| 10000 \| 0.4573 \| 0.8703 \| 0.8704 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_34M", "model-index": [{"name": "GUE_EMP_H3-seqsight_16384_512_34M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3-seqsight_16384_512_34M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_16384_512_34M", "region:us" ]	null	2024-04-29T22:18:44+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	lunarsylph/mooncell_v33	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T22:20:17+00:00
null	null		{"license": "openrail"}	leeloli/shuhua-by-leelo	null	[ "license:openrail", "region:us" ]	null	2024-04-29T22:21:22+00:00
text-to-image	diffusers	# nagatsuki_mix API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/18504034401710498398.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "nagatsukimix" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/nagatsukimix) Model link: [View model](https://modelslab.com/models/nagatsukimix) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "nagatsukimix", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN	{"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true}	stablediffusionapi/nagatsukimix	null	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	null	2024-04-29T22:23:09+00:00
null	null		{}	ivykopal/sksquad_sk_prompt_100k	null	[ "region:us" ]	null	2024-04-29T22:23:56+00:00
text-generation	keras-nlp	This is a [`GPT2` model](https://keras.io/api/keras_nlp/models/gpt2) uploaded using the KerasNLP library and can be used with JAX, TensorFlow, and PyTorch backends. This model is related to a `CausalLM` task. Model config: * name: gpt2_backbone * trainable: True * vocabulary_size: 50257 * num_layers: 12 * num_heads: 12 * hidden_dim: 768 * intermediate_dim: 3072 * dropout: 0.1 * max_sequence_length: 1024 This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information.	{"library_name": "keras-nlp", "pipeline_tag": "text-generation"}	samanehs/finetuned_gpt2	null	[ "keras-nlp", "text-generation", "region:us" ]	null	2024-04-29T22:24:27+00:00
null	null		{}	ivykopal/cssquad_cs_prompt_100k	null	[ "region:us" ]	null	2024-04-29T22:25:11+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	cilantro9246/31anfwt	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-29T22:26:02+00:00
null	transformers	# Uploaded model - Developed by: tingting - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	tingting/llama3_lora_model_Data_3200	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-29T22:28:38+00:00
null	null		{}	Juzie/Bb	null	[ "region:us" ]	null	2024-04-29T22:28:48+00:00

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