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yalhessi/lemexp-task5-template_deriv_full-deepseek-coder-1.3b-base-4gpu-bs4-8lr-12epochs-normal-eos-8bit
lemexp-task5-template_deriv_full-deepseek-coder-1.3b-base-4gpu-bs4-8lr-12epochs-normal-eos-8bit is a machine learning model from yalhessi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as other.
should probably proofread and complete it, then remove this comment. --
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From the Hugging Face model README
This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-base on the None dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5476 | 0.2001 | 720 | 0.3815 |
| 0.3642 | 0.4002 | 1440 | 0.3325 |
| 0.3018 | 0.6003 | 2160 | 0.3054 |
| 0.2847 | 0.8004 | 2880 | 0.2860 |
| 0.2684 | 1.0006 | 3600 | 0.2856 |
| 0.2595 | 1.2007 | 4320 | 0.2708 |
| 0.2471 | 1.4008 | 5040 | 0.2601 |
| 0.2465 | 1.6009 | 5760 | 0.2531 |
| 0.2403 | 1.8010 | 6480 | 0.2519 |
| 0.2324 | 2.0011 | 7200 | 0.2407 |
| 0.2247 | 2.2012 | 7920 | 0.2477 |
| 0.2178 | 2.4013 | 8640 | 0.2346 |
| 0.2171 | 2.6014 | 9360 | 0.2357 |
| 0.2173 | 2.8016 | 10080 | 0.2311 |
| 0.2153 | 3.0017 | 10800 | 0.2244 |
| 0.2006 | 3.2018 | 11520 | 0.2242 |
| 0.2002 | 3.4019 | 12240 | 0.2196 |
| 0.2012 | 3.6020 | 12960 | 0.2230 |
| 0.1987 | 3.8021 | 13680 | 0.2173 |
| 0.198 | 4.0022 | 14400 | 0.2114 |
| 0.1836 | 4.2023 | 15120 | 0.2114 |
| 0.1871 | 4.4024 | 15840 | 0.2061 |
| 0.1837 | 4.6026 | 16560 | 0.2070 |
| 0.1823 | 4.8027 | 17280 | 0.2032 |
| 0.1799 | 5.0028 | 18000 | 0.2007 |
| 0.167 | 5.2029 | 18720 | 0.2007 |
| 0.1716 | 5.4030 | 19440 | 0.1969 |
| 0.1682 | 5.6031 | 20160 | 0.1974 |
| 0.168 | 5.8032 | 20880 | 0.1974 |
| 0.1679 | 6.0033 | 21600 | 0.1864 |
| 0.1543 | 6.2034 | 22320 | 0.1903 |
| 0.156 | 6.4036 | 23040 | 0.1858 |
| 0.1569 | 6.6037 | 23760 | 0.1864 |
| 0.1538 | 6.8038 | 24480 | 0.1824 |
| 0.153 | 7.0039 | 25200 | 0.1845 |
| 0.1419 | 7.2040 | 25920 | 0.1857 |
| 0.1428 | 7.4041 | 26640 | 0.1766 |
| 0.1415 | 7.6042 | 27360 | 0.1749 |
| 0.1404 | 7.8043 | 28080 | 0.1750 |
| 0.1395 | 8.0044 | 28800 | 0.1734 |
| 0.1266 | 8.2046 | 29520 | 0.1728 |
| 0.1261 | 8.4047 | 30240 | 0.1724 |
| 0.1257 | 8.6048 | 30960 | 0.1695 |
| 0.1262 | 8.8049 | 31680 | 0.1710 |
| 0.1261 | 9.0050 | 32400 | 0.1683 |
| 0.112 | 9.2051 | 33120 | 0.1654 |
| 0.1107 | 9.4052 | 33840 | 0.1666 |
| 0.1111 | 9.6053 | 34560 | 0.1613 |
| 0.1104 | 9.8054 | 35280 | 0.1611 |
| 0.1106 | 10.0056 | 36000 | 0.1638 |
| 0.0952 | 10.2057 | 36720 | 0.1656 |
| 0.0981 | 10.4058 | 37440 | 0.1636 |
| 0.0978 | 10.6059 | 38160 | 0.1627 |
| 0.096 | 10.8060 | 38880 | 0.1627 |
| 0.0958 | 11.0061 | 39600 | 0.1639 |
| 0.0876 | 11.2062 | 40320 | 0.1684 |
| 0.0844 | 11.4063 | 41040 | 0.1679 |
| 0.0857 | 11.6064 | 41760 | 0.1669 |
| 0.0855 | 11.8066 | 42480 | 0.1664 |