librarian-bots/model_cards_with_metadata_with_embeddings

modelId stringlengths 4 122	author stringlengths 2 42 ⌀	last_modified timestamp[us, tz=UTC]	likes int64 0 9.67k	library_name stringlengths 2 84 ⌀	tags list	pipeline_tag stringlengths 5 30 ⌀	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k	embedding list
kwwww/bert-base-uncased_8_1039_token	kwwww	2023-12-14T22:38:11Z	0	null	[ "pytorch", "generated_from_trainer", "dataset:bionlp2004", "license:apache-2.0", "region:us" ]	null	2023-12-14T16:33:49Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - bionlp2004 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-uncased_8_1039_token results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased_8_1039_token This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the bionlp2004 dataset. It achieves the following results on the evaluation set: - Loss: 0.7191 - Precision: 0.7311 - Recall: 0.7648 - F1: 0.7476 - Accuracy: 0.9327 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 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: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 65 \| 0.3785 \| 0.3534 \| 0.4596 \| 0.3996 \| 0.8659 \| \| No log \| 2.0 \| 130 \| 0.2718 \| 0.5366 \| 0.6877 \| 0.6028 \| 0.9072 \| \| No log \| 3.0 \| 195 \| 0.2542 \| 0.6101 \| 0.6434 \| 0.6263 \| 0.9140 \| \| No log \| 4.0 \| 260 \| 0.2302 \| 0.6304 \| 0.7054 \| 0.6658 \| 0.9248 \| \| No log \| 5.0 \| 325 \| 0.2327 \| 0.6415 \| 0.7315 \| 0.6836 \| 0.9255 \| \| No log \| 6.0 \| 390 \| 0.2576 \| 0.6277 \| 0.7418 \| 0.6800 \| 0.9193 \| \| No log \| 7.0 \| 455 \| 0.2431 \| 0.6615 \| 0.7425 \| 0.6996 \| 0.9263 \| \| 0.2489 \| 8.0 \| 520 \| 0.2490 \| 0.6756 \| 0.7184 \| 0.6963 \| 0.9276 \| \| 0.2489 \| 9.0 \| 585 \| 0.2702 \| 0.6773 \| 0.7268 \| 0.7012 \| 0.9262 \| \| 0.2489 \| 10.0 \| 650 \| 0.2941 \| 0.6335 \| 0.6632 \| 0.6480 \| 0.9193 \| \| 0.2489 \| 11.0 \| 715 \| 0.2777 \| 0.6975 \| 0.7502 \| 0.7229 \| 0.9305 \| \| 0.2489 \| 12.0 \| 780 \| 0.2879 \| 0.6850 \| 0.7517 \| 0.7168 \| 0.9290 \| \| 0.2489 \| 13.0 \| 845 \| 0.3211 \| 0.6839 \| 0.6834 \| 0.6837 \| 0.9240 \| \| 0.2489 \| 14.0 \| 910 \| 0.3036 \| 0.6808 \| 0.6910 \| 0.6859 \| 0.9259 \| \| 0.2489 \| 15.0 \| 975 \| 0.3149 \| 0.6666 \| 0.7108 \| 0.6880 \| 0.9241 \| \| 0.0766 \| 16.0 \| 1040 \| 0.3416 \| 0.6635 \| 0.6917 \| 0.6773 \| 0.9232 \| \| 0.0766 \| 17.0 \| 1105 \| 0.3478 \| 0.6630 \| 0.7302 \| 0.6950 \| 0.9239 \| \| 0.0766 \| 18.0 \| 1170 \| 0.3316 \| 0.6703 \| 0.7581 \| 0.7115 \| 0.9273 \| \| 0.0766 \| 19.0 \| 1235 \| 0.3510 \| 0.6918 \| 0.7054 \| 0.6985 \| 0.9254 \| \| 0.0766 \| 20.0 \| 1300 \| 0.3560 \| 0.6817 \| 0.7299 \| 0.7050 \| 0.9269 \| \| 0.0766 \| 21.0 \| 1365 \| 0.3641 \| 0.6678 \| 0.7313 \| 0.6981 \| 0.9262 \| \| 0.0766 \| 22.0 \| 1430 \| 0.3408 \| 0.6826 \| 0.7292 \| 0.7051 \| 0.9268 \| \| 0.0766 \| 23.0 \| 1495 \| 0.3689 \| 0.6772 \| 0.7238 \| 0.6997 \| 0.9259 \| \| 0.0406 \| 24.0 \| 1560 \| 0.3782 \| 0.6674 \| 0.7443 \| 0.7037 \| 0.9259 \| \| 0.0406 \| 25.0 \| 1625 \| 0.4102 \| 0.6744 \| 0.6771 \| 0.6758 \| 0.9205 \| \| 0.0406 \| 26.0 \| 1690 \| 0.3654 \| 0.6750 \| 0.7297 \| 0.7013 \| 0.9277 \| \| 0.0406 \| 27.0 \| 1755 \| 0.4032 \| 0.6721 \| 0.7556 \| 0.7114 \| 0.9253 \| \| 0.0406 \| 28.0 \| 1820 \| 0.4372 \| 0.6936 \| 0.6865 \| 0.6900 \| 0.9231 \| \| 0.0406 \| 29.0 \| 1885 \| 0.3914 \| 0.6761 \| 0.7290 \| 0.7016 \| 0.9264 \| \| 0.0406 \| 30.0 \| 1950 \| 0.3967 \| 0.6844 \| 0.7337 \| 0.7082 \| 0.9266 \| \| 0.0307 \| 31.0 \| 2015 \| 0.4035 \| 0.6858 \| 0.7441 \| 0.7138 \| 0.9267 \| \| 0.0307 \| 32.0 \| 2080 \| 0.4173 \| 0.6899 \| 0.7461 \| 0.7169 \| 0.9284 \| \| 0.0307 \| 33.0 \| 2145 \| 0.3992 \| 0.6826 \| 0.7223 \| 0.7019 \| 0.9280 \| \| 0.0307 \| 34.0 \| 2210 \| 0.4192 \| 0.6677 \| 0.7637 \| 0.7125 \| 0.9280 \| \| 0.0307 \| 35.0 \| 2275 \| 0.4110 \| 0.6990 \| 0.7373 \| 0.7176 \| 0.9302 \| \| 0.0307 \| 36.0 \| 2340 \| 0.4688 \| 0.6739 \| 0.7034 \| 0.6883 \| 0.9210 \| \| 0.0307 \| 37.0 \| 2405 \| 0.4203 \| 0.6834 \| 0.7311 \| 0.7065 \| 0.9285 \| \| 0.0307 \| 38.0 \| 2470 \| 0.4597 \| 0.6923 \| 0.7452 \| 0.7178 \| 0.9277 \| \| 0.0235 \| 39.0 \| 2535 \| 0.4246 \| 0.6790 \| 0.7484 \| 0.7120 \| 0.9264 \| \| 0.0235 \| 40.0 \| 2600 \| 0.4335 \| 0.6906 \| 0.7286 \| 0.7091 \| 0.9274 \| \| 0.0235 \| 41.0 \| 2665 \| 0.4571 \| 0.6786 \| 0.7239 \| 0.7005 \| 0.9251 \| \| 0.0235 \| 42.0 \| 2730 \| 0.4273 \| 0.6930 \| 0.7340 \| 0.7129 \| 0.9282 \| \| 0.0235 \| 43.0 \| 2795 \| 0.4471 \| 0.6981 \| 0.7167 \| 0.7073 \| 0.9271 \| \| 0.0235 \| 44.0 \| 2860 \| 0.4520 \| 0.6959 \| 0.7290 \| 0.7120 \| 0.9273 \| \| 0.0235 \| 45.0 \| 2925 \| 0.4540 \| 0.6946 \| 0.7349 \| 0.7142 \| 0.9272 \| \| 0.0235 \| 46.0 \| 2990 \| 0.4478 \| 0.6826 \| 0.7497 \| 0.7146 \| 0.9286 \| \| 0.0188 \| 47.0 \| 3055 \| 0.4538 \| 0.6951 \| 0.7414 \| 0.7175 \| 0.9278 \| \| 0.0188 \| 48.0 \| 3120 \| 0.4738 \| 0.6954 \| 0.7247 \| 0.7097 \| 0.9276 \| \| 0.0188 \| 49.0 \| 3185 \| 0.4915 \| 0.7057 \| 0.7338 \| 0.7195 \| 0.9287 \| \| 0.0188 \| 50.0 \| 3250 \| 0.4568 \| 0.6989 \| 0.7382 \| 0.7180 \| 0.9285 \| \| 0.0188 \| 51.0 \| 3315 \| 0.5172 \| 0.6901 \| 0.7137 \| 0.7017 \| 0.9223 \| \| 0.0188 \| 52.0 \| 3380 \| 0.4803 \| 0.6842 \| 0.7290 \| 0.7059 \| 0.9267 \| \| 0.0188 \| 53.0 \| 3445 \| 0.4312 \| 0.7015 \| 0.7405 \| 0.7205 \| 0.9297 \| \| 0.0169 \| 54.0 \| 3510 \| 0.4340 \| 0.6825 \| 0.7430 \| 0.7115 \| 0.9286 \| \| 0.0169 \| 55.0 \| 3575 \| 0.4535 \| 0.6941 \| 0.7583 \| 0.7248 \| 0.9292 \| \| 0.0169 \| 56.0 \| 3640 \| 0.4922 \| 0.6897 \| 0.7630 \| 0.7245 \| 0.9292 \| \| 0.0169 \| 57.0 \| 3705 \| 0.4889 \| 0.6900 \| 0.7551 \| 0.7211 \| 0.9283 \| \| 0.0169 \| 58.0 \| 3770 \| 0.4814 \| 0.6869 \| 0.7396 \| 0.7123 \| 0.9266 \| \| 0.0169 \| 59.0 \| 3835 \| 0.4799 \| 0.6764 \| 0.7659 \| 0.7184 \| 0.9275 \| \| 0.0169 \| 60.0 \| 3900 \| 0.4813 \| 0.6687 \| 0.7524 \| 0.7081 \| 0.9275 \| \| 0.0169 \| 61.0 \| 3965 \| 0.4561 \| 0.7081 \| 0.7526 \| 0.7296 \| 0.9297 \| \| 0.0165 \| 62.0 \| 4030 \| 0.4537 \| 0.7002 \| 0.7360 \| 0.7176 \| 0.9277 \| \| 0.0165 \| 63.0 \| 4095 \| 0.4793 \| 0.6933 \| 0.7509 \| 0.7210 \| 0.9268 \| \| 0.0165 \| 64.0 \| 4160 \| 0.4645 \| 0.7157 \| 0.7162 \| 0.7159 \| 0.9274 \| \| 0.0165 \| 65.0 \| 4225 \| 0.4886 \| 0.6939 \| 0.7169 \| 0.7052 \| 0.9263 \| \| 0.0165 \| 66.0 \| 4290 \| 0.4707 \| 0.6714 \| 0.7639 \| 0.7147 \| 0.9253 \| \| 0.0165 \| 67.0 \| 4355 \| 0.4768 \| 0.6783 \| 0.7535 \| 0.7139 \| 0.9292 \| \| 0.0165 \| 68.0 \| 4420 \| 0.4601 \| 0.6868 \| 0.7430 \| 0.7138 \| 0.9280 \| \| 0.0165 \| 69.0 \| 4485 \| 0.4411 \| 0.7076 \| 0.7371 \| 0.7221 \| 0.9301 \| \| 0.0134 \| 70.0 \| 4550 \| 0.5118 \| 0.6811 \| 0.7383 \| 0.7085 \| 0.9277 \| \| 0.0134 \| 71.0 \| 4615 \| 0.4723 \| 0.6908 \| 0.7455 \| 0.7171 \| 0.9283 \| \| 0.0134 \| 72.0 \| 4680 \| 0.4940 \| 0.6939 \| 0.7196 \| 0.7065 \| 0.9257 \| \| 0.0134 \| 73.0 \| 4745 \| 0.5030 \| 0.6870 \| 0.7443 \| 0.7145 \| 0.9276 \| \| 0.0134 \| 74.0 \| 4810 \| 0.4606 \| 0.6834 \| 0.7398 \| 0.7105 \| 0.9283 \| \| 0.0134 \| 75.0 \| 4875 \| 0.4554 \| 0.6904 \| 0.7614 \| 0.7242 \| 0.9305 \| \| 0.0134 \| 76.0 \| 4940 \| 0.5056 \| 0.6926 \| 0.7241 \| 0.7080 \| 0.9263 \| \| 0.0136 \| 77.0 \| 5005 \| 0.5137 \| 0.6537 \| 0.7371 \| 0.6929 \| 0.9224 \| \| 0.0136 \| 78.0 \| 5070 \| 0.4599 \| 0.6891 \| 0.7252 \| 0.7067 \| 0.9264 \| \| 0.0136 \| 79.0 \| 5135 \| 0.4919 \| 0.6916 \| 0.7641 \| 0.7260 \| 0.9292 \| \| 0.0136 \| 80.0 \| 5200 \| 0.4656 \| 0.6801 \| 0.7513 \| 0.7140 \| 0.9279 \| \| 0.0136 \| 81.0 \| 5265 \| 0.4895 \| 0.6856 \| 0.7592 \| 0.7206 \| 0.9282 \| \| 0.0136 \| 82.0 \| 5330 \| 0.4975 \| 0.69 \| 0.7331 \| 0.7109 \| 0.9268 \| \| 0.0136 \| 83.0 \| 5395 \| 0.4889 \| 0.6984 \| 0.7679 \| 0.7315 \| 0.9291 \| \| 0.0136 \| 84.0 \| 5460 \| 0.4989 \| 0.6934 \| 0.7446 \| 0.7181 \| 0.9279 \| \| 0.0142 \| 85.0 \| 5525 \| 0.4927 \| 0.7045 \| 0.7382 \| 0.7210 \| 0.9292 \| \| 0.0142 \| 86.0 \| 5590 \| 0.5044 \| 0.6862 \| 0.7322 \| 0.7085 \| 0.9253 \| \| 0.0142 \| 87.0 \| 5655 \| 0.4977 \| 0.6834 \| 0.7434 \| 0.7122 \| 0.9263 \| \| 0.0142 \| 88.0 \| 5720 \| 0.5068 \| 0.6750 \| 0.7675 \| 0.7183 \| 0.9275 \| \| 0.0142 \| 89.0 \| 5785 \| 0.5193 \| 0.6946 \| 0.7531 \| 0.7227 \| 0.9286 \| \| 0.0142 \| 90.0 \| 5850 \| 0.5418 \| 0.6953 \| 0.7497 \| 0.7215 \| 0.9282 \| \| 0.0142 \| 91.0 \| 5915 \| 0.5780 \| 0.7030 \| 0.7315 \| 0.7170 \| 0.9273 \| \| 0.0142 \| 92.0 \| 5980 \| 0.5278 \| 0.6825 \| 0.6937 \| 0.6880 \| 0.9224 \| \| 0.0112 \| 93.0 \| 6045 \| 0.5053 \| 0.7089 \| 0.7333 \| 0.7209 \| 0.9275 \| \| 0.0112 \| 94.0 \| 6110 \| 0.5550 \| 0.7067 \| 0.6926 \| 0.6996 \| 0.9231 \| \| 0.0112 \| 95.0 \| 6175 \| 0.5253 \| 0.6830 \| 0.7610 \| 0.7199 \| 0.9262 \| \| 0.0112 \| 96.0 \| 6240 \| 0.5264 \| 0.6906 \| 0.7495 \| 0.7188 \| 0.9267 \| \| 0.0112 \| 97.0 \| 6305 \| 0.5120 \| 0.6988 \| 0.7533 \| 0.7250 \| 0.9284 \| \| 0.0112 \| 98.0 \| 6370 \| 0.5490 \| 0.7072 \| 0.7160 \| 0.7116 \| 0.9266 \| \| 0.0112 \| 99.0 \| 6435 \| 0.4795 \| 0.7063 \| 0.7526 \| 0.7287 \| 0.9298 \| \| 0.0115 \| 100.0 \| 6500 \| 0.5181 \| 0.6969 \| 0.7382 \| 0.7169 \| 0.9281 \| \| 0.0115 \| 101.0 \| 6565 \| 0.5245 \| 0.6909 \| 0.7355 \| 0.7125 \| 0.9273 \| \| 0.0115 \| 102.0 \| 6630 \| 0.5008 \| 0.6837 \| 0.7704 \| 0.7245 \| 0.9282 \| \| 0.0115 \| 103.0 \| 6695 \| 0.5059 \| 0.6833 \| 0.7481 \| 0.7142 \| 0.9267 \| \| 0.0115 \| 104.0 \| 6760 \| 0.5299 \| 0.7057 \| 0.7279 \| 0.7166 \| 0.9280 \| \| 0.0115 \| 105.0 \| 6825 \| 0.4829 \| 0.7054 \| 0.7416 \| 0.7230 \| 0.9299 \| \| 0.0115 \| 106.0 \| 6890 \| 0.5247 \| 0.7062 \| 0.7463 \| 0.7257 \| 0.9284 \| \| 0.0115 \| 107.0 \| 6955 \| 0.5306 \| 0.7067 \| 0.7603 \| 0.7325 \| 0.9309 \| \| 0.0096 \| 108.0 \| 7020 \| 0.5252 \| 0.6885 \| 0.7481 \| 0.7171 \| 0.9263 \| \| 0.0096 \| 109.0 \| 7085 \| 0.4961 \| 0.7094 \| 0.7407 \| 0.7247 \| 0.9306 \| \| 0.0096 \| 110.0 \| 7150 \| 0.5659 \| 0.6912 \| 0.7369 \| 0.7133 \| 0.9266 \| \| 0.0096 \| 111.0 \| 7215 \| 0.5572 \| 0.6765 \| 0.7682 \| 0.7195 \| 0.9268 \| \| 0.0096 \| 112.0 \| 7280 \| 0.5170 \| 0.7190 \| 0.7360 \| 0.7274 \| 0.9305 \| \| 0.0096 \| 113.0 \| 7345 \| 0.5140 \| 0.6973 \| 0.7274 \| 0.7120 \| 0.9276 \| \| 0.0096 \| 114.0 \| 7410 \| 0.5507 \| 0.7096 \| 0.7520 \| 0.7302 \| 0.9298 \| \| 0.0096 \| 115.0 \| 7475 \| 0.5813 \| 0.6944 \| 0.7337 \| 0.7135 \| 0.9271 \| \| 0.0087 \| 116.0 \| 7540 \| 0.5431 \| 0.7079 \| 0.7423 \| 0.7247 \| 0.9288 \| \| 0.0087 \| 117.0 \| 7605 \| 0.5649 \| 0.6867 \| 0.7596 \| 0.7213 \| 0.9275 \| \| 0.0087 \| 118.0 \| 7670 \| 0.5577 \| 0.6856 \| 0.7576 \| 0.7198 \| 0.9267 \| \| 0.0087 \| 119.0 \| 7735 \| 0.5189 \| 0.7107 \| 0.7448 \| 0.7273 \| 0.9296 \| \| 0.0087 \| 120.0 \| 7800 \| 0.4997 \| 0.7024 \| 0.7268 \| 0.7144 \| 0.9283 \| \| 0.0087 \| 121.0 \| 7865 \| 0.5347 \| 0.6838 \| 0.7594 \| 0.7196 \| 0.9274 \| \| 0.0087 \| 122.0 \| 7930 \| 0.5074 \| 0.6986 \| 0.7329 \| 0.7154 \| 0.9280 \| \| 0.0087 \| 123.0 \| 7995 \| 0.5455 \| 0.6925 \| 0.7661 \| 0.7274 \| 0.9279 \| \| 0.0094 \| 124.0 \| 8060 \| 0.5187 \| 0.6858 \| 0.7681 \| 0.7246 \| 0.9295 \| \| 0.0094 \| 125.0 \| 8125 \| 0.5379 \| 0.6918 \| 0.7574 \| 0.7231 \| 0.9290 \| \| 0.0094 \| 126.0 \| 8190 \| 0.5108 \| 0.6963 \| 0.7500 \| 0.7221 \| 0.9286 \| \| 0.0094 \| 127.0 \| 8255 \| 0.5201 \| 0.7093 \| 0.7612 \| 0.7344 \| 0.9300 \| \| 0.0094 \| 128.0 \| 8320 \| 0.5498 \| 0.6975 \| 0.7536 \| 0.7245 \| 0.9290 \| \| 0.0094 \| 129.0 \| 8385 \| 0.5157 \| 0.7128 \| 0.7464 \| 0.7292 \| 0.9293 \| \| 0.0094 \| 130.0 \| 8450 \| 0.5246 \| 0.7044 \| 0.7446 \| 0.7240 \| 0.9281 \| \| 0.0083 \| 131.0 \| 8515 \| 0.5290 \| 0.6857 \| 0.7391 \| 0.7114 \| 0.9254 \| \| 0.0083 \| 132.0 \| 8580 \| 0.5479 \| 0.6919 \| 0.7400 \| 0.7151 \| 0.9266 \| \| 0.0083 \| 133.0 \| 8645 \| 0.5500 \| 0.7084 \| 0.7207 \| 0.7145 \| 0.9279 \| \| 0.0083 \| 134.0 \| 8710 \| 0.5646 \| 0.6755 \| 0.7572 \| 0.7140 \| 0.9235 \| \| 0.0083 \| 135.0 \| 8775 \| 0.5731 \| 0.6871 \| 0.7290 \| 0.7074 \| 0.9247 \| \| 0.0083 \| 136.0 \| 8840 \| 0.5839 \| 0.6946 \| 0.7421 \| 0.7176 \| 0.9279 \| \| 0.0083 \| 137.0 \| 8905 \| 0.5546 \| 0.6814 \| 0.7715 \| 0.7236 \| 0.9275 \| \| 0.0083 \| 138.0 \| 8970 \| 0.5774 \| 0.7080 \| 0.7205 \| 0.7142 \| 0.9252 \| \| 0.008 \| 139.0 \| 9035 \| 0.5790 \| 0.7026 \| 0.7585 \| 0.7295 \| 0.9292 \| \| 0.008 \| 140.0 \| 9100 \| 0.5969 \| 0.7078 \| 0.7389 \| 0.7230 \| 0.9290 \| \| 0.008 \| 141.0 \| 9165 \| 0.5879 \| 0.7129 \| 0.7335 \| 0.7231 \| 0.9270 \| \| 0.008 \| 142.0 \| 9230 \| 0.5642 \| 0.6920 \| 0.7749 \| 0.7311 \| 0.9286 \| \| 0.008 \| 143.0 \| 9295 \| 0.5660 \| 0.6920 \| 0.7317 \| 0.7113 \| 0.9243 \| \| 0.008 \| 144.0 \| 9360 \| 0.5383 \| 0.7210 \| 0.7454 \| 0.7330 \| 0.9303 \| \| 0.008 \| 145.0 \| 9425 \| 0.5516 \| 0.6899 \| 0.7625 \| 0.7244 \| 0.9285 \| \| 0.008 \| 146.0 \| 9490 \| 0.5800 \| 0.7001 \| 0.7504 \| 0.7244 \| 0.9279 \| \| 0.0077 \| 147.0 \| 9555 \| 0.5994 \| 0.6904 \| 0.7538 \| 0.7207 \| 0.9271 \| \| 0.0077 \| 148.0 \| 9620 \| 0.5490 \| 0.6971 \| 0.7605 \| 0.7274 \| 0.9286 \| \| 0.0077 \| 149.0 \| 9685 \| 0.5419 \| 0.7037 \| 0.7331 \| 0.7181 \| 0.9277 \| \| 0.0077 \| 150.0 \| 9750 \| 0.5677 \| 0.7030 \| 0.7590 \| 0.7299 \| 0.9286 \| \| 0.0077 \| 151.0 \| 9815 \| 0.5506 \| 0.7133 \| 0.7576 \| 0.7348 \| 0.9306 \| \| 0.0077 \| 152.0 \| 9880 \| 0.5485 \| 0.7170 \| 0.7531 \| 0.7346 \| 0.9305 \| \| 0.0077 \| 153.0 \| 9945 \| 0.5387 \| 0.7140 \| 0.7508 \| 0.7319 \| 0.9305 \| \| 0.0074 \| 154.0 \| 10010 \| 0.5367 \| 0.7082 \| 0.7409 \| 0.7242 \| 0.9285 \| \| 0.0074 \| 155.0 \| 10075 \| 0.5347 \| 0.7194 \| 0.7434 \| 0.7312 \| 0.9313 \| \| 0.0074 \| 156.0 \| 10140 \| 0.5098 \| 0.7002 \| 0.7493 \| 0.7239 \| 0.9303 \| \| 0.0074 \| 157.0 \| 10205 \| 0.5244 \| 0.7072 \| 0.7556 \| 0.7306 \| 0.9313 \| \| 0.0074 \| 158.0 \| 10270 \| 0.5308 \| 0.6972 \| 0.7459 \| 0.7207 \| 0.9286 \| \| 0.0074 \| 159.0 \| 10335 \| 0.5254 \| 0.7082 \| 0.7418 \| 0.7246 \| 0.9302 \| \| 0.0074 \| 160.0 \| 10400 \| 0.5290 \| 0.7012 \| 0.7627 \| 0.7306 \| 0.9308 \| \| 0.0074 \| 161.0 \| 10465 \| 0.5579 \| 0.6890 \| 0.7450 \| 0.7159 \| 0.9271 \| \| 0.0063 \| 162.0 \| 10530 \| 0.5626 \| 0.7027 \| 0.7740 \| 0.7367 \| 0.9286 \| \| 0.0063 \| 163.0 \| 10595 \| 0.5592 \| 0.6973 \| 0.7587 \| 0.7267 \| 0.9287 \| \| 0.0063 \| 164.0 \| 10660 \| 0.5477 \| 0.7021 \| 0.7484 \| 0.7245 \| 0.9286 \| \| 0.0063 \| 165.0 \| 10725 \| 0.5554 \| 0.6906 \| 0.7400 \| 0.7144 \| 0.9269 \| \| 0.0063 \| 166.0 \| 10790 \| 0.5559 \| 0.7128 \| 0.7527 \| 0.7322 \| 0.9296 \| \| 0.0063 \| 167.0 \| 10855 \| 0.5043 \| 0.7081 \| 0.7333 \| 0.7205 \| 0.9281 \| \| 0.0063 \| 168.0 \| 10920 \| 0.5417 \| 0.7009 \| 0.7556 \| 0.7272 \| 0.9287 \| \| 0.0063 \| 169.0 \| 10985 \| 0.5428 \| 0.7145 \| 0.7410 \| 0.7275 \| 0.9281 \| \| 0.0075 \| 170.0 \| 11050 \| 0.5807 \| 0.6804 \| 0.7576 \| 0.7169 \| 0.9259 \| \| 0.0075 \| 171.0 \| 11115 \| 0.6278 \| 0.6838 \| 0.7409 \| 0.7112 \| 0.9255 \| \| 0.0075 \| 172.0 \| 11180 \| 0.5789 \| 0.7049 \| 0.7360 \| 0.7201 \| 0.9276 \| \| 0.0075 \| 173.0 \| 11245 \| 0.5873 \| 0.6980 \| 0.7256 \| 0.7115 \| 0.9270 \| \| 0.0075 \| 174.0 \| 11310 \| 0.5459 \| 0.6911 \| 0.7774 \| 0.7317 \| 0.9290 \| \| 0.0075 \| 175.0 \| 11375 \| 0.5281 \| 0.7018 \| 0.7535 \| 0.7267 \| 0.9294 \| \| 0.0075 \| 176.0 \| 11440 \| 0.5366 \| 0.7141 \| 0.7506 \| 0.7319 \| 0.9294 \| \| 0.006 \| 177.0 \| 11505 \| 0.5741 \| 0.7062 \| 0.7531 \| 0.7289 \| 0.9298 \| \| 0.006 \| 178.0 \| 11570 \| 0.5661 \| 0.6983 \| 0.7589 \| 0.7273 \| 0.9290 \| \| 0.006 \| 179.0 \| 11635 \| 0.5913 \| 0.7179 \| 0.7373 \| 0.7274 \| 0.9293 \| \| 0.006 \| 180.0 \| 11700 \| 0.5723 \| 0.7236 \| 0.7452 \| 0.7342 \| 0.9300 \| \| 0.006 \| 181.0 \| 11765 \| 0.6079 \| 0.7217 \| 0.7405 \| 0.7310 \| 0.9287 \| \| 0.006 \| 182.0 \| 11830 \| 0.5710 \| 0.6981 \| 0.7162 \| 0.7070 \| 0.9238 \| \| 0.006 \| 183.0 \| 11895 \| 0.5895 \| 0.7091 \| 0.7468 \| 0.7275 \| 0.9289 \| \| 0.006 \| 184.0 \| 11960 \| 0.5970 \| 0.6882 \| 0.7652 \| 0.7247 \| 0.9285 \| \| 0.0059 \| 185.0 \| 12025 \| 0.5658 \| 0.6849 \| 0.7580 \| 0.7196 \| 0.9278 \| \| 0.0059 \| 186.0 \| 12090 \| 0.5282 \| 0.6981 \| 0.7320 \| 0.7147 \| 0.9267 \| \| 0.0059 \| 187.0 \| 12155 \| 0.5622 \| 0.7084 \| 0.7502 \| 0.7287 \| 0.9293 \| \| 0.0059 \| 188.0 \| 12220 \| 0.5713 \| 0.6991 \| 0.7497 \| 0.7235 \| 0.9289 \| \| 0.0059 \| 189.0 \| 12285 \| 0.5848 \| 0.6926 \| 0.7789 \| 0.7332 \| 0.9270 \| \| 0.0059 \| 190.0 \| 12350 \| 0.5561 \| 0.7055 \| 0.7657 \| 0.7344 \| 0.9298 \| \| 0.0059 \| 191.0 \| 12415 \| 0.5761 \| 0.7040 \| 0.7405 \| 0.7218 \| 0.9269 \| \| 0.0059 \| 192.0 \| 12480 \| 0.6216 \| 0.6911 \| 0.7430 \| 0.7161 \| 0.9264 \| \| 0.0059 \| 193.0 \| 12545 \| 0.6137 \| 0.7116 \| 0.7459 \| 0.7283 \| 0.9295 \| \| 0.0059 \| 194.0 \| 12610 \| 0.5797 \| 0.6929 \| 0.7630 \| 0.7263 \| 0.9289 \| \| 0.0059 \| 195.0 \| 12675 \| 0.5176 \| 0.7081 \| 0.7675 \| 0.7366 \| 0.9319 \| \| 0.0059 \| 196.0 \| 12740 \| 0.5698 \| 0.7042 \| 0.7356 \| 0.7196 \| 0.9273 \| \| 0.0059 \| 197.0 \| 12805 \| 0.5592 \| 0.7042 \| 0.7396 \| 0.7215 \| 0.9293 \| \| 0.0059 \| 198.0 \| 12870 \| 0.5740 \| 0.7015 \| 0.7313 \| 0.7161 \| 0.9271 \| \| 0.0059 \| 199.0 \| 12935 \| 0.5589 \| 0.7190 \| 0.7311 \| 0.7250 \| 0.9297 \| \| 0.0058 \| 200.0 \| 13000 \| 0.5749 \| 0.6847 \| 0.7717 \| 0.7256 \| 0.9287 \| \| 0.0058 \| 201.0 \| 13065 \| 0.5486 \| 0.7161 \| 0.7663 \| 0.7403 \| 0.9313 \| \| 0.0058 \| 202.0 \| 13130 \| 0.5279 \| 0.7108 \| 0.7218 \| 0.7162 \| 0.9281 \| \| 0.0058 \| 203.0 \| 13195 \| 0.5850 \| 0.7102 \| 0.7252 \| 0.7176 \| 0.9273 \| \| 0.0058 \| 204.0 \| 13260 \| 0.6231 \| 0.6969 \| 0.7771 \| 0.7348 \| 0.9283 \| \| 0.0058 \| 205.0 \| 13325 \| 0.5637 \| 0.6968 \| 0.7740 \| 0.7334 \| 0.9277 \| \| 0.0058 \| 206.0 \| 13390 \| 0.5366 \| 0.7017 \| 0.7308 \| 0.7159 \| 0.9279 \| \| 0.0058 \| 207.0 \| 13455 \| 0.5978 \| 0.7098 \| 0.7250 \| 0.7173 \| 0.9267 \| \| 0.0055 \| 208.0 \| 13520 \| 0.5603 \| 0.6980 \| 0.7599 \| 0.7276 \| 0.9295 \| \| 0.0055 \| 209.0 \| 13585 \| 0.5780 \| 0.6930 \| 0.7407 \| 0.7161 \| 0.9275 \| \| 0.0055 \| 210.0 \| 13650 \| 0.5846 \| 0.7038 \| 0.7572 \| 0.7295 \| 0.9283 \| \| 0.0055 \| 211.0 \| 13715 \| 0.6297 \| 0.7018 \| 0.7250 \| 0.7132 \| 0.9241 \| \| 0.0055 \| 212.0 \| 13780 \| 0.5818 \| 0.7143 \| 0.7173 \| 0.7158 \| 0.9276 \| \| 0.0055 \| 213.0 \| 13845 \| 0.6260 \| 0.6879 \| 0.7454 \| 0.7155 \| 0.9252 \| \| 0.0055 \| 214.0 \| 13910 \| 0.6031 \| 0.7072 \| 0.7578 \| 0.7316 \| 0.9284 \| \| 0.0055 \| 215.0 \| 13975 \| 0.5846 \| 0.6954 \| 0.7684 \| 0.7301 \| 0.9279 \| \| 0.0044 \| 216.0 \| 14040 \| 0.5741 \| 0.7067 \| 0.7328 \| 0.7195 \| 0.9265 \| \| 0.0044 \| 217.0 \| 14105 \| 0.5848 \| 0.7186 \| 0.7389 \| 0.7286 \| 0.9291 \| \| 0.0044 \| 218.0 \| 14170 \| 0.5496 \| 0.7163 \| 0.7248 \| 0.7206 \| 0.9269 \| \| 0.0044 \| 219.0 \| 14235 \| 0.5700 \| 0.7089 \| 0.7520 \| 0.7298 \| 0.9288 \| \| 0.0044 \| 220.0 \| 14300 \| 0.5664 \| 0.7063 \| 0.7427 \| 0.7240 \| 0.9280 \| \| 0.0044 \| 221.0 \| 14365 \| 0.5573 \| 0.7173 \| 0.7618 \| 0.7389 \| 0.9324 \| \| 0.0044 \| 222.0 \| 14430 \| 0.5588 \| 0.7151 \| 0.7690 \| 0.7411 \| 0.9324 \| \| 0.0044 \| 223.0 \| 14495 \| 0.5809 \| 0.7129 \| 0.7455 \| 0.7289 \| 0.9294 \| \| 0.0037 \| 224.0 \| 14560 \| 0.6135 \| 0.6921 \| 0.7621 \| 0.7254 \| 0.9281 \| \| 0.0037 \| 225.0 \| 14625 \| 0.6160 \| 0.6890 \| 0.7383 \| 0.7128 \| 0.9251 \| \| 0.0037 \| 226.0 \| 14690 \| 0.5938 \| 0.6887 \| 0.7508 \| 0.7184 \| 0.9270 \| \| 0.0037 \| 227.0 \| 14755 \| 0.5687 \| 0.7076 \| 0.7473 \| 0.7269 \| 0.9296 \| \| 0.0037 \| 228.0 \| 14820 \| 0.5785 \| 0.7097 \| 0.7517 \| 0.7301 \| 0.9283 \| \| 0.0037 \| 229.0 \| 14885 \| 0.5675 \| 0.6869 \| 0.7470 \| 0.7157 \| 0.9270 \| \| 0.0037 \| 230.0 \| 14950 \| 0.5937 \| 0.7077 \| 0.7524 \| 0.7293 \| 0.9289 \| \| 0.0046 \| 231.0 \| 15015 \| 0.5650 \| 0.7097 \| 0.7414 \| 0.7252 \| 0.9294 \| \| 0.0046 \| 232.0 \| 15080 \| 0.5820 \| 0.7038 \| 0.7745 \| 0.7375 \| 0.9307 \| \| 0.0046 \| 233.0 \| 15145 \| 0.6118 \| 0.7124 \| 0.7432 \| 0.7275 \| 0.9284 \| \| 0.0046 \| 234.0 \| 15210 \| 0.6100 \| 0.7055 \| 0.7544 \| 0.7291 \| 0.9297 \| \| 0.0046 \| 235.0 \| 15275 \| 0.5869 \| 0.7149 \| 0.7592 \| 0.7364 \| 0.9302 \| \| 0.0046 \| 236.0 \| 15340 \| 0.5881 \| 0.7169 \| 0.7616 \| 0.7386 \| 0.9305 \| \| 0.0046 \| 237.0 \| 15405 \| 0.5616 \| 0.7188 \| 0.7380 \| 0.7283 \| 0.9315 \| \| 0.0046 \| 238.0 \| 15470 \| 0.5785 \| 0.7176 \| 0.7320 \| 0.7247 \| 0.9281 \| \| 0.0047 \| 239.0 \| 15535 \| 0.5979 \| 0.7159 \| 0.7466 \| 0.7310 \| 0.9279 \| \| 0.0047 \| 240.0 \| 15600 \| 0.5732 \| 0.7075 \| 0.7533 \| 0.7297 \| 0.9303 \| \| 0.0047 \| 241.0 \| 15665 \| 0.5732 \| 0.7111 \| 0.7421 \| 0.7263 \| 0.9294 \| \| 0.0047 \| 242.0 \| 15730 \| 0.5516 \| 0.7198 \| 0.7382 \| 0.7288 \| 0.9292 \| \| 0.0047 \| 243.0 \| 15795 \| 0.5505 \| 0.7073 \| 0.7697 \| 0.7372 \| 0.9310 \| \| 0.0047 \| 244.0 \| 15860 \| 0.5677 \| 0.7163 \| 0.7428 \| 0.7293 \| 0.9303 \| \| 0.0047 \| 245.0 \| 15925 \| 0.5815 \| 0.7191 \| 0.7302 \| 0.7246 \| 0.9286 \| \| 0.0047 \| 246.0 \| 15990 \| 0.6044 \| 0.7099 \| 0.7497 \| 0.7293 \| 0.9287 \| \| 0.0042 \| 247.0 \| 16055 \| 0.5540 \| 0.7144 \| 0.7558 \| 0.7345 \| 0.9302 \| \| 0.0042 \| 248.0 \| 16120 \| 0.5498 \| 0.7219 \| 0.7581 \| 0.7396 \| 0.9320 \| \| 0.0042 \| 249.0 \| 16185 \| 0.6078 \| 0.7152 \| 0.7428 \| 0.7287 \| 0.9303 \| \| 0.0042 \| 250.0 \| 16250 \| 0.6523 \| 0.7152 \| 0.7286 \| 0.7219 \| 0.9279 \| \| 0.0042 \| 251.0 \| 16315 \| 0.6075 \| 0.7184 \| 0.7621 \| 0.7396 \| 0.9319 \| \| 0.0042 \| 252.0 \| 16380 \| 0.6071 \| 0.7108 \| 0.7648 \| 0.7368 \| 0.9302 \| \| 0.0042 \| 253.0 \| 16445 \| 0.5849 \| 0.7196 \| 0.7367 \| 0.7281 \| 0.9308 \| \| 0.0031 \| 254.0 \| 16510 \| 0.5892 \| 0.7141 \| 0.7652 \| 0.7388 \| 0.9316 \| \| 0.0031 \| 255.0 \| 16575 \| 0.5991 \| 0.7174 \| 0.7310 \| 0.7241 \| 0.9273 \| \| 0.0031 \| 256.0 \| 16640 \| 0.6051 \| 0.7240 \| 0.7358 \| 0.7298 \| 0.9288 \| \| 0.0031 \| 257.0 \| 16705 \| 0.5621 \| 0.7267 \| 0.7560 \| 0.7410 \| 0.9320 \| \| 0.0031 \| 258.0 \| 16770 \| 0.5838 \| 0.7165 \| 0.7607 \| 0.7379 \| 0.9314 \| \| 0.0031 \| 259.0 \| 16835 \| 0.5713 \| 0.7092 \| 0.7709 \| 0.7388 \| 0.9318 \| \| 0.0031 \| 260.0 \| 16900 \| 0.5902 \| 0.7243 \| 0.7499 \| 0.7369 \| 0.9314 \| \| 0.0031 \| 261.0 \| 16965 \| 0.6098 \| 0.716 \| 0.7414 \| 0.7285 \| 0.9288 \| \| 0.0029 \| 262.0 \| 17030 \| 0.5979 \| 0.7193 \| 0.7502 \| 0.7344 \| 0.9308 \| \| 0.0029 \| 263.0 \| 17095 \| 0.6314 \| 0.7077 \| 0.7455 \| 0.7261 \| 0.9287 \| \| 0.0029 \| 264.0 \| 17160 \| 0.6227 \| 0.6870 \| 0.7360 \| 0.7107 \| 0.9261 \| \| 0.0029 \| 265.0 \| 17225 \| 0.5925 \| 0.6997 \| 0.7509 \| 0.7244 \| 0.9283 \| \| 0.0029 \| 266.0 \| 17290 \| 0.6090 \| 0.7049 \| 0.7428 \| 0.7234 \| 0.9274 \| \| 0.0029 \| 267.0 \| 17355 \| 0.5929 \| 0.7082 \| 0.7508 \| 0.7288 \| 0.9294 \| \| 0.0029 \| 268.0 \| 17420 \| 0.5661 \| 0.7013 \| 0.7560 \| 0.7276 \| 0.9293 \| \| 0.0029 \| 269.0 \| 17485 \| 0.6482 \| 0.7223 \| 0.7439 \| 0.7330 \| 0.9277 \| \| 0.0036 \| 270.0 \| 17550 \| 0.5838 \| 0.7123 \| 0.7479 \| 0.7297 \| 0.9300 \| \| 0.0036 \| 271.0 \| 17615 \| 0.5947 \| 0.7220 \| 0.7194 \| 0.7207 \| 0.9272 \| \| 0.0036 \| 272.0 \| 17680 \| 0.5878 \| 0.7198 \| 0.7416 \| 0.7305 \| 0.9292 \| \| 0.0036 \| 273.0 \| 17745 \| 0.5592 \| 0.7120 \| 0.7684 \| 0.7391 \| 0.9320 \| \| 0.0036 \| 274.0 \| 17810 \| 0.5885 \| 0.7163 \| 0.7513 \| 0.7334 \| 0.9297 \| \| 0.0036 \| 275.0 \| 17875 \| 0.5953 \| 0.7091 \| 0.7497 \| 0.7288 \| 0.9282 \| \| 0.0036 \| 276.0 \| 17940 \| 0.6116 \| 0.7178 \| 0.7457 \| 0.7315 \| 0.9294 \| \| 0.0028 \| 277.0 \| 18005 \| 0.6008 \| 0.7201 \| 0.7614 \| 0.7402 \| 0.9313 \| \| 0.0028 \| 278.0 \| 18070 \| 0.5998 \| 0.7236 \| 0.7452 \| 0.7342 \| 0.9311 \| \| 0.0028 \| 279.0 \| 18135 \| 0.6165 \| 0.7018 \| 0.7594 \| 0.7295 \| 0.9292 \| \| 0.0028 \| 280.0 \| 18200 \| 0.5834 \| 0.7262 \| 0.7452 \| 0.7356 \| 0.9312 \| \| 0.0028 \| 281.0 \| 18265 \| 0.6081 \| 0.7072 \| 0.7643 \| 0.7346 \| 0.9302 \| \| 0.0028 \| 282.0 \| 18330 \| 0.5909 \| 0.6981 \| 0.7580 \| 0.7268 \| 0.9301 \| \| 0.0028 \| 283.0 \| 18395 \| 0.6065 \| 0.7155 \| 0.7313 \| 0.7233 \| 0.9277 \| \| 0.0028 \| 284.0 \| 18460 \| 0.5695 \| 0.7201 \| 0.7454 \| 0.7325 \| 0.9306 \| \| 0.0031 \| 285.0 \| 18525 \| 0.6007 \| 0.7173 \| 0.7455 \| 0.7311 \| 0.9289 \| \| 0.0031 \| 286.0 \| 18590 \| 0.5894 \| 0.7041 \| 0.7488 \| 0.7258 \| 0.9284 \| \| 0.0031 \| 287.0 \| 18655 \| 0.6207 \| 0.7082 \| 0.7301 \| 0.7190 \| 0.9277 \| \| 0.0031 \| 288.0 \| 18720 \| 0.5907 \| 0.7171 \| 0.7488 \| 0.7326 \| 0.9303 \| \| 0.0031 \| 289.0 \| 18785 \| 0.6165 \| 0.7199 \| 0.7374 \| 0.7286 \| 0.9298 \| \| 0.0031 \| 290.0 \| 18850 \| 0.6304 \| 0.7277 \| 0.7419 \| 0.7347 \| 0.9297 \| \| 0.0031 \| 291.0 \| 18915 \| 0.6379 \| 0.7132 \| 0.7605 \| 0.7361 \| 0.9310 \| \| 0.0031 \| 292.0 \| 18980 \| 0.6186 \| 0.7226 \| 0.7553 \| 0.7386 \| 0.9307 \| \| 0.0027 \| 293.0 \| 19045 \| 0.6400 \| 0.7125 \| 0.7551 \| 0.7332 \| 0.9297 \| \| 0.0027 \| 294.0 \| 19110 \| 0.6244 \| 0.7197 \| 0.7527 \| 0.7359 \| 0.9305 \| \| 0.0027 \| 295.0 \| 19175 \| 0.6283 \| 0.7200 \| 0.7648 \| 0.7417 \| 0.9309 \| \| 0.0027 \| 296.0 \| 19240 \| 0.6233 \| 0.7166 \| 0.7563 \| 0.7359 \| 0.9304 \| \| 0.0027 \| 297.0 \| 19305 \| 0.6241 \| 0.7288 \| 0.7583 \| 0.7433 \| 0.9318 \| \| 0.0027 \| 298.0 \| 19370 \| 0.6316 \| 0.7266 \| 0.7450 \| 0.7357 \| 0.9299 \| \| 0.0027 \| 299.0 \| 19435 \| 0.6733 \| 0.7185 \| 0.7434 \| 0.7307 \| 0.9290 \| \| 0.0018 \| 300.0 \| 19500 \| 0.5783 \| 0.7280 \| 0.7506 \| 0.7391 \| 0.9323 \| \| 0.0018 \| 301.0 \| 19565 \| 0.6198 \| 0.7086 \| 0.7693 \| 0.7377 \| 0.9303 \| \| 0.0018 \| 302.0 \| 19630 \| 0.6555 \| 0.7151 \| 0.7508 \| 0.7325 \| 0.9273 \| \| 0.0018 \| 303.0 \| 19695 \| 0.6122 \| 0.7132 \| 0.7616 \| 0.7366 \| 0.9281 \| \| 0.0018 \| 304.0 \| 19760 \| 0.6051 \| 0.7082 \| 0.7542 \| 0.7304 \| 0.9294 \| \| 0.0018 \| 305.0 \| 19825 \| 0.6306 \| 0.7282 \| 0.7405 \| 0.7343 \| 0.9306 \| \| 0.0018 \| 306.0 \| 19890 \| 0.5903 \| 0.7161 \| 0.7650 \| 0.7397 \| 0.9313 \| \| 0.0018 \| 307.0 \| 19955 \| 0.6170 \| 0.7169 \| 0.7711 \| 0.7430 \| 0.9318 \| \| 0.0023 \| 308.0 \| 20020 \| 0.5948 \| 0.7229 \| 0.7596 \| 0.7408 \| 0.9314 \| \| 0.0023 \| 309.0 \| 20085 \| 0.6176 \| 0.7187 \| 0.7652 \| 0.7412 \| 0.9312 \| \| 0.0023 \| 310.0 \| 20150 \| 0.6166 \| 0.7219 \| 0.7576 \| 0.7393 \| 0.9305 \| \| 0.0023 \| 311.0 \| 20215 \| 0.6239 \| 0.7230 \| 0.7646 \| 0.7432 \| 0.9305 \| \| 0.0023 \| 312.0 \| 20280 \| 0.6458 \| 0.7172 \| 0.7641 \| 0.7399 \| 0.9311 \| \| 0.0023 \| 313.0 \| 20345 \| 0.6546 \| 0.7112 \| 0.7841 \| 0.7459 \| 0.9310 \| \| 0.0023 \| 314.0 \| 20410 \| 0.6326 \| 0.7273 \| 0.7627 \| 0.7445 \| 0.9312 \| \| 0.0023 \| 315.0 \| 20475 \| 0.6073 \| 0.7248 \| 0.7574 \| 0.7408 \| 0.9314 \| \| 0.0016 \| 316.0 \| 20540 \| 0.6442 \| 0.7169 \| 0.7673 \| 0.7412 \| 0.9303 \| \| 0.0016 \| 317.0 \| 20605 \| 0.6380 \| 0.7215 \| 0.7608 \| 0.7406 \| 0.9305 \| \| 0.0016 \| 318.0 \| 20670 \| 0.6590 \| 0.7126 \| 0.7461 \| 0.7290 \| 0.9280 \| \| 0.0016 \| 319.0 \| 20735 \| 0.6474 \| 0.7178 \| 0.7594 \| 0.7380 \| 0.9292 \| \| 0.0016 \| 320.0 \| 20800 \| 0.6006 \| 0.7071 \| 0.7729 \| 0.7385 \| 0.9302 \| \| 0.0016 \| 321.0 \| 20865 \| 0.6207 \| 0.7274 \| 0.7488 \| 0.7380 \| 0.9309 \| \| 0.0016 \| 322.0 \| 20930 \| 0.6735 \| 0.7032 \| 0.7679 \| 0.7341 \| 0.9277 \| \| 0.0016 \| 323.0 \| 20995 \| 0.6733 \| 0.7056 \| 0.7376 \| 0.7213 \| 0.9271 \| \| 0.0018 \| 324.0 \| 21060 \| 0.6851 \| 0.6920 \| 0.7288 \| 0.7099 \| 0.9245 \| \| 0.0018 \| 325.0 \| 21125 \| 0.6069 \| 0.7245 \| 0.7562 \| 0.7400 \| 0.9308 \| \| 0.0018 \| 326.0 \| 21190 \| 0.6677 \| 0.7148 \| 0.7562 \| 0.7349 \| 0.9299 \| \| 0.0018 \| 327.0 \| 21255 \| 0.6671 \| 0.7057 \| 0.7371 \| 0.7210 \| 0.9270 \| \| 0.0018 \| 328.0 \| 21320 \| 0.6393 \| 0.7088 \| 0.7506 \| 0.7291 \| 0.9285 \| \| 0.0018 \| 329.0 \| 21385 \| 0.6455 \| 0.7186 \| 0.7628 \| 0.7400 \| 0.9309 \| \| 0.0018 \| 330.0 \| 21450 \| 0.6320 \| 0.7349 \| 0.7358 \| 0.7354 \| 0.9301 \| \| 0.0023 \| 331.0 \| 21515 \| 0.6373 \| 0.7281 \| 0.7645 \| 0.7458 \| 0.9322 \| \| 0.0023 \| 332.0 \| 21580 \| 0.6545 \| 0.7221 \| 0.7504 \| 0.7360 \| 0.9305 \| \| 0.0023 \| 333.0 \| 21645 \| 0.6490 \| 0.7188 \| 0.7499 \| 0.7340 \| 0.9298 \| \| 0.0023 \| 334.0 \| 21710 \| 0.6504 \| 0.7053 \| 0.7623 \| 0.7327 \| 0.9305 \| \| 0.0023 \| 335.0 \| 21775 \| 0.6364 \| 0.7196 \| 0.7578 \| 0.7382 \| 0.9318 \| \| 0.0023 \| 336.0 \| 21840 \| 0.6697 \| 0.7205 \| 0.7389 \| 0.7296 \| 0.9293 \| \| 0.0023 \| 337.0 \| 21905 \| 0.6477 \| 0.7156 \| 0.7594 \| 0.7369 \| 0.9297 \| \| 0.0023 \| 338.0 \| 21970 \| 0.6673 \| 0.6943 \| 0.7634 \| 0.7272 \| 0.9280 \| \| 0.0015 \| 339.0 \| 22035 \| 0.6527 \| 0.7253 \| 0.7545 \| 0.7396 \| 0.9301 \| \| 0.0015 \| 340.0 \| 22100 \| 0.6479 \| 0.7083 \| 0.7310 \| 0.7194 \| 0.9262 \| \| 0.0015 \| 341.0 \| 22165 \| 0.6228 \| 0.7179 \| 0.7508 \| 0.7340 \| 0.9288 \| \| 0.0015 \| 342.0 \| 22230 \| 0.6324 \| 0.6975 \| 0.7706 \| 0.7322 \| 0.9280 \| \| 0.0015 \| 343.0 \| 22295 \| 0.6646 \| 0.7212 \| 0.7434 \| 0.7321 \| 0.9289 \| \| 0.0015 \| 344.0 \| 22360 \| 0.6601 \| 0.7298 \| 0.7329 \| 0.7314 \| 0.9281 \| \| 0.0015 \| 345.0 \| 22425 \| 0.6535 \| 0.7191 \| 0.7495 \| 0.7340 \| 0.9298 \| \| 0.0015 \| 346.0 \| 22490 \| 0.6840 \| 0.7189 \| 0.7437 \| 0.7311 \| 0.9284 \| \| 0.0021 \| 347.0 \| 22555 \| 0.6934 \| 0.7214 \| 0.7266 \| 0.7240 \| 0.9274 \| \| 0.0021 \| 348.0 \| 22620 \| 0.6449 \| 0.7097 \| 0.7326 \| 0.7210 \| 0.9282 \| \| 0.0021 \| 349.0 \| 22685 \| 0.6576 \| 0.7119 \| 0.7520 \| 0.7314 \| 0.9281 \| \| 0.0021 \| 350.0 \| 22750 \| 0.6672 \| 0.7265 \| 0.7473 \| 0.7368 \| 0.9301 \| \| 0.0021 \| 351.0 \| 22815 \| 0.6704 \| 0.7193 \| 0.7639 \| 0.7410 \| 0.9305 \| \| 0.0021 \| 352.0 \| 22880 \| 0.6893 \| 0.7146 \| 0.7648 \| 0.7389 \| 0.9299 \| \| 0.0021 \| 353.0 \| 22945 \| 0.6747 \| 0.7299 \| 0.7589 \| 0.7441 \| 0.9307 \| \| 0.0013 \| 354.0 \| 23010 \| 0.6942 \| 0.7119 \| 0.7650 \| 0.7375 \| 0.9285 \| \| 0.0013 \| 355.0 \| 23075 \| 0.7042 \| 0.7223 \| 0.7347 \| 0.7284 \| 0.9280 \| \| 0.0013 \| 356.0 \| 23140 \| 0.7009 \| 0.7184 \| 0.7666 \| 0.7417 \| 0.9313 \| \| 0.0013 \| 357.0 \| 23205 \| 0.6892 \| 0.7262 \| 0.7756 \| 0.7501 \| 0.9317 \| \| 0.0013 \| 358.0 \| 23270 \| 0.7022 \| 0.7312 \| 0.7607 \| 0.7456 \| 0.9311 \| \| 0.0013 \| 359.0 \| 23335 \| 0.7169 \| 0.7067 \| 0.7657 \| 0.7350 \| 0.9277 \| \| 0.0013 \| 360.0 \| 23400 \| 0.7329 \| 0.7179 \| 0.7551 \| 0.7360 \| 0.9273 \| \| 0.0013 \| 361.0 \| 23465 \| 0.7030 \| 0.7242 \| 0.7391 \| 0.7316 \| 0.9288 \| \| 0.0007 \| 362.0 \| 23530 \| 0.7063 \| 0.7185 \| 0.7529 \| 0.7353 \| 0.9290 \| \| 0.0007 \| 363.0 \| 23595 \| 0.7068 \| 0.7239 \| 0.7466 \| 0.7351 \| 0.9293 \| \| 0.0007 \| 364.0 \| 23660 \| 0.6959 \| 0.7159 \| 0.7722 \| 0.7430 \| 0.9303 \| \| 0.0007 \| 365.0 \| 23725 \| 0.7632 \| 0.7015 \| 0.7450 \| 0.7226 \| 0.9258 \| \| 0.0007 \| 366.0 \| 23790 \| 0.7022 \| 0.7246 \| 0.7301 \| 0.7273 \| 0.9268 \| \| 0.0007 \| 367.0 \| 23855 \| 0.6818 \| 0.7161 \| 0.7720 \| 0.7430 \| 0.9305 \| \| 0.0007 \| 368.0 \| 23920 \| 0.6895 \| 0.7275 \| 0.7578 \| 0.7423 \| 0.9311 \| \| 0.0007 \| 369.0 \| 23985 \| 0.6987 \| 0.7124 \| 0.7473 \| 0.7295 \| 0.9293 \| \| 0.0016 \| 370.0 \| 24050 \| 0.6857 \| 0.7301 \| 0.7531 \| 0.7414 \| 0.9310 \| \| 0.0016 \| 371.0 \| 24115 \| 0.7022 \| 0.7271 \| 0.7418 \| 0.7344 \| 0.9297 \| \| 0.0016 \| 372.0 \| 24180 \| 0.6936 \| 0.7277 \| 0.7672 \| 0.7469 \| 0.9313 \| \| 0.0016 \| 373.0 \| 24245 \| 0.6836 \| 0.7240 \| 0.7659 \| 0.7444 \| 0.9316 \| \| 0.0016 \| 374.0 \| 24310 \| 0.6868 \| 0.7221 \| 0.7576 \| 0.7394 \| 0.9311 \| \| 0.0016 \| 375.0 \| 24375 \| 0.6903 \| 0.7315 \| 0.7648 \| 0.7478 \| 0.9314 \| \| 0.0016 \| 376.0 \| 24440 \| 0.6973 \| 0.7172 \| 0.7700 \| 0.7427 \| 0.9315 \| \| 0.0008 \| 377.0 \| 24505 \| 0.6768 \| 0.7233 \| 0.7470 \| 0.7349 \| 0.9297 \| \| 0.0008 \| 378.0 \| 24570 \| 0.7004 \| 0.7347 \| 0.7171 \| 0.7258 \| 0.9273 \| \| 0.0008 \| 379.0 \| 24635 \| 0.6794 \| 0.7267 \| 0.7464 \| 0.7364 \| 0.9291 \| \| 0.0008 \| 380.0 \| 24700 \| 0.6835 \| 0.7236 \| 0.7599 \| 0.7413 \| 0.9298 \| \| 0.0008 \| 381.0 \| 24765 \| 0.6758 \| 0.7188 \| 0.7663 \| 0.7417 \| 0.9295 \| \| 0.0008 \| 382.0 \| 24830 \| 0.6731 \| 0.7247 \| 0.7542 \| 0.7391 \| 0.9304 \| \| 0.0008 \| 383.0 \| 24895 \| 0.6734 \| 0.7232 \| 0.7513 \| 0.7370 \| 0.9302 \| \| 0.0008 \| 384.0 \| 24960 \| 0.6849 \| 0.7207 \| 0.7673 \| 0.7433 \| 0.9305 \| \| 0.001 \| 385.0 \| 25025 \| 0.6711 \| 0.7216 \| 0.7533 \| 0.7371 \| 0.9297 \| \| 0.001 \| 386.0 \| 25090 \| 0.6792 \| 0.7326 \| 0.7508 \| 0.7416 \| 0.9298 \| \| 0.001 \| 387.0 \| 25155 \| 0.6708 \| 0.7074 \| 0.7603 \| 0.7329 \| 0.9284 \| \| 0.001 \| 388.0 \| 25220 \| 0.6708 \| 0.7157 \| 0.7468 \| 0.7309 \| 0.9282 \| \| 0.001 \| 389.0 \| 25285 \| 0.6720 \| 0.7191 \| 0.7639 \| 0.7408 \| 0.9292 \| \| 0.001 \| 390.0 \| 25350 \| 0.6690 \| 0.7097 \| 0.7738 \| 0.7404 \| 0.9291 \| \| 0.001 \| 391.0 \| 25415 \| 0.6585 \| 0.7209 \| 0.7599 \| 0.7399 \| 0.9304 \| \| 0.001 \| 392.0 \| 25480 \| 0.6568 \| 0.7131 \| 0.7717 \| 0.7412 \| 0.9313 \| \| 0.0009 \| 393.0 \| 25545 \| 0.6936 \| 0.7104 \| 0.7581 \| 0.7335 \| 0.9287 \| \| 0.0009 \| 394.0 \| 25610 \| 0.6739 \| 0.7264 \| 0.7639 \| 0.7447 \| 0.9316 \| \| 0.0009 \| 395.0 \| 25675 \| 0.7106 \| 0.7215 \| 0.7520 \| 0.7364 \| 0.9301 \| \| 0.0009 \| 396.0 \| 25740 \| 0.6742 \| 0.7276 \| 0.7490 \| 0.7381 \| 0.9309 \| \| 0.0009 \| 397.0 \| 25805 \| 0.6745 \| 0.7238 \| 0.7484 \| 0.7359 \| 0.9311 \| \| 0.0009 \| 398.0 \| 25870 \| 0.6849 \| 0.7247 \| 0.7614 \| 0.7426 \| 0.9297 \| \| 0.0009 \| 399.0 \| 25935 \| 0.6664 \| 0.7242 \| 0.7558 \| 0.7397 \| 0.9308 \| \| 0.001 \| 400.0 \| 26000 \| 0.7093 \| 0.7172 \| 0.7403 \| 0.7286 \| 0.9287 \| \| 0.001 \| 401.0 \| 26065 \| 0.6641 \| 0.7202 \| 0.7580 \| 0.7386 \| 0.9307 \| \| 0.001 \| 402.0 \| 26130 \| 0.6643 \| 0.7137 \| 0.7711 \| 0.7413 \| 0.9303 \| \| 0.001 \| 403.0 \| 26195 \| 0.6608 \| 0.7263 \| 0.7551 \| 0.7404 \| 0.9307 \| \| 0.001 \| 404.0 \| 26260 \| 0.6820 \| 0.7251 \| 0.7632 \| 0.7436 \| 0.9314 \| \| 0.001 \| 405.0 \| 26325 \| 0.6736 \| 0.7293 \| 0.7623 \| 0.7454 \| 0.9314 \| \| 0.001 \| 406.0 \| 26390 \| 0.6834 \| 0.7274 \| 0.7509 \| 0.7390 \| 0.9303 \| \| 0.001 \| 407.0 \| 26455 \| 0.6767 \| 0.7218 \| 0.7513 \| 0.7363 \| 0.9312 \| \| 0.0006 \| 408.0 \| 26520 \| 0.6918 \| 0.7176 \| 0.7738 \| 0.7446 \| 0.9315 \| \| 0.0006 \| 409.0 \| 26585 \| 0.6917 \| 0.7276 \| 0.7639 \| 0.7453 \| 0.9324 \| \| 0.0006 \| 410.0 \| 26650 \| 0.7120 \| 0.7111 \| 0.7603 \| 0.7349 \| 0.9297 \| \| 0.0006 \| 411.0 \| 26715 \| 0.6895 \| 0.7251 \| 0.7535 \| 0.7390 \| 0.9313 \| \| 0.0006 \| 412.0 \| 26780 \| 0.6915 \| 0.7220 \| 0.7627 \| 0.7417 \| 0.9316 \| \| 0.0006 \| 413.0 \| 26845 \| 0.7040 \| 0.7071 \| 0.7756 \| 0.7398 \| 0.9299 \| \| 0.0006 \| 414.0 \| 26910 \| 0.6899 \| 0.7248 \| 0.7526 \| 0.7384 \| 0.9308 \| \| 0.0006 \| 415.0 \| 26975 \| 0.6993 \| 0.7229 \| 0.7639 \| 0.7428 \| 0.9315 \| \| 0.0005 \| 416.0 \| 27040 \| 0.7063 \| 0.7224 \| 0.7672 \| 0.7441 \| 0.9316 \| \| 0.0005 \| 417.0 \| 27105 \| 0.6930 \| 0.7241 \| 0.7708 \| 0.7467 \| 0.9327 \| \| 0.0005 \| 418.0 \| 27170 \| 0.6957 \| 0.7245 \| 0.7610 \| 0.7423 \| 0.9320 \| \| 0.0005 \| 419.0 \| 27235 \| 0.6978 \| 0.7256 \| 0.7538 \| 0.7394 \| 0.9317 \| \| 0.0005 \| 420.0 \| 27300 \| 0.6905 \| 0.7285 \| 0.7562 \| 0.7421 \| 0.9327 \| \| 0.0005 \| 421.0 \| 27365 \| 0.7016 \| 0.7140 \| 0.7598 \| 0.7362 \| 0.9312 \| \| 0.0005 \| 422.0 \| 27430 \| 0.7007 \| 0.7206 \| 0.7590 \| 0.7393 \| 0.9323 \| \| 0.0005 \| 423.0 \| 27495 \| 0.7106 \| 0.7175 \| 0.7554 \| 0.7360 \| 0.9315 \| \| 0.0006 \| 424.0 \| 27560 \| 0.6984 \| 0.7304 \| 0.7778 \| 0.7534 \| 0.9331 \| \| 0.0006 \| 425.0 \| 27625 \| 0.6956 \| 0.7310 \| 0.7771 \| 0.7533 \| 0.9330 \| \| 0.0006 \| 426.0 \| 27690 \| 0.7069 \| 0.7149 \| 0.7583 \| 0.7360 \| 0.9303 \| \| 0.0006 \| 427.0 \| 27755 \| 0.7152 \| 0.7233 \| 0.7389 \| 0.7310 \| 0.9289 \| \| 0.0006 \| 428.0 \| 27820 \| 0.7047 \| 0.7269 \| 0.7639 \| 0.7449 \| 0.9319 \| \| 0.0006 \| 429.0 \| 27885 \| 0.7078 \| 0.7295 \| 0.7556 \| 0.7423 \| 0.9318 \| \| 0.0006 \| 430.0 \| 27950 \| 0.7091 \| 0.7322 \| 0.7610 \| 0.7463 \| 0.9321 \| \| 0.0006 \| 431.0 \| 28015 \| 0.7219 \| 0.7340 \| 0.7464 \| 0.7402 \| 0.9310 \| \| 0.0006 \| 432.0 \| 28080 \| 0.7183 \| 0.7205 \| 0.7571 \| 0.7383 \| 0.9303 \| \| 0.0006 \| 433.0 \| 28145 \| 0.7059 \| 0.7262 \| 0.7562 \| 0.7409 \| 0.9317 \| \| 0.0006 \| 434.0 \| 28210 \| 0.7006 \| 0.7260 \| 0.7654 \| 0.7452 \| 0.9316 \| \| 0.0006 \| 435.0 \| 28275 \| 0.7073 \| 0.7209 \| 0.7623 \| 0.7410 \| 0.9308 \| \| 0.0006 \| 436.0 \| 28340 \| 0.7080 \| 0.7270 \| 0.7720 \| 0.7488 \| 0.9324 \| \| 0.0006 \| 437.0 \| 28405 \| 0.7054 \| 0.7218 \| 0.7619 \| 0.7413 \| 0.9312 \| \| 0.0006 \| 438.0 \| 28470 \| 0.7128 \| 0.7294 \| 0.7529 \| 0.7410 \| 0.9314 \| \| 0.0004 \| 439.0 \| 28535 \| 0.7119 \| 0.7234 \| 0.7587 \| 0.7406 \| 0.9314 \| \| 0.0004 \| 440.0 \| 28600 \| 0.7244 \| 0.7175 \| 0.7632 \| 0.7396 \| 0.9313 \| \| 0.0004 \| 441.0 \| 28665 \| 0.7293 \| 0.7297 \| 0.7587 \| 0.7439 \| 0.9315 \| \| 0.0004 \| 442.0 \| 28730 \| 0.7422 \| 0.7194 \| 0.7587 \| 0.7385 \| 0.9310 \| \| 0.0004 \| 443.0 \| 28795 \| 0.7304 \| 0.7264 \| 0.7645 \| 0.7449 \| 0.9313 \| \| 0.0004 \| 444.0 \| 28860 \| 0.7224 \| 0.7307 \| 0.7551 \| 0.7427 \| 0.9315 \| \| 0.0004 \| 445.0 \| 28925 \| 0.7144 \| 0.7317 \| 0.7598 \| 0.7455 \| 0.9325 \| \| 0.0004 \| 446.0 \| 28990 \| 0.7153 \| 0.7210 \| 0.7627 \| 0.7412 \| 0.9325 \| \| 0.0004 \| 447.0 \| 29055 \| 0.7269 \| 0.7273 \| 0.7540 \| 0.7404 \| 0.9315 \| \| 0.0004 \| 448.0 \| 29120 \| 0.7487 \| 0.7198 \| 0.7608 \| 0.7397 \| 0.9304 \| \| 0.0004 \| 449.0 \| 29185 \| 0.7341 \| 0.7238 \| 0.7580 \| 0.7405 \| 0.9312 \| \| 0.0004 \| 450.0 \| 29250 \| 0.7212 \| 0.7325 \| 0.7697 \| 0.7506 \| 0.9327 \| \| 0.0004 \| 451.0 \| 29315 \| 0.7180 \| 0.7302 \| 0.7565 \| 0.7431 \| 0.9317 \| \| 0.0004 \| 452.0 \| 29380 \| 0.7197 \| 0.7257 \| 0.7724 \| 0.7483 \| 0.9324 \| \| 0.0004 \| 453.0 \| 29445 \| 0.7183 \| 0.7194 \| 0.7536 \| 0.7361 \| 0.9307 \| \| 0.0003 \| 454.0 \| 29510 \| 0.7264 \| 0.7178 \| 0.7679 \| 0.7420 \| 0.9308 \| \| 0.0003 \| 455.0 \| 29575 \| 0.7120 \| 0.7283 \| 0.7608 \| 0.7442 \| 0.9322 \| \| 0.0003 \| 456.0 \| 29640 \| 0.7410 \| 0.7290 \| 0.7513 \| 0.7400 \| 0.9305 \| \| 0.0003 \| 457.0 \| 29705 \| 0.7289 \| 0.7338 \| 0.7574 \| 0.7454 \| 0.9317 \| \| 0.0003 \| 458.0 \| 29770 \| 0.7051 \| 0.7332 \| 0.7576 \| 0.7452 \| 0.9321 \| \| 0.0003 \| 459.0 \| 29835 \| 0.7243 \| 0.7292 \| 0.7608 \| 0.7447 \| 0.9311 \| \| 0.0003 \| 460.0 \| 29900 \| 0.7140 \| 0.7314 \| 0.7562 \| 0.7436 \| 0.9318 \| \| 0.0003 \| 461.0 \| 29965 \| 0.7157 \| 0.7257 \| 0.7599 \| 0.7424 \| 0.9315 \| \| 0.0003 \| 462.0 \| 30030 \| 0.7115 \| 0.7277 \| 0.7646 \| 0.7457 \| 0.9324 \| \| 0.0003 \| 463.0 \| 30095 \| 0.7073 \| 0.7296 \| 0.7765 \| 0.7523 \| 0.9331 \| \| 0.0003 \| 464.0 \| 30160 \| 0.6973 \| 0.7352 \| 0.7594 \| 0.7471 \| 0.9336 \| \| 0.0003 \| 465.0 \| 30225 \| 0.6869 \| 0.7332 \| 0.7700 \| 0.7512 \| 0.9331 \| \| 0.0003 \| 466.0 \| 30290 \| 0.6951 \| 0.7304 \| 0.7704 \| 0.7499 \| 0.9327 \| \| 0.0003 \| 467.0 \| 30355 \| 0.7065 \| 0.7250 \| 0.7691 \| 0.7464 \| 0.9318 \| \| 0.0003 \| 468.0 \| 30420 \| 0.7112 \| 0.7280 \| 0.7679 \| 0.7474 \| 0.9319 \| \| 0.0003 \| 469.0 \| 30485 \| 0.7115 \| 0.7303 \| 0.7695 \| 0.7494 \| 0.9321 \| \| 0.0004 \| 470.0 \| 30550 \| 0.7132 \| 0.7288 \| 0.7681 \| 0.7479 \| 0.9324 \| \| 0.0004 \| 471.0 \| 30615 \| 0.7132 \| 0.7278 \| 0.7699 \| 0.7482 \| 0.9321 \| \| 0.0004 \| 472.0 \| 30680 \| 0.7200 \| 0.7259 \| 0.7630 \| 0.7440 \| 0.9313 \| \| 0.0004 \| 473.0 \| 30745 \| 0.7191 \| 0.7233 \| 0.7772 \| 0.7493 \| 0.9319 \| \| 0.0004 \| 474.0 \| 30810 \| 0.7178 \| 0.7320 \| 0.7679 \| 0.7495 \| 0.9327 \| \| 0.0004 \| 475.0 \| 30875 \| 0.7182 \| 0.7331 \| 0.7659 \| 0.7492 \| 0.9325 \| \| 0.0004 \| 476.0 \| 30940 \| 0.7216 \| 0.7309 \| 0.7654 \| 0.7477 \| 0.9322 \| \| 0.0002 \| 477.0 \| 31005 \| 0.7226 \| 0.7309 \| 0.7648 \| 0.7474 \| 0.9322 \| \| 0.0002 \| 478.0 \| 31070 \| 0.7241 \| 0.7259 \| 0.7697 \| 0.7471 \| 0.9319 \| \| 0.0002 \| 479.0 \| 31135 \| 0.7259 \| 0.7280 \| 0.7697 \| 0.7482 \| 0.9320 \| \| 0.0002 \| 480.0 \| 31200 \| 0.7258 \| 0.7247 \| 0.7738 \| 0.7485 \| 0.9319 \| \| 0.0002 \| 481.0 \| 31265 \| 0.7239 \| 0.7274 \| 0.7765 \| 0.7512 \| 0.9327 \| \| 0.0002 \| 482.0 \| 31330 \| 0.7243 \| 0.7289 \| 0.7763 \| 0.7519 \| 0.9327 \| \| 0.0002 \| 483.0 \| 31395 \| 0.7231 \| 0.7267 \| 0.7771 \| 0.7510 \| 0.9328 \| \| 0.0002 \| 484.0 \| 31460 \| 0.7221 \| 0.7283 \| 0.7744 \| 0.7506 \| 0.9328 \| \| 0.0001 \| 485.0 \| 31525 \| 0.7217 \| 0.7309 \| 0.7688 \| 0.7493 \| 0.9326 \| \| 0.0001 \| 486.0 \| 31590 \| 0.7211 \| 0.7317 \| 0.7616 \| 0.7463 \| 0.9323 \| \| 0.0001 \| 487.0 \| 31655 \| 0.7206 \| 0.7330 \| 0.7610 \| 0.7468 \| 0.9325 \| \| 0.0001 \| 488.0 \| 31720 \| 0.7198 \| 0.7326 \| 0.7580 \| 0.7451 \| 0.9323 \| \| 0.0001 \| 489.0 \| 31785 \| 0.7203 \| 0.7311 \| 0.7598 \| 0.7451 \| 0.9321 \| \| 0.0001 \| 490.0 \| 31850 \| 0.7205 \| 0.7312 \| 0.7614 \| 0.7460 \| 0.9323 \| \| 0.0001 \| 491.0 \| 31915 \| 0.7167 \| 0.7294 \| 0.7673 \| 0.7479 \| 0.9328 \| \| 0.0001 \| 492.0 \| 31980 \| 0.7164 \| 0.7311 \| 0.7646 \| 0.7475 \| 0.9328 \| \| 0.0003 \| 493.0 \| 32045 \| 0.7169 \| 0.7312 \| 0.7655 \| 0.7480 \| 0.9328 \| \| 0.0003 \| 494.0 \| 32110 \| 0.7184 \| 0.7324 \| 0.7675 \| 0.7496 \| 0.9330 \| \| 0.0003 \| 495.0 \| 32175 \| 0.7188 \| 0.7320 \| 0.7664 \| 0.7488 \| 0.9329 \| \| 0.0003 \| 496.0 \| 32240 \| 0.7187 \| 0.7320 \| 0.7655 \| 0.7484 \| 0.9328 \| \| 0.0003 \| 497.0 \| 32305 \| 0.7189 \| 0.7321 \| 0.7659 \| 0.7486 \| 0.9329 \| \| 0.0003 \| 498.0 \| 32370 \| 0.7192 \| 0.7315 \| 0.7659 \| 0.7483 \| 0.9328 \| \| 0.0003 \| 499.0 \| 32435 \| 0.7191 \| 0.7313 \| 0.7650 \| 0.7478 \| 0.9327 \| \| 0.0001 \| 500.0 \| 32500 \| 0.7191 \| 0.7311 \| 0.7648 \| 0.7476 \| 0.9327 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3	[ -0.7027727961540222, -0.6241275668144226, 0.32486221194267273, 0.07315682619810104, -0.06576995551586151, 0.15181133151054382, 0.033089492470026016, 0.08525516092777252, 0.9334553480148315, 0.4844721853733063, -0.6719618439674377, -0.6788055896759033, -0.6688582301139832, -0.26132991909980774, 0.26526641845703125, 0.6556826233863831, 0.1824091374874115, -0.3999754786491394, -0.11088170111179352, -0.2265876829624176, -0.1804906129837036, 0.10354218631982803, -0.7669479846954346, -0.03476816043257713, 0.18063853681087494, 0.6815066337585449, 0.8950104117393494, 0.7748779654502869, 0.47113990783691406, 0.4191838800907135, -0.29636791348457336, 0.15064148604869843, -0.34222936630249023, -0.7846683859825134, 0.059743884950876236, -0.644518256187439, -0.2960253059864044, -0.047374412417411804, 0.40871796011924744, 0.7732676863670349, -0.06747933477163315, 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model-hub/mistral-8x7b-chat	model-hub	2023-12-14T16:33:53Z	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-12-14T16:33:53Z	A very capable chat model built on top of the new Mistral MoE model, trained on the SlimOrca dataset for 1 epoch, using QLoRA. Inference: ```py import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("mattshumer/mistral-8x7b-chat", low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True) tok = AutoTokenizer.from_pretrained("mattshumer/mistral-8x7b-chat") x = tok.encode(PROMPT_GOES_HERE, return_tensors="pt").cuda() x = model.generate(x, max_new_tokens=512).cpu() print(tok.batch_decode(x)) ``` Prompt Template: ``` <\|im_start\|>system You are an AI assistant.<\|im_end\|> <\|im_start\|>user Hi, how are you?<\|im_end\|> <\|im_start\|>assistant I'm doing well, thanks for asking!<\|im_end\|> <\|im_start\|>user Write me a poem about AI.<\|im_end\|> ``` Trained w/ Axolotl on 6x H100s for nine hours.	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model-hub/stablelm-zephyr-3b	model-hub	2023-12-14T16:34:04Z	0	transformers	[ "transformers", "safetensors", "stablelm_epoch", "text-generation", "causal-lm", "custom_code", "en", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:HuggingFaceH4/ultrafeedback_binarized", "dataset:meta-math/MetaMathQA", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:Intel/orca_dpo_pairs", "arxiv:2305.18290", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-12-14T16:34:04Z	--- datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - Intel/orca_dpo_pairs language: - en tags: - causal-lm extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox license: other --- # `StableLM Zephyr 3B` ## Model Description `StableLM Zephyr 3B` is a 3 billion parameter instruction tuned inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline this model was trained on a mix of publicly available datasets, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290), evaluation for this model based on [MT Bench](https://tatsu-lab.github.io/alpaca_eval/) and [Alpaca Benchmark](https://tatsu-lab.github.io/alpaca_eval/) ## Usage `StableLM Zephyr 3B` uses the following instruction format: ``` <\|user\|> List 3 synonyms for the word "tiny"<\|endoftext\|> <\|assistant\|> 1. Dwarf 2. Little 3. Petite<\|endoftext\|> ``` This format is also available through the tokenizer's `apply_chat_template` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-zephyr-3b') model = AutoModelForCausalLM.from_pretrained( 'stabilityai/stablelm-zephyr-3b', trust_remote_code=True, device_map="auto" ) prompt = [{'role': 'user', 'content': 'List 3 synonyms for the word "tiny"'}] inputs = tokenizer.apply_chat_template( prompt, add_generation_prompt=True, return_tensors='pt' ) tokens = model.generate( inputs.to(model.device), max_new_tokens=1024, temperature=0.8, do_sample=True ) print(tokenizer.decode(tokens[0], skip_special_tokens=False)) ``` You can also see how to run a performance optimized version of this model [here](https://github.com/eaidova/openvino_notebooks/blob/ea/stateful_chatbot/notebooks/273-stable-zephyr-3b-chatbot/273-stable-zephyr-3b-chatbot.ipynb) using [OpenVINO](https://docs.openvino.ai/2023.2/home.html) from Intel. ## Model Details * Developed by: [Stability AI](https://stability.ai/) * Model type: `StableLM Zephyr 3B` model is an auto-regressive language model based on the transformer decoder architecture. * Language(s): English * Library: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git) * Finetuned from model: [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) * License: [StabilityAI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-zephyr-3b/raw/main/LICENSE). If you want to use this model for your commercial products or purposes, please contact us [here](https://stability.ai/contact) to learn more. * Contact: For questions and comments about the model, please email `[email protected]` ### Training Dataset The dataset is comprised of a mixture of open datasets large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): 1. SFT Datasets - HuggingFaceH4/ultrachat_200k - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - Open-Orca/SlimOrca 2. Preference Datasets: - HuggingFaceH4/ultrafeedback_binarized - Intel/orca_dpo_pairs ## Performance ### MT-Bench and Alpaca Bench <img src="https://cdn-uploads.huggingface.co/production/uploads/6310474ca119d49bc1eb0d80/8WIZS6dAlu5kSH-382pMl.png" alt="mt_bench_plot" width="600"/> \| Model \| Size \| Alignment \| MT-Bench (score) \| AlpacaEval (win rate %) \| \|-------------\|-----\|----\|---------------\|--------------\| \| StableLM Zephyr 3B 🪁 \| 3B \| DPO \| 6.64 \| 76.00 \| \| StableLM Zephyr (SFT only) \| 3B \| SFT \| 6.04 \| 71.15 \| \| Capybara v1.9 \| 3B \| dSFT \| 5.94 \| - \| \| MPT-Chat \| 7B \|dSFT \|5.42\| -\| \| Xwin-LM v0.1 \| 7B\| dPPO\| 6.19\| 87.83\| \| Mistral-Instruct v0.1 \| 7B\| - \| 6.84 \|-\| \| Zephyr-7b-α \|7B\| dDPO\| 6.88\| -\| \| Zephyr-7b-β\| 7B \| dDPO \| 7.34 \| 90.60 \| \| Falcon-Instruct \| 40B \|dSFT \|5.17 \|45.71\| \| Guanaco \| 65B \| SFT \|6.41\| 71.80\| \| Llama2-Chat \| 70B \|RLHF \|6.86\| 92.66\| \| Vicuna v1.3 \| 33B \|dSFT \|7.12 \|88.99\| \| WizardLM v1.0 \| 70B \|dSFT \|7.71 \|-\| \| Xwin-LM v0.1 \| 70B \|dPPO \|- \|95.57\| \| GPT-3.5-turbo \| - \|RLHF \|7.94 \|89.37\| \| Claude 2 \| - \|RLHF \|8.06\| 91.36\| \| GPT-4 \| -\| RLHF \|8.99\| 95.28\| ## Other benchmarks: \| Task \| Value \| \|-----------------------\|---------------------------\| \| ARC (25-shot) \| 47.0 \| \| HellaSwag (10-shot) \| 74.2 \| \| MMLU (5-shot) \| 46.3 \| \| TruthfulQA (0-shot) \| 46.5 \| \| Winogrande (5-shot) \| 65.5 \| \| GSM8K (5-shot) \| 42.3 \| \| BigBench (Avg) \| 35.26 \| \| AGI Benchmark (Avg) \| 33.23 \| ### Training Infrastructure * Hardware: `StableLM Zephyr 3B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes. * Code Base: We use our internal script for SFT steps and used [HuggingFace Alignment Handbook script](https://github.com/huggingface/alignment-handbook) for DPO training. ## Commitment to Ethical AI In line with our responsibility towards ethical AI development, `StableLM Zephyr 3B` is released with a focus on ensuring safety, reliability, and appropriateness in its applications. To this end, we have evaluated `StableLM Zephyr 3B` on 488 malicious prompts and used standard protocols to assess the harmfulness of its outputs. Compared to Zephyr-7b-β, `StableLM Zephyr 3B` reduces the number of harmful outputs as assessed by GPT-4 by 55. Additionally, we performed an internal red teaming event targeting the following abuse areas: * Self-Harm Methods: (Suicide Methods, Encouragement of Self-Harm, Methods and encouragement of Eating Disorders) * Misinformation: (Health, Conspiracy Theories, Social Unrest/Conflict, Political Misinformation, & Climate change) * Hate Speech: (Race, Stereotypes, Immigrants, Gender, Personally Identifiable Information such as Social security numbers, Full names, ID numbers, Email addresses, and telephone numbers) We have incorporated the findings of our malicious prompts evaluation and red teaming event into our release. Users are encouraged to fine-tune and evaluate the model to suit their specific needs, considering the potential biases and limitations found in `StableLM Zephyr 3B` and inherent in other LLM models. ## Use and Limitations ### Intended Use The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. ### Limitations and Bias This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses. Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it is willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.	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fsicoli/whisper-large-v3-pt-cv15-fleurs-mls-td-co	fsicoli	2023-12-17T21:19:33Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-14T16:34:31Z	Entry not found	[ -0.3227650821208954, -0.22568479180335999, 0.8622263669967651, 0.4346153140068054, -0.5282987952232361, 0.7012966871261597, 0.7915722727775574, 0.07618651539087296, 0.7746027112007141, 0.2563222348690033, -0.7852821350097656, -0.225738525390625, -0.910447895526886, 0.5715667009353638, -0.3992331027984619, 0.5791246294975281, -0.14494472742080688, -0.10751137882471085, 0.28233787417411804, -0.27689510583877563, -0.5409226417541504, -0.36855220794677734, -1.1902778148651123, 0.06149188429117203, 0.5316580533981323, 0.7435144186019897, 0.7584060430526733, 0.36521705985069275, 0.6432579159736633, 0.3932287096977234, -0.23138964176177979, 0.48270511627197266, -0.04171810299158096, 0.002603815635666251, 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model-hub/mixtral-7b-8expert	model-hub	2023-12-14T16:34:37Z	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "en", "fr", "it", "es", "de", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-12-14T16:34:37Z	--- license: apache-2.0 language: - en - fr - it - es - de --- # Mixtral 7b 8 Expert ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62e3b6ab0c2a907c388e4965/6m3e2d2BNXDjy6_qHd2LT.png) This is a preliminary HuggingFace implementation of the newly released MoE model by MistralAi. Make sure to load with `trust_remote_code=True`. Thanks to @dzhulgakov for his early implementation (https://github.com/dzhulgakov/llama-mistral) that helped me find a working setup. Also many thanks to our friends at [LAION](https://laion.ai) and [HessianAI](https://hessian.ai/) for the compute used for these projects! Benchmark scores: ``` hella swag: 0.8661 winogrande: 0.824 truthfulqa_mc2: 0.4855 arc_challenge: 0.6638 gsm8k: 0.5709 MMLU: 0.7173 ``` # Basic Inference setup ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("DiscoResearch/mixtral-7b-8expert", low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True) tok = AutoTokenizer.from_pretrained("DiscoResearch/mixtral-7b-8expert") x = tok.encode("The mistral wind in is a phenomenon ", return_tensors="pt").cuda() x = model.generate(x, max_new_tokens=128).cpu() print(tok.batch_decode(x)) ``` # Conversion Use `convert_mistral_moe_weights_to_hf.py --input_dir ./input_dir --model_size 7B --output_dir ./output` to convert the original consolidated weights to this HF setup. 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model-hub/falcon-40b	model-hub	2023-12-14T16:35:44Z	0	transformers	[ "transformers", "pytorch", "falcon", "text-generation", "custom_code", "en", "de", "es", "fr", "dataset:tiiuae/falcon-refinedweb", "arxiv:2205.14135", "arxiv:1911.02150", "arxiv:2101.00027", "arxiv:2005.14165", "arxiv:2104.09864", "arxiv:2306.01116", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-12-14T16:35:43Z	--- datasets: - tiiuae/falcon-refinedweb language: - en - de - es - fr inference: false license: apache-2.0 --- # 🚀 Falcon-40B Falcon-40B is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license. Paper coming soon 😊. 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-40B? * It is the best open-source model currently available. Falcon-40B outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * It features an architecture optimized for inference, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). * It is made available under a permissive Apache 2.0 license allowing for commercial use, without any royalties or restrictions. * ⚠️ This is a raw, pretrained model, which should be further finetuned for most usecases. If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct). 💸 Looking for a smaller, less expensive model? [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) is Falcon-40B's little brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-40b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 Falcon LLMs require PyTorch 2.0 for use with `transformers`! For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need at least 85-100GB of memory to swiftly run inference with Falcon-40B. # Model Card for Falcon-40B ## Model Details ### Model Description - Developed by: [https://www.tii.ae](https://www.tii.ae); - Model type: Causal decoder-only; - Language(s) (NLP): English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish); - License: Apache 2.0 license. ### Model Source - Paper: coming soon. ## Uses ### Direct Use Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.) ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-40B is trained mostly on English, German, Spanish, French, with limited capabilities also in in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-40B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-40b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-40B was trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)). \| Data source \| Fraction \| Tokens \| Sources \| \|--------------------\|--------------\|------------\|-----------------------------------\| \| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) \| 75% \| 750B \| massive web crawl \| \| RefinedWeb-Europe \| 7% \| 70B \| European massive web crawl \| \| Books \| 6% \| 60B \| \| \| Conversations \| 5% \| 50B \| Reddit, StackOverflow, HackerNews \| \| Code \| 5% \| 50B \| \| \| Technical \| 2% \| 20B \| arXiv, PubMed, USPTO, etc. \| RefinedWeb-Europe is made of the following languages: \| Language \| Fraction of multilingual data \| Tokens \| \|--------------\|-----------------------------------\|------------\| \| German \| 26% \| 18B \| \| Spanish \| 24% \| 17B \| \| French \| 23% \| 16B \| \| _Italian_ \| 7% \| 5B \| \| _Portuguese_ \| 4% \| 3B \| \| _Polish_ \| 4% \| 3B \| \| _Dutch_ \| 4% \| 3B \| \| _Romanian_ \| 3% \| 2B \| \| _Czech_ \| 3% \| 2B \| \| _Swedish_ \| 2% \| 1B \| The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ### Training Procedure Falcon-40B was trained on 384 A100 40GB GPUs, using a 3D parallelism strategy (TP=8, PP=4, DP=12) combined with ZeRO. #### Training Hyperparameters \| Hyperparameter \| Value \| Comment \| \|--------------------\|------------\|-------------------------------------------\| \| Precision \| `bfloat16` \| \| \| Optimizer \| AdamW \| \| \| Learning rate \| 1.85e-4 \| 4B tokens warm-up, cosine decay to 1.85e-5 \| \| Weight decay \| 1e-1 \| \| \| Z-loss \| 1e-4 \| \| \| Batch size \| 1152 \| 100B tokens ramp-up \| #### Speeds, Sizes, Times Training started in December 2022 and took two months. ## Evaluation Paper coming soon. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. ## Technical Specifications ### Model Architecture and Objective Falcon-40B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * Positionnal embeddings: rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * Attention: multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * Decoder-block: parallel attention/MLP with a two layer norms. For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree. \| Hyperparameter \| Value \| Comment \| \|--------------------\|-----------\|----------------------------------------\| \| Layers \| 60 \| \| \| `d_model` \| 8192 \| \| \| `head_dim` \| 64 \| Reduced to optimise for FlashAttention \| \| Vocabulary \| 65024 \| \| \| Sequence length \| 2048 \| \| ### Compute Infrastructure #### Hardware Falcon-40B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances. #### Software Falcon-40B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation Paper coming soon 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-40B is made available under the Apache 2.0 license. ## Contact [email protected]	[ -0.6592786908149719, -0.7868486046791077, 0.010901639237999916, 0.36640459299087524, -0.10425491631031036, 0.08819595724344254, -0.27059435844421387, -0.58452308177948, 0.3333091735839844, 0.21808256208896637, -0.611575722694397, -0.49244844913482666, -0.7128450274467468, 0.20112183690071106, -0.33882012963294983, 1.1297610998153687, 0.03640494495630264, -0.006382067687809467, 0.17350046336650848, 0.023711327463388443, 0.09735822677612305, -0.4932476580142975, -0.9098092317581177, -0.1490991711616516, 0.40003496408462524, 0.3046843111515045, 0.5313785672187805, 0.9207436442375183, 0.618840217590332, 0.340817391872406, -0.2826478183269501, 0.20963981747627258, -0.493297815322876, -0.26147446036338806, -0.0827292948961258, -0.35846632719039917, -0.3261575996875763, -0.1773846596479416, 0.6603475213050842, 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pariakashani/en-multinerd-ner-more-training	pariakashani	2023-12-14T18:05:46Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-12-14T16:38:26Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: en-multinerd-ner-more-training results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # en-multinerd-ner-more-training This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0563 - Precision: 0.8942 - Recall: 0.9050 - F1: 0.8996 - Accuracy: 0.9836 - Per-precision: 0.9952 - Per-recall: 0.9965 - Per-f1: 0.9959 - Org-precision: 0.9412 - Org-recall: 0.9474 - Org-f1: 0.9443 - Loc-precision: 0.9691 - Loc-recall: 0.9752 - Loc-f1: 0.9721 - Dis-precision: 0.7157 - Dis-recall: 0.7440 - Dis-f1: 0.7295 - Anim-precision: 0.6870 - Anim-recall: 0.7240 - Anim-f1: 0.7050 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| Per-precision \| Per-recall \| Per-f1 \| Org-precision \| Org-recall \| Org-f1 \| Loc-precision \| Loc-recall \| Loc-f1 \| Dis-precision \| Dis-recall \| Dis-f1 \| Anim-precision \| Anim-recall \| Anim-f1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|:-------------:\|:----------:\|:------:\|:-------------:\|:----------:\|:------:\|:-------------:\|:----------:\|:------:\|:-------------:\|:----------:\|:------:\|:--------------:\|:-----------:\|:-------:\| \| 0.0447 \| 1.0 \| 8205 \| 0.0524 \| 0.8583 \| 0.9010 \| 0.8791 \| 0.9808 \| 0.9939 \| 0.9959 \| 0.9949 \| 0.8846 \| 0.9547 \| 0.9183 \| 0.9626 \| 0.9727 \| 0.9676 \| 0.6472 \| 0.7457 \| 0.6930 \| 0.6607 \| 0.7522 \| 0.7035 \| \| 0.0309 \| 2.0 \| 16410 \| 0.0503 \| 0.8800 \| 0.9028 \| 0.8913 \| 0.9825 \| 0.9940 \| 0.9965 \| 0.9953 \| 0.9304 \| 0.9474 \| 0.9388 \| 0.9640 \| 0.9712 \| 0.9676 \| 0.7072 \| 0.7369 \| 0.7218 \| 0.6765 \| 0.7469 \| 0.7100 \| \| 0.0209 \| 3.0 \| 24615 \| 0.0520 \| 0.8908 \| 0.9042 \| 0.8975 \| 0.9836 \| 0.9953 \| 0.9967 \| 0.9960 \| 0.9365 \| 0.9478 \| 0.9421 \| 0.9680 \| 0.9750 \| 0.9715 \| 0.7163 \| 0.7268 \| 0.7215 \| 0.7013 \| 0.7434 \| 0.7217 \| \| 0.0138 \| 4.0 \| 32820 \| 0.0563 \| 0.8942 \| 0.9050 \| 0.8996 \| 0.9836 \| 0.9952 \| 0.9965 \| 0.9959 \| 0.9412 \| 0.9474 \| 0.9443 \| 0.9691 \| 0.9752 \| 0.9721 \| 0.7157 \| 0.7440 \| 0.7295 \| 0.6870 \| 0.7240 \| 0.7050 \| ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ -0.8221696019172668, -0.5964378714561462, 0.19352400302886963, 0.08934751898050308, -0.13578826189041138, -0.09063386917114258, 0.014320050366222858, -0.09530863910913467, 0.5937643647193909, 0.2770642638206482, -0.7780588865280151, -0.7255472540855408, -0.8376081585884094, -0.012129715643823147, -0.022156843915581703, 0.9450185894966125, 0.07926038652658463, -0.07741529494524002, -0.01953677460551262, -0.14939969778060913, 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Kooten/DaringMaid-20B-GGUF	Kooten	2023-12-15T18:16:27Z	1	null	[ "gguf", "text-generation", "en", "license:cc-by-nc-4.0", "region:us" ]	text-generation	2023-12-14T16:40:18Z	--- license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation --- # This repo contains GGUF quants of DaringMaid-20B [Q3_K_M](https://huggingface.co/Kooten/DaringMaid-20B-GGUF/blob/main/DaringMaid-20B-Q3_K_M.gguf) - [Q4_K_M](https://huggingface.co/Kooten/DaringMaid-20B-GGUF/blob/main/DaringMaid-20B-Q4_K_M.gguf) - [Q5_K_M](https://huggingface.co/Kooten/DaringMaid-20B-GGUF/blob/main/DaringMaid-20B-Q5_K_M.gguf) # DaringMaid-20B My goal was to make a Noromaid that's smarter and better at following instructions. After trying a bunch of different recipes I think this one turned out pretty good - I used [sequelbox/DynamicFactor](https://huggingface.co/sequelbox/DynamicFactor) as a base to as its supposed "improve overall knowledge, precise communication, conceptual understanding, and technical skill" over the base llama2. - [NeverSleep/Noromaid](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1.1) of course. - [Undi95/Utopia](https://huggingface.co/Undi95/Utopia-13B) has been recommended again recently and its still really good so in the mixer it goes - I enjoyed [tavtav/Rose](https://huggingface.co/tavtav/Rose-20B) so i threw in a bit of [CalderaAI/Thorns](https://huggingface.co/CalderaAI/13B-Thorns-l2) - There was recently a model that tried to pass itself off as [Gryphe/MythoMax](https://huggingface.co/Gryphe/MythoMax-L2-13b), i made a merge with that model before it was revealed to be MythoMax and it turned out pretty good so i used it. The .yml config files for mergekit with the exact merges can be found in the ["Recipe"](https://huggingface.co/Kooten/DaringMaid/tree/main/Recipe) folder in the [fp16 repo](https://huggingface.co/Kooten/DaringMaid) # Quants [GGUF](https://huggingface.co/Kooten/DaringMaid-20B-GGUF), EXL2: [6bpw](https://huggingface.co/Kooten/DaringMaid-20B-6bpw-exl2), [3bpw](https://huggingface.co/Kooten/DaringMaid-20B-3bpw-exl2) ## Prompt template: I have been using Undi/Ikaris SillyTavern presets for Noromaid: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json). ### Alpaca: ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not include descriptions of non-visual qualities such as personality, movements, scents, mental traits, or anything which could not be seen in a still photograph. Do not write in full sentences. Prefix your description with the phrase 'full body portrait,' ### Instruction: {prompt} ### Response: ```	[ -0.31068551540374756, -0.511763334274292, 0.39474374055862427, 0.29772308468818665, -0.3129016160964966, -0.09155939519405365, 0.25367650389671326, -0.3739205598831177, 0.647099494934082, 0.8036719560623169, -0.917527973651886, -0.7854746580123901, -0.5444824695587158, 0.16755935549736023, -0.005583596881479025, 1.107669711112976, -0.13814961910247803, -0.22065097093582153, -0.07122090458869934, -0.288526713848114, -0.5137187242507935, -0.046365126967430115, -0.585667073726654, -0.21215829253196716, 0.45252880454063416, 0.5705445408821106, 0.702981173992157, 0.7694346904754639, 0.21310578286647797, 0.22849813103675842, -0.221190944314003, 0.31528356671333313, -0.4768419563770294, 0.2194664627313614, -0.1736706644296646, -0.5068528056144714, -0.8413267135620117, 0.2547916769981384, 0.5275412797927856, 0.2883797883987427, -0.09467717260122299, 0.21226580440998077, 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sproos/e5-base-v2-de	sproos	2023-12-14T16:43:29Z	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "endpoints_compatible", "region:us" ]	feature-extraction	2023-12-14T16:43:16Z	Entry not found	[ -0.3227651119232178, -0.22568456828594208, 0.8622261881828308, 0.43461447954177856, -0.5282989740371704, 0.7012965083122253, 0.7915719747543335, 0.0761861652135849, 0.7746025323867798, 0.25632235407829285, -0.7852817177772522, -0.22573819756507874, -0.9104477763175964, 0.5715669393539429, -0.39923295378685, 0.5791240334510803, -0.14494486153125763, -0.10751186311244965, 0.2823379635810852, -0.27689534425735474, -0.5409222841262817, -0.3685528635978699, -1.1902779340744019, 0.06149144098162651, 0.5316579937934875, 0.7435142993927002, 0.7584061026573181, 0.365217387676239, 0.643257737159729, 0.39322930574417114, -0.23138955235481262, 0.4827052056789398, -0.04171820357441902, 0.0026043057441711426, -0.3524433374404907, -0.5516901016235352, -0.2859658896923065, 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balzanilo/dialogs-emotions-classifier-bert-base-seed512	balzanilo	2023-12-14T17:17:00Z	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T16:46:42Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, 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ishorn5/RTLCoder-Z-GPTQ4bit-v1.0	ishorn5	2023-12-14T16:59:09Z	0	transformers	[ "transformers", "mistral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-12-14T16:53:39Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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ans123/Brain-tumor-segmentor	ans123	2023-12-14T16:57:40Z	0	diffusers	[ "diffusers", "medical", "biology", "fill-mask", "an", "license:apache-2.0", "region:us" ]	fill-mask	2023-12-14T16:55:11Z	--- license: apache-2.0 language: - an metrics: - code_eval library_name: diffusers pipeline_tag: fill-mask tags: - medical - biology ---	[ -0.12853378057479858, -0.18616795539855957, 0.6529127359390259, 0.49436259269714355, -0.1931932121515274, 0.23607474565505981, 0.36071982979774475, 0.0505632683634758, 0.5793652534484863, 0.740013837814331, -0.6508103609085083, -0.2378396987915039, -0.7102251052856445, -0.0478258840739727, -0.38947516679763794, 0.8470761775970459, -0.09598260372877121, 0.024004995822906494, 0.047120094299316406, -0.143178328871727, -0.6121038198471069, -0.047717198729515076, -1.0524537563323975, -0.06787501275539398, 0.30022794008255005, 0.5120974183082581, 0.8275895118713379, 0.3960292339324951, 0.5030564665794373, 1.7515556812286377, -0.08836917579174042, 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hkivancoral/smids_5x_deit_tiny_adamax_00001_fold4	hkivancoral	2023-12-14T18:03:15Z	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-tiny-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-14T17:00:18Z	--- license: apache-2.0 base_model: facebook/deit-small-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_deit_tiny_adamax_00001_fold4 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8716666666666667 --- <!-- 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. --> # smids_5x_deit_tiny_adamax_00001_fold4 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3354 - Accuracy: 0.8717 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.2847 \| 1.0 \| 375 \| 0.3588 \| 0.86 \| \| 0.2399 \| 2.0 \| 750 \| 0.3497 \| 0.8783 \| \| 0.1069 \| 3.0 \| 1125 \| 0.3839 \| 0.8683 \| \| 0.1588 \| 4.0 \| 1500 \| 0.3944 \| 0.8767 \| \| 0.0651 \| 5.0 \| 1875 \| 0.4658 \| 0.8717 \| \| 0.0408 \| 6.0 \| 2250 \| 0.5268 \| 0.8767 \| \| 0.026 \| 7.0 \| 2625 \| 0.6145 \| 0.865 \| \| 0.0213 \| 8.0 \| 3000 \| 0.6639 \| 0.8717 \| \| 0.0032 \| 9.0 \| 3375 \| 0.8259 \| 0.8583 \| \| 0.0148 \| 10.0 \| 3750 \| 0.8114 \| 0.8717 \| \| 0.015 \| 11.0 \| 4125 \| 0.8747 \| 0.87 \| \| 0.0014 \| 12.0 \| 4500 \| 0.9000 \| 0.8783 \| \| 0.0001 \| 13.0 \| 4875 \| 0.9761 \| 0.87 \| \| 0.015 \| 14.0 \| 5250 \| 0.9964 \| 0.87 \| \| 0.0001 \| 15.0 \| 5625 \| 1.0366 \| 0.87 \| \| 0.0 \| 16.0 \| 6000 \| 1.0464 \| 0.8733 \| \| 0.0 \| 17.0 \| 6375 \| 1.0647 \| 0.8733 \| \| 0.0197 \| 18.0 \| 6750 \| 1.1060 \| 0.87 \| \| 0.0 \| 19.0 \| 7125 \| 1.1223 \| 0.87 \| \| 0.0 \| 20.0 \| 7500 \| 1.1307 \| 0.8717 \| \| 0.0 \| 21.0 \| 7875 \| 1.1563 \| 0.8667 \| \| 0.0 \| 22.0 \| 8250 \| 1.1659 \| 0.8717 \| \| 0.0 \| 23.0 \| 8625 \| 1.1873 \| 0.8717 \| \| 0.0 \| 24.0 \| 9000 \| 1.1966 \| 0.875 \| \| 0.0 \| 25.0 \| 9375 \| 1.2159 \| 0.8667 \| \| 0.0 \| 26.0 \| 9750 \| 1.2192 \| 0.8667 \| \| 0.0 \| 27.0 \| 10125 \| 1.2446 \| 0.865 \| \| 0.0 \| 28.0 \| 10500 \| 1.2249 \| 0.8683 \| \| 0.0 \| 29.0 \| 10875 \| 1.2382 \| 0.8717 \| \| 0.0 \| 30.0 \| 11250 \| 1.2391 \| 0.87 \| \| 0.0 \| 31.0 \| 11625 \| 1.2547 \| 0.8733 \| \| 0.0 \| 32.0 \| 12000 \| 1.2771 \| 0.87 \| \| 0.0 \| 33.0 \| 12375 \| 1.2797 \| 0.8717 \| \| 0.0 \| 34.0 \| 12750 \| 1.2938 \| 0.87 \| \| 0.0 \| 35.0 \| 13125 \| 1.2859 \| 0.87 \| \| 0.0 \| 36.0 \| 13500 \| 1.2998 \| 0.8717 \| \| 0.0 \| 37.0 \| 13875 \| 1.2941 \| 0.8683 \| \| 0.0015 \| 38.0 \| 14250 \| 1.3001 \| 0.8683 \| \| 0.0 \| 39.0 \| 14625 \| 1.3082 \| 0.8717 \| \| 0.0 \| 40.0 \| 15000 \| 1.3151 \| 0.8717 \| \| 0.0 \| 41.0 \| 15375 \| 1.3188 \| 0.8717 \| \| 0.0 \| 42.0 \| 15750 \| 1.3223 \| 0.8717 \| \| 0.0 \| 43.0 \| 16125 \| 1.3285 \| 0.87 \| \| 0.0 \| 44.0 \| 16500 \| 1.3298 \| 0.87 \| \| 0.0 \| 45.0 \| 16875 \| 1.3317 \| 0.87 \| \| 0.0 \| 46.0 \| 17250 \| 1.3330 \| 0.87 \| \| 0.0 \| 47.0 \| 17625 \| 1.3343 \| 0.87 \| \| 0.0 \| 48.0 \| 18000 \| 1.3352 \| 0.87 \| \| 0.0 \| 49.0 \| 18375 \| 1.3354 \| 0.8717 \| \| 0.0 \| 50.0 \| 18750 \| 1.3354 \| 0.8717 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2	[ -0.7380917072296143, -0.6195871829986572, 0.22829468548297882, 0.022310668602585793, -0.04170401021838188, -0.09780906140804291, 0.0381142720580101, -0.06387541443109512, 0.7231950759887695, 0.2661881148815155, -0.7790468335151672, -0.7735416889190674, -0.7122335433959961, -0.12222565710544586, -0.01400807499885559, 0.7205064296722412, 0.03560638800263405, -0.18115514516830444, -0.0913430005311966, -0.23984313011169434, -0.2875629961490631, 0.06734496355056763, -0.9018640518188477, -0.2523210048675537, 0.27877718210220337, 0.6052154302597046, 0.9655664563179016, 0.9526546001434326, 0.5828103423118591, 0.4043845534324646, -0.4181898832321167, 0.10869523137807846, -0.35045474767684937, -0.7272021174430847, 0.13198089599609375, -0.4723503887653351, -0.4231421947479248, 0.053570955991744995, 0.6097908020019531, 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zuazo/whisper-large-es	zuazo	2023-12-17T21:07:22Z	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-14T17:03:11Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, -0.5516899228096008, 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bdsqlsz/PowerPaint-V1-stable-diffusion-inpainting	bdsqlsz	2023-12-14T18:01:19Z	9	null	[ "arxiv:2312.03594", "region:us" ]	null	2023-12-14T17:05:22Z	# A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting [Project Page](https://powerpaint.github.io/) \| [Paper](https://arxiv.org/abs/2312.03594) This README provides a step-by-step guide to download the repository, set up the required virtual environment named "PowerPaint" using conda, and run PowerPaint with or without ControlNet. Stronger Model Weights and Online Demo Coming Soon！ <img src='https://github.com/open-mmlab/mmagic/assets/12782558/acd01391-c73f-4997-aafd-0869aebcc915'/> ## Getting Started Download and use in Fooocus、sd-webui or comfyui Feel free to explore and create stunning images with PowerPaint! ## BibTeX ``` @misc{zhuang2023task, title={A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting}, author={Junhao Zhuang and Yanhong Zeng and Wenran Liu and Chun Yuan and Kai Chen}, year={2023}, eprint={2312.03594}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```	[ -0.1721809208393097, -0.40549689531326294, 0.3456360101699829, 0.39867517352104187, -0.5631033778190613, -0.06899036467075348, -0.15624316036701202, -0.04522031918168068, 0.3440663814544678, 0.7736735343933105, -0.893446683883667, -0.3969506323337555, -0.6403005719184875, -0.19200396537780762, -0.09747298806905746, 0.8549341559410095, -0.23303109407424927, 0.047928664833307266, -0.29318130016326904, 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Cordmail/Feminist-Mistral-7Bv3.Q5_K_M	Cordmail	2023-12-14T18:00:32Z	0	null	[ "gguf", "region:us" ]	null	2023-12-14T17:05:23Z	Entry not found	[ -0.322765052318573, -0.22568443417549133, 0.862225353717804, 0.43461543321609497, -0.5282990336418152, 0.7012964487075806, 0.7915717363357544, 0.07618646323680878, 0.7746022939682007, 0.25632232427597046, -0.7852814197540283, -0.2257380485534668, -0.9104474782943726, 0.5715667009353638, -0.39923304319381714, 0.5791245698928833, -0.14494505524635315, -0.10751169919967651, 0.2823375165462494, -0.27689534425735474, -0.5409221053123474, -0.36855247616767883, -1.1902778148651123, 0.061491310596466064, 0.5316579341888428, 0.7435140609741211, 0.7584059238433838, 0.365217000246048, 0.6432581543922424, 0.39322906732559204, -0.2313895970582962, 0.48270535469055176, -0.04171810299158096, 0.0026040554512292147, -0.35244348645210266, -0.5516899824142456, -0.2859657108783722, 0.07584784924983978, 1.09613037109375, 0.9666878581047058, -0.28466349840164185, 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Jeevesh8/multibert_seed0_20k_qqp_ft_84	Jeevesh8	2023-12-15T01:54:16Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_79	Jeevesh8	2023-12-15T01:33:29Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_83	Jeevesh8	2023-12-15T01:32:10Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_71	Jeevesh8	2023-12-15T01:10:06Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_87	Jeevesh8	2023-12-15T01:06:33Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_88	Jeevesh8	2023-12-15T01:10:32Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_81	Jeevesh8	2023-12-15T01:58:43Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_80	Jeevesh8	2023-12-15T01:55:44Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_86	Jeevesh8	2023-12-14T19:53:30Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_76	Jeevesh8	2023-12-15T01:48:54Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_85	Jeevesh8	2023-12-15T01:29:45Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_73	Jeevesh8	2023-12-15T01:39:03Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:40Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_74	Jeevesh8	2023-12-15T01:47:18Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:06:41Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_93	Jeevesh8	2023-12-15T01:35:04Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:09:00Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_91	Jeevesh8	2023-12-15T01:09:20Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:09:00Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_92	Jeevesh8	2023-12-15T01:32:55Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:09:00Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_89	Jeevesh8	2023-12-15T01:33:25Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:09:00Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_94	Jeevesh8	2023-12-15T01:37:07Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:09:00Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_90	Jeevesh8	2023-12-15T01:36:32Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:09:00Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_95	Jeevesh8	2023-12-15T01:35:48Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:12:20Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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Jeevesh8/multibert_seed0_20k_qqp_ft_96	Jeevesh8	2023-12-15T01:31:46Z	0	transformers	[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:12:20Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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ishorn5/RTLCoder-Z-v1.0	ishorn5	2023-12-14T17:36:42Z	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-12-14T17:12:51Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, 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balzanilo/dialogs-emotions-classifier-bert-base-seed1024	balzanilo	2023-12-14T17:43:04Z	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:17:09Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 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EthanRhys/Bowser	EthanRhys	2023-12-14T17:22:05Z	0	null	[ "license:openrail", "region:us" ]	null	2023-12-14T17:21:03Z	--- license: openrail ---	[ -0.12853378057479858, -0.18616795539855957, 0.6529127359390259, 0.49436259269714355, -0.1931932121515274, 0.23607474565505981, 0.36071982979774475, 0.0505632683634758, 0.5793652534484863, 0.740013837814331, -0.6508103609085083, -0.2378396987915039, -0.7102251052856445, -0.0478258840739727, -0.38947516679763794, 0.8470761775970459, -0.09598260372877121, 0.024004995822906494, 0.047120094299316406, -0.143178328871727, -0.6121038198471069, -0.047717198729515076, -1.0524537563323975, -0.06787501275539398, 0.30022794008255005, 0.5120974183082581, 0.8275895118713379, 0.3960292339324951, 0.5030564665794373, 1.7515556812286377, -0.08836917579174042, -0.22754409909248352, -0.4589202404022217, 0.4223068952560425, -0.3327736258506775, -0.42133715748786926, -0.2624164819717407, -0.07449344545602798, 0.3238038420677185, 0.7903714179992676, 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kompiangg/svm-hate-speech-classification	kompiangg	2023-12-14T17:53:23Z	0	null	[ "region:us", "has_space" ]	null	2023-12-14T17:21:10Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, -0.5516899228096008, -0.28596577048301697, 0.07584742456674576, 1.09613037109375, 0.9666878581047058, -0.2846634089946747, 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TSC7/Nasu	TSC7	2023-12-14T17:25:45Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2023-12-14T17:23:23Z	--- license: apache-2.0 ---	[ -0.12853378057479858, -0.18616795539855957, 0.6529127359390259, 0.49436259269714355, -0.1931932121515274, 0.23607474565505981, 0.36071982979774475, 0.0505632683634758, 0.5793652534484863, 0.740013837814331, -0.6508103609085083, -0.2378396987915039, -0.7102251052856445, -0.0478258840739727, -0.38947516679763794, 0.8470761775970459, -0.09598260372877121, 0.024004995822906494, 0.047120094299316406, -0.143178328871727, -0.6121038198471069, -0.047717198729515076, -1.0524537563323975, -0.06787501275539398, 0.30022794008255005, 0.5120974183082581, 0.8275895118713379, 0.3960292339324951, 0.5030564665794373, 1.7515556812286377, -0.08836917579174042, -0.22754409909248352, -0.4589202404022217, 0.4223068952560425, -0.3327736258506775, -0.42133715748786926, -0.2624164819717407, -0.07449344545602798, 0.3238038420677185, 0.7903714179992676, -0.38104113936424255, 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h4iku/codet5-small-t5apr-javascript	h4iku	2023-12-14T20:36:26Z	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2023-12-14T17:23:26Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, 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athirdpath/Nu-17b-GGUF	athirdpath	2023-12-14T18:45:13Z	0	null	[ "en", "dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW", "dataset:athirdpath/alpaca_dpo_pairs", "license:cc-by-nc-4.0", "region:us" ]	null	2023-12-14T17:25:50Z	--- license: cc-by-nc-4.0 datasets: - athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW - athirdpath/alpaca_dpo_pairs language: - en --- <p align="center"><font size="6"> <b>Needs storytelling finetuning, but this is MUCH better.</b> </font></p> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/633a809fa4a8f33508dce32c/C9gBkxnxVU7vNpzr6XQhO.png) ### Recipe models: - model: athirdpath/Lambda-17b - model: athirdpath/Mu-17b merge_method: slerp base_model: athirdpath/Lambda-17b t: 0.2 dtype: float16	[ -0.373089998960495, -0.47073885798454285, 0.4274030029773712, 0.11031865328550339, -0.48893287777900696, 0.16912373900413513, 0.04790885001420975, -0.5411646962165833, 0.5975250601768494, 0.7234681248664856, -0.9412215352058411, -0.39721184968948364, 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delayedkarma/distilbert-base-uncased-finetuned-imdb	delayedkarma	2023-12-14T17:34:06Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-12-14T17:27:17Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4890 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.682 \| 1.0 \| 157 \| 2.5037 \| \| 2.5854 \| 2.0 \| 314 \| 2.4486 \| \| 2.526 \| 3.0 \| 471 \| 2.4817 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	[ -0.6098368167877197, -0.6422547101974487, 0.11770539730787277, 0.08601097762584686, 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AdnanRiaz107/coderbert-basePEFTLORAtest	AdnanRiaz107	2023-12-14T17:28:27Z	0	null	[ "region:us" ]	null	2023-12-14T17:28:26Z	Entry not found	[ -0.3227648138999939, -0.22568483650684357, 0.8622256517410278, 0.43461519479751587, -0.5282990336418152, 0.7012965679168701, 0.7915716767311096, 0.07618631422519684, 0.7746025323867798, 0.25632259249687195, -0.7852814793586731, -0.22573857009410858, -0.910447895526886, 0.5715669393539429, -0.39923328161239624, 0.5791246891021729, -0.14494486153125763, -0.10751142352819443, 0.2823377251625061, -0.2768954634666443, -0.5409225821495056, -0.36855238676071167, -1.1902778148651123, 0.061491332948207855, 0.5316581726074219, 0.7435142993927002, 0.7584061622619629, 0.3652169108390808, 0.643258273601532, 0.3932289481163025, -0.23138928413391113, 0.48270535469055176, -0.041718292981386185, 0.00260428199544549, -0.3524433970451355, -0.5516899228096008, -0.28596562147140503, 0.0758475661277771, 1.0961300134658813, 0.9666879773139954, -0.28466370701789856, 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hoangtb203/Translation_Model_10	hoangtb203	2023-12-14T17:34:47Z	0	null	[ "license:mit", "region:us" ]	null	2023-12-14T17:31:59Z	--- license: mit ---	[ -0.12853378057479858, -0.18616795539855957, 0.6529127359390259, 0.49436259269714355, -0.1931932121515274, 0.23607474565505981, 0.36071982979774475, 0.0505632683634758, 0.5793652534484863, 0.740013837814331, -0.6508103609085083, -0.2378396987915039, -0.7102251052856445, -0.0478258840739727, -0.38947516679763794, 0.8470761775970459, -0.09598260372877121, 0.024004995822906494, 0.047120094299316406, -0.143178328871727, -0.6121038198471069, -0.047717198729515076, -1.0524537563323975, -0.06787501275539398, 0.30022794008255005, 0.5120974183082581, 0.8275895118713379, 0.3960292339324951, 0.5030564665794373, 1.7515556812286377, -0.08836917579174042, -0.22754409909248352, -0.4589202404022217, 0.4223068952560425, -0.3327736258506775, -0.42133715748786926, -0.2624164819717407, -0.07449344545602798, 0.3238038420677185, 0.7903714179992676, 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Cordmail/Feminist-Mistral-7Bv3.Q4_K_M	Cordmail	2023-12-14T17:51:19Z	0	null	[ "gguf", "region:us" ]	null	2023-12-14T17:33:03Z	Entry not found	[ -0.32276487350463867, -0.22568444907665253, 0.8622258901596069, 0.43461498618125916, -0.5282989740371704, 0.7012968063354492, 0.7915717959403992, 0.07618644088506699, 0.7746023535728455, 0.25632238388061523, -0.785281777381897, -0.22573837637901306, -0.9104480743408203, 0.5715668797492981, -0.3992334008216858, 0.5791248083114624, -0.14494499564170837, -0.10751183331012726, 0.2823374271392822, -0.2768949866294861, -0.5409224629402161, -0.3685525953769684, -1.1902778148651123, 0.06149108335375786, 0.5316582322120667, 0.7435141801834106, 0.7584060430526733, 0.3652164936065674, 0.6432580351829529, 0.39322906732559204, -0.23138932883739471, 0.4827057421207428, -0.04171822965145111, 0.002603852655738592, -0.3524436950683594, -0.5516901612281799, -0.2859659194946289, 0.07584758847951889, 1.096130132675171, 0.966687798500061, -0.284663587808609, 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tittuvmathew/q-FrozenLake-v1-4x4-noSlippery	tittuvmathew	2023-12-14T17:33:29Z	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-12-14T17:33:27Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="tittuvmathew/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	[ 0.04006510227918625, -0.41619083285331726, 0.05250820890069008, 0.019562438130378723, -0.29276496171951294, 0.2927261292934418, 0.488381952047348, -0.004692478105425835, 0.13257569074630737, 0.5688955783843994, -0.7312718629837036, -0.5076682567596436, -0.3322146236896515, -0.25537946820259094, -0.028640680015087128, 0.810086727142334, 0.21593375504016876, 0.3691132068634033, -0.2503170073032379, -0.017479488626122475, -0.488768607378006, -0.31191229820251465, -1.0522842407226562, -0.8505184054374695, 0.08377701789140701, 0.3234734535217285, 1.041437029838562, 0.6022459268569946, 0.006991017609834671, 0.474191278219223, -0.0114793935790658, -0.43353506922721863, -0.5684546232223511, 0.13897910714149475, -0.13747772574424744, -0.754787266254425, -0.8710242509841919, 0.22031845152378082, 0.8221479058265686, -0.13917604088783264, 0.05043540149927139, 0.10899873077869415, -0.35947754979133606, 0.47084715962409973, -0.2684358060359955, 0.3032093942165375, 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gkallergis/bbbp_model_da	gkallergis	2023-12-14T17:54:31Z	0	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2023-12-14T17:33:38Z	--- license: cc-by-nc-nd-4.0 ---	[ -0.12853389978408813, -0.18616794049739838, 0.6529129147529602, 0.49436238408088684, -0.19319312274456024, 0.2360745072364807, 0.36071979999542236, 0.05056321248412132, 0.5793654918670654, 0.7400141358375549, -0.6508101224899292, -0.2378395050764084, -0.710224986076355, -0.04782600700855255, -0.3894750475883484, 0.847076416015625, -0.09598265588283539, 0.02400507777929306, 0.04712001234292984, -0.1431782841682434, -0.6121038198471069, -0.04771745204925537, -1.052453875541687, -0.06787474453449249, 0.3002281188964844, 0.5120974779129028, 0.827589750289917, 0.39602941274642944, 0.5030565857887268, 1.7515558004379272, -0.08836935460567474, -0.2275441586971283, -0.45892053842544556, 0.42230695486068726, -0.3327735960483551, -0.421336829662323, -0.26241639256477356, -0.07449369877576828, 0.3238040506839752, 0.790371298789978, 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tittuvmathew/q-Taxi-pickup-dropoff	tittuvmathew	2023-12-14T17:37:53Z	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-12-14T17:37:51Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-pickup-dropoff results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="tittuvmathew/q-Taxi-pickup-dropoff", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	[ -0.04615474119782448, -0.2538559138774872, 0.061736978590488434, 0.2792685329914093, -0.2940858006477356, 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bio-datasets/Mistral-7B-Instruct-v0.1-blooming-thunder-195-epoch-100	bio-datasets	2023-12-14T17:39:48Z	0	peft	[ "peft", "safetensors", "region:us" ]	null	2023-12-14T17:39:45Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0	[ -0.4019758701324463, -0.1770414263010025, 0.30504828691482544, 0.918927013874054, -0.22498556971549988, 0.03766064718365669, 0.6600639224052429, -0.032512079924345016, 0.15127845108509064, 0.9004538655281067, -0.6599444150924683, -0.2624579966068268, -0.4278685450553894, 0.06369946151971817, -0.46948984265327454, 0.8085747957229614, -0.27128374576568604, 0.32459795475006104, 0.23183807730674744, 0.05647203326225281, 0.06490640342235565, -0.8316478729248047, -0.8786255717277527, -0.6577088832855225, 0.5207829475402832, 0.7058131694793701, 0.10134033113718033, 0.3649715185165405, 0.6818897128105164, 0.19839316606521606, -0.034440454095602036, -0.9248972535133362, -0.3319840729236603, 0.03142951801419258, -0.2656098008155823, 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abhishek/3iqe-6zi8-5xf7-0	abhishek	2023-12-14T17:40:15Z	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "autotrain", "dataset:abhishek/autotrain-data-3iqe-6zi8-5xf7", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-14T17:40:06Z	--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - abhishek/autotrain-data-3iqe-6zi8-5xf7 --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: nan f1_macro: 0.06153846153846154 f1_micro: 0.18181818181818182 f1_weighted: 0.055944055944055944 precision_macro: 0.03636363636363636 precision_micro: 0.18181818181818182 precision_weighted: 0.03305785123966942 recall_macro: 0.2 recall_micro: 0.18181818181818182 recall_weighted: 0.18181818181818182 accuracy: 0.18181818181818182	[ -0.36743873357772827, -0.2159474939107895, 0.226772278547287, 0.32527613639831543, -0.30571267008781433, -0.2528270184993744, 0.04824429750442505, -0.006654363125562668, -0.09340719878673553, -0.10108229517936707, -0.47797465324401855, -0.5758273601531982, -0.9261990785598755, -0.10764119774103165, -0.6288039088249207, 0.8731536865234375, -0.3748730421066284, 0.3794270157814026, 0.02681177295744419, -0.6189607977867126, -0.5415999293327332, -0.6618170738220215, -0.9400075078010559, -0.055450309067964554, 0.4874958097934723, 0.4583222568035126, 0.689714252948761, 0.7603844404220581, 0.7940047383308411, 0.3620595335960388, 0.10339487344026566, -0.13683925569057465, -0.3118596076965332, -0.851909339427948, 0.30084553360939026, -0.8246803283691406, -0.5981877446174622, 0.23219704627990723, 0.5459132194519043, 0.47240039706230164, 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RudrakshSB/mistral-finetuned-alpaca	RudrakshSB	2023-12-15T16:59:51Z	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	2023-12-14T17:40:33Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, -0.5516899228096008, -0.28596577048301697, 0.07584742456674576, 1.09613037109375, 0.9666878581047058, 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netoferraz/distilbert-base-uncased-finetuned-pad-clf-corpus	netoferraz	2023-12-14T17:48:47Z	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:42:23Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-pad-clf-corpus results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-pad-clf-corpus This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1507 - Accuracy: 0.9667 - F1: 0.9667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 4 \| 0.6603 \| 0.55 \| 0.3903 \| \| No log \| 2.0 \| 8 \| 0.5784 \| 0.9083 \| 0.9082 \| \| No log \| 3.0 \| 12 \| 0.4524 \| 0.9167 \| 0.9169 \| \| 0.593 \| 4.0 \| 16 \| 0.3400 \| 0.9417 \| 0.9418 \| \| 0.593 \| 5.0 \| 20 \| 0.2622 \| 0.95 \| 0.9501 \| \| 0.593 \| 6.0 \| 24 \| 0.2090 \| 0.9667 \| 0.9667 \| \| 0.263 \| 7.0 \| 28 \| 0.1811 \| 0.9667 \| 0.9667 \| \| 0.263 \| 8.0 \| 32 \| 0.1640 \| 0.9667 \| 0.9667 \| \| 0.263 \| 9.0 \| 36 \| 0.1544 \| 0.9667 \| 0.9667 \| \| 0.263 \| 10.0 \| 40 \| 0.1507 \| 0.9667 \| 0.9667 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	[ -0.5406284332275391, -0.7307304739952087, 0.15955637395381927, 0.17964376509189606, -0.2777397036552429, -0.22545666992664337, -0.10492011904716492, -0.14641238749027252, 0.2820703089237213, 0.28837981820106506, -0.6888733506202698, -0.8001357913017273, -0.8582533597946167, -0.15749849379062653, -0.3151175379753113, 1.2620657682418823, 0.13524147868156433, 0.22691506147384644, -0.09810744971036911, -0.009797029197216034, -0.4701603055000305, -0.6939336061477661, -0.7470559477806091, -0.61226886510849, 0.18873263895511627, 0.3916220963001251, 0.9331905245780945, 0.8603231310844421, 0.6055114269256592, 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balzanilo/dialogs-emotions-classifier-bert-base-seed2048	balzanilo	2023-12-14T18:09:01Z	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:43:12Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 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delayedkarma/distilbert-base-uncased-finetuned-imdb-accelerate	delayedkarma	2023-12-14T18:28:06Z	0	transformers	[ "transformers", "safetensors", "distilbert", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-12-14T17:45:25Z	--- license: apache-2.0 ---	[ -0.12853378057479858, -0.18616795539855957, 0.6529127359390259, 0.49436259269714355, -0.1931932121515274, 0.23607474565505981, 0.36071982979774475, 0.0505632683634758, 0.5793652534484863, 0.740013837814331, -0.6508103609085083, -0.2378396987915039, -0.7102251052856445, -0.0478258840739727, -0.38947516679763794, 0.8470761775970459, -0.09598260372877121, 0.024004995822906494, 0.047120094299316406, -0.143178328871727, -0.6121038198471069, -0.047717198729515076, -1.0524537563323975, -0.06787501275539398, 0.30022794008255005, 0.5120974183082581, 0.8275895118713379, 0.3960292339324951, 0.5030564665794373, 1.7515556812286377, -0.08836917579174042, -0.22754409909248352, 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CLMBR/preemption_testing-full-transformer-121212	CLMBR	2023-12-14T22:38:03Z	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-12-14T17:45:32Z	--- tags: - generated_from_trainer model-index: - name: preemption_testing-full-transformer-121212 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # preemption_testing-full-transformer-121212 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0214 ## 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: 121212 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3052726 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 4.2242 \| 0.03 \| 76319 \| 4.1911 \| \| 4.0182 \| 1.03 \| 152638 \| 4.0214 \| \| 4.0182 \| 1.03 \| 152638 \| 4.0214 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3	[ -0.6366506218910217, -0.5370630621910095, 0.21360909938812256, 0.26914429664611816, -0.3388326168060303, 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UCaps/UCaps	UCaps	2023-12-14T17:47:42Z	0	null	[ "region:us" ]	null	2023-12-14T17:46:41Z	U Caps, herhangi bir yan etki olmaksızın işitme sorunlarından kurtulmanızı sağlayan etkili bir işitme desteği formülüdür. U Caps Turkey <a href="https://www.excaliburnutrition.com/UCaTur">U Caps Şimdi al!! Daha fazla bilgi için Aşağıdaki Bağlantıya tıklayın ve şimdi %50 indirim kazanın !! acele etmek!!</a> Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/UCaTur">www.UCaps.com</a> <p><a href="https://www.excaliburnutrition.com/UCaTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJnB3qtwUTtJSlRIeky1PHGgKG6KZznoocsdMXrF3HI3p1oMZf_o5E6hA0fIb9LU1VaLm74vwOWwNSieN1cVNjy0klzI6fGOJQoIA1GoN6JMMuji88ZDxBYGOd8wnXC8Njvl87u65O198lUEknsKI_VuK0NI_E0yO-mZruFKAxYY6KkehYmniRtFdIDYQ/w648-h334/U%20Caps%20Turkey.png" alt="enter image description here"> </a></p> ØÜrün Adı — U Caps ØKategori - Sağlıklı İşitme Desteği ØAna Faydaları — işitme desteği Formülü Ø Bileşimi — Doğal Organik Bileşik ➢ Yan Etkiler—YOK ØNihai Derecelendirme: — 4,8 ➢ Kullanılabilirlik — Çevrimiçi ➢Teklifler ve İndirimler; BUGÜN KAYDET! ÖZEL TEKLİF satın almak için ŞİMDİ ALIŞVERİŞ YAPIN!!! U Caps NEDİR? U Caps, Tinnitus ve diğer işitme problemlerini hafifletmek için %100 doğal bir formüldür. İçerikler vitaminler, amino asitler, şifalı bitkiler ve bitki özlerinden oluşur. U Caps işitme sağlığını geliştirir ve her kişide verimli bir şekilde çalışır. KBB (kulak-burun-boğaz) ürünü saftır ve doğal olarak çalışır. Yan etkilere yol açmadan hızlı bir rahatlama sağlar. U Caps ile kulak ameliyatı olmanıza veya olumsuz yan etkilere neden olabilecek reçeteli ilaçları kullanmanıza gerek kalmaz. Kullanımdan birkaç hafta veya ay sonra kulak sağlığını mükemmel şekilde geri kazandırır. Kulakla ilgili diğer sorunları durdurur ve daha fazla hasarı önler. <a href="https://www.excaliburnutrition.com/UCaTur">U Caps Şimdi al!! Daha fazla bilgi için Aşağıdaki Bağlantıya tıklayın ve şimdi %50 indirim kazanın !! acele etmek!!</a> Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/UCaTur">www.UCaps.com</a> <p><a href="https://www.excaliburnutrition.com/UCaTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJnB3qtwUTtJSlRIeky1PHGgKG6KZznoocsdMXrF3HI3p1oMZf_o5E6hA0fIb9LU1VaLm74vwOWwNSieN1cVNjy0klzI6fGOJQoIA1GoN6JMMuji88ZDxBYGOd8wnXC8Njvl87u65O198lUEknsKI_VuK0NI_E0yO-mZruFKAxYY6KkehYmniRtFdIDYQ/w648-h334/U%20Caps%20Turkey.png" alt="enter image description here"> </a></p> U Caps U Caps Hap U Caps kapsül U Caps tablet U Caps Fiyat U Caps yorumlar U Caps İçindekiler U Caps Faydalar U Caps Yan etkiler U Caps kapsül fiyatı U Caps kapsül incelemeleri U Caps kompozisyon U Caps şikayet U Caps Nereden Alınır U Caps Nasıl kullanılır U Caps maliyet U Caps İşler U Caps forum U Caps orijinal U Caps eczane U Caps NASIL ÇALIŞIR? U Caps, işitme duyunuzu iyileştirebilen ve kulaklarınızı iyileştirebilen bir besin takviyesidir. İnsanlar yaşlandıkça işitme duyularını kaybederler ve işitme yetenekleri azalır. Bu noktada işitmeyle ilgili nöronlar bozulmaya başlar. Bu durumda, boyundaki ve beyindeki diğer duyu nöronları, hasar gören işitme hücrelerini telafi etmek zorunda oldukları için gerilir. Bunun sonucunda boyundaki ve beyindeki nöronlar aşırı aktif hale gelerek çınlama sesi çıkarır. ÇÖZÜM: İşitme kaybını tedavi etmek ve tersine çevirmek için başka hiçbir doğal ürün U Caps kadar çok bitki, kök ve bitki özü içermez. İster genç ister yaşlı olsun, sürekli kullanan herkeste işe yaraması garanti edilir. Vücudunuz iyileşir, beyin hücreleriniz yenilenir ve her dozda zil sesi kesilerek işitme cihazı ve ilaç kullanmayı bırakmanıza olanak tanır. U Caps hakkında daha fazla bilgi edinmek için bugün resmi web sitesini ziyaret edin! <a href="https://www.excaliburnutrition.com/UCaTur">U Caps Şimdi al!! Daha fazla bilgi için Aşağıdaki Bağlantıya tıklayın ve şimdi %50 indirim kazanın !! acele etmek!!</a> Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/UCaTur">www.UCaps.com</a> <p><a href="https://www.excaliburnutrition.com/UCaTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJnB3qtwUTtJSlRIeky1PHGgKG6KZznoocsdMXrF3HI3p1oMZf_o5E6hA0fIb9LU1VaLm74vwOWwNSieN1cVNjy0klzI6fGOJQoIA1GoN6JMMuji88ZDxBYGOd8wnXC8Njvl87u65O198lUEknsKI_VuK0NI_E0yO-mZruFKAxYY6KkehYmniRtFdIDYQ/w648-h334/U%20Caps%20Turkey.png" alt="enter image description here"> </a></p> U Caps Nereden Satın Alınır? U Caps şirketin web sitesinden satın alınabilir. U Caps'ü satın almak istiyorsanız lütfen aşağıda verilen URL'yi kullanın. U Caps U Caps Hap U Caps kapsül U Caps tablet U Caps Fiyat U Caps yorumlar U Caps İçindekiler U Caps Faydalar U Caps Yan etkiler U Caps kapsül fiyatı U Caps kapsül incelemeleri U Caps kompozisyon U Caps şikayet U Caps Nereden Alınır U Caps Nasıl kullanılır U Caps maliyet U Caps İşler U Caps forum U Caps orijinal U Caps eczane devamını oku: https://www.excaliburnutrition.com/UCaTur https://www.excaliburnutrition.com/UCaTur https://sites.google.com/view/ucapsturkey/home https://healthtoned.blogspot.com/2023/12/u-caps-kulak-cnlamas-ve-isitme.html https://www.weddingwire.com/website/u-caps-turkey https://medium.com/@healthytalk24x7/u-caps-turkey-1acbf98f99e2 https://medium.com/@healthytalk24x7/u-caps-kulak-%C3%A7%C4%B1nlamas%C4%B1-ve-i%CC%87%C5%9Fitme-sorunlar%C4%B1n%C4%B1-gidermeye-yard%C4%B1mc%C4%B1-olur-c497585540af https://infogram.com/u-caps-turkey-1hnp27mwqpx3y2g?live https://softisenilspain.hashnode.dev/u-caps-turkey https://ucaps.company.site/ 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kubernetes-bad/testing123-gguf	kubernetes-bad	2023-12-14T17:55:43Z	0	null	[ "gguf", "region:us" ]	null	2023-12-14T17:47:00Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, -0.5516899228096008, -0.28596577048301697, 0.07584742456674576, 1.09613037109375, 0.9666878581047058, -0.2846634089946747, 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gkallergis/bace_da	gkallergis	2023-12-14T17:59:58Z	0	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2023-12-14T17:50:14Z	--- license: cc-by-nc-nd-4.0 ---	[ -0.12853378057479858, -0.18616795539855957, 0.6529127359390259, 0.49436259269714355, -0.1931932121515274, 0.23607474565505981, 0.36071982979774475, 0.0505632683634758, 0.5793652534484863, 0.740013837814331, -0.6508103609085083, -0.2378396987915039, -0.7102251052856445, -0.0478258840739727, -0.38947516679763794, 0.8470761775970459, -0.09598260372877121, 0.024004995822906494, 0.047120094299316406, -0.143178328871727, -0.6121038198471069, -0.047717198729515076, -1.0524537563323975, -0.06787501275539398, 0.30022794008255005, 0.5120974183082581, 0.8275895118713379, 0.3960292339324951, 0.5030564665794373, 1.7515556812286377, -0.08836917579174042, -0.22754409909248352, -0.4589202404022217, 0.4223068952560425, -0.3327736258506775, -0.42133715748786926, -0.2624164819717407, -0.07449344545602798, 0.3238038420677185, 0.7903714179992676, 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abhishek/3iqe-6zi8-5xf72-0	abhishek	2023-12-14T17:53:50Z	0	transformers	[ "transformers", "safetensors", "resnet", "image-classification", "autotrain", "dataset:abhishek/autotrain-data-3iqe-6zi8-5xf72", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-14T17:53:46Z	--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - abhishek/autotrain-data-3iqe-6zi8-5xf72 --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: nan f1_macro: 0.06153846153846154 f1_micro: 0.18181818181818182 f1_weighted: 0.055944055944055944 precision_macro: 0.03636363636363636 precision_micro: 0.18181818181818182 precision_weighted: 0.03305785123966942 recall_macro: 0.2 recall_micro: 0.18181818181818182 recall_weighted: 0.18181818181818182 accuracy: 0.18181818181818182	[ -0.36743873357772827, -0.2159474939107895, 0.226772278547287, 0.32527613639831543, -0.30571267008781433, -0.2528270184993744, 0.04824429750442505, -0.006654363125562668, -0.09340719878673553, -0.10108229517936707, -0.47797465324401855, -0.5758273601531982, -0.9261990785598755, -0.10764119774103165, -0.6288039088249207, 0.8731536865234375, -0.3748730421066284, 0.3794270157814026, 0.02681177295744419, -0.6189607977867126, -0.5415999293327332, -0.6618170738220215, -0.9400075078010559, -0.055450309067964554, 0.4874958097934723, 0.4583222568035126, 0.689714252948761, 0.7603844404220581, 0.7940047383308411, 0.3620595335960388, 0.10339487344026566, -0.13683925569057465, -0.3118596076965332, -0.851909339427948, 0.30084553360939026, -0.8246803283691406, -0.5981877446174622, 0.23219704627990723, 0.5459132194519043, 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CLMBR/preemption_testing-full-transformer-10101	CLMBR	2023-12-15T09:58:14Z	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-12-14T17:55:25Z	Entry not found	[ -0.32276463508605957, -0.22568437457084656, 0.8622260093688965, 0.43461504578590393, -0.5282986760139465, 0.7012966275215149, 0.7915719747543335, 0.07618647813796997, 0.7746024131774902, 0.2563219368457794, -0.7852815389633179, -0.22573824226856232, -0.910447895526886, 0.5715669393539429, -0.3992336392402649, 0.5791244506835938, -0.14494502544403076, -0.1075114980340004, 0.28233784437179565, -0.27689528465270996, -0.5409225225448608, -0.3685522973537445, -1.1902774572372437, 0.0614912286400795, 0.5316581726074219, 0.7435142397880554, 0.758405864238739, 0.36521655321121216, 0.6432578563690186, 0.3932291567325592, -0.23138923943042755, 0.482705295085907, -0.041718240827322006, 0.002604067325592041, 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abhishek/3iqe-6zi8-5xf73-0	abhishek	2023-12-14T17:55:53Z	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "autotrain", "dataset:abhishek/autotrain-data-3iqe-6zi8-5xf73", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-14T17:55:48Z	--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - abhishek/autotrain-data-3iqe-6zi8-5xf73 --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: nan f1_macro: 0.09132538710036761 f1_micro: 0.1690909090909091 f1_weighted: 0.08854425957244272 precision_macro: 0.07907624633431085 precision_micro: 0.1690909090909091 precision_weighted: 0.08039189549453478 recall_macro: 0.18150387596899226 recall_micro: 0.1690909090909091 recall_weighted: 0.1690909090909091 accuracy: 0.1690909090909091	[ -0.343136727809906, -0.26392480731010437, 0.15391339361667633, 0.30455589294433594, -0.3763940632343292, -0.22391429543495178, 0.0440719798207283, -0.04245664179325104, -0.09670347720384598, -0.14260175824165344, -0.44099682569503784, -0.6450890302658081, -0.9667718410491943, -0.13404682278633118, -0.6678832173347473, 0.833280622959137, -0.39087897539138794, 0.39118701219558716, 0.018748974427580833, -0.5705023407936096, -0.6168144345283508, -0.6693314909934998, -0.9615611433982849, -0.04363134875893593, 0.41256678104400635, 0.4233945906162262, 0.6706294417381287, 0.691556990146637, 0.728310227394104, 0.3643913269042969, 0.0205523781478405, -0.12037456780672073, -0.3913828730583191, -0.7950143218040466, 0.3449329137802124, -0.8266025185585022, -0.6042538285255432, 0.21090325713157654, 0.5476865768432617, 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Chenchuhui/Icelandic-finetuned-no-data-augmentation	Chenchuhui	2023-12-17T19:18:48Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-14T17:56:05Z	--- license: cc-by-4.0 base_model: carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h tags: - generated_from_trainer model-index: - name: Icelandic-finetuned-no-data-augmentation results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Icelandic-finetuned-no-data-augmentation This model is a fine-tuned version of [carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h](https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - 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kanishka/smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-3e-4	kanishka	2023-12-15T07:10:41Z	0	transformers	[ "transformers", "safetensors", "opt", "text-generation", "generated_from_trainer", "dataset:kanishka/counterfactual_babylm_aann_excess_adj_removal", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-12-14T17:56:44Z	--- tags: - generated_from_trainer datasets: - kanishka/counterfactual_babylm_aann_excess_adj_removal metrics: - accuracy model-index: - name: smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-3e-4 results: - task: name: Causal Language Modeling type: text-generation dataset: name: kanishka/counterfactual_babylm_aann_excess_adj_removal type: kanishka/counterfactual_babylm_aann_excess_adj_removal metrics: - name: Accuracy type: accuracy value: 0.40517314741870225 --- <!-- 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. --> # smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-3e-4 This model was trained from scratch on the kanishka/counterfactual_babylm_aann_excess_adj_removal dataset. It achieves the following results on the evaluation set: - Loss: 3.4663 - Accuracy: 0.4052 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 3.7291 \| 1.0 \| 18629 \| 3.9229 \| 0.3464 \| \| 3.4237 \| 2.0 \| 37258 \| 3.6542 \| 0.3747 \| \| 3.2842 \| 3.0 \| 55887 \| 3.5183 \| 0.3880 \| \| 3.1952 \| 4.0 \| 74516 \| 3.4748 \| 0.3938 \| \| 3.1351 \| 5.0 \| 93145 \| 3.4493 \| 0.3972 \| \| 3.0844 \| 6.0 \| 111774 \| 3.4156 \| 0.4005 \| \| 3.0442 \| 7.0 \| 130403 \| 3.3854 \| 0.4032 \| \| 3.008 \| 8.0 \| 149032 \| 3.4062 \| 0.4030 \| \| 2.9768 \| 9.0 \| 167661 \| 3.3970 \| 0.4047 \| \| 2.9498 \| 10.0 \| 186290 \| 3.4024 \| 0.4047 \| \| 2.917 \| 11.0 \| 204919 \| 3.4242 \| 0.4039 \| \| 2.9005 \| 12.0 \| 223548 \| 3.4093 \| 0.4049 \| \| 2.8747 \| 13.0 \| 242177 \| 3.4192 \| 0.4051 \| \| 2.8542 \| 14.0 \| 260806 \| 3.4233 \| 0.4053 \| \| 2.8326 \| 15.0 \| 279435 \| 3.4314 \| 0.4054 \| \| 2.8125 \| 16.0 \| 298064 \| 3.4404 \| 0.4052 \| \| 2.7911 \| 17.0 \| 316693 \| 3.4450 \| 0.4054 \| \| 2.7682 \| 18.0 \| 335322 \| 3.4488 \| 0.4054 \| \| 2.7512 \| 19.0 \| 353951 \| 3.4581 \| 0.4054 \| \| 2.7331 \| 20.0 \| 372580 \| 3.4663 \| 0.4052 \| ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	[ -0.6968598961830139, -0.5994974970817566, 0.15337125957012177, 0.036934059113264084, -0.32599979639053345, 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TheBloke/Solar-10.7B-SLERP-GGUF	TheBloke	2023-12-14T19:21:11Z	6	transformers	[ "transformers", "gguf", "solar", "en", "base_model:janhq/Solar-10.7B-SLERP", "license:apache-2.0", "region:us" ]	null	2023-12-14T17:57:10Z	--- base_model: janhq/Solar-10.7B-SLERP inference: false language: - en license: apache-2.0 model_creator: Jan model_name: Solar 10.7B SLERP model_type: solar prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Solar 10.7B SLERP - GGUF - Model creator: [Jan](https://huggingface.co/janhq) - Original model: [Solar 10.7B SLERP](https://huggingface.co/janhq/Solar-10.7B-SLERP) <!-- description start --> ## Description This repo contains GGUF format model files for [Jan's Solar 10.7B SLERP](https://huggingface.co/janhq/Solar-10.7B-SLERP). <!-- 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). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF) * [Jan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/janhq/Solar-10.7B-SLERP) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## 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 Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [solar-10.7b-slerp.Q2_K.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q2_K.gguf) \| Q2_K \| 2 \| 4.55 GB\| 7.05 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [solar-10.7b-slerp.Q3_K_S.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 4.67 GB\| 7.17 GB \| very small, high quality loss \| \| [solar-10.7b-slerp.Q3_K_M.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 5.19 GB\| 7.69 GB \| very small, high quality loss \| \| [solar-10.7b-slerp.Q3_K_L.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 5.65 GB\| 8.15 GB \| small, substantial quality loss \| \| [solar-10.7b-slerp.Q4_0.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q4_0.gguf) \| Q4_0 \| 4 \| 6.07 GB\| 8.57 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [solar-10.7b-slerp.Q4_K_S.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 6.10 GB\| 8.60 GB \| small, greater quality loss \| \| [solar-10.7b-slerp.Q4_K_M.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 6.46 GB\| 8.96 GB \| medium, balanced quality - recommended \| \| [solar-10.7b-slerp.Q5_0.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q5_0.gguf) \| Q5_0 \| 5 \| 7.40 GB\| 9.90 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [solar-10.7b-slerp.Q5_K_S.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 7.40 GB\| 9.90 GB \| large, low quality loss - recommended \| \| [solar-10.7b-slerp.Q5_K_M.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 7.60 GB\| 10.10 GB \| large, very low quality loss - recommended \| \| [solar-10.7b-slerp.Q6_K.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q6_K.gguf) \| Q6_K \| 6 \| 8.81 GB\| 11.31 GB \| very large, extremely low quality loss \| \| [solar-10.7b-slerp.Q8_0.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q8_0.gguf) \| Q8_0 \| 8 \| 11.41 GB\| 13.91 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files 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 file. 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: TheBloke/Solar-10.7B-SLERP-GGUF and below it, a specific filename to download, such as: solar-10.7b-slerp.Q4_K_M.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 TheBloke/Solar-10.7B-SLERP-GGUF solar-10.7b-slerp.Q4_K_M.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 TheBloke/Solar-10.7B-SLERP-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 hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Solar-10.7B-SLERP-GGUF solar-10.7b-slerp.Q4_K_M.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 solar-10.7b-slerp.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` 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="./solar-10.7b-slerp.Q4_K_M.gguf", # Download the model file first n_ctx=4096, # 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( "<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant", # 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="./solar-10.7b-slerp.Q4_K_M.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 start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Jan's Solar 10.7B SLERP <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a > - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This model uses the `Slerp` merge method from the best models on 14th Dec on the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard): 1. [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) 2. [janhq/Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B) - base model: [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) The yaml config file for this model is here: ```yaml slices: - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [0, 48] - model: janhq/Pandora-v1-10.7B layer_range: [0, 48] merge_method: slerp base_model: upstage/SOLAR-10.7B-Instruct-v1.0 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` # Prompt template - ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` # Run this model You can run this model using [Jan](https://jan.ai/) on Mac, Windows, or Linux. Jan is an open source, ChatGPT alternative that is: 💻 100% offline on your machine: Your conversations remain confidential, and visible only to you. 🗂️ An Open File Format: Conversations and model settings stay on your computer and can be exported or deleted at any time. 🌐 OpenAI Compatible: Local server on port ` 1337` with OpenAI compatible endpoints 🌍 Open Source & Free: We build in public; check out our [Github](https://github.com/janhq) - Please use the [Solar-10.7B-SLERP-GGUF](https://huggingface.co/janhq/Solar-10.7B-SLERP-GGUF) when using on Jan. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Merger This is a test project for merging models. # Open LLM Leaderboard Evaluation Results Detailed results can be found here. \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| ?\| \| ARC (25-shot) \| ? \| \| HellaSwag (10-shot) \| ? \| \| MMLU (5-shot) \| ?\| \| TruthfulQA (0-shot) \| ? \| \| Winogrande (5-shot) \| ? \| \| GSM8K (5-shot) \| ? \| # Acknowlegement - [mergekit](https://github.com/cg123/mergekit) - [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md) - [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) - [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) <!-- original-model-card end -->	[ -0.6068943738937378, -0.6311302781105042, 0.43282538652420044, 0.2659408748149872, -0.31904155015945435, -0.05857985466718674, 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bio-datasets/Mistral-7B-Instruct-v0.1-fast-pond-196-epoch-0	bio-datasets	2023-12-14T17:57:25Z	0	peft	[ "peft", "safetensors", "region:us" ]	null	2023-12-14T17:57:22Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0	[ -0.40197592973709106, -0.1770414263010025, 0.30504855513572693, 0.9189266562461853, -0.2249852418899536, 0.0376606360077858, 0.6600638628005981, -0.03251168876886368, 0.151278555393219, 0.9004541635513306, -0.6599443554878235, -0.26245781779289246, -0.4278685450553894, 0.06369954347610474, -0.4694896936416626, 0.8085750937461853, -0.271283894777298, 0.324598103761673, 0.23183782398700714, 0.05647220090031624, 0.0649067610502243, -0.8316479921340942, -0.8786254525184631, -0.6577085852622986, 0.520783007144928, 0.7058131694793701, 0.10134003311395645, 0.3649715483188629, 0.6818897724151611, 0.1983930915594101, -0.03444051370024681, -0.9248970150947571, -0.3319839835166931, 0.03142935410141945, -0.2656101584434509, 0.24455444514751434, 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svenbl80/roberta-base_contracts	svenbl80	2023-12-14T18:49:22Z	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-14T17:57:38Z	Entry not found	[ -0.3227648437023163, -0.22568444907665253, 0.8622260093688965, 0.43461522459983826, -0.5282989740371704, 0.7012965083122253, 0.791571855545044, 0.07618637382984161, 0.7746025323867798, 0.25632187724113464, -0.7852814793586731, -0.22573819756507874, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791244506835938, -0.14494505524635315, -0.1075114980340004, 0.2823377251625061, -0.27689528465270996, -0.5409227013587952, -0.3685525059700012, -1.1902776956558228, 0.06149137765169144, 0.5316580533981323, 0.7435140609741211, 0.7584060430526733, 0.3652169406414032, 0.6432583928108215, 0.3932291567325592, -0.23138920962810516, 0.4827055037021637, -0.041717953979969025, 0.002604186534881592, 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Likalto4/vindr-siemens-256-5em6	Likalto4	2023-12-15T14:20:00Z	0	diffusers	[ "diffusers", "safetensors", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	null	2023-12-14T17:58:47Z	Entry not found	[ -0.3227648138999939, -0.2256845235824585, 0.8622258901596069, 0.43461498618125916, -0.5282987356185913, 0.7012966275215149, 0.7915717959403992, 0.076186403632164, 0.7746021747589111, 0.25632184743881226, -0.7852813005447388, -0.22573807835578918, -0.9104479551315308, 0.5715667605400085, -0.39923328161239624, 0.5791248679161072, -0.14494501054286957, -0.10751153528690338, 0.282337486743927, -0.27689552307128906, -0.5409223437309265, -0.36855241656303406, -1.1902776956558228, 0.0614909753203392, 0.5316579341888428, 0.7435141801834106, 0.758405864238739, 0.3652164340019226, 0.6432579755783081, 0.3932289779186249, -0.2313891351222992, 0.4827054440975189, -0.0417182631790638, 0.0026039122603833675, -0.3524433672428131, -0.5516899228096008, -0.28596577048301697, 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gkallergis/bbbp_intrinsic	gkallergis	2023-12-15T01:50:54Z	0	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2023-12-14T18:01:02Z	--- license: cc-by-nc-nd-4.0 ---	[ -0.12853378057479858, -0.18616795539855957, 0.6529127359390259, 0.49436259269714355, -0.1931932121515274, 0.23607474565505981, 0.36071982979774475, 0.0505632683634758, 0.5793652534484863, 0.740013837814331, -0.6508103609085083, -0.2378396987915039, -0.7102251052856445, -0.0478258840739727, -0.38947516679763794, 0.8470761775970459, -0.09598260372877121, 0.024004995822906494, 0.047120094299316406, -0.143178328871727, -0.6121038198471069, -0.047717198729515076, -1.0524537563323975, -0.06787501275539398, 0.30022794008255005, 0.5120974183082581, 0.8275895118713379, 0.3960292339324951, 0.5030564665794373, 1.7515556812286377, -0.08836917579174042, -0.22754409909248352, -0.4589202404022217, 0.4223068952560425, -0.3327736258506775, -0.42133715748786926, -0.2624164819717407, -0.07449344545602798, 0.3238038420677185, 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jan-hq/pandora-v1-10.7b-GGUF	jan-hq	2023-12-15T06:56:21Z	0	null	[ "gguf", "en", "base_model:janhq/Pandora-v1-10.7B", "license:apache-2.0", "region:us" ]	null	2023-12-14T18:01:12Z	--- license: apache-2.0 language: - en base_model: janhq/Pandora-v1-10.7B model_creator: janhq model_name: Pandora-v1-10.7B quantized_by: JanHQ --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This is a GGUF version of [janhq/Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B) - Model creator: [janhq](https://huggingface.co/janhq) - Original model: [Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B) - Model description: [Readme](https://huggingface.co/janhq/Pandora-v1-10.7B/blob/main/README.md) # Run this model You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux. Jan is an open source, ChatGPT alternative that is: - 💻 100% offline on your machine: Your conversations remain confidential, and visible only to you. - 🗂️ An Open File Format: Conversations and model settings stay on your computer and can be exported or deleted at any time. - 🌐 OpenAI Compatible: Local server on port `1337` with OpenAI compatible endpoints - 🌍 Open Source & Free: We build in public; check out our [Github](https://github.com/janhq) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Converter This is a repository for the [open-source converter](https://github.com/janhq/model-converter). We would be grateful if the community could contribute and strengthen this repository. 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gkallergis/clintox_da	gkallergis	2023-12-14T18:25:55Z	0	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2023-12-14T18:01:59Z	--- license: cc-by-nc-nd-4.0 ---	[ -0.12853394448757172, -0.1861671805381775, 0.6529130339622498, 0.49436283111572266, -0.1931932270526886, 0.23607474565505981, 0.3607197403907776, 0.05056331306695938, 0.5793652534484863, 0.7400139570236206, -0.6508102416992188, -0.23783963918685913, -0.7102248668670654, -0.04782587289810181, -0.3894752860069275, 0.8470762968063354, -0.09598271548748016, 0.02400510013103485, 0.047119975090026855, -0.14317838847637177, -0.6121038198471069, -0.04771730303764343, -1.0524539947509766, -0.06787487864494324, 0.3002283573150635, 0.5120975971221924, 0.8275899291038513, 0.3960294723510742, 0.5030567049980164, 1.7515556812286377, -0.08836932480335236, -0.22754409909248352, -0.45892059803009033, 0.422306627035141, -0.33277350664138794, -0.42133671045303345, -0.2624160945415497, -0.0744936615228653, 0.32380393147468567, 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Vinibarcley/Anahis	Vinibarcley	2023-12-14T18:02:54Z	0	null	[ "license:openrail", "region:us" ]	null	2023-12-14T18:02:24Z	--- license: openrail ---	[ -0.12853378057479858, -0.18616795539855957, 0.6529127359390259, 0.49436259269714355, -0.1931932121515274, 0.23607474565505981, 0.36071982979774475, 0.0505632683634758, 0.5793652534484863, 0.740013837814331, -0.6508103609085083, -0.2378396987915039, -0.7102251052856445, -0.0478258840739727, -0.38947516679763794, 0.8470761775970459, -0.09598260372877121, 0.024004995822906494, 0.047120094299316406, -0.143178328871727, -0.6121038198471069, -0.047717198729515076, -1.0524537563323975, -0.06787501275539398, 0.30022794008255005, 0.5120974183082581, 0.8275895118713379, 0.3960292339324951, 0.5030564665794373, 1.7515556812286377, -0.08836917579174042, -0.22754409909248352, -0.4589202404022217, 0.4223068952560425, -0.3327736258506775, -0.42133715748786926, -0.2624164819717407, -0.07449344545602798, 0.3238038420677185, 0.7903714179992676, 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kwwww/bert-base-uncased_8_1039_token

kwwww

2023-12-14T22:38:11Z

0

null

[ "pytorch", "generated_from_trainer", "dataset:bionlp2004", "license:apache-2.0", "region:us" ]

null

2023-12-14T16:33:49Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - bionlp2004 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-uncased_8_1039_token results: [] ---  # bert-base-uncased_8_1039_token This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the bionlp2004 dataset. It achieves the following results on the evaluation set: - Loss: 0.7191 - Precision: 0.7311 - Recall: 0.7648 - F1: 0.7476 - Accuracy: 0.9327 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 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: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 65 | 0.3785 | 0.3534 | 0.4596 | 0.3996 | 0.8659 | | No log | 2.0 | 130 | 0.2718 | 0.5366 | 0.6877 | 0.6028 | 0.9072 | | No log | 3.0 | 195 | 0.2542 | 0.6101 | 0.6434 | 0.6263 | 0.9140 | | No log | 4.0 | 260 | 0.2302 | 0.6304 | 0.7054 | 0.6658 | 0.9248 | | No log | 5.0 | 325 | 0.2327 | 0.6415 | 0.7315 | 0.6836 | 0.9255 | | No log | 6.0 | 390 | 0.2576 | 0.6277 | 0.7418 | 0.6800 | 0.9193 | | No log | 7.0 | 455 | 0.2431 | 0.6615 | 0.7425 | 0.6996 | 0.9263 | | 0.2489 | 8.0 | 520 | 0.2490 | 0.6756 | 0.7184 | 0.6963 | 0.9276 | | 0.2489 | 9.0 | 585 | 0.2702 | 0.6773 | 0.7268 | 0.7012 | 0.9262 | | 0.2489 | 10.0 | 650 | 0.2941 | 0.6335 | 0.6632 | 0.6480 | 0.9193 | | 0.2489 | 11.0 | 715 | 0.2777 | 0.6975 | 0.7502 | 0.7229 | 0.9305 | | 0.2489 | 12.0 | 780 | 0.2879 | 0.6850 | 0.7517 | 0.7168 | 0.9290 | | 0.2489 | 13.0 | 845 | 0.3211 | 0.6839 | 0.6834 | 0.6837 | 0.9240 | | 0.2489 | 14.0 | 910 | 0.3036 | 0.6808 | 0.6910 | 0.6859 | 0.9259 | | 0.2489 | 15.0 | 975 | 0.3149 | 0.6666 | 0.7108 | 0.6880 | 0.9241 | | 0.0766 | 16.0 | 1040 | 0.3416 | 0.6635 | 0.6917 | 0.6773 | 0.9232 | | 0.0766 | 17.0 | 1105 | 0.3478 | 0.6630 | 0.7302 | 0.6950 | 0.9239 | | 0.0766 | 18.0 | 1170 | 0.3316 | 0.6703 | 0.7581 | 0.7115 | 0.9273 | | 0.0766 | 19.0 | 1235 | 0.3510 | 0.6918 | 0.7054 | 0.6985 | 0.9254 | | 0.0766 | 20.0 | 1300 | 0.3560 | 0.6817 | 0.7299 | 0.7050 | 0.9269 | | 0.0766 | 21.0 | 1365 | 0.3641 | 0.6678 | 0.7313 | 0.6981 | 0.9262 | | 0.0766 | 22.0 | 1430 | 0.3408 | 0.6826 | 0.7292 | 0.7051 | 0.9268 | | 0.0766 | 23.0 | 1495 | 0.3689 | 0.6772 | 0.7238 | 0.6997 | 0.9259 | | 0.0406 | 24.0 | 1560 | 0.3782 | 0.6674 | 0.7443 | 0.7037 | 0.9259 | | 0.0406 | 25.0 | 1625 | 0.4102 | 0.6744 | 0.6771 | 0.6758 | 0.9205 | | 0.0406 | 26.0 | 1690 | 0.3654 | 0.6750 | 0.7297 | 0.7013 | 0.9277 | | 0.0406 | 27.0 | 1755 | 0.4032 | 0.6721 | 0.7556 | 0.7114 | 0.9253 | | 0.0406 | 28.0 | 1820 | 0.4372 | 0.6936 | 0.6865 | 0.6900 | 0.9231 | | 0.0406 | 29.0 | 1885 | 0.3914 | 0.6761 | 0.7290 | 0.7016 | 0.9264 | | 0.0406 | 30.0 | 1950 | 0.3967 | 0.6844 | 0.7337 | 0.7082 | 0.9266 | | 0.0307 | 31.0 | 2015 | 0.4035 | 0.6858 | 0.7441 | 0.7138 | 0.9267 | | 0.0307 | 32.0 | 2080 | 0.4173 | 0.6899 | 0.7461 | 0.7169 | 0.9284 | | 0.0307 | 33.0 | 2145 | 0.3992 | 0.6826 | 0.7223 | 0.7019 | 0.9280 | | 0.0307 | 34.0 | 2210 | 0.4192 | 0.6677 | 0.7637 | 0.7125 | 0.9280 | | 0.0307 | 35.0 | 2275 | 0.4110 | 0.6990 | 0.7373 | 0.7176 | 0.9302 | | 0.0307 | 36.0 | 2340 | 0.4688 | 0.6739 | 0.7034 | 0.6883 | 0.9210 | | 0.0307 | 37.0 | 2405 | 0.4203 | 0.6834 | 0.7311 | 0.7065 | 0.9285 | | 0.0307 | 38.0 | 2470 | 0.4597 | 0.6923 | 0.7452 | 0.7178 | 0.9277 | | 0.0235 | 39.0 | 2535 | 0.4246 | 0.6790 | 0.7484 | 0.7120 | 0.9264 | | 0.0235 | 40.0 | 2600 | 0.4335 | 0.6906 | 0.7286 | 0.7091 | 0.9274 | | 0.0235 | 41.0 | 2665 | 0.4571 | 0.6786 | 0.7239 | 0.7005 | 0.9251 | | 0.0235 | 42.0 | 2730 | 0.4273 | 0.6930 | 0.7340 | 0.7129 | 0.9282 | | 0.0235 | 43.0 | 2795 | 0.4471 | 0.6981 | 0.7167 | 0.7073 | 0.9271 | | 0.0235 | 44.0 | 2860 | 0.4520 | 0.6959 | 0.7290 | 0.7120 | 0.9273 | | 0.0235 | 45.0 | 2925 | 0.4540 | 0.6946 | 0.7349 | 0.7142 | 0.9272 | | 0.0235 | 46.0 | 2990 | 0.4478 | 0.6826 | 0.7497 | 0.7146 | 0.9286 | | 0.0188 | 47.0 | 3055 | 0.4538 | 0.6951 | 0.7414 | 0.7175 | 0.9278 | | 0.0188 | 48.0 | 3120 | 0.4738 | 0.6954 | 0.7247 | 0.7097 | 0.9276 | | 0.0188 | 49.0 | 3185 | 0.4915 | 0.7057 | 0.7338 | 0.7195 | 0.9287 | | 0.0188 | 50.0 | 3250 | 0.4568 | 0.6989 | 0.7382 | 0.7180 | 0.9285 | | 0.0188 | 51.0 | 3315 | 0.5172 | 0.6901 | 0.7137 | 0.7017 | 0.9223 | | 0.0188 | 52.0 | 3380 | 0.4803 | 0.6842 | 0.7290 | 0.7059 | 0.9267 | | 0.0188 | 53.0 | 3445 | 0.4312 | 0.7015 | 0.7405 | 0.7205 | 0.9297 | | 0.0169 | 54.0 | 3510 | 0.4340 | 0.6825 | 0.7430 | 0.7115 | 0.9286 | | 0.0169 | 55.0 | 3575 | 0.4535 | 0.6941 | 0.7583 | 0.7248 | 0.9292 | | 0.0169 | 56.0 | 3640 | 0.4922 | 0.6897 | 0.7630 | 0.7245 | 0.9292 | | 0.0169 | 57.0 | 3705 | 0.4889 | 0.6900 | 0.7551 | 0.7211 | 0.9283 | | 0.0169 | 58.0 | 3770 | 0.4814 | 0.6869 | 0.7396 | 0.7123 | 0.9266 | | 0.0169 | 59.0 | 3835 | 0.4799 | 0.6764 | 0.7659 | 0.7184 | 0.9275 | | 0.0169 | 60.0 | 3900 | 0.4813 | 0.6687 | 0.7524 | 0.7081 | 0.9275 | | 0.0169 | 61.0 | 3965 | 0.4561 | 0.7081 | 0.7526 | 0.7296 | 0.9297 | | 0.0165 | 62.0 | 4030 | 0.4537 | 0.7002 | 0.7360 | 0.7176 | 0.9277 | | 0.0165 | 63.0 | 4095 | 0.4793 | 0.6933 | 0.7509 | 0.7210 | 0.9268 | | 0.0165 | 64.0 | 4160 | 0.4645 | 0.7157 | 0.7162 | 0.7159 | 0.9274 | | 0.0165 | 65.0 | 4225 | 0.4886 | 0.6939 | 0.7169 | 0.7052 | 0.9263 | | 0.0165 | 66.0 | 4290 | 0.4707 | 0.6714 | 0.7639 | 0.7147 | 0.9253 | | 0.0165 | 67.0 | 4355 | 0.4768 | 0.6783 | 0.7535 | 0.7139 | 0.9292 | | 0.0165 | 68.0 | 4420 | 0.4601 | 0.6868 | 0.7430 | 0.7138 | 0.9280 | | 0.0165 | 69.0 | 4485 | 0.4411 | 0.7076 | 0.7371 | 0.7221 | 0.9301 | | 0.0134 | 70.0 | 4550 | 0.5118 | 0.6811 | 0.7383 | 0.7085 | 0.9277 | | 0.0134 | 71.0 | 4615 | 0.4723 | 0.6908 | 0.7455 | 0.7171 | 0.9283 | | 0.0134 | 72.0 | 4680 | 0.4940 | 0.6939 | 0.7196 | 0.7065 | 0.9257 | | 0.0134 | 73.0 | 4745 | 0.5030 | 0.6870 | 0.7443 | 0.7145 | 0.9276 | | 0.0134 | 74.0 | 4810 | 0.4606 | 0.6834 | 0.7398 | 0.7105 | 0.9283 | | 0.0134 | 75.0 | 4875 | 0.4554 | 0.6904 | 0.7614 | 0.7242 | 0.9305 | | 0.0134 | 76.0 | 4940 | 0.5056 | 0.6926 | 0.7241 | 0.7080 | 0.9263 | | 0.0136 | 77.0 | 5005 | 0.5137 | 0.6537 | 0.7371 | 0.6929 | 0.9224 | | 0.0136 | 78.0 | 5070 | 0.4599 | 0.6891 | 0.7252 | 0.7067 | 0.9264 | | 0.0136 | 79.0 | 5135 | 0.4919 | 0.6916 | 0.7641 | 0.7260 | 0.9292 | | 0.0136 | 80.0 | 5200 | 0.4656 | 0.6801 | 0.7513 | 0.7140 | 0.9279 | | 0.0136 | 81.0 | 5265 | 0.4895 | 0.6856 | 0.7592 | 0.7206 | 0.9282 | | 0.0136 | 82.0 | 5330 | 0.4975 | 0.69 | 0.7331 | 0.7109 | 0.9268 | | 0.0136 | 83.0 | 5395 | 0.4889 | 0.6984 | 0.7679 | 0.7315 | 0.9291 | | 0.0136 | 84.0 | 5460 | 0.4989 | 0.6934 | 0.7446 | 0.7181 | 0.9279 | | 0.0142 | 85.0 | 5525 | 0.4927 | 0.7045 | 0.7382 | 0.7210 | 0.9292 | | 0.0142 | 86.0 | 5590 | 0.5044 | 0.6862 | 0.7322 | 0.7085 | 0.9253 | | 0.0142 | 87.0 | 5655 | 0.4977 | 0.6834 | 0.7434 | 0.7122 | 0.9263 | | 0.0142 | 88.0 | 5720 | 0.5068 | 0.6750 | 0.7675 | 0.7183 | 0.9275 | | 0.0142 | 89.0 | 5785 | 0.5193 | 0.6946 | 0.7531 | 0.7227 | 0.9286 | | 0.0142 | 90.0 | 5850 | 0.5418 | 0.6953 | 0.7497 | 0.7215 | 0.9282 | | 0.0142 | 91.0 | 5915 | 0.5780 | 0.7030 | 0.7315 | 0.7170 | 0.9273 | | 0.0142 | 92.0 | 5980 | 0.5278 | 0.6825 | 0.6937 | 0.6880 | 0.9224 | | 0.0112 | 93.0 | 6045 | 0.5053 | 0.7089 | 0.7333 | 0.7209 | 0.9275 | | 0.0112 | 94.0 | 6110 | 0.5550 | 0.7067 | 0.6926 | 0.6996 | 0.9231 | | 0.0112 | 95.0 | 6175 | 0.5253 | 0.6830 | 0.7610 | 0.7199 | 0.9262 | | 0.0112 | 96.0 | 6240 | 0.5264 | 0.6906 | 0.7495 | 0.7188 | 0.9267 | | 0.0112 | 97.0 | 6305 | 0.5120 | 0.6988 | 0.7533 | 0.7250 | 0.9284 | | 0.0112 | 98.0 | 6370 | 0.5490 | 0.7072 | 0.7160 | 0.7116 | 0.9266 | | 0.0112 | 99.0 | 6435 | 0.4795 | 0.7063 | 0.7526 | 0.7287 | 0.9298 | | 0.0115 | 100.0 | 6500 | 0.5181 | 0.6969 | 0.7382 | 0.7169 | 0.9281 | | 0.0115 | 101.0 | 6565 | 0.5245 | 0.6909 | 0.7355 | 0.7125 | 0.9273 | | 0.0115 | 102.0 | 6630 | 0.5008 | 0.6837 | 0.7704 | 0.7245 | 0.9282 | | 0.0115 | 103.0 | 6695 | 0.5059 | 0.6833 | 0.7481 | 0.7142 | 0.9267 | | 0.0115 | 104.0 | 6760 | 0.5299 | 0.7057 | 0.7279 | 0.7166 | 0.9280 | | 0.0115 | 105.0 | 6825 | 0.4829 | 0.7054 | 0.7416 | 0.7230 | 0.9299 | | 0.0115 | 106.0 | 6890 | 0.5247 | 0.7062 | 0.7463 | 0.7257 | 0.9284 | | 0.0115 | 107.0 | 6955 | 0.5306 | 0.7067 | 0.7603 | 0.7325 | 0.9309 | | 0.0096 | 108.0 | 7020 | 0.5252 | 0.6885 | 0.7481 | 0.7171 | 0.9263 | | 0.0096 | 109.0 | 7085 | 0.4961 | 0.7094 | 0.7407 | 0.7247 | 0.9306 | | 0.0096 | 110.0 | 7150 | 0.5659 | 0.6912 | 0.7369 | 0.7133 | 0.9266 | | 0.0096 | 111.0 | 7215 | 0.5572 | 0.6765 | 0.7682 | 0.7195 | 0.9268 | | 0.0096 | 112.0 | 7280 | 0.5170 | 0.7190 | 0.7360 | 0.7274 | 0.9305 | | 0.0096 | 113.0 | 7345 | 0.5140 | 0.6973 | 0.7274 | 0.7120 | 0.9276 | | 0.0096 | 114.0 | 7410 | 0.5507 | 0.7096 | 0.7520 | 0.7302 | 0.9298 | | 0.0096 | 115.0 | 7475 | 0.5813 | 0.6944 | 0.7337 | 0.7135 | 0.9271 | | 0.0087 | 116.0 | 7540 | 0.5431 | 0.7079 | 0.7423 | 0.7247 | 0.9288 | | 0.0087 | 117.0 | 7605 | 0.5649 | 0.6867 | 0.7596 | 0.7213 | 0.9275 | | 0.0087 | 118.0 | 7670 | 0.5577 | 0.6856 | 0.7576 | 0.7198 | 0.9267 | | 0.0087 | 119.0 | 7735 | 0.5189 | 0.7107 | 0.7448 | 0.7273 | 0.9296 | | 0.0087 | 120.0 | 7800 | 0.4997 | 0.7024 | 0.7268 | 0.7144 | 0.9283 | | 0.0087 | 121.0 | 7865 | 0.5347 | 0.6838 | 0.7594 | 0.7196 | 0.9274 | | 0.0087 | 122.0 | 7930 | 0.5074 | 0.6986 | 0.7329 | 0.7154 | 0.9280 | | 0.0087 | 123.0 | 7995 | 0.5455 | 0.6925 | 0.7661 | 0.7274 | 0.9279 | | 0.0094 | 124.0 | 8060 | 0.5187 | 0.6858 | 0.7681 | 0.7246 | 0.9295 | | 0.0094 | 125.0 | 8125 | 0.5379 | 0.6918 | 0.7574 | 0.7231 | 0.9290 | | 0.0094 | 126.0 | 8190 | 0.5108 | 0.6963 | 0.7500 | 0.7221 | 0.9286 | | 0.0094 | 127.0 | 8255 | 0.5201 | 0.7093 | 0.7612 | 0.7344 | 0.9300 | | 0.0094 | 128.0 | 8320 | 0.5498 | 0.6975 | 0.7536 | 0.7245 | 0.9290 | | 0.0094 | 129.0 | 8385 | 0.5157 | 0.7128 | 0.7464 | 0.7292 | 0.9293 | | 0.0094 | 130.0 | 8450 | 0.5246 | 0.7044 | 0.7446 | 0.7240 | 0.9281 | | 0.0083 | 131.0 | 8515 | 0.5290 | 0.6857 | 0.7391 | 0.7114 | 0.9254 | | 0.0083 | 132.0 | 8580 | 0.5479 | 0.6919 | 0.7400 | 0.7151 | 0.9266 | | 0.0083 | 133.0 | 8645 | 0.5500 | 0.7084 | 0.7207 | 0.7145 | 0.9279 | | 0.0083 | 134.0 | 8710 | 0.5646 | 0.6755 | 0.7572 | 0.7140 | 0.9235 | | 0.0083 | 135.0 | 8775 | 0.5731 | 0.6871 | 0.7290 | 0.7074 | 0.9247 | | 0.0083 | 136.0 | 8840 | 0.5839 | 0.6946 | 0.7421 | 0.7176 | 0.9279 | | 0.0083 | 137.0 | 8905 | 0.5546 | 0.6814 | 0.7715 | 0.7236 | 0.9275 | | 0.0083 | 138.0 | 8970 | 0.5774 | 0.7080 | 0.7205 | 0.7142 | 0.9252 | | 0.008 | 139.0 | 9035 | 0.5790 | 0.7026 | 0.7585 | 0.7295 | 0.9292 | | 0.008 | 140.0 | 9100 | 0.5969 | 0.7078 | 0.7389 | 0.7230 | 0.9290 | | 0.008 | 141.0 | 9165 | 0.5879 | 0.7129 | 0.7335 | 0.7231 | 0.9270 | | 0.008 | 142.0 | 9230 | 0.5642 | 0.6920 | 0.7749 | 0.7311 | 0.9286 | | 0.008 | 143.0 | 9295 | 0.5660 | 0.6920 | 0.7317 | 0.7113 | 0.9243 | | 0.008 | 144.0 | 9360 | 0.5383 | 0.7210 | 0.7454 | 0.7330 | 0.9303 | | 0.008 | 145.0 | 9425 | 0.5516 | 0.6899 | 0.7625 | 0.7244 | 0.9285 | | 0.008 | 146.0 | 9490 | 0.5800 | 0.7001 | 0.7504 | 0.7244 | 0.9279 | | 0.0077 | 147.0 | 9555 | 0.5994 | 0.6904 | 0.7538 | 0.7207 | 0.9271 | | 0.0077 | 148.0 | 9620 | 0.5490 | 0.6971 | 0.7605 | 0.7274 | 0.9286 | | 0.0077 | 149.0 | 9685 | 0.5419 | 0.7037 | 0.7331 | 0.7181 | 0.9277 | | 0.0077 | 150.0 | 9750 | 0.5677 | 0.7030 | 0.7590 | 0.7299 | 0.9286 | | 0.0077 | 151.0 | 9815 | 0.5506 | 0.7133 | 0.7576 | 0.7348 | 0.9306 | | 0.0077 | 152.0 | 9880 | 0.5485 | 0.7170 | 0.7531 | 0.7346 | 0.9305 | | 0.0077 | 153.0 | 9945 | 0.5387 | 0.7140 | 0.7508 | 0.7319 | 0.9305 | | 0.0074 | 154.0 | 10010 | 0.5367 | 0.7082 | 0.7409 | 0.7242 | 0.9285 | | 0.0074 | 155.0 | 10075 | 0.5347 | 0.7194 | 0.7434 | 0.7312 | 0.9313 | | 0.0074 | 156.0 | 10140 | 0.5098 | 0.7002 | 0.7493 | 0.7239 | 0.9303 | | 0.0074 | 157.0 | 10205 | 0.5244 | 0.7072 | 0.7556 | 0.7306 | 0.9313 | | 0.0074 | 158.0 | 10270 | 0.5308 | 0.6972 | 0.7459 | 0.7207 | 0.9286 | | 0.0074 | 159.0 | 10335 | 0.5254 | 0.7082 | 0.7418 | 0.7246 | 0.9302 | | 0.0074 | 160.0 | 10400 | 0.5290 | 0.7012 | 0.7627 | 0.7306 | 0.9308 | | 0.0074 | 161.0 | 10465 | 0.5579 | 0.6890 | 0.7450 | 0.7159 | 0.9271 | | 0.0063 | 162.0 | 10530 | 0.5626 | 0.7027 | 0.7740 | 0.7367 | 0.9286 | | 0.0063 | 163.0 | 10595 | 0.5592 | 0.6973 | 0.7587 | 0.7267 | 0.9287 | | 0.0063 | 164.0 | 10660 | 0.5477 | 0.7021 | 0.7484 | 0.7245 | 0.9286 | | 0.0063 | 165.0 | 10725 | 0.5554 | 0.6906 | 0.7400 | 0.7144 | 0.9269 | | 0.0063 | 166.0 | 10790 | 0.5559 | 0.7128 | 0.7527 | 0.7322 | 0.9296 | | 0.0063 | 167.0 | 10855 | 0.5043 | 0.7081 | 0.7333 | 0.7205 | 0.9281 | | 0.0063 | 168.0 | 10920 | 0.5417 | 0.7009 | 0.7556 | 0.7272 | 0.9287 | | 0.0063 | 169.0 | 10985 | 0.5428 | 0.7145 | 0.7410 | 0.7275 | 0.9281 | | 0.0075 | 170.0 | 11050 | 0.5807 | 0.6804 | 0.7576 | 0.7169 | 0.9259 | | 0.0075 | 171.0 | 11115 | 0.6278 | 0.6838 | 0.7409 | 0.7112 | 0.9255 | | 0.0075 | 172.0 | 11180 | 0.5789 | 0.7049 | 0.7360 | 0.7201 | 0.9276 | | 0.0075 | 173.0 | 11245 | 0.5873 | 0.6980 | 0.7256 | 0.7115 | 0.9270 | | 0.0075 | 174.0 | 11310 | 0.5459 | 0.6911 | 0.7774 | 0.7317 | 0.9290 | | 0.0075 | 175.0 | 11375 | 0.5281 | 0.7018 | 0.7535 | 0.7267 | 0.9294 | | 0.0075 | 176.0 | 11440 | 0.5366 | 0.7141 | 0.7506 | 0.7319 | 0.9294 | | 0.006 | 177.0 | 11505 | 0.5741 | 0.7062 | 0.7531 | 0.7289 | 0.9298 | | 0.006 | 178.0 | 11570 | 0.5661 | 0.6983 | 0.7589 | 0.7273 | 0.9290 | | 0.006 | 179.0 | 11635 | 0.5913 | 0.7179 | 0.7373 | 0.7274 | 0.9293 | | 0.006 | 180.0 | 11700 | 0.5723 | 0.7236 | 0.7452 | 0.7342 | 0.9300 | | 0.006 | 181.0 | 11765 | 0.6079 | 0.7217 | 0.7405 | 0.7310 | 0.9287 | | 0.006 | 182.0 | 11830 | 0.5710 | 0.6981 | 0.7162 | 0.7070 | 0.9238 | | 0.006 | 183.0 | 11895 | 0.5895 | 0.7091 | 0.7468 | 0.7275 | 0.9289 | | 0.006 | 184.0 | 11960 | 0.5970 | 0.6882 | 0.7652 | 0.7247 | 0.9285 | | 0.0059 | 185.0 | 12025 | 0.5658 | 0.6849 | 0.7580 | 0.7196 | 0.9278 | | 0.0059 | 186.0 | 12090 | 0.5282 | 0.6981 | 0.7320 | 0.7147 | 0.9267 | | 0.0059 | 187.0 | 12155 | 0.5622 | 0.7084 | 0.7502 | 0.7287 | 0.9293 | | 0.0059 | 188.0 | 12220 | 0.5713 | 0.6991 | 0.7497 | 0.7235 | 0.9289 | | 0.0059 | 189.0 | 12285 | 0.5848 | 0.6926 | 0.7789 | 0.7332 | 0.9270 | | 0.0059 | 190.0 | 12350 | 0.5561 | 0.7055 | 0.7657 | 0.7344 | 0.9298 | | 0.0059 | 191.0 | 12415 | 0.5761 | 0.7040 | 0.7405 | 0.7218 | 0.9269 | | 0.0059 | 192.0 | 12480 | 0.6216 | 0.6911 | 0.7430 | 0.7161 | 0.9264 | | 0.0059 | 193.0 | 12545 | 0.6137 | 0.7116 | 0.7459 | 0.7283 | 0.9295 | | 0.0059 | 194.0 | 12610 | 0.5797 | 0.6929 | 0.7630 | 0.7263 | 0.9289 | | 0.0059 | 195.0 | 12675 | 0.5176 | 0.7081 | 0.7675 | 0.7366 | 0.9319 | | 0.0059 | 196.0 | 12740 | 0.5698 | 0.7042 | 0.7356 | 0.7196 | 0.9273 | | 0.0059 | 197.0 | 12805 | 0.5592 | 0.7042 | 0.7396 | 0.7215 | 0.9293 | | 0.0059 | 198.0 | 12870 | 0.5740 | 0.7015 | 0.7313 | 0.7161 | 0.9271 | | 0.0059 | 199.0 | 12935 | 0.5589 | 0.7190 | 0.7311 | 0.7250 | 0.9297 | | 0.0058 | 200.0 | 13000 | 0.5749 | 0.6847 | 0.7717 | 0.7256 | 0.9287 | | 0.0058 | 201.0 | 13065 | 0.5486 | 0.7161 | 0.7663 | 0.7403 | 0.9313 | | 0.0058 | 202.0 | 13130 | 0.5279 | 0.7108 | 0.7218 | 0.7162 | 0.9281 | | 0.0058 | 203.0 | 13195 | 0.5850 | 0.7102 | 0.7252 | 0.7176 | 0.9273 | | 0.0058 | 204.0 | 13260 | 0.6231 | 0.6969 | 0.7771 | 0.7348 | 0.9283 | | 0.0058 | 205.0 | 13325 | 0.5637 | 0.6968 | 0.7740 | 0.7334 | 0.9277 | | 0.0058 | 206.0 | 13390 | 0.5366 | 0.7017 | 0.7308 | 0.7159 | 0.9279 | | 0.0058 | 207.0 | 13455 | 0.5978 | 0.7098 | 0.7250 | 0.7173 | 0.9267 | | 0.0055 | 208.0 | 13520 | 0.5603 | 0.6980 | 0.7599 | 0.7276 | 0.9295 | | 0.0055 | 209.0 | 13585 | 0.5780 | 0.6930 | 0.7407 | 0.7161 | 0.9275 | | 0.0055 | 210.0 | 13650 | 0.5846 | 0.7038 | 0.7572 | 0.7295 | 0.9283 | | 0.0055 | 211.0 | 13715 | 0.6297 | 0.7018 | 0.7250 | 0.7132 | 0.9241 | | 0.0055 | 212.0 | 13780 | 0.5818 | 0.7143 | 0.7173 | 0.7158 | 0.9276 | | 0.0055 | 213.0 | 13845 | 0.6260 | 0.6879 | 0.7454 | 0.7155 | 0.9252 | | 0.0055 | 214.0 | 13910 | 0.6031 | 0.7072 | 0.7578 | 0.7316 | 0.9284 | | 0.0055 | 215.0 | 13975 | 0.5846 | 0.6954 | 0.7684 | 0.7301 | 0.9279 | | 0.0044 | 216.0 | 14040 | 0.5741 | 0.7067 | 0.7328 | 0.7195 | 0.9265 | | 0.0044 | 217.0 | 14105 | 0.5848 | 0.7186 | 0.7389 | 0.7286 | 0.9291 | | 0.0044 | 218.0 | 14170 | 0.5496 | 0.7163 | 0.7248 | 0.7206 | 0.9269 | | 0.0044 | 219.0 | 14235 | 0.5700 | 0.7089 | 0.7520 | 0.7298 | 0.9288 | | 0.0044 | 220.0 | 14300 | 0.5664 | 0.7063 | 0.7427 | 0.7240 | 0.9280 | | 0.0044 | 221.0 | 14365 | 0.5573 | 0.7173 | 0.7618 | 0.7389 | 0.9324 | | 0.0044 | 222.0 | 14430 | 0.5588 | 0.7151 | 0.7690 | 0.7411 | 0.9324 | | 0.0044 | 223.0 | 14495 | 0.5809 | 0.7129 | 0.7455 | 0.7289 | 0.9294 | | 0.0037 | 224.0 | 14560 | 0.6135 | 0.6921 | 0.7621 | 0.7254 | 0.9281 | | 0.0037 | 225.0 | 14625 | 0.6160 | 0.6890 | 0.7383 | 0.7128 | 0.9251 | | 0.0037 | 226.0 | 14690 | 0.5938 | 0.6887 | 0.7508 | 0.7184 | 0.9270 | | 0.0037 | 227.0 | 14755 | 0.5687 | 0.7076 | 0.7473 | 0.7269 | 0.9296 | | 0.0037 | 228.0 | 14820 | 0.5785 | 0.7097 | 0.7517 | 0.7301 | 0.9283 | | 0.0037 | 229.0 | 14885 | 0.5675 | 0.6869 | 0.7470 | 0.7157 | 0.9270 | | 0.0037 | 230.0 | 14950 | 0.5937 | 0.7077 | 0.7524 | 0.7293 | 0.9289 | | 0.0046 | 231.0 | 15015 | 0.5650 | 0.7097 | 0.7414 | 0.7252 | 0.9294 | | 0.0046 | 232.0 | 15080 | 0.5820 | 0.7038 | 0.7745 | 0.7375 | 0.9307 | | 0.0046 | 233.0 | 15145 | 0.6118 | 0.7124 | 0.7432 | 0.7275 | 0.9284 | | 0.0046 | 234.0 | 15210 | 0.6100 | 0.7055 | 0.7544 | 0.7291 | 0.9297 | | 0.0046 | 235.0 | 15275 | 0.5869 | 0.7149 | 0.7592 | 0.7364 | 0.9302 | | 0.0046 | 236.0 | 15340 | 0.5881 | 0.7169 | 0.7616 | 0.7386 | 0.9305 | | 0.0046 | 237.0 | 15405 | 0.5616 | 0.7188 | 0.7380 | 0.7283 | 0.9315 | | 0.0046 | 238.0 | 15470 | 0.5785 | 0.7176 | 0.7320 | 0.7247 | 0.9281 | | 0.0047 | 239.0 | 15535 | 0.5979 | 0.7159 | 0.7466 | 0.7310 | 0.9279 | | 0.0047 | 240.0 | 15600 | 0.5732 | 0.7075 | 0.7533 | 0.7297 | 0.9303 | | 0.0047 | 241.0 | 15665 | 0.5732 | 0.7111 | 0.7421 | 0.7263 | 0.9294 | | 0.0047 | 242.0 | 15730 | 0.5516 | 0.7198 | 0.7382 | 0.7288 | 0.9292 | | 0.0047 | 243.0 | 15795 | 0.5505 | 0.7073 | 0.7697 | 0.7372 | 0.9310 | | 0.0047 | 244.0 | 15860 | 0.5677 | 0.7163 | 0.7428 | 0.7293 | 0.9303 | | 0.0047 | 245.0 | 15925 | 0.5815 | 0.7191 | 0.7302 | 0.7246 | 0.9286 | | 0.0047 | 246.0 | 15990 | 0.6044 | 0.7099 | 0.7497 | 0.7293 | 0.9287 | | 0.0042 | 247.0 | 16055 | 0.5540 | 0.7144 | 0.7558 | 0.7345 | 0.9302 | | 0.0042 | 248.0 | 16120 | 0.5498 | 0.7219 | 0.7581 | 0.7396 | 0.9320 | | 0.0042 | 249.0 | 16185 | 0.6078 | 0.7152 | 0.7428 | 0.7287 | 0.9303 | | 0.0042 | 250.0 | 16250 | 0.6523 | 0.7152 | 0.7286 | 0.7219 | 0.9279 | | 0.0042 | 251.0 | 16315 | 0.6075 | 0.7184 | 0.7621 | 0.7396 | 0.9319 | | 0.0042 | 252.0 | 16380 | 0.6071 | 0.7108 | 0.7648 | 0.7368 | 0.9302 | | 0.0042 | 253.0 | 16445 | 0.5849 | 0.7196 | 0.7367 | 0.7281 | 0.9308 | | 0.0031 | 254.0 | 16510 | 0.5892 | 0.7141 | 0.7652 | 0.7388 | 0.9316 | | 0.0031 | 255.0 | 16575 | 0.5991 | 0.7174 | 0.7310 | 0.7241 | 0.9273 | | 0.0031 | 256.0 | 16640 | 0.6051 | 0.7240 | 0.7358 | 0.7298 | 0.9288 | | 0.0031 | 257.0 | 16705 | 0.5621 | 0.7267 | 0.7560 | 0.7410 | 0.9320 | | 0.0031 | 258.0 | 16770 | 0.5838 | 0.7165 | 0.7607 | 0.7379 | 0.9314 | | 0.0031 | 259.0 | 16835 | 0.5713 | 0.7092 | 0.7709 | 0.7388 | 0.9318 | | 0.0031 | 260.0 | 16900 | 0.5902 | 0.7243 | 0.7499 | 0.7369 | 0.9314 | | 0.0031 | 261.0 | 16965 | 0.6098 | 0.716 | 0.7414 | 0.7285 | 0.9288 | | 0.0029 | 262.0 | 17030 | 0.5979 | 0.7193 | 0.7502 | 0.7344 | 0.9308 | | 0.0029 | 263.0 | 17095 | 0.6314 | 0.7077 | 0.7455 | 0.7261 | 0.9287 | | 0.0029 | 264.0 | 17160 | 0.6227 | 0.6870 | 0.7360 | 0.7107 | 0.9261 | | 0.0029 | 265.0 | 17225 | 0.5925 | 0.6997 | 0.7509 | 0.7244 | 0.9283 | | 0.0029 | 266.0 | 17290 | 0.6090 | 0.7049 | 0.7428 | 0.7234 | 0.9274 | | 0.0029 | 267.0 | 17355 | 0.5929 | 0.7082 | 0.7508 | 0.7288 | 0.9294 | | 0.0029 | 268.0 | 17420 | 0.5661 | 0.7013 | 0.7560 | 0.7276 | 0.9293 | | 0.0029 | 269.0 | 17485 | 0.6482 | 0.7223 | 0.7439 | 0.7330 | 0.9277 | | 0.0036 | 270.0 | 17550 | 0.5838 | 0.7123 | 0.7479 | 0.7297 | 0.9300 | | 0.0036 | 271.0 | 17615 | 0.5947 | 0.7220 | 0.7194 | 0.7207 | 0.9272 | | 0.0036 | 272.0 | 17680 | 0.5878 | 0.7198 | 0.7416 | 0.7305 | 0.9292 | | 0.0036 | 273.0 | 17745 | 0.5592 | 0.7120 | 0.7684 | 0.7391 | 0.9320 | | 0.0036 | 274.0 | 17810 | 0.5885 | 0.7163 | 0.7513 | 0.7334 | 0.9297 | | 0.0036 | 275.0 | 17875 | 0.5953 | 0.7091 | 0.7497 | 0.7288 | 0.9282 | | 0.0036 | 276.0 | 17940 | 0.6116 | 0.7178 | 0.7457 | 0.7315 | 0.9294 | | 0.0028 | 277.0 | 18005 | 0.6008 | 0.7201 | 0.7614 | 0.7402 | 0.9313 | | 0.0028 | 278.0 | 18070 | 0.5998 | 0.7236 | 0.7452 | 0.7342 | 0.9311 | | 0.0028 | 279.0 | 18135 | 0.6165 | 0.7018 | 0.7594 | 0.7295 | 0.9292 | | 0.0028 | 280.0 | 18200 | 0.5834 | 0.7262 | 0.7452 | 0.7356 | 0.9312 | | 0.0028 | 281.0 | 18265 | 0.6081 | 0.7072 | 0.7643 | 0.7346 | 0.9302 | | 0.0028 | 282.0 | 18330 | 0.5909 | 0.6981 | 0.7580 | 0.7268 | 0.9301 | | 0.0028 | 283.0 | 18395 | 0.6065 | 0.7155 | 0.7313 | 0.7233 | 0.9277 | | 0.0028 | 284.0 | 18460 | 0.5695 | 0.7201 | 0.7454 | 0.7325 | 0.9306 | | 0.0031 | 285.0 | 18525 | 0.6007 | 0.7173 | 0.7455 | 0.7311 | 0.9289 | | 0.0031 | 286.0 | 18590 | 0.5894 | 0.7041 | 0.7488 | 0.7258 | 0.9284 | | 0.0031 | 287.0 | 18655 | 0.6207 | 0.7082 | 0.7301 | 0.7190 | 0.9277 | | 0.0031 | 288.0 | 18720 | 0.5907 | 0.7171 | 0.7488 | 0.7326 | 0.9303 | | 0.0031 | 289.0 | 18785 | 0.6165 | 0.7199 | 0.7374 | 0.7286 | 0.9298 | | 0.0031 | 290.0 | 18850 | 0.6304 | 0.7277 | 0.7419 | 0.7347 | 0.9297 | | 0.0031 | 291.0 | 18915 | 0.6379 | 0.7132 | 0.7605 | 0.7361 | 0.9310 | | 0.0031 | 292.0 | 18980 | 0.6186 | 0.7226 | 0.7553 | 0.7386 | 0.9307 | | 0.0027 | 293.0 | 19045 | 0.6400 | 0.7125 | 0.7551 | 0.7332 | 0.9297 | | 0.0027 | 294.0 | 19110 | 0.6244 | 0.7197 | 0.7527 | 0.7359 | 0.9305 | | 0.0027 | 295.0 | 19175 | 0.6283 | 0.7200 | 0.7648 | 0.7417 | 0.9309 | | 0.0027 | 296.0 | 19240 | 0.6233 | 0.7166 | 0.7563 | 0.7359 | 0.9304 | | 0.0027 | 297.0 | 19305 | 0.6241 | 0.7288 | 0.7583 | 0.7433 | 0.9318 | | 0.0027 | 298.0 | 19370 | 0.6316 | 0.7266 | 0.7450 | 0.7357 | 0.9299 | | 0.0027 | 299.0 | 19435 | 0.6733 | 0.7185 | 0.7434 | 0.7307 | 0.9290 | | 0.0018 | 300.0 | 19500 | 0.5783 | 0.7280 | 0.7506 | 0.7391 | 0.9323 | | 0.0018 | 301.0 | 19565 | 0.6198 | 0.7086 | 0.7693 | 0.7377 | 0.9303 | | 0.0018 | 302.0 | 19630 | 0.6555 | 0.7151 | 0.7508 | 0.7325 | 0.9273 | | 0.0018 | 303.0 | 19695 | 0.6122 | 0.7132 | 0.7616 | 0.7366 | 0.9281 | | 0.0018 | 304.0 | 19760 | 0.6051 | 0.7082 | 0.7542 | 0.7304 | 0.9294 | | 0.0018 | 305.0 | 19825 | 0.6306 | 0.7282 | 0.7405 | 0.7343 | 0.9306 | | 0.0018 | 306.0 | 19890 | 0.5903 | 0.7161 | 0.7650 | 0.7397 | 0.9313 | | 0.0018 | 307.0 | 19955 | 0.6170 | 0.7169 | 0.7711 | 0.7430 | 0.9318 | | 0.0023 | 308.0 | 20020 | 0.5948 | 0.7229 | 0.7596 | 0.7408 | 0.9314 | | 0.0023 | 309.0 | 20085 | 0.6176 | 0.7187 | 0.7652 | 0.7412 | 0.9312 | | 0.0023 | 310.0 | 20150 | 0.6166 | 0.7219 | 0.7576 | 0.7393 | 0.9305 | | 0.0023 | 311.0 | 20215 | 0.6239 | 0.7230 | 0.7646 | 0.7432 | 0.9305 | | 0.0023 | 312.0 | 20280 | 0.6458 | 0.7172 | 0.7641 | 0.7399 | 0.9311 | | 0.0023 | 313.0 | 20345 | 0.6546 | 0.7112 | 0.7841 | 0.7459 | 0.9310 | | 0.0023 | 314.0 | 20410 | 0.6326 | 0.7273 | 0.7627 | 0.7445 | 0.9312 | | 0.0023 | 315.0 | 20475 | 0.6073 | 0.7248 | 0.7574 | 0.7408 | 0.9314 | | 0.0016 | 316.0 | 20540 | 0.6442 | 0.7169 | 0.7673 | 0.7412 | 0.9303 | | 0.0016 | 317.0 | 20605 | 0.6380 | 0.7215 | 0.7608 | 0.7406 | 0.9305 | | 0.0016 | 318.0 | 20670 | 0.6590 | 0.7126 | 0.7461 | 0.7290 | 0.9280 | | 0.0016 | 319.0 | 20735 | 0.6474 | 0.7178 | 0.7594 | 0.7380 | 0.9292 | | 0.0016 | 320.0 | 20800 | 0.6006 | 0.7071 | 0.7729 | 0.7385 | 0.9302 | | 0.0016 | 321.0 | 20865 | 0.6207 | 0.7274 | 0.7488 | 0.7380 | 0.9309 | | 0.0016 | 322.0 | 20930 | 0.6735 | 0.7032 | 0.7679 | 0.7341 | 0.9277 | | 0.0016 | 323.0 | 20995 | 0.6733 | 0.7056 | 0.7376 | 0.7213 | 0.9271 | | 0.0018 | 324.0 | 21060 | 0.6851 | 0.6920 | 0.7288 | 0.7099 | 0.9245 | | 0.0018 | 325.0 | 21125 | 0.6069 | 0.7245 | 0.7562 | 0.7400 | 0.9308 | | 0.0018 | 326.0 | 21190 | 0.6677 | 0.7148 | 0.7562 | 0.7349 | 0.9299 | | 0.0018 | 327.0 | 21255 | 0.6671 | 0.7057 | 0.7371 | 0.7210 | 0.9270 | | 0.0018 | 328.0 | 21320 | 0.6393 | 0.7088 | 0.7506 | 0.7291 | 0.9285 | | 0.0018 | 329.0 | 21385 | 0.6455 | 0.7186 | 0.7628 | 0.7400 | 0.9309 | | 0.0018 | 330.0 | 21450 | 0.6320 | 0.7349 | 0.7358 | 0.7354 | 0.9301 | | 0.0023 | 331.0 | 21515 | 0.6373 | 0.7281 | 0.7645 | 0.7458 | 0.9322 | | 0.0023 | 332.0 | 21580 | 0.6545 | 0.7221 | 0.7504 | 0.7360 | 0.9305 | | 0.0023 | 333.0 | 21645 | 0.6490 | 0.7188 | 0.7499 | 0.7340 | 0.9298 | | 0.0023 | 334.0 | 21710 | 0.6504 | 0.7053 | 0.7623 | 0.7327 | 0.9305 | | 0.0023 | 335.0 | 21775 | 0.6364 | 0.7196 | 0.7578 | 0.7382 | 0.9318 | | 0.0023 | 336.0 | 21840 | 0.6697 | 0.7205 | 0.7389 | 0.7296 | 0.9293 | | 0.0023 | 337.0 | 21905 | 0.6477 | 0.7156 | 0.7594 | 0.7369 | 0.9297 | | 0.0023 | 338.0 | 21970 | 0.6673 | 0.6943 | 0.7634 | 0.7272 | 0.9280 | | 0.0015 | 339.0 | 22035 | 0.6527 | 0.7253 | 0.7545 | 0.7396 | 0.9301 | | 0.0015 | 340.0 | 22100 | 0.6479 | 0.7083 | 0.7310 | 0.7194 | 0.9262 | | 0.0015 | 341.0 | 22165 | 0.6228 | 0.7179 | 0.7508 | 0.7340 | 0.9288 | | 0.0015 | 342.0 | 22230 | 0.6324 | 0.6975 | 0.7706 | 0.7322 | 0.9280 | | 0.0015 | 343.0 | 22295 | 0.6646 | 0.7212 | 0.7434 | 0.7321 | 0.9289 | | 0.0015 | 344.0 | 22360 | 0.6601 | 0.7298 | 0.7329 | 0.7314 | 0.9281 | | 0.0015 | 345.0 | 22425 | 0.6535 | 0.7191 | 0.7495 | 0.7340 | 0.9298 | | 0.0015 | 346.0 | 22490 | 0.6840 | 0.7189 | 0.7437 | 0.7311 | 0.9284 | | 0.0021 | 347.0 | 22555 | 0.6934 | 0.7214 | 0.7266 | 0.7240 | 0.9274 | | 0.0021 | 348.0 | 22620 | 0.6449 | 0.7097 | 0.7326 | 0.7210 | 0.9282 | | 0.0021 | 349.0 | 22685 | 0.6576 | 0.7119 | 0.7520 | 0.7314 | 0.9281 | | 0.0021 | 350.0 | 22750 | 0.6672 | 0.7265 | 0.7473 | 0.7368 | 0.9301 | | 0.0021 | 351.0 | 22815 | 0.6704 | 0.7193 | 0.7639 | 0.7410 | 0.9305 | | 0.0021 | 352.0 | 22880 | 0.6893 | 0.7146 | 0.7648 | 0.7389 | 0.9299 | | 0.0021 | 353.0 | 22945 | 0.6747 | 0.7299 | 0.7589 | 0.7441 | 0.9307 | | 0.0013 | 354.0 | 23010 | 0.6942 | 0.7119 | 0.7650 | 0.7375 | 0.9285 | | 0.0013 | 355.0 | 23075 | 0.7042 | 0.7223 | 0.7347 | 0.7284 | 0.9280 | | 0.0013 | 356.0 | 23140 | 0.7009 | 0.7184 | 0.7666 | 0.7417 | 0.9313 | | 0.0013 | 357.0 | 23205 | 0.6892 | 0.7262 | 0.7756 | 0.7501 | 0.9317 | | 0.0013 | 358.0 | 23270 | 0.7022 | 0.7312 | 0.7607 | 0.7456 | 0.9311 | | 0.0013 | 359.0 | 23335 | 0.7169 | 0.7067 | 0.7657 | 0.7350 | 0.9277 | | 0.0013 | 360.0 | 23400 | 0.7329 | 0.7179 | 0.7551 | 0.7360 | 0.9273 | | 0.0013 | 361.0 | 23465 | 0.7030 | 0.7242 | 0.7391 | 0.7316 | 0.9288 | | 0.0007 | 362.0 | 23530 | 0.7063 | 0.7185 | 0.7529 | 0.7353 | 0.9290 | | 0.0007 | 363.0 | 23595 | 0.7068 | 0.7239 | 0.7466 | 0.7351 | 0.9293 | | 0.0007 | 364.0 | 23660 | 0.6959 | 0.7159 | 0.7722 | 0.7430 | 0.9303 | | 0.0007 | 365.0 | 23725 | 0.7632 | 0.7015 | 0.7450 | 0.7226 | 0.9258 | | 0.0007 | 366.0 | 23790 | 0.7022 | 0.7246 | 0.7301 | 0.7273 | 0.9268 | | 0.0007 | 367.0 | 23855 | 0.6818 | 0.7161 | 0.7720 | 0.7430 | 0.9305 | | 0.0007 | 368.0 | 23920 | 0.6895 | 0.7275 | 0.7578 | 0.7423 | 0.9311 | | 0.0007 | 369.0 | 23985 | 0.6987 | 0.7124 | 0.7473 | 0.7295 | 0.9293 | | 0.0016 | 370.0 | 24050 | 0.6857 | 0.7301 | 0.7531 | 0.7414 | 0.9310 | | 0.0016 | 371.0 | 24115 | 0.7022 | 0.7271 | 0.7418 | 0.7344 | 0.9297 | | 0.0016 | 372.0 | 24180 | 0.6936 | 0.7277 | 0.7672 | 0.7469 | 0.9313 | | 0.0016 | 373.0 | 24245 | 0.6836 | 0.7240 | 0.7659 | 0.7444 | 0.9316 | | 0.0016 | 374.0 | 24310 | 0.6868 | 0.7221 | 0.7576 | 0.7394 | 0.9311 | | 0.0016 | 375.0 | 24375 | 0.6903 | 0.7315 | 0.7648 | 0.7478 | 0.9314 | | 0.0016 | 376.0 | 24440 | 0.6973 | 0.7172 | 0.7700 | 0.7427 | 0.9315 | | 0.0008 | 377.0 | 24505 | 0.6768 | 0.7233 | 0.7470 | 0.7349 | 0.9297 | | 0.0008 | 378.0 | 24570 | 0.7004 | 0.7347 | 0.7171 | 0.7258 | 0.9273 | | 0.0008 | 379.0 | 24635 | 0.6794 | 0.7267 | 0.7464 | 0.7364 | 0.9291 | | 0.0008 | 380.0 | 24700 | 0.6835 | 0.7236 | 0.7599 | 0.7413 | 0.9298 | | 0.0008 | 381.0 | 24765 | 0.6758 | 0.7188 | 0.7663 | 0.7417 | 0.9295 | | 0.0008 | 382.0 | 24830 | 0.6731 | 0.7247 | 0.7542 | 0.7391 | 0.9304 | | 0.0008 | 383.0 | 24895 | 0.6734 | 0.7232 | 0.7513 | 0.7370 | 0.9302 | | 0.0008 | 384.0 | 24960 | 0.6849 | 0.7207 | 0.7673 | 0.7433 | 0.9305 | | 0.001 | 385.0 | 25025 | 0.6711 | 0.7216 | 0.7533 | 0.7371 | 0.9297 | | 0.001 | 386.0 | 25090 | 0.6792 | 0.7326 | 0.7508 | 0.7416 | 0.9298 | | 0.001 | 387.0 | 25155 | 0.6708 | 0.7074 | 0.7603 | 0.7329 | 0.9284 | | 0.001 | 388.0 | 25220 | 0.6708 | 0.7157 | 0.7468 | 0.7309 | 0.9282 | | 0.001 | 389.0 | 25285 | 0.6720 | 0.7191 | 0.7639 | 0.7408 | 0.9292 | | 0.001 | 390.0 | 25350 | 0.6690 | 0.7097 | 0.7738 | 0.7404 | 0.9291 | | 0.001 | 391.0 | 25415 | 0.6585 | 0.7209 | 0.7599 | 0.7399 | 0.9304 | | 0.001 | 392.0 | 25480 | 0.6568 | 0.7131 | 0.7717 | 0.7412 | 0.9313 | | 0.0009 | 393.0 | 25545 | 0.6936 | 0.7104 | 0.7581 | 0.7335 | 0.9287 | | 0.0009 | 394.0 | 25610 | 0.6739 | 0.7264 | 0.7639 | 0.7447 | 0.9316 | | 0.0009 | 395.0 | 25675 | 0.7106 | 0.7215 | 0.7520 | 0.7364 | 0.9301 | | 0.0009 | 396.0 | 25740 | 0.6742 | 0.7276 | 0.7490 | 0.7381 | 0.9309 | | 0.0009 | 397.0 | 25805 | 0.6745 | 0.7238 | 0.7484 | 0.7359 | 0.9311 | | 0.0009 | 398.0 | 25870 | 0.6849 | 0.7247 | 0.7614 | 0.7426 | 0.9297 | | 0.0009 | 399.0 | 25935 | 0.6664 | 0.7242 | 0.7558 | 0.7397 | 0.9308 | | 0.001 | 400.0 | 26000 | 0.7093 | 0.7172 | 0.7403 | 0.7286 | 0.9287 | | 0.001 | 401.0 | 26065 | 0.6641 | 0.7202 | 0.7580 | 0.7386 | 0.9307 | | 0.001 | 402.0 | 26130 | 0.6643 | 0.7137 | 0.7711 | 0.7413 | 0.9303 | | 0.001 | 403.0 | 26195 | 0.6608 | 0.7263 | 0.7551 | 0.7404 | 0.9307 | | 0.001 | 404.0 | 26260 | 0.6820 | 0.7251 | 0.7632 | 0.7436 | 0.9314 | | 0.001 | 405.0 | 26325 | 0.6736 | 0.7293 | 0.7623 | 0.7454 | 0.9314 | | 0.001 | 406.0 | 26390 | 0.6834 | 0.7274 | 0.7509 | 0.7390 | 0.9303 | | 0.001 | 407.0 | 26455 | 0.6767 | 0.7218 | 0.7513 | 0.7363 | 0.9312 | | 0.0006 | 408.0 | 26520 | 0.6918 | 0.7176 | 0.7738 | 0.7446 | 0.9315 | | 0.0006 | 409.0 | 26585 | 0.6917 | 0.7276 | 0.7639 | 0.7453 | 0.9324 | | 0.0006 | 410.0 | 26650 | 0.7120 | 0.7111 | 0.7603 | 0.7349 | 0.9297 | | 0.0006 | 411.0 | 26715 | 0.6895 | 0.7251 | 0.7535 | 0.7390 | 0.9313 | | 0.0006 | 412.0 | 26780 | 0.6915 | 0.7220 | 0.7627 | 0.7417 | 0.9316 | | 0.0006 | 413.0 | 26845 | 0.7040 | 0.7071 | 0.7756 | 0.7398 | 0.9299 | | 0.0006 | 414.0 | 26910 | 0.6899 | 0.7248 | 0.7526 | 0.7384 | 0.9308 | | 0.0006 | 415.0 | 26975 | 0.6993 | 0.7229 | 0.7639 | 0.7428 | 0.9315 | | 0.0005 | 416.0 | 27040 | 0.7063 | 0.7224 | 0.7672 | 0.7441 | 0.9316 | | 0.0005 | 417.0 | 27105 | 0.6930 | 0.7241 | 0.7708 | 0.7467 | 0.9327 | | 0.0005 | 418.0 | 27170 | 0.6957 | 0.7245 | 0.7610 | 0.7423 | 0.9320 | | 0.0005 | 419.0 | 27235 | 0.6978 | 0.7256 | 0.7538 | 0.7394 | 0.9317 | | 0.0005 | 420.0 | 27300 | 0.6905 | 0.7285 | 0.7562 | 0.7421 | 0.9327 | | 0.0005 | 421.0 | 27365 | 0.7016 | 0.7140 | 0.7598 | 0.7362 | 0.9312 | | 0.0005 | 422.0 | 27430 | 0.7007 | 0.7206 | 0.7590 | 0.7393 | 0.9323 | | 0.0005 | 423.0 | 27495 | 0.7106 | 0.7175 | 0.7554 | 0.7360 | 0.9315 | | 0.0006 | 424.0 | 27560 | 0.6984 | 0.7304 | 0.7778 | 0.7534 | 0.9331 | | 0.0006 | 425.0 | 27625 | 0.6956 | 0.7310 | 0.7771 | 0.7533 | 0.9330 | | 0.0006 | 426.0 | 27690 | 0.7069 | 0.7149 | 0.7583 | 0.7360 | 0.9303 | | 0.0006 | 427.0 | 27755 | 0.7152 | 0.7233 | 0.7389 | 0.7310 | 0.9289 | | 0.0006 | 428.0 | 27820 | 0.7047 | 0.7269 | 0.7639 | 0.7449 | 0.9319 | | 0.0006 | 429.0 | 27885 | 0.7078 | 0.7295 | 0.7556 | 0.7423 | 0.9318 | | 0.0006 | 430.0 | 27950 | 0.7091 | 0.7322 | 0.7610 | 0.7463 | 0.9321 | | 0.0006 | 431.0 | 28015 | 0.7219 | 0.7340 | 0.7464 | 0.7402 | 0.9310 | | 0.0006 | 432.0 | 28080 | 0.7183 | 0.7205 | 0.7571 | 0.7383 | 0.9303 | | 0.0006 | 433.0 | 28145 | 0.7059 | 0.7262 | 0.7562 | 0.7409 | 0.9317 | | 0.0006 | 434.0 | 28210 | 0.7006 | 0.7260 | 0.7654 | 0.7452 | 0.9316 | | 0.0006 | 435.0 | 28275 | 0.7073 | 0.7209 | 0.7623 | 0.7410 | 0.9308 | | 0.0006 | 436.0 | 28340 | 0.7080 | 0.7270 | 0.7720 | 0.7488 | 0.9324 | | 0.0006 | 437.0 | 28405 | 0.7054 | 0.7218 | 0.7619 | 0.7413 | 0.9312 | | 0.0006 | 438.0 | 28470 | 0.7128 | 0.7294 | 0.7529 | 0.7410 | 0.9314 | | 0.0004 | 439.0 | 28535 | 0.7119 | 0.7234 | 0.7587 | 0.7406 | 0.9314 | | 0.0004 | 440.0 | 28600 | 0.7244 | 0.7175 | 0.7632 | 0.7396 | 0.9313 | | 0.0004 | 441.0 | 28665 | 0.7293 | 0.7297 | 0.7587 | 0.7439 | 0.9315 | | 0.0004 | 442.0 | 28730 | 0.7422 | 0.7194 | 0.7587 | 0.7385 | 0.9310 | | 0.0004 | 443.0 | 28795 | 0.7304 | 0.7264 | 0.7645 | 0.7449 | 0.9313 | | 0.0004 | 444.0 | 28860 | 0.7224 | 0.7307 | 0.7551 | 0.7427 | 0.9315 | | 0.0004 | 445.0 | 28925 | 0.7144 | 0.7317 | 0.7598 | 0.7455 | 0.9325 | | 0.0004 | 446.0 | 28990 | 0.7153 | 0.7210 | 0.7627 | 0.7412 | 0.9325 | | 0.0004 | 447.0 | 29055 | 0.7269 | 0.7273 | 0.7540 | 0.7404 | 0.9315 | | 0.0004 | 448.0 | 29120 | 0.7487 | 0.7198 | 0.7608 | 0.7397 | 0.9304 | | 0.0004 | 449.0 | 29185 | 0.7341 | 0.7238 | 0.7580 | 0.7405 | 0.9312 | | 0.0004 | 450.0 | 29250 | 0.7212 | 0.7325 | 0.7697 | 0.7506 | 0.9327 | | 0.0004 | 451.0 | 29315 | 0.7180 | 0.7302 | 0.7565 | 0.7431 | 0.9317 | | 0.0004 | 452.0 | 29380 | 0.7197 | 0.7257 | 0.7724 | 0.7483 | 0.9324 | | 0.0004 | 453.0 | 29445 | 0.7183 | 0.7194 | 0.7536 | 0.7361 | 0.9307 | | 0.0003 | 454.0 | 29510 | 0.7264 | 0.7178 | 0.7679 | 0.7420 | 0.9308 | | 0.0003 | 455.0 | 29575 | 0.7120 | 0.7283 | 0.7608 | 0.7442 | 0.9322 | | 0.0003 | 456.0 | 29640 | 0.7410 | 0.7290 | 0.7513 | 0.7400 | 0.9305 | | 0.0003 | 457.0 | 29705 | 0.7289 | 0.7338 | 0.7574 | 0.7454 | 0.9317 | | 0.0003 | 458.0 | 29770 | 0.7051 | 0.7332 | 0.7576 | 0.7452 | 0.9321 | | 0.0003 | 459.0 | 29835 | 0.7243 | 0.7292 | 0.7608 | 0.7447 | 0.9311 | | 0.0003 | 460.0 | 29900 | 0.7140 | 0.7314 | 0.7562 | 0.7436 | 0.9318 | | 0.0003 | 461.0 | 29965 | 0.7157 | 0.7257 | 0.7599 | 0.7424 | 0.9315 | | 0.0003 | 462.0 | 30030 | 0.7115 | 0.7277 | 0.7646 | 0.7457 | 0.9324 | | 0.0003 | 463.0 | 30095 | 0.7073 | 0.7296 | 0.7765 | 0.7523 | 0.9331 | | 0.0003 | 464.0 | 30160 | 0.6973 | 0.7352 | 0.7594 | 0.7471 | 0.9336 | | 0.0003 | 465.0 | 30225 | 0.6869 | 0.7332 | 0.7700 | 0.7512 | 0.9331 | | 0.0003 | 466.0 | 30290 | 0.6951 | 0.7304 | 0.7704 | 0.7499 | 0.9327 | | 0.0003 | 467.0 | 30355 | 0.7065 | 0.7250 | 0.7691 | 0.7464 | 0.9318 | | 0.0003 | 468.0 | 30420 | 0.7112 | 0.7280 | 0.7679 | 0.7474 | 0.9319 | | 0.0003 | 469.0 | 30485 | 0.7115 | 0.7303 | 0.7695 | 0.7494 | 0.9321 | | 0.0004 | 470.0 | 30550 | 0.7132 | 0.7288 | 0.7681 | 0.7479 | 0.9324 | | 0.0004 | 471.0 | 30615 | 0.7132 | 0.7278 | 0.7699 | 0.7482 | 0.9321 | | 0.0004 | 472.0 | 30680 | 0.7200 | 0.7259 | 0.7630 | 0.7440 | 0.9313 | | 0.0004 | 473.0 | 30745 | 0.7191 | 0.7233 | 0.7772 | 0.7493 | 0.9319 | | 0.0004 | 474.0 | 30810 | 0.7178 | 0.7320 | 0.7679 | 0.7495 | 0.9327 | | 0.0004 | 475.0 | 30875 | 0.7182 | 0.7331 | 0.7659 | 0.7492 | 0.9325 | | 0.0004 | 476.0 | 30940 | 0.7216 | 0.7309 | 0.7654 | 0.7477 | 0.9322 | | 0.0002 | 477.0 | 31005 | 0.7226 | 0.7309 | 0.7648 | 0.7474 | 0.9322 | | 0.0002 | 478.0 | 31070 | 0.7241 | 0.7259 | 0.7697 | 0.7471 | 0.9319 | | 0.0002 | 479.0 | 31135 | 0.7259 | 0.7280 | 0.7697 | 0.7482 | 0.9320 | | 0.0002 | 480.0 | 31200 | 0.7258 | 0.7247 | 0.7738 | 0.7485 | 0.9319 | | 0.0002 | 481.0 | 31265 | 0.7239 | 0.7274 | 0.7765 | 0.7512 | 0.9327 | | 0.0002 | 482.0 | 31330 | 0.7243 | 0.7289 | 0.7763 | 0.7519 | 0.9327 | | 0.0002 | 483.0 | 31395 | 0.7231 | 0.7267 | 0.7771 | 0.7510 | 0.9328 | | 0.0002 | 484.0 | 31460 | 0.7221 | 0.7283 | 0.7744 | 0.7506 | 0.9328 | | 0.0001 | 485.0 | 31525 | 0.7217 | 0.7309 | 0.7688 | 0.7493 | 0.9326 | | 0.0001 | 486.0 | 31590 | 0.7211 | 0.7317 | 0.7616 | 0.7463 | 0.9323 | | 0.0001 | 487.0 | 31655 | 0.7206 | 0.7330 | 0.7610 | 0.7468 | 0.9325 | | 0.0001 | 488.0 | 31720 | 0.7198 | 0.7326 | 0.7580 | 0.7451 | 0.9323 | | 0.0001 | 489.0 | 31785 | 0.7203 | 0.7311 | 0.7598 | 0.7451 | 0.9321 | | 0.0001 | 490.0 | 31850 | 0.7205 | 0.7312 | 0.7614 | 0.7460 | 0.9323 | | 0.0001 | 491.0 | 31915 | 0.7167 | 0.7294 | 0.7673 | 0.7479 | 0.9328 | | 0.0001 | 492.0 | 31980 | 0.7164 | 0.7311 | 0.7646 | 0.7475 | 0.9328 | | 0.0003 | 493.0 | 32045 | 0.7169 | 0.7312 | 0.7655 | 0.7480 | 0.9328 | | 0.0003 | 494.0 | 32110 | 0.7184 | 0.7324 | 0.7675 | 0.7496 | 0.9330 | | 0.0003 | 495.0 | 32175 | 0.7188 | 0.7320 | 0.7664 | 0.7488 | 0.9329 | | 0.0003 | 496.0 | 32240 | 0.7187 | 0.7320 | 0.7655 | 0.7484 | 0.9328 | | 0.0003 | 497.0 | 32305 | 0.7189 | 0.7321 | 0.7659 | 0.7486 | 0.9329 | | 0.0003 | 498.0 | 32370 | 0.7192 | 0.7315 | 0.7659 | 0.7483 | 0.9328 | | 0.0003 | 499.0 | 32435 | 0.7191 | 0.7313 | 0.7650 | 0.7478 | 0.9327 | | 0.0001 | 500.0 | 32500 | 0.7191 | 0.7311 | 0.7648 | 0.7476 | 0.9327 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

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model-hub/mistral-8x7b-chat

model-hub

2023-12-14T16:33:53Z

0

transformers

[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-12-14T16:33:53Z

A very capable chat model built on top of the new Mistral MoE model, trained on the SlimOrca dataset for 1 epoch, using QLoRA. Inference: ```py import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("mattshumer/mistral-8x7b-chat", low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True) tok = AutoTokenizer.from_pretrained("mattshumer/mistral-8x7b-chat") x = tok.encode(PROMPT_GOES_HERE, return_tensors="pt").cuda() x = model.generate(x, max_new_tokens=512).cpu() print(tok.batch_decode(x)) ``` Prompt Template: ``` <|im_start|>system You are an AI assistant.<|im_end|> <|im_start|>user Hi, how are you?<|im_end|> <|im_start|>assistant I'm doing well, thanks for asking!<|im_end|> <|im_start|>user Write me a poem about AI.<|im_end|> ``` Trained w/ Axolotl on 6x H100s for nine hours.

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model-hub/stablelm-zephyr-3b

model-hub

2023-12-14T16:34:04Z

0

transformers

[ "transformers", "safetensors", "stablelm_epoch", "text-generation", "causal-lm", "custom_code", "en", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:HuggingFaceH4/ultrafeedback_binarized", "dataset:meta-math/MetaMathQA", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:Intel/orca_dpo_pairs", "arxiv:2305.18290", "license:other", "autotrain_compatible", "region:us" ]

text-generation

2023-12-14T16:34:04Z

--- datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - Intel/orca_dpo_pairs language: - en tags: - causal-lm extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox license: other --- # `StableLM Zephyr 3B` ## Model Description `StableLM Zephyr 3B` is a 3 billion parameter instruction tuned inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline this model was trained on a mix of publicly available datasets, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290), evaluation for this model based on [MT Bench](https://tatsu-lab.github.io/alpaca_eval/) and [Alpaca Benchmark](https://tatsu-lab.github.io/alpaca_eval/) ## Usage `StableLM Zephyr 3B` uses the following instruction format: ``` <|user|> List 3 synonyms for the word "tiny"<|endoftext|> <|assistant|> 1. Dwarf 2. Little 3. Petite<|endoftext|> ``` This format is also available through the tokenizer's `apply_chat_template` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-zephyr-3b') model = AutoModelForCausalLM.from_pretrained( 'stabilityai/stablelm-zephyr-3b', trust_remote_code=True, device_map="auto" ) prompt = [{'role': 'user', 'content': 'List 3 synonyms for the word "tiny"'}] inputs = tokenizer.apply_chat_template( prompt, add_generation_prompt=True, return_tensors='pt' ) tokens = model.generate( inputs.to(model.device), max_new_tokens=1024, temperature=0.8, do_sample=True ) print(tokenizer.decode(tokens[0], skip_special_tokens=False)) ``` You can also see how to run a performance optimized version of this model [here](https://github.com/eaidova/openvino_notebooks/blob/ea/stateful_chatbot/notebooks/273-stable-zephyr-3b-chatbot/273-stable-zephyr-3b-chatbot.ipynb) using [OpenVINO](https://docs.openvino.ai/2023.2/home.html) from Intel. ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: `StableLM Zephyr 3B` model is an auto-regressive language model based on the transformer decoder architecture. * **Language(s)**: English * **Library**: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git) * **Finetuned from model**: [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) * **License**: [StabilityAI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-zephyr-3b/raw/main/LICENSE). If you want to use this model for your commercial products or purposes, please contact us [here](https://stability.ai/contact) to learn more. * **Contact**: For questions and comments about the model, please email `[email protected]` ### Training Dataset The dataset is comprised of a mixture of open datasets large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): 1. SFT Datasets - HuggingFaceH4/ultrachat_200k - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - Open-Orca/SlimOrca 2. Preference Datasets: - HuggingFaceH4/ultrafeedback_binarized - Intel/orca_dpo_pairs ## Performance ### MT-Bench and Alpaca Bench <img src="https://cdn-uploads.huggingface.co/production/uploads/6310474ca119d49bc1eb0d80/8WIZS6dAlu5kSH-382pMl.png" alt="mt_bench_plot" width="600"/> | Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) | |-------------|-----|----|---------------|--------------| | **StableLM Zephyr 3B** 🪁 | 3B | DPO | 6.64 | 76.00 | | StableLM Zephyr (SFT only) | 3B | SFT | 6.04 | 71.15 | | Capybara v1.9 | 3B | dSFT | 5.94 | - | | MPT-Chat | 7B |dSFT |5.42| -| | Xwin-LM v0.1 | 7B| dPPO| 6.19| 87.83| | Mistral-Instruct v0.1 | 7B| - | 6.84 |-| | Zephyr-7b-α |7B| dDPO| 6.88| -| | Zephyr-7b-β| 7B | dDPO | 7.34 | 90.60 | | Falcon-Instruct | 40B |dSFT |5.17 |45.71| | Guanaco | 65B | SFT |6.41| 71.80| | Llama2-Chat | 70B |RLHF |6.86| 92.66| | Vicuna v1.3 | 33B |dSFT |7.12 |88.99| | WizardLM v1.0 | 70B |dSFT |7.71 |-| | Xwin-LM v0.1 | 70B |dPPO |- |95.57| | GPT-3.5-turbo | - |RLHF |7.94 |89.37| | Claude 2 | - |RLHF |8.06| 91.36| | GPT-4 | -| RLHF |8.99| 95.28| ## Other benchmarks: | Task | Value | |-----------------------|---------------------------| | ARC (25-shot) | 47.0 | | HellaSwag (10-shot) | 74.2 | | MMLU (5-shot) | 46.3 | | TruthfulQA (0-shot) | 46.5 | | Winogrande (5-shot) | 65.5 | | GSM8K (5-shot) | 42.3 | | BigBench (Avg) | 35.26 | | AGI Benchmark (Avg) | 33.23 | ### Training Infrastructure * **Hardware**: `StableLM Zephyr 3B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes. * **Code Base**: We use our internal script for SFT steps and used [HuggingFace Alignment Handbook script](https://github.com/huggingface/alignment-handbook) for DPO training. ## Commitment to Ethical AI In line with our responsibility towards ethical AI development, `StableLM Zephyr 3B` is released with a focus on ensuring safety, reliability, and appropriateness in its applications. To this end, we have evaluated `StableLM Zephyr 3B` on 488 malicious prompts and used standard protocols to assess the harmfulness of its outputs. Compared to Zephyr-7b-β, `StableLM Zephyr 3B` reduces the number of harmful outputs as assessed by GPT-4 by 55. Additionally, we performed an internal red teaming event targeting the following abuse areas: * **Self-Harm Methods**: (Suicide Methods, Encouragement of Self-Harm, Methods and encouragement of Eating Disorders) * **Misinformation**: (Health, Conspiracy Theories, Social Unrest/Conflict, Political Misinformation, & Climate change) * **Hate Speech**: (Race, Stereotypes, Immigrants, Gender, Personally Identifiable Information such as Social security numbers, Full names, ID numbers, Email addresses, and telephone numbers) We have incorporated the findings of our malicious prompts evaluation and red teaming event into our release. Users are encouraged to fine-tune and evaluate the model to suit their specific needs, considering the potential biases and limitations found in `StableLM Zephyr 3B` and inherent in other LLM models. ## Use and Limitations ### Intended Use The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. ### Limitations and Bias This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses. Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it is willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.

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fsicoli/whisper-large-v3-pt-cv15-fleurs-mls-td-co

fsicoli

2023-12-17T21:19:33Z

0

transformers

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

automatic-speech-recognition

2023-12-14T16:34:31Z

Entry not found

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model-hub/mixtral-7b-8expert

model-hub

2023-12-14T16:34:37Z

0

transformers

[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "en", "fr", "it", "es", "de", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-12-14T16:34:37Z

--- license: apache-2.0 language: - en - fr - it - es - de --- # Mixtral 7b 8 Expert ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62e3b6ab0c2a907c388e4965/6m3e2d2BNXDjy6_qHd2LT.png) This is a preliminary HuggingFace implementation of the newly released MoE model by MistralAi. Make sure to load with `trust_remote_code=True`. Thanks to @dzhulgakov for his early implementation (https://github.com/dzhulgakov/llama-mistral) that helped me find a working setup. Also many thanks to our friends at [LAION](https://laion.ai) and [HessianAI](https://hessian.ai/) for the compute used for these projects! Benchmark scores: ``` hella swag: 0.8661 winogrande: 0.824 truthfulqa_mc2: 0.4855 arc_challenge: 0.6638 gsm8k: 0.5709 MMLU: 0.7173 ``` # Basic Inference setup ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("DiscoResearch/mixtral-7b-8expert", low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True) tok = AutoTokenizer.from_pretrained("DiscoResearch/mixtral-7b-8expert") x = tok.encode("The mistral wind in is a phenomenon ", return_tensors="pt").cuda() x = model.generate(x, max_new_tokens=128).cpu() print(tok.batch_decode(x)) ``` # Conversion Use `convert_mistral_moe_weights_to_hf.py --input_dir ./input_dir --model_size 7B --output_dir ./output` to convert the original consolidated weights to this HF setup. Come chat about this in our [Disco(rd)](https://discord.gg/S8W8B5nz3v)! :)

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model-hub/falcon-40b

model-hub

2023-12-14T16:35:44Z

0

transformers

[ "transformers", "pytorch", "falcon", "text-generation", "custom_code", "en", "de", "es", "fr", "dataset:tiiuae/falcon-refinedweb", "arxiv:2205.14135", "arxiv:1911.02150", "arxiv:2101.00027", "arxiv:2005.14165", "arxiv:2104.09864", "arxiv:2306.01116", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-12-14T16:35:43Z

--- datasets: - tiiuae/falcon-refinedweb language: - en - de - es - fr inference: false license: apache-2.0 --- # 🚀 Falcon-40B **Falcon-40B is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.** *Paper coming soon 😊.* 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-40B? * **It is the best open-source model currently available.** Falcon-40B outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). * **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions. * ⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct). 💸 **Looking for a smaller, less expensive model?** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) is Falcon-40B's little brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-40b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!** For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need **at least 85-100GB of memory** to swiftly run inference with Falcon-40B. # Model Card for Falcon-40B ## Model Details ### Model Description - **Developed by:** [https://www.tii.ae](https://www.tii.ae); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish); - **License:** Apache 2.0 license. ### Model Source - **Paper:** *coming soon*. ## Uses ### Direct Use Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.) ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-40B is trained mostly on English, German, Spanish, French, with limited capabilities also in in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-40B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-40b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-40B was trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)). | **Data source** | **Fraction** | **Tokens** | **Sources** | |--------------------|--------------|------------|-----------------------------------| | [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 75% | 750B | massive web crawl | | RefinedWeb-Europe | 7% | 70B | European massive web crawl | | Books | 6% | 60B | | | Conversations | 5% | 50B | Reddit, StackOverflow, HackerNews | | Code | 5% | 50B | | | Technical | 2% | 20B | arXiv, PubMed, USPTO, etc. | RefinedWeb-Europe is made of the following languages: | **Language** | **Fraction of multilingual data** | **Tokens** | |--------------|-----------------------------------|------------| | German | 26% | 18B | | Spanish | 24% | 17B | | French | 23% | 16B | | _Italian_ | 7% | 5B | | _Portuguese_ | 4% | 3B | | _Polish_ | 4% | 3B | | _Dutch_ | 4% | 3B | | _Romanian_ | 3% | 2B | | _Czech_ | 3% | 2B | | _Swedish_ | 2% | 1B | The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ### Training Procedure Falcon-40B was trained on 384 A100 40GB GPUs, using a 3D parallelism strategy (TP=8, PP=4, DP=12) combined with ZeRO. #### Training Hyperparameters | **Hyperparameter** | **Value** | **Comment** | |--------------------|------------|-------------------------------------------| | Precision | `bfloat16` | | | Optimizer | AdamW | | | Learning rate | 1.85e-4 | 4B tokens warm-up, cosine decay to 1.85e-5 | | Weight decay | 1e-1 | | | Z-loss | 1e-4 | | | Batch size | 1152 | 100B tokens ramp-up | #### Speeds, Sizes, Times Training started in December 2022 and took two months. ## Evaluation *Paper coming soon.* See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. ## Technical Specifications ### Model Architecture and Objective Falcon-40B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * **Decoder-block:** parallel attention/MLP with a two layer norms. For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree. | **Hyperparameter** | **Value** | **Comment** | |--------------------|-----------|----------------------------------------| | Layers | 60 | | | `d_model` | 8192 | | | `head_dim` | 64 | Reduced to optimise for FlashAttention | | Vocabulary | 65024 | | | Sequence length | 2048 | | ### Compute Infrastructure #### Hardware Falcon-40B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances. #### Software Falcon-40B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation *Paper coming soon* 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-40B is made available under the Apache 2.0 license. ## Contact [email protected]

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pariakashani/en-multinerd-ner-more-training

pariakashani

2023-12-14T18:05:46Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-12-14T16:38:26Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: en-multinerd-ner-more-training results: [] ---  # en-multinerd-ner-more-training This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0563 - Precision: 0.8942 - Recall: 0.9050 - F1: 0.8996 - Accuracy: 0.9836 - Per-precision: 0.9952 - Per-recall: 0.9965 - Per-f1: 0.9959 - Org-precision: 0.9412 - Org-recall: 0.9474 - Org-f1: 0.9443 - Loc-precision: 0.9691 - Loc-recall: 0.9752 - Loc-f1: 0.9721 - Dis-precision: 0.7157 - Dis-recall: 0.7440 - Dis-f1: 0.7295 - Anim-precision: 0.6870 - Anim-recall: 0.7240 - Anim-f1: 0.7050 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Per-precision | Per-recall | Per-f1 | Org-precision | Org-recall | Org-f1 | Loc-precision | Loc-recall | Loc-f1 | Dis-precision | Dis-recall | Dis-f1 | Anim-precision | Anim-recall | Anim-f1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:| | 0.0447 | 1.0 | 8205 | 0.0524 | 0.8583 | 0.9010 | 0.8791 | 0.9808 | 0.9939 | 0.9959 | 0.9949 | 0.8846 | 0.9547 | 0.9183 | 0.9626 | 0.9727 | 0.9676 | 0.6472 | 0.7457 | 0.6930 | 0.6607 | 0.7522 | 0.7035 | | 0.0309 | 2.0 | 16410 | 0.0503 | 0.8800 | 0.9028 | 0.8913 | 0.9825 | 0.9940 | 0.9965 | 0.9953 | 0.9304 | 0.9474 | 0.9388 | 0.9640 | 0.9712 | 0.9676 | 0.7072 | 0.7369 | 0.7218 | 0.6765 | 0.7469 | 0.7100 | | 0.0209 | 3.0 | 24615 | 0.0520 | 0.8908 | 0.9042 | 0.8975 | 0.9836 | 0.9953 | 0.9967 | 0.9960 | 0.9365 | 0.9478 | 0.9421 | 0.9680 | 0.9750 | 0.9715 | 0.7163 | 0.7268 | 0.7215 | 0.7013 | 0.7434 | 0.7217 | | 0.0138 | 4.0 | 32820 | 0.0563 | 0.8942 | 0.9050 | 0.8996 | 0.9836 | 0.9952 | 0.9965 | 0.9959 | 0.9412 | 0.9474 | 0.9443 | 0.9691 | 0.9752 | 0.9721 | 0.7157 | 0.7440 | 0.7295 | 0.6870 | 0.7240 | 0.7050 | ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

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hafsanito/output_whisper

hafsanito

2023-12-14T16:38:35Z

0

null

[ "region:us" ]

null

2023-12-14T16:38:34Z

Entry not found

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Kooten/DaringMaid-20B-GGUF

Kooten

2023-12-15T18:16:27Z

0

1

null

[ "gguf", "text-generation", "en", "license:cc-by-nc-4.0", "region:us" ]

text-generation

2023-12-14T16:40:18Z

--- license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation --- # This repo contains GGUF quants of DaringMaid-20B [Q3_K_M](https://huggingface.co/Kooten/DaringMaid-20B-GGUF/blob/main/DaringMaid-20B-Q3_K_M.gguf) - [Q4_K_M](https://huggingface.co/Kooten/DaringMaid-20B-GGUF/blob/main/DaringMaid-20B-Q4_K_M.gguf) - [Q5_K_M](https://huggingface.co/Kooten/DaringMaid-20B-GGUF/blob/main/DaringMaid-20B-Q5_K_M.gguf) # DaringMaid-20B My goal was to make a Noromaid that's smarter and better at following instructions. After trying a bunch of different recipes I think this one turned out pretty good - I used [sequelbox/DynamicFactor](https://huggingface.co/sequelbox/DynamicFactor) as a base to as its supposed "improve overall knowledge, precise communication, conceptual understanding, and technical skill" over the base llama2. - [NeverSleep/Noromaid](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1.1) of course. - [Undi95/Utopia](https://huggingface.co/Undi95/Utopia-13B) has been recommended again recently and its still really good so in the mixer it goes - I enjoyed [tavtav/Rose](https://huggingface.co/tavtav/Rose-20B) so i threw in a bit of [CalderaAI/Thorns](https://huggingface.co/CalderaAI/13B-Thorns-l2) - There was recently a model that tried to pass itself off as [Gryphe/MythoMax](https://huggingface.co/Gryphe/MythoMax-L2-13b), i made a merge with that model before it was revealed to be MythoMax and it turned out pretty good so i used it. The .yml config files for mergekit with the exact merges can be found in the ["Recipe"](https://huggingface.co/Kooten/DaringMaid/tree/main/Recipe) folder in the [fp16 repo](https://huggingface.co/Kooten/DaringMaid) # Quants [GGUF](https://huggingface.co/Kooten/DaringMaid-20B-GGUF), EXL2: [6bpw](https://huggingface.co/Kooten/DaringMaid-20B-6bpw-exl2), [3bpw](https://huggingface.co/Kooten/DaringMaid-20B-3bpw-exl2) ## Prompt template: I have been using Undi/Ikaris SillyTavern presets for Noromaid: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json). ### Alpaca: ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not include descriptions of non-visual qualities such as personality, movements, scents, mental traits, or anything which could not be seen in a still photograph. Do not write in full sentences. Prefix your description with the phrase 'full body portrait,' ### Instruction: {prompt} ### Response: ```

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sproos/e5-base-v2-de

sproos

2023-12-14T16:43:29Z

0

transformers

[ "transformers", "safetensors", "bert", "feature-extraction", "endpoints_compatible", "region:us" ]

feature-extraction

2023-12-14T16:43:16Z

Entry not found

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balzanilo/dialogs-emotions-classifier-bert-base-seed512

balzanilo

2023-12-14T17:17:00Z

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T16:46:42Z

Entry not found

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Mehdixt910/Ilon

Mehdixt910

2023-12-14T16:47:22Z

0

null

[ "region:us" ]

null

2023-12-14T16:47:22Z

Entry not found

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EmmaGthn/results_prefix_100_moji

EmmaGthn

2023-12-14T16:47:40Z

0

null

[ "region:us" ]

null

2023-12-14T16:47:40Z

Entry not found

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ishorn5/RTLCoder-Z-GPTQ4bit-v1.0

ishorn5

2023-12-14T16:59:09Z

0

transformers

[ "transformers", "mistral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-12-14T16:53:39Z

Entry not found

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ans123/Brain-tumor-segmentor

ans123

2023-12-14T16:57:40Z

0

diffusers

[ "diffusers", "medical", "biology", "fill-mask", "an", "license:apache-2.0", "region:us" ]

fill-mask

2023-12-14T16:55:11Z

--- license: apache-2.0 language: - an metrics: - code_eval library_name: diffusers pipeline_tag: fill-mask tags: - medical - biology ---

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EmmaGthn/runs_lora_100_moji

EmmaGthn

2023-12-14T16:55:54Z

0

null

[ "region:us" ]

null

2023-12-14T16:55:54Z

Entry not found

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ashk72/business-quant

ashk72

2023-12-14T16:58:05Z

0

null

[ "region:us" ]

null

2023-12-14T16:58:05Z

Entry not found

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jiyoun-kim/vit-base-patch16-224-in21k-finetuned-lora-r_16food101

jiyoun-kim

2023-12-14T17:07:41Z

0

null

[ "region:us" ]

null

2023-12-14T17:00:08Z

Entry not found

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EmmaGthn/runs_prefix_100_moji

EmmaGthn

2023-12-14T17:00:12Z

0

null

[ "region:us" ]

null

2023-12-14T17:00:12Z

Entry not found

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hkivancoral/smids_5x_deit_tiny_adamax_00001_fold4

hkivancoral

2023-12-14T18:03:15Z

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-tiny-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-12-14T17:00:18Z

--- license: apache-2.0 base_model: facebook/deit-small-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_deit_tiny_adamax_00001_fold4 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8716666666666667 ---  # smids_5x_deit_tiny_adamax_00001_fold4 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3354 - Accuracy: 0.8717 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2847 | 1.0 | 375 | 0.3588 | 0.86 | | 0.2399 | 2.0 | 750 | 0.3497 | 0.8783 | | 0.1069 | 3.0 | 1125 | 0.3839 | 0.8683 | | 0.1588 | 4.0 | 1500 | 0.3944 | 0.8767 | | 0.0651 | 5.0 | 1875 | 0.4658 | 0.8717 | | 0.0408 | 6.0 | 2250 | 0.5268 | 0.8767 | | 0.026 | 7.0 | 2625 | 0.6145 | 0.865 | | 0.0213 | 8.0 | 3000 | 0.6639 | 0.8717 | | 0.0032 | 9.0 | 3375 | 0.8259 | 0.8583 | | 0.0148 | 10.0 | 3750 | 0.8114 | 0.8717 | | 0.015 | 11.0 | 4125 | 0.8747 | 0.87 | | 0.0014 | 12.0 | 4500 | 0.9000 | 0.8783 | | 0.0001 | 13.0 | 4875 | 0.9761 | 0.87 | | 0.015 | 14.0 | 5250 | 0.9964 | 0.87 | | 0.0001 | 15.0 | 5625 | 1.0366 | 0.87 | | 0.0 | 16.0 | 6000 | 1.0464 | 0.8733 | | 0.0 | 17.0 | 6375 | 1.0647 | 0.8733 | | 0.0197 | 18.0 | 6750 | 1.1060 | 0.87 | | 0.0 | 19.0 | 7125 | 1.1223 | 0.87 | | 0.0 | 20.0 | 7500 | 1.1307 | 0.8717 | | 0.0 | 21.0 | 7875 | 1.1563 | 0.8667 | | 0.0 | 22.0 | 8250 | 1.1659 | 0.8717 | | 0.0 | 23.0 | 8625 | 1.1873 | 0.8717 | | 0.0 | 24.0 | 9000 | 1.1966 | 0.875 | | 0.0 | 25.0 | 9375 | 1.2159 | 0.8667 | | 0.0 | 26.0 | 9750 | 1.2192 | 0.8667 | | 0.0 | 27.0 | 10125 | 1.2446 | 0.865 | | 0.0 | 28.0 | 10500 | 1.2249 | 0.8683 | | 0.0 | 29.0 | 10875 | 1.2382 | 0.8717 | | 0.0 | 30.0 | 11250 | 1.2391 | 0.87 | | 0.0 | 31.0 | 11625 | 1.2547 | 0.8733 | | 0.0 | 32.0 | 12000 | 1.2771 | 0.87 | | 0.0 | 33.0 | 12375 | 1.2797 | 0.8717 | | 0.0 | 34.0 | 12750 | 1.2938 | 0.87 | | 0.0 | 35.0 | 13125 | 1.2859 | 0.87 | | 0.0 | 36.0 | 13500 | 1.2998 | 0.8717 | | 0.0 | 37.0 | 13875 | 1.2941 | 0.8683 | | 0.0015 | 38.0 | 14250 | 1.3001 | 0.8683 | | 0.0 | 39.0 | 14625 | 1.3082 | 0.8717 | | 0.0 | 40.0 | 15000 | 1.3151 | 0.8717 | | 0.0 | 41.0 | 15375 | 1.3188 | 0.8717 | | 0.0 | 42.0 | 15750 | 1.3223 | 0.8717 | | 0.0 | 43.0 | 16125 | 1.3285 | 0.87 | | 0.0 | 44.0 | 16500 | 1.3298 | 0.87 | | 0.0 | 45.0 | 16875 | 1.3317 | 0.87 | | 0.0 | 46.0 | 17250 | 1.3330 | 0.87 | | 0.0 | 47.0 | 17625 | 1.3343 | 0.87 | | 0.0 | 48.0 | 18000 | 1.3352 | 0.87 | | 0.0 | 49.0 | 18375 | 1.3354 | 0.8717 | | 0.0 | 50.0 | 18750 | 1.3354 | 0.8717 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

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zuazo/whisper-large-es

zuazo

2023-12-17T21:07:22Z

0

transformers

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

automatic-speech-recognition

2023-12-14T17:03:11Z

Entry not found

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Entrapeer-Organization/news-llama-2-7b_v2

Entrapeer-Organization

2023-12-14T17:04:26Z

0

null

[ "region:us" ]

null

2023-12-14T17:03:51Z

Entry not found

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bdsqlsz/PowerPaint-V1-stable-diffusion-inpainting

bdsqlsz

2023-12-14T18:01:19Z

0

9

null

[ "arxiv:2312.03594", "region:us" ]

null

2023-12-14T17:05:22Z

# A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting **[Project Page](https://powerpaint.github.io/) | [Paper](https://arxiv.org/abs/2312.03594)** This README provides a step-by-step guide to download the repository, set up the required virtual environment named "PowerPaint" using conda, and run PowerPaint with or without ControlNet. **Stronger Model Weights and Online Demo Coming Soon！** <img src='https://github.com/open-mmlab/mmagic/assets/12782558/acd01391-c73f-4997-aafd-0869aebcc915'/> ## Getting Started Download and use in Fooocus、sd-webui or comfyui Feel free to explore and create stunning images with PowerPaint! ## BibTeX ``` @misc{zhuang2023task, title={A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting}, author={Junhao Zhuang and Yanhong Zeng and Wenran Liu and Chun Yuan and Kai Chen}, year={2023}, eprint={2312.03594}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

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Cordmail/Feminist-Mistral-7Bv3.Q5_K_M

Cordmail

2023-12-14T18:00:32Z

0

null

[ "gguf", "region:us" ]

null

2023-12-14T17:05:23Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_82

Jeevesh8

2023-12-14T17:06:40Z

0

null

[ "region:us" ]

null

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_84

Jeevesh8

2023-12-15T01:54:16Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_79

Jeevesh8

2023-12-15T01:33:29Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_83

Jeevesh8

2023-12-15T01:32:10Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_78

Jeevesh8

2023-12-14T17:06:40Z

0

null

[ "region:us" ]

null

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_71

Jeevesh8

2023-12-15T01:10:06Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_87

Jeevesh8

2023-12-15T01:06:33Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_88

Jeevesh8

2023-12-15T01:10:32Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_77

Jeevesh8

2023-12-14T17:06:40Z

0

null

[ "region:us" ]

null

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_81

Jeevesh8

2023-12-15T01:58:43Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_80

Jeevesh8

2023-12-15T01:55:44Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_86

Jeevesh8

2023-12-14T19:53:30Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_76

Jeevesh8

2023-12-15T01:48:54Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_75

Jeevesh8

2023-12-14T17:06:40Z

0

null

[ "region:us" ]

null

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_85

Jeevesh8

2023-12-15T01:29:45Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_73

Jeevesh8

2023-12-15T01:39:03Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_72

Jeevesh8

2023-12-14T17:06:40Z

0

null

[ "region:us" ]

null

2023-12-14T17:06:40Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_74

Jeevesh8

2023-12-15T01:47:18Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:06:41Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_93

Jeevesh8

2023-12-15T01:35:04Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:09:00Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_91

Jeevesh8

2023-12-15T01:09:20Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:09:00Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_92

Jeevesh8

2023-12-15T01:32:55Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:09:00Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_89

Jeevesh8

2023-12-15T01:33:25Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:09:00Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_94

Jeevesh8

2023-12-15T01:37:07Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:09:00Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_90

Jeevesh8

2023-12-15T01:36:32Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:09:00Z

Entry not found

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Mithilss/whisper-large-finetune

Mithilss

2023-12-14T17:11:05Z

0

null

[ "region:us" ]

null

2023-12-14T17:11:04Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_95

Jeevesh8

2023-12-15T01:35:48Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:12:20Z

Entry not found

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Jeevesh8/multibert_seed0_20k_qqp_ft_96

Jeevesh8

2023-12-15T01:31:46Z

0

transformers

[ "transformers", "jax", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:12:20Z

Entry not found

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ishorn5/RTLCoder-Z-v1.0

ishorn5

2023-12-14T17:36:42Z

0

transformers

[ "transformers", "pytorch", "mistral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-12-14T17:12:51Z

Entry not found

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wade001/yolov8_driver_drowsiness_detection

wade001

2023-12-14T17:13:58Z

0

null

[ "region:us" ]

null

2023-12-14T17:13:58Z

Entry not found

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balzanilo/dialogs-emotions-classifier-bert-base-seed1024

balzanilo

2023-12-14T17:43:04Z

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:17:09Z

Entry not found

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EthanRhys/Bowser

EthanRhys

2023-12-14T17:22:05Z

0

null

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

null

2023-12-14T17:21:03Z

--- license: openrail ---

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kompiangg/svm-hate-speech-classification

kompiangg

2023-12-14T17:53:23Z

0

null

[ "region:us", "has_space" ]

null

2023-12-14T17:21:10Z

Entry not found

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TSC7/Nasu

TSC7

2023-12-14T17:25:45Z

0

null

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

null

2023-12-14T17:23:23Z

--- license: apache-2.0 ---

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h4iku/codet5-small-t5apr-javascript

h4iku

2023-12-14T20:36:26Z

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text2text-generation

2023-12-14T17:23:26Z

Entry not found

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athirdpath/Nu-17b-GGUF

athirdpath

2023-12-14T18:45:13Z

0

null

[ "en", "dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW", "dataset:athirdpath/alpaca_dpo_pairs", "license:cc-by-nc-4.0", "region:us" ]

null

2023-12-14T17:25:50Z

--- license: cc-by-nc-4.0 datasets: - athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW - athirdpath/alpaca_dpo_pairs language: - en --- <p align="center"><font size="6"> <b>Needs storytelling finetuning, but this is MUCH better.</b> </font></p> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/633a809fa4a8f33508dce32c/C9gBkxnxVU7vNpzr6XQhO.png) ### Recipe models: - model: athirdpath/Lambda-17b - model: athirdpath/Mu-17b merge_method: slerp base_model: athirdpath/Lambda-17b t: 0.2 dtype: float16

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jiyoun-kim/mit-b0-scene-parse-150-lora

jiyoun-kim

2023-12-14T17:26:56Z

0

null

[ "region:us" ]

null

2023-12-14T17:26:56Z

Entry not found

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delayedkarma/distilbert-base-uncased-finetuned-imdb

delayedkarma

2023-12-14T17:34:06Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-12-14T17:27:17Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] ---  # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4890 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.682 | 1.0 | 157 | 2.5037 | | 2.5854 | 2.0 | 314 | 2.4486 | | 2.526 | 3.0 | 471 | 2.4817 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

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AdnanRiaz107/coderbert-basePEFTLORAtest

AdnanRiaz107

2023-12-14T17:28:27Z

0

null

[ "region:us" ]

null

2023-12-14T17:28:26Z

Entry not found

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AlvaroVasquezAI/Vane

AlvaroVasquezAI

2023-12-14T18:08:19Z

0

null

[ "region:us" ]

null

2023-12-14T17:31:53Z

Entry not found

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hoangtb203/Translation_Model_10

hoangtb203

2023-12-14T17:34:47Z

0

null

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

null

2023-12-14T17:31:59Z

--- license: mit ---

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Cordmail/Feminist-Mistral-7Bv3.Q4_K_M

Cordmail

2023-12-14T17:51:19Z

0

null

[ "gguf", "region:us" ]

null

2023-12-14T17:33:03Z

Entry not found

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

tittuvmathew

2023-12-14T17:33:29Z

0

null

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

reinforcement-learning

2023-12-14T17:33:27Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="tittuvmathew/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

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gkallergis/bbbp_model_da

gkallergis

2023-12-14T17:54:31Z

0

null

[ "license:cc-by-nc-nd-4.0", "region:us" ]

null

2023-12-14T17:33:38Z

--- license: cc-by-nc-nd-4.0 ---

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tittuvmathew/q-Taxi-pickup-dropoff

tittuvmathew

2023-12-14T17:37:53Z

0

null

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

reinforcement-learning

2023-12-14T17:37:51Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-pickup-dropoff results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="tittuvmathew/q-Taxi-pickup-dropoff", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

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bio-datasets/Mistral-7B-Instruct-v0.1-blooming-thunder-195-epoch-100

bio-datasets

2023-12-14T17:39:48Z

0

peft

[ "peft", "safetensors", "region:us" ]

null

2023-12-14T17:39:45Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

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abhishek/3iqe-6zi8-5xf7-0

abhishek

2023-12-14T17:40:15Z

0

transformers

[ "transformers", "safetensors", "vit", "image-classification", "autotrain", "dataset:abhishek/autotrain-data-3iqe-6zi8-5xf7", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-12-14T17:40:06Z

--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - abhishek/autotrain-data-3iqe-6zi8-5xf7 --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: nan f1_macro: 0.06153846153846154 f1_micro: 0.18181818181818182 f1_weighted: 0.055944055944055944 precision_macro: 0.03636363636363636 precision_micro: 0.18181818181818182 precision_weighted: 0.03305785123966942 recall_macro: 0.2 recall_micro: 0.18181818181818182 recall_weighted: 0.18181818181818182 accuracy: 0.18181818181818182

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RudrakshSB/mistral-finetuned-alpaca

RudrakshSB

2023-12-15T16:59:51Z

0

null

[ "tensorboard", "safetensors", "region:us" ]

null

2023-12-14T17:40:33Z

Entry not found

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Nikt271/Jachas

Nikt271

2023-12-14T17:43:05Z

0

null

[ "region:us" ]

null

2023-12-14T17:42:06Z

Entry not found

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netoferraz/distilbert-base-uncased-finetuned-pad-clf-corpus

netoferraz

2023-12-14T17:48:47Z

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:42:23Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-pad-clf-corpus results: [] ---  # distilbert-base-uncased-finetuned-pad-clf-corpus This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1507 - Accuracy: 0.9667 - F1: 0.9667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 4 | 0.6603 | 0.55 | 0.3903 | | No log | 2.0 | 8 | 0.5784 | 0.9083 | 0.9082 | | No log | 3.0 | 12 | 0.4524 | 0.9167 | 0.9169 | | 0.593 | 4.0 | 16 | 0.3400 | 0.9417 | 0.9418 | | 0.593 | 5.0 | 20 | 0.2622 | 0.95 | 0.9501 | | 0.593 | 6.0 | 24 | 0.2090 | 0.9667 | 0.9667 | | 0.263 | 7.0 | 28 | 0.1811 | 0.9667 | 0.9667 | | 0.263 | 8.0 | 32 | 0.1640 | 0.9667 | 0.9667 | | 0.263 | 9.0 | 36 | 0.1544 | 0.9667 | 0.9667 | | 0.263 | 10.0 | 40 | 0.1507 | 0.9667 | 0.9667 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

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saadan5/hat

saadan5

2023-12-14T17:50:02Z

0

null

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

null

2023-12-14T17:42:42Z

--- license: unknown ---

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balzanilo/dialogs-emotions-classifier-bert-base-seed2048

balzanilo

2023-12-14T18:09:01Z

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:43:12Z

Entry not found

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geraq11/Cas

geraq11

2023-12-14T17:43:51Z

0

null

[ "region:us" ]

null

2023-12-14T17:43:51Z

Entry not found

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pminha/roberta-base-squad-i4-f32-p5

pminha

2023-12-14T17:44:07Z

0

null

[ "region:us" ]

null

2023-12-14T17:44:07Z

Entry not found

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delayedkarma/distilbert-base-uncased-finetuned-imdb-accelerate

delayedkarma

2023-12-14T18:28:06Z

0

transformers

[ "transformers", "safetensors", "distilbert", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-12-14T17:45:25Z

--- license: apache-2.0 ---

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CLMBR/preemption_testing-full-transformer-121212

CLMBR

2023-12-14T22:38:03Z

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-12-14T17:45:32Z

--- tags: - generated_from_trainer model-index: - name: preemption_testing-full-transformer-121212 results: [] ---  # preemption_testing-full-transformer-121212 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0214 ## 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: 121212 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3052726 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 4.2242 | 0.03 | 76319 | 4.1911 | | 4.0182 | 1.03 | 152638 | 4.0214 | | 4.0182 | 1.03 | 152638 | 4.0214 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3

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UCaps/UCaps

UCaps

2023-12-14T17:47:42Z

0

null

[ "region:us" ]

null

2023-12-14T17:46:41Z

U Caps, herhangi bir yan etki olmaksızın işitme sorunlarından kurtulmanızı sağlayan etkili bir işitme desteği formülüdür. U Caps Turkey <a href="https://www.excaliburnutrition.com/UCaTur">U Caps Şimdi al!! Daha fazla bilgi için Aşağıdaki Bağlantıya tıklayın ve şimdi %50 indirim kazanın !! acele etmek!!</a> Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/UCaTur">www.UCaps.com</a> <p><a href="https://www.excaliburnutrition.com/UCaTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJnB3qtwUTtJSlRIeky1PHGgKG6KZznoocsdMXrF3HI3p1oMZf_o5E6hA0fIb9LU1VaLm74vwOWwNSieN1cVNjy0klzI6fGOJQoIA1GoN6JMMuji88ZDxBYGOd8wnXC8Njvl87u65O198lUEknsKI_VuK0NI_E0yO-mZruFKAxYY6KkehYmniRtFdIDYQ/w648-h334/U%20Caps%20Turkey.png" alt="enter image description here"> </a></p> ØÜrün Adı — U Caps ØKategori - Sağlıklı İşitme Desteği ØAna Faydaları — işitme desteği Formülü Ø Bileşimi — Doğal Organik Bileşik ➢ Yan Etkiler—YOK ØNihai Derecelendirme: — 4,8 ➢ Kullanılabilirlik — Çevrimiçi ➢Teklifler ve İndirimler; BUGÜN KAYDET! ÖZEL TEKLİF satın almak için ŞİMDİ ALIŞVERİŞ YAPIN!!! U Caps NEDİR? U Caps, Tinnitus ve diğer işitme problemlerini hafifletmek için %100 doğal bir formüldür. İçerikler vitaminler, amino asitler, şifalı bitkiler ve bitki özlerinden oluşur. U Caps işitme sağlığını geliştirir ve her kişide verimli bir şekilde çalışır. KBB (kulak-burun-boğaz) ürünü saftır ve doğal olarak çalışır. Yan etkilere yol açmadan hızlı bir rahatlama sağlar. U Caps ile kulak ameliyatı olmanıza veya olumsuz yan etkilere neden olabilecek reçeteli ilaçları kullanmanıza gerek kalmaz. Kullanımdan birkaç hafta veya ay sonra kulak sağlığını mükemmel şekilde geri kazandırır. Kulakla ilgili diğer sorunları durdurur ve daha fazla hasarı önler. <a href="https://www.excaliburnutrition.com/UCaTur">U Caps Şimdi al!! Daha fazla bilgi için Aşağıdaki Bağlantıya tıklayın ve şimdi %50 indirim kazanın !! acele etmek!!</a> Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/UCaTur">www.UCaps.com</a> <p><a href="https://www.excaliburnutrition.com/UCaTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJnB3qtwUTtJSlRIeky1PHGgKG6KZznoocsdMXrF3HI3p1oMZf_o5E6hA0fIb9LU1VaLm74vwOWwNSieN1cVNjy0klzI6fGOJQoIA1GoN6JMMuji88ZDxBYGOd8wnXC8Njvl87u65O198lUEknsKI_VuK0NI_E0yO-mZruFKAxYY6KkehYmniRtFdIDYQ/w648-h334/U%20Caps%20Turkey.png" alt="enter image description here"> </a></p> U Caps U Caps Hap U Caps kapsül U Caps tablet U Caps Fiyat U Caps yorumlar U Caps İçindekiler U Caps Faydalar U Caps Yan etkiler U Caps kapsül fiyatı U Caps kapsül incelemeleri U Caps kompozisyon U Caps şikayet U Caps Nereden Alınır U Caps Nasıl kullanılır U Caps maliyet U Caps İşler U Caps forum U Caps orijinal U Caps eczane U Caps NASIL ÇALIŞIR? U Caps, işitme duyunuzu iyileştirebilen ve kulaklarınızı iyileştirebilen bir besin takviyesidir. İnsanlar yaşlandıkça işitme duyularını kaybederler ve işitme yetenekleri azalır. Bu noktada işitmeyle ilgili nöronlar bozulmaya başlar. Bu durumda, boyundaki ve beyindeki diğer duyu nöronları, hasar gören işitme hücrelerini telafi etmek zorunda oldukları için gerilir. Bunun sonucunda boyundaki ve beyindeki nöronlar aşırı aktif hale gelerek çınlama sesi çıkarır. ÇÖZÜM: İşitme kaybını tedavi etmek ve tersine çevirmek için başka hiçbir doğal ürün U Caps kadar çok bitki, kök ve bitki özü içermez. İster genç ister yaşlı olsun, sürekli kullanan herkeste işe yaraması garanti edilir. Vücudunuz iyileşir, beyin hücreleriniz yenilenir ve her dozda zil sesi kesilerek işitme cihazı ve ilaç kullanmayı bırakmanıza olanak tanır. U Caps hakkında daha fazla bilgi edinmek için bugün resmi web sitesini ziyaret edin! <a href="https://www.excaliburnutrition.com/UCaTur">U Caps Şimdi al!! Daha fazla bilgi için Aşağıdaki Bağlantıya tıklayın ve şimdi %50 indirim kazanın !! acele etmek!!</a> Resmi internet sitesi: <a href="https://www.excaliburnutrition.com/UCaTur">www.UCaps.com</a> <p><a href="https://www.excaliburnutrition.com/UCaTur"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJnB3qtwUTtJSlRIeky1PHGgKG6KZznoocsdMXrF3HI3p1oMZf_o5E6hA0fIb9LU1VaLm74vwOWwNSieN1cVNjy0klzI6fGOJQoIA1GoN6JMMuji88ZDxBYGOd8wnXC8Njvl87u65O198lUEknsKI_VuK0NI_E0yO-mZruFKAxYY6KkehYmniRtFdIDYQ/w648-h334/U%20Caps%20Turkey.png" alt="enter image description here"> </a></p> U Caps Nereden Satın Alınır? U Caps şirketin web sitesinden satın alınabilir. U Caps'ü satın almak istiyorsanız lütfen aşağıda verilen URL'yi kullanın. U Caps U Caps Hap U Caps kapsül U Caps tablet U Caps Fiyat U Caps yorumlar U Caps İçindekiler U Caps Faydalar U Caps Yan etkiler U Caps kapsül fiyatı U Caps kapsül incelemeleri U Caps kompozisyon U Caps şikayet U Caps Nereden Alınır U Caps Nasıl kullanılır U Caps maliyet U Caps İşler U Caps forum U Caps orijinal U Caps eczane devamını oku: https://www.excaliburnutrition.com/UCaTur https://www.excaliburnutrition.com/UCaTur https://sites.google.com/view/ucapsturkey/home https://healthtoned.blogspot.com/2023/12/u-caps-kulak-cnlamas-ve-isitme.html https://www.weddingwire.com/website/u-caps-turkey https://medium.com/@healthytalk24x7/u-caps-turkey-1acbf98f99e2 https://medium.com/@healthytalk24x7/u-caps-kulak-%C3%A7%C4%B1nlamas%C4%B1-ve-i%CC%87%C5%9Fitme-sorunlar%C4%B1n%C4%B1-gidermeye-yard%C4%B1mc%C4%B1-olur-c497585540af https://infogram.com/u-caps-turkey-1hnp27mwqpx3y2g?live https://softisenilspain.hashnode.dev/u-caps-turkey https://ucaps.company.site/ https://sway.office.com/4IR1GMz56qn2FXhv https://gamma.app/docs/U-Caps-Turkey-ngr337ouffviijf?mode=doc https://healthytalk24x7.wixsite.com/sunshine/post/u-caps-turkey https://groups.google.com/g/snshine/c/8sIP5S-dCd8 https://www.sunflower-cissp.com/glossary/cissp/7254/u-caps-kulak-cinlamasi-ve-isitme-sorunlarini-gidermeye-yardimci-olur-fiyat-ve-indirimi-okuyun-turkey https://replit.com/@ucapskapsul

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kubernetes-bad/testing123-gguf

kubernetes-bad

2023-12-14T17:55:43Z

0

null

[ "gguf", "region:us" ]

null

2023-12-14T17:47:00Z

Entry not found

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gkallergis/bace_da

gkallergis

2023-12-14T17:59:58Z

0

null

[ "license:cc-by-nc-nd-4.0", "region:us" ]

null

2023-12-14T17:50:14Z

--- license: cc-by-nc-nd-4.0 ---

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multimodalart/test-poli-face-2

multimodalart

2023-12-14T17:53:41Z

0

null

[ "region:us" ]

null

2023-12-14T17:53:41Z

Entry not found

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abhishek/3iqe-6zi8-5xf72-0

abhishek

2023-12-14T17:53:50Z

0

transformers

[ "transformers", "safetensors", "resnet", "image-classification", "autotrain", "dataset:abhishek/autotrain-data-3iqe-6zi8-5xf72", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-12-14T17:53:46Z

--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - abhishek/autotrain-data-3iqe-6zi8-5xf72 --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: nan f1_macro: 0.06153846153846154 f1_micro: 0.18181818181818182 f1_weighted: 0.055944055944055944 precision_macro: 0.03636363636363636 precision_micro: 0.18181818181818182 precision_weighted: 0.03305785123966942 recall_macro: 0.2 recall_micro: 0.18181818181818182 recall_weighted: 0.18181818181818182 accuracy: 0.18181818181818182

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CLMBR/preemption_testing-full-transformer-10101

CLMBR

2023-12-15T09:58:14Z

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-12-14T17:55:25Z

Entry not found

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abhishek/3iqe-6zi8-5xf73-0

abhishek

2023-12-14T17:55:53Z

0

transformers

[ "transformers", "safetensors", "vit", "image-classification", "autotrain", "dataset:abhishek/autotrain-data-3iqe-6zi8-5xf73", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-12-14T17:55:48Z

--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - abhishek/autotrain-data-3iqe-6zi8-5xf73 --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: nan f1_macro: 0.09132538710036761 f1_micro: 0.1690909090909091 f1_weighted: 0.08854425957244272 precision_macro: 0.07907624633431085 precision_micro: 0.1690909090909091 precision_weighted: 0.08039189549453478 recall_macro: 0.18150387596899226 recall_micro: 0.1690909090909091 recall_weighted: 0.1690909090909091 accuracy: 0.1690909090909091

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Chenchuhui/Icelandic-finetuned-no-data-augmentation

Chenchuhui

2023-12-17T19:18:48Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-12-14T17:56:05Z

--- license: cc-by-4.0 base_model: carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h tags: - generated_from_trainer model-index: - name: Icelandic-finetuned-no-data-augmentation results: [] ---  # Icelandic-finetuned-no-data-augmentation This model is a fine-tuned version of [carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h](https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

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multimodalart/test-poli-face-2-no-prior-pres

multimodalart

2023-12-14T17:56:40Z

0

null

[ "region:us" ]

null

2023-12-14T17:56:40Z

Entry not found

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kanishka/smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-3e-4

kanishka

2023-12-15T07:10:41Z

0

transformers

[ "transformers", "safetensors", "opt", "text-generation", "generated_from_trainer", "dataset:kanishka/counterfactual_babylm_aann_excess_adj_removal", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-12-14T17:56:44Z

--- tags: - generated_from_trainer datasets: - kanishka/counterfactual_babylm_aann_excess_adj_removal metrics: - accuracy model-index: - name: smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-3e-4 results: - task: name: Causal Language Modeling type: text-generation dataset: name: kanishka/counterfactual_babylm_aann_excess_adj_removal type: kanishka/counterfactual_babylm_aann_excess_adj_removal metrics: - name: Accuracy type: accuracy value: 0.40517314741870225 ---  # smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-3e-4 This model was trained from scratch on the kanishka/counterfactual_babylm_aann_excess_adj_removal dataset. It achieves the following results on the evaluation set: - Loss: 3.4663 - Accuracy: 0.4052 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.7291 | 1.0 | 18629 | 3.9229 | 0.3464 | | 3.4237 | 2.0 | 37258 | 3.6542 | 0.3747 | | 3.2842 | 3.0 | 55887 | 3.5183 | 0.3880 | | 3.1952 | 4.0 | 74516 | 3.4748 | 0.3938 | | 3.1351 | 5.0 | 93145 | 3.4493 | 0.3972 | | 3.0844 | 6.0 | 111774 | 3.4156 | 0.4005 | | 3.0442 | 7.0 | 130403 | 3.3854 | 0.4032 | | 3.008 | 8.0 | 149032 | 3.4062 | 0.4030 | | 2.9768 | 9.0 | 167661 | 3.3970 | 0.4047 | | 2.9498 | 10.0 | 186290 | 3.4024 | 0.4047 | | 2.917 | 11.0 | 204919 | 3.4242 | 0.4039 | | 2.9005 | 12.0 | 223548 | 3.4093 | 0.4049 | | 2.8747 | 13.0 | 242177 | 3.4192 | 0.4051 | | 2.8542 | 14.0 | 260806 | 3.4233 | 0.4053 | | 2.8326 | 15.0 | 279435 | 3.4314 | 0.4054 | | 2.8125 | 16.0 | 298064 | 3.4404 | 0.4052 | | 2.7911 | 17.0 | 316693 | 3.4450 | 0.4054 | | 2.7682 | 18.0 | 335322 | 3.4488 | 0.4054 | | 2.7512 | 19.0 | 353951 | 3.4581 | 0.4054 | | 2.7331 | 20.0 | 372580 | 3.4663 | 0.4052 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

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TheBloke/Solar-10.7B-SLERP-GGUF

TheBloke

2023-12-14T19:21:11Z

0

6

transformers

[ "transformers", "gguf", "solar", "en", "base_model:janhq/Solar-10.7B-SLERP", "license:apache-2.0", "region:us" ]

null

2023-12-14T17:57:10Z

--- base_model: janhq/Solar-10.7B-SLERP inference: false language: - en license: apache-2.0 model_creator: Jan model_name: Solar 10.7B SLERP model_type: solar prompt_template: '<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ' quantized_by: TheBloke ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Solar 10.7B SLERP - GGUF - Model creator: [Jan](https://huggingface.co/janhq) - Original model: [Solar 10.7B SLERP](https://huggingface.co/janhq/Solar-10.7B-SLERP)  ## Description This repo contains GGUF format model files for [Jan's Solar 10.7B SLERP](https://huggingface.co/janhq/Solar-10.7B-SLERP).   ### 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). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF) * [Jan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/janhq/Solar-10.7B-SLERP)   ## Prompt template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ```   ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## 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 Refer to the Provided Files table below to see what files use which methods, and how. </details>   ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [solar-10.7b-slerp.Q2_K.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q2_K.gguf) | Q2_K | 2 | 4.55 GB| 7.05 GB | smallest, significant quality loss - not recommended for most purposes | | [solar-10.7b-slerp.Q3_K_S.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q3_K_S.gguf) | Q3_K_S | 3 | 4.67 GB| 7.17 GB | very small, high quality loss | | [solar-10.7b-slerp.Q3_K_M.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q3_K_M.gguf) | Q3_K_M | 3 | 5.19 GB| 7.69 GB | very small, high quality loss | | [solar-10.7b-slerp.Q3_K_L.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q3_K_L.gguf) | Q3_K_L | 3 | 5.65 GB| 8.15 GB | small, substantial quality loss | | [solar-10.7b-slerp.Q4_0.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q4_0.gguf) | Q4_0 | 4 | 6.07 GB| 8.57 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [solar-10.7b-slerp.Q4_K_S.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q4_K_S.gguf) | Q4_K_S | 4 | 6.10 GB| 8.60 GB | small, greater quality loss | | [solar-10.7b-slerp.Q4_K_M.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q4_K_M.gguf) | Q4_K_M | 4 | 6.46 GB| 8.96 GB | medium, balanced quality - recommended | | [solar-10.7b-slerp.Q5_0.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q5_0.gguf) | Q5_0 | 5 | 7.40 GB| 9.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [solar-10.7b-slerp.Q5_K_S.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q5_K_S.gguf) | Q5_K_S | 5 | 7.40 GB| 9.90 GB | large, low quality loss - recommended | | [solar-10.7b-slerp.Q5_K_M.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q5_K_M.gguf) | Q5_K_M | 5 | 7.60 GB| 10.10 GB | large, very low quality loss - recommended | | [solar-10.7b-slerp.Q6_K.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q6_K.gguf) | Q6_K | 6 | 8.81 GB| 11.31 GB | very large, extremely low quality loss | | [solar-10.7b-slerp.Q8_0.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q8_0.gguf) | Q8_0 | 8 | 11.41 GB| 13.91 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.   ## 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 file. 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: TheBloke/Solar-10.7B-SLERP-GGUF and below it, a specific filename to download, such as: solar-10.7b-slerp.Q4_K_M.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 TheBloke/Solar-10.7B-SLERP-GGUF solar-10.7b-slerp.Q4_K_M.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 TheBloke/Solar-10.7B-SLERP-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` 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 hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Solar-10.7B-SLERP-GGUF solar-10.7b-slerp.Q4_K_M.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>   ## 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 solar-10.7b-slerp.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` 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="./solar-10.7b-slerp.Q4_K_M.gguf", # Download the model file first n_ctx=4096, # 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( "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # 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="./solar-10.7b-slerp.Q4_K_M.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)    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.   # Original model card: Jan's Solar 10.7B SLERP   <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a > - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p>  # Model Description This model uses the `Slerp` merge method from the best models on 14th Dec on the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard): 1. [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) 2. [janhq/Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B) - base model: [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) The yaml config file for this model is here: ```yaml slices: - sources: - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [0, 48] - model: janhq/Pandora-v1-10.7B layer_range: [0, 48] merge_method: slerp base_model: upstage/SOLAR-10.7B-Instruct-v1.0 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` # Prompt template - **ChatML** ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` # Run this model You can run this model using [Jan](https://jan.ai/) on Mac, Windows, or Linux. **Jan is an open source, ChatGPT alternative that is:** 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you. 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time. 🌐 **OpenAI Compatible**: Local server on port ` 1337` with OpenAI compatible endpoints 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq) - Please use the [Solar-10.7B-SLERP-GGUF](https://huggingface.co/janhq/Solar-10.7B-SLERP-GGUF) when using on Jan. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Merger This is a test project for merging models. # Open LLM Leaderboard Evaluation Results Detailed results can be found here. | Metric | Value | |-----------------------|---------------------------| | Avg. | ?| | ARC (25-shot) | ? | | HellaSwag (10-shot) | ? | | MMLU (5-shot) | ?| | TruthfulQA (0-shot) | ? | | Winogrande (5-shot) | ? | | GSM8K (5-shot) | ? | # Acknowlegement - [mergekit](https://github.com/cg123/mergekit) - [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md) - [SLERP](https://github.com/Digitous/LLM-SLERP-Merge) - [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)

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bio-datasets/Mistral-7B-Instruct-v0.1-fast-pond-196-epoch-0

bio-datasets

2023-12-14T17:57:25Z

0

peft

[ "peft", "safetensors", "region:us" ]

null

2023-12-14T17:57:22Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

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svenbl80/roberta-base_contracts

svenbl80

2023-12-14T18:49:22Z

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-14T17:57:38Z

Entry not found

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Lez94/instruct-pix2pix-model

Lez94

2023-12-14T17:58:29Z

0

null

[ "region:us" ]

null

2023-12-14T17:58:29Z

Entry not found

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Likalto4/vindr-siemens-256-5em6

Likalto4

2023-12-15T14:20:00Z

0

diffusers

[ "diffusers", "safetensors", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

null

2023-12-14T17:58:47Z

Entry not found

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gkallergis/bbbp_intrinsic

gkallergis

2023-12-15T01:50:54Z

0

null

[ "license:cc-by-nc-nd-4.0", "region:us" ]

null

2023-12-14T18:01:02Z

--- license: cc-by-nc-nd-4.0 ---

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arxyzan/roberta-sentiment

arxyzan

2023-12-14T18:01:11Z

0

null

[ "region:us" ]

null

2023-12-14T18:01:09Z

Entry not found

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jan-hq/pandora-v1-10.7b-GGUF

jan-hq

2023-12-15T06:56:21Z

0

null

[ "gguf", "en", "base_model:janhq/Pandora-v1-10.7B", "license:apache-2.0", "region:us" ]

null

2023-12-14T18:01:12Z

--- license: apache-2.0 language: - en base_model: janhq/Pandora-v1-10.7B model_creator: janhq model_name: Pandora-v1-10.7B quantized_by: JanHQ ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p>  # Model Description This is a GGUF version of [janhq/Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B) - Model creator: [janhq](https://huggingface.co/janhq) - Original model: [Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B) - Model description: [Readme](https://huggingface.co/janhq/Pandora-v1-10.7B/blob/main/README.md) # Run this model You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux. Jan is an open source, ChatGPT alternative that is: - 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you. - 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time. - 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints - 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Converter This is a repository for the [open-source converter](https://github.com/janhq/model-converter). We would be grateful if the community could contribute and strengthen this repository. We are aiming to expand the repo that can convert into various types of format

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gkallergis/clintox_da

gkallergis

2023-12-14T18:25:55Z

0

null

[ "license:cc-by-nc-nd-4.0", "region:us" ]

null

2023-12-14T18:01:59Z

--- license: cc-by-nc-nd-4.0 ---

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Vinibarcley/Anahis

Vinibarcley

2023-12-14T18:02:54Z

0

null

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

null

2023-12-14T18:02:24Z

--- license: openrail ---

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pierd/pokemon-lora

pierd

2023-12-14T18:03:55Z

0

null

[ "region:us" ]

null

2023-12-14T18:03:55Z

Entry not found

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