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End of training

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README.md CHANGED
@@ -18,7 +18,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2781
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  ## Model description
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@@ -50,165 +50,169 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:------:|:-----:|:---------------:|
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- | 0.1903 | 2.5238 | 13000 | 0.2652 |
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- | 0.1723 | 2.5335 | 13050 | 0.2645 |
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- | 0.17 | 2.5432 | 13100 | 0.2640 |
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- | 0.1681 | 2.5529 | 13150 | 0.2656 |
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- | 0.1865 | 2.5626 | 13200 | 0.2655 |
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- | 0.1855 | 2.5723 | 13250 | 0.2640 |
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- | 0.1657 | 2.5820 | 13300 | 0.2664 |
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- | 0.197 | 2.5917 | 13350 | 0.2621 |
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- | 0.1912 | 2.6014 | 13400 | 0.2645 |
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- | 0.1787 | 2.6111 | 13450 | 0.2605 |
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- | 0.2003 | 2.6209 | 13500 | 0.2609 |
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- | 0.1812 | 2.6306 | 13550 | 0.2631 |
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- | 0.1964 | 2.6403 | 13600 | 0.2630 |
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- | 0.1774 | 2.6500 | 13650 | 0.2626 |
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- | 0.1907 | 2.6597 | 13700 | 0.2615 |
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- | 0.1835 | 2.6694 | 13750 | 0.2610 |
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- | 0.1812 | 2.6791 | 13800 | 0.2620 |
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- | 0.1778 | 2.6888 | 13850 | 0.2636 |
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- | 0.1738 | 2.6985 | 13900 | 0.2624 |
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- | 0.1795 | 2.7082 | 13950 | 0.2644 |
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- | 0.1773 | 2.7179 | 14000 | 0.2619 |
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- | 0.1951 | 2.7276 | 14050 | 0.2594 |
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- | 0.178 | 2.7373 | 14100 | 0.2609 |
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- | 0.1711 | 2.7470 | 14150 | 0.2620 |
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- | 0.1712 | 2.7567 | 14200 | 0.2617 |
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- | 0.1762 | 2.7665 | 14250 | 0.2620 |
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- | 0.1893 | 2.7762 | 14300 | 0.2612 |
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- | 0.1715 | 2.7859 | 14350 | 0.2625 |
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- | 0.166 | 2.7956 | 14400 | 0.2615 |
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- | 0.1771 | 2.8053 | 14450 | 0.2618 |
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- | 0.16 | 2.8150 | 14500 | 0.2614 |
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- | 0.1754 | 2.8247 | 14550 | 0.2603 |
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- | 0.1677 | 2.8344 | 14600 | 0.2610 |
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- | 0.1828 | 2.8441 | 14650 | 0.2610 |
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- | 0.1708 | 2.8538 | 14700 | 0.2606 |
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- | 0.1853 | 2.8635 | 14750 | 0.2582 |
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- | 0.1823 | 2.8732 | 14800 | 0.2601 |
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- | 0.1863 | 2.8829 | 14850 | 0.2601 |
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- | 0.182 | 2.8926 | 14900 | 0.2598 |
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- | 0.1874 | 2.9023 | 14950 | 0.2590 |
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- | 0.1813 | 2.9121 | 15000 | 0.2587 |
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- | 0.1803 | 2.9218 | 15050 | 0.2577 |
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- | 0.1652 | 2.9315 | 15100 | 0.2596 |
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- | 0.2131 | 2.9412 | 15150 | 0.2586 |
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- | 0.1699 | 2.9509 | 15200 | 0.2579 |
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- | 0.175 | 2.9606 | 15250 | 0.2575 |
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- | 0.1604 | 2.9703 | 15300 | 0.2588 |
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- | 0.191 | 2.9800 | 15350 | 0.2575 |
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- | 0.1911 | 2.9897 | 15400 | 0.2584 |
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- | 0.1945 | 2.9994 | 15450 | 0.2564 |
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- | 0.1149 | 3.0091 | 15500 | 0.2782 |
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- | 0.1198 | 3.0188 | 15550 | 0.2785 |
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- | 0.137 | 3.0285 | 15600 | 0.2770 |
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- | 0.1292 | 3.0382 | 15650 | 0.2778 |
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- | 0.1227 | 3.0480 | 15700 | 0.2814 |
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- | 0.1349 | 3.0577 | 15750 | 0.2810 |
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- | 0.117 | 3.0674 | 15800 | 0.2815 |
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- | 0.13 | 3.0771 | 15850 | 0.2790 |
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- | 0.1297 | 3.0868 | 15900 | 0.2788 |
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- | 0.1238 | 3.0965 | 15950 | 0.2799 |
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- | 0.1303 | 3.1062 | 16000 | 0.2792 |
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- | 0.1259 | 3.1159 | 16050 | 0.2799 |
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- | 0.1217 | 3.1256 | 16100 | 0.2807 |
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- | 0.1268 | 3.1353 | 16150 | 0.2825 |
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- | 0.1201 | 3.1450 | 16200 | 0.2788 |
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- | 0.1363 | 3.1547 | 16250 | 0.2795 |
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- | 0.1108 | 3.1644 | 16300 | 0.2817 |
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- | 0.1227 | 3.1741 | 16350 | 0.2798 |
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- | 0.1397 | 3.1838 | 16400 | 0.2808 |
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- | 0.1193 | 3.1936 | 16450 | 0.2788 |
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- | 0.1257 | 3.2033 | 16500 | 0.2807 |
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- | 0.1279 | 3.2130 | 16550 | 0.2805 |
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- | 0.1148 | 3.2227 | 16600 | 0.2794 |
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- | 0.1323 | 3.2324 | 16650 | 0.2810 |
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- | 0.112 | 3.2421 | 16700 | 0.2802 |
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- | 0.1369 | 3.2518 | 16750 | 0.2799 |
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- | 0.1109 | 3.2615 | 16800 | 0.2823 |
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- | 0.1188 | 3.2712 | 16850 | 0.2813 |
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- | 0.1211 | 3.2809 | 16900 | 0.2804 |
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- | 0.1256 | 3.2906 | 16950 | 0.2820 |
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- | 0.1215 | 3.3003 | 17000 | 0.2819 |
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- | 0.1241 | 3.3100 | 17050 | 0.2804 |
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- | 0.121 | 3.3197 | 17100 | 0.2813 |
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- | 0.1207 | 3.3295 | 17150 | 0.2803 |
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- | 0.1241 | 3.3392 | 17200 | 0.2794 |
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- | 0.1115 | 3.3489 | 17250 | 0.2810 |
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- | 0.1113 | 3.3586 | 17300 | 0.2804 |
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- | 0.13 | 3.3683 | 17350 | 0.2805 |
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- | 0.1202 | 3.3780 | 17400 | 0.2796 |
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- | 0.1322 | 3.3877 | 17450 | 0.2798 |
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- | 0.127 | 3.3974 | 17500 | 0.2793 |
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- | 0.1272 | 3.4071 | 17550 | 0.2783 |
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- | 0.1251 | 3.4168 | 17600 | 0.2811 |
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- | 0.1229 | 3.4265 | 17650 | 0.2814 |
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- | 0.122 | 3.4362 | 17700 | 0.2800 |
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- | 0.1246 | 3.4459 | 17750 | 0.2793 |
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- | 0.1113 | 3.4556 | 17800 | 0.2801 |
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- | 0.1229 | 3.4653 | 17850 | 0.2799 |
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- | 0.1246 | 3.4751 | 17900 | 0.2792 |
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- | 0.1217 | 3.4848 | 17950 | 0.2798 |
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- | 0.1129 | 3.4945 | 18000 | 0.2791 |
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- | 0.1234 | 3.5042 | 18050 | 0.2793 |
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- | 0.1187 | 3.5139 | 18100 | 0.2801 |
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- | 0.1239 | 3.5236 | 18150 | 0.2798 |
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- | 0.1134 | 3.5333 | 18200 | 0.2795 |
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- | 0.1307 | 3.5430 | 18250 | 0.2787 |
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- | 0.1232 | 3.5527 | 18300 | 0.2798 |
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- | 0.1271 | 3.5624 | 18350 | 0.2795 |
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- | 0.1176 | 3.5721 | 18400 | 0.2803 |
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- | 0.1273 | 3.5818 | 18450 | 0.2775 |
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- | 0.1186 | 3.5915 | 18500 | 0.2794 |
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- | 0.1267 | 3.6012 | 18550 | 0.2786 |
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- | 0.1316 | 3.6109 | 18600 | 0.2789 |
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- | 0.1132 | 3.6207 | 18650 | 0.2801 |
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- | 0.1292 | 3.6304 | 18700 | 0.2787 |
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- | 0.1211 | 3.6401 | 18750 | 0.2798 |
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- | 0.126 | 3.6498 | 18800 | 0.2796 |
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- | 0.1074 | 3.6595 | 18850 | 0.2781 |
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- | 0.1399 | 3.6692 | 18900 | 0.2779 |
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- | 0.1216 | 3.6789 | 18950 | 0.2775 |
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- | 0.1232 | 3.6886 | 19000 | 0.2780 |
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- | 0.1357 | 3.6983 | 19050 | 0.2796 |
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- | 0.1174 | 3.7080 | 19100 | 0.2792 |
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- | 0.1312 | 3.7177 | 19150 | 0.2790 |
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- | 0.1213 | 3.7274 | 19200 | 0.2794 |
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- | 0.1233 | 3.7371 | 19250 | 0.2789 |
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- | 0.1156 | 3.7468 | 19300 | 0.2794 |
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- | 0.1074 | 3.7566 | 19350 | 0.2785 |
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- | 0.125 | 3.7663 | 19400 | 0.2784 |
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- | 0.1151 | 3.7760 | 19450 | 0.2785 |
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- | 0.1237 | 3.7857 | 19500 | 0.2782 |
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- | 0.1158 | 3.7954 | 19550 | 0.2773 |
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- | 0.1193 | 3.8051 | 19600 | 0.2774 |
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- | 0.128 | 3.8148 | 19650 | 0.2770 |
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- | 0.1069 | 3.8245 | 19700 | 0.2775 |
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- | 0.1137 | 3.8342 | 19750 | 0.2778 |
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- | 0.1183 | 3.8439 | 19800 | 0.2784 |
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- | 0.1077 | 3.8536 | 19850 | 0.2780 |
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- | 0.1297 | 3.8633 | 19900 | 0.2775 |
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- | 0.1202 | 3.8730 | 19950 | 0.2776 |
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- | 0.1278 | 3.8827 | 20000 | 0.2772 |
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- | 0.1182 | 3.8924 | 20050 | 0.2769 |
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- | 0.1265 | 3.9022 | 20100 | 0.2772 |
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- | 0.1169 | 3.9119 | 20150 | 0.2774 |
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- | 0.1191 | 3.9216 | 20200 | 0.2776 |
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- | 0.1115 | 3.9313 | 20250 | 0.2777 |
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- | 0.1257 | 3.9410 | 20300 | 0.2777 |
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- | 0.1134 | 3.9507 | 20350 | 0.2781 |
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- | 0.1243 | 3.9604 | 20400 | 0.2780 |
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- | 0.1183 | 3.9701 | 20450 | 0.2781 |
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- | 0.1189 | 3.9798 | 20500 | 0.2781 |
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- | 0.117 | 3.9895 | 20550 | 0.2781 |
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- | 0.1205 | 3.9992 | 20600 | 0.2781 |
 
 
 
 
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  ### Framework versions
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  - PEFT 0.12.0
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- - Transformers 4.43.3
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  - Pytorch 2.3.1+cu121
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- - Datasets 2.20.0
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  - Tokenizers 0.19.1
 
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  This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2953
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:------:|:-----:|:---------------:|
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+ | 0.1975 | 2.4942 | 13000 | 0.2857 |
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+ | 0.1754 | 2.5038 | 13050 | 0.2851 |
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+ | 0.1716 | 2.5134 | 13100 | 0.2843 |
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+ | 0.1896 | 2.5230 | 13150 | 0.2843 |
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+ | 0.172 | 2.5326 | 13200 | 0.2849 |
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+ | 0.1967 | 2.5422 | 13250 | 0.2805 |
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+ | 0.1778 | 2.5518 | 13300 | 0.2820 |
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+ | 0.2003 | 2.5614 | 13350 | 0.2818 |
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+ | 0.1669 | 2.5710 | 13400 | 0.2818 |
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+ | 0.1713 | 2.5806 | 13450 | 0.2819 |
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+ | 0.1813 | 2.5902 | 13500 | 0.2826 |
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+ | 0.2075 | 2.5998 | 13550 | 0.2810 |
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+ | 0.1683 | 2.6094 | 13600 | 0.2830 |
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+ | 0.1985 | 2.6190 | 13650 | 0.2819 |
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+ | 0.1759 | 2.6285 | 13700 | 0.2840 |
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+ | 0.1854 | 2.6381 | 13750 | 0.2821 |
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+ | 0.1922 | 2.6477 | 13800 | 0.2824 |
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+ | 0.1707 | 2.6573 | 13850 | 0.2828 |
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+ | 0.1759 | 2.6669 | 13900 | 0.2811 |
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+ | 0.1747 | 2.6765 | 13950 | 0.2814 |
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+ | 0.1965 | 2.6861 | 14000 | 0.2837 |
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+ | 0.1917 | 2.6957 | 14050 | 0.2821 |
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+ | 0.186 | 2.7053 | 14100 | 0.2832 |
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+ | 0.1878 | 2.7149 | 14150 | 0.2823 |
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+ | 0.1906 | 2.7245 | 14200 | 0.2792 |
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+ | 0.1551 | 2.7341 | 14250 | 0.2793 |
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+ | 0.2018 | 2.7437 | 14300 | 0.2779 |
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+ | 0.1939 | 2.7533 | 14350 | 0.2799 |
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+ | 0.1659 | 2.7629 | 14400 | 0.2785 |
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+ | 0.1845 | 2.7724 | 14450 | 0.2783 |
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+ | 0.1727 | 2.7820 | 14500 | 0.2780 |
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+ | 0.1793 | 2.7916 | 14550 | 0.2787 |
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+ | 0.1753 | 2.8012 | 14600 | 0.2780 |
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+ | 0.1857 | 2.8108 | 14650 | 0.2781 |
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+ | 0.1759 | 2.8204 | 14700 | 0.2776 |
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+ | 0.1925 | 2.8300 | 14750 | 0.2769 |
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+ | 0.1666 | 2.8396 | 14800 | 0.2780 |
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+ | 0.2118 | 2.8492 | 14850 | 0.2762 |
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+ | 0.169 | 2.8588 | 14900 | 0.2779 |
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+ | 0.1604 | 2.8684 | 14950 | 0.2765 |
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+ | 0.1746 | 2.8780 | 15000 | 0.2788 |
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+ | 0.1795 | 2.8876 | 15050 | 0.2771 |
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+ | 0.1661 | 2.8972 | 15100 | 0.2799 |
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+ | 0.1893 | 2.9068 | 15150 | 0.2781 |
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+ | 0.1665 | 2.9163 | 15200 | 0.2763 |
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+ | 0.1781 | 2.9259 | 15250 | 0.2768 |
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+ | 0.1634 | 2.9355 | 15300 | 0.2774 |
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+ | 0.1713 | 2.9451 | 15350 | 0.2774 |
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+ | 0.1676 | 2.9547 | 15400 | 0.2774 |
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+ | 0.1927 | 2.9643 | 15450 | 0.2758 |
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+ | 0.1667 | 2.9739 | 15500 | 0.2753 |
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+ | 0.1791 | 2.9835 | 15550 | 0.2755 |
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+ | 0.1817 | 2.9931 | 15600 | 0.2751 |
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+ | 0.1526 | 3.0027 | 15650 | 0.2775 |
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+ | 0.128 | 3.0123 | 15700 | 0.2942 |
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+ | 0.1235 | 3.0219 | 15750 | 0.2959 |
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+ | 0.1385 | 3.0315 | 15800 | 0.2932 |
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+ | 0.1259 | 3.0411 | 15850 | 0.2946 |
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+ | 0.1148 | 3.0507 | 15900 | 0.2958 |
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+ | 0.121 | 3.0602 | 15950 | 0.2957 |
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+ | 0.1137 | 3.0698 | 16000 | 0.2982 |
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+ | 0.1181 | 3.0794 | 16050 | 0.2982 |
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+ | 0.1185 | 3.0890 | 16100 | 0.2949 |
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+ | 0.1254 | 3.0986 | 16150 | 0.2965 |
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+ | 0.1261 | 3.1082 | 16200 | 0.2965 |
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+ | 0.1202 | 3.1178 | 16250 | 0.3008 |
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+ | 0.1151 | 3.1274 | 16300 | 0.2980 |
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+ | 0.1284 | 3.1370 | 16350 | 0.2976 |
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+ | 0.1353 | 3.1466 | 16400 | 0.2961 |
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+ | 0.1149 | 3.1562 | 16450 | 0.2995 |
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+ | 0.1254 | 3.1658 | 16500 | 0.3002 |
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+ | 0.1318 | 3.1754 | 16550 | 0.2975 |
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+ | 0.1289 | 3.1850 | 16600 | 0.2955 |
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+ | 0.1286 | 3.1946 | 16650 | 0.3016 |
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+ | 0.1104 | 3.2041 | 16700 | 0.2981 |
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+ | 0.1087 | 3.2137 | 16750 | 0.2984 |
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+ | 0.1325 | 3.2233 | 16800 | 0.2953 |
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+ | 0.113 | 3.2329 | 16850 | 0.2972 |
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+ | 0.1335 | 3.2425 | 16900 | 0.2984 |
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+ | 0.1189 | 3.2521 | 16950 | 0.2981 |
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+ | 0.1177 | 3.2617 | 17000 | 0.3003 |
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+ | 0.1065 | 3.2713 | 17050 | 0.2980 |
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+ | 0.1262 | 3.2809 | 17100 | 0.2970 |
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+ | 0.1248 | 3.2905 | 17150 | 0.2972 |
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+ | 0.1229 | 3.3001 | 17200 | 0.2961 |
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+ | 0.1339 | 3.3097 | 17250 | 0.2953 |
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+ | 0.1216 | 3.3193 | 17300 | 0.2984 |
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+ | 0.1239 | 3.3289 | 17350 | 0.2974 |
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+ | 0.1182 | 3.3384 | 17400 | 0.2956 |
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+ | 0.1237 | 3.3480 | 17450 | 0.2967 |
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+ | 0.1208 | 3.3576 | 17500 | 0.2973 |
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+ | 0.1378 | 3.3672 | 17550 | 0.2994 |
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+ | 0.1199 | 3.3768 | 17600 | 0.2975 |
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+ | 0.1295 | 3.3864 | 17650 | 0.2990 |
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+ | 0.127 | 3.3960 | 17700 | 0.2985 |
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+ | 0.129 | 3.4056 | 17750 | 0.3004 |
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+ | 0.1227 | 3.4152 | 17800 | 0.2993 |
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+ | 0.1188 | 3.4248 | 17850 | 0.2994 |
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+ | 0.1243 | 3.4344 | 17900 | 0.2992 |
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+ | 0.1246 | 3.4440 | 17950 | 0.2973 |
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+ | 0.1252 | 3.4536 | 18000 | 0.2989 |
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+ | 0.128 | 3.4632 | 18050 | 0.2976 |
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+ | 0.1359 | 3.4728 | 18100 | 0.2966 |
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+ | 0.1194 | 3.4823 | 18150 | 0.2959 |
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+ | 0.1213 | 3.4919 | 18200 | 0.2964 |
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+ | 0.1161 | 3.5015 | 18250 | 0.2965 |
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+ | 0.1209 | 3.5111 | 18300 | 0.2974 |
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+ | 0.1255 | 3.5207 | 18350 | 0.2955 |
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+ | 0.1319 | 3.5303 | 18400 | 0.2965 |
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+ | 0.118 | 3.5399 | 18450 | 0.2990 |
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+ | 0.1317 | 3.5495 | 18500 | 0.2964 |
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+ | 0.1302 | 3.5591 | 18550 | 0.2956 |
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+ | 0.1289 | 3.5687 | 18600 | 0.2968 |
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+ | 0.1363 | 3.5783 | 18650 | 0.2959 |
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+ | 0.1301 | 3.5879 | 18700 | 0.2954 |
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+ | 0.123 | 3.5975 | 18750 | 0.2969 |
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+ | 0.1189 | 3.6071 | 18800 | 0.2969 |
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+ | 0.1151 | 3.6167 | 18850 | 0.2951 |
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+ | 0.1296 | 3.6262 | 18900 | 0.2963 |
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+ | 0.1264 | 3.6358 | 18950 | 0.2958 |
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+ | 0.1151 | 3.6454 | 19000 | 0.2967 |
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+ | 0.1193 | 3.6550 | 19050 | 0.2974 |
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+ | 0.13 | 3.6646 | 19100 | 0.2962 |
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+ | 0.1324 | 3.6742 | 19150 | 0.2956 |
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+ | 0.1126 | 3.6838 | 19200 | 0.2975 |
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+ | 0.1223 | 3.6934 | 19250 | 0.2965 |
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+ | 0.1257 | 3.7030 | 19300 | 0.2970 |
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+ | 0.1236 | 3.7126 | 19350 | 0.2976 |
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+ | 0.1255 | 3.7222 | 19400 | 0.2970 |
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+ | 0.1174 | 3.7318 | 19450 | 0.2974 |
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+ | 0.1256 | 3.7414 | 19500 | 0.2966 |
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+ | 0.1131 | 3.7510 | 19550 | 0.2971 |
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+ | 0.1296 | 3.7606 | 19600 | 0.2961 |
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+ | 0.1291 | 3.7701 | 19650 | 0.2947 |
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+ | 0.1232 | 3.7797 | 19700 | 0.2951 |
188
+ | 0.1257 | 3.7893 | 19750 | 0.2958 |
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+ | 0.1123 | 3.7989 | 19800 | 0.2954 |
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+ | 0.1234 | 3.8085 | 19850 | 0.2952 |
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+ | 0.1224 | 3.8181 | 19900 | 0.2951 |
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+ | 0.1125 | 3.8277 | 19950 | 0.2960 |
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+ | 0.1349 | 3.8373 | 20000 | 0.2951 |
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+ | 0.1274 | 3.8469 | 20050 | 0.2950 |
195
+ | 0.1169 | 3.8565 | 20100 | 0.2948 |
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+ | 0.1202 | 3.8661 | 20150 | 0.2953 |
197
+ | 0.1239 | 3.8757 | 20200 | 0.2960 |
198
+ | 0.1237 | 3.8853 | 20250 | 0.2956 |
199
+ | 0.1126 | 3.8949 | 20300 | 0.2957 |
200
+ | 0.121 | 3.9045 | 20350 | 0.2956 |
201
+ | 0.1319 | 3.9140 | 20400 | 0.2953 |
202
+ | 0.1274 | 3.9236 | 20450 | 0.2952 |
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+ | 0.116 | 3.9332 | 20500 | 0.2951 |
204
+ | 0.1189 | 3.9428 | 20550 | 0.2953 |
205
+ | 0.1224 | 3.9524 | 20600 | 0.2956 |
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+ | 0.1133 | 3.9620 | 20650 | 0.2958 |
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+ | 0.1216 | 3.9716 | 20700 | 0.2956 |
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+ | 0.1229 | 3.9812 | 20750 | 0.2954 |
209
+ | 0.1228 | 3.9908 | 20800 | 0.2953 |
210
 
211
 
212
  ### Framework versions
213
 
214
  - PEFT 0.12.0
215
+ - Transformers 4.44.2
216
  - Pytorch 2.3.1+cu121
217
+ - Datasets 2.21.0
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  - Tokenizers 0.19.1
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