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yalhessi/lemexp-task1-v2-template_small-deepseek-coder-1.3b-base-8lr-12epochs-normal-eos
lemexp-task1-v2-template_small-deepseek-coder-1.3b-base-8lr-12epochs-normal-eos 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 an unknown 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 |
|---|---|---|---|
| 0.3895 | 0.2001 | 720 | 0.2991 |
| 0.2897 | 0.4001 | 1440 | 0.2637 |
| 0.2536 | 0.6002 | 2160 | 0.2559 |
| 0.2453 | 0.8002 | 2880 | 0.2426 |
| 0.2335 | 1.0003 | 3600 | 0.2423 |
| 0.2214 | 1.2003 | 4320 | 0.2344 |
| 0.217 | 1.4004 | 5040 | 0.2214 |
| 0.2154 | 1.6004 | 5760 | 0.2222 |
| 0.2133 | 1.8005 | 6480 | 0.2249 |
| 0.2095 | 2.0006 | 7200 | 0.2111 |
| 0.1984 | 2.2006 | 7920 | 0.2192 |
| 0.1973 | 2.4007 | 8640 | 0.2109 |
| 0.1938 | 2.6007 | 9360 | 0.1993 |
| 0.1941 | 2.8008 | 10080 | 0.2004 |
| 0.1962 | 3.0008 | 10800 | 0.1977 |
| 0.1813 | 3.2009 | 11520 | 0.2014 |
| 0.1822 | 3.4009 | 12240 | 0.2025 |
| 0.1814 | 3.6010 | 12960 | 0.1900 |
| 0.1788 | 3.8011 | 13680 | 0.1898 |
| 0.1795 | 4.0011 | 14400 | 0.1856 |
| 0.1678 | 4.2012 | 15120 | 0.1905 |
| 0.1693 | 4.4012 | 15840 | 0.1903 |
| 0.1675 | 4.6013 | 16560 | 0.1858 |
| 0.1681 | 4.8013 | 17280 | 0.1844 |
| 0.1635 | 5.0014 | 18000 | 0.1824 |
| 0.1537 | 5.2014 | 18720 | 0.1821 |
| 0.155 | 5.4015 | 19440 | 0.1762 |
| 0.1559 | 5.6016 | 20160 | 0.1837 |
| 0.1565 | 5.8016 | 20880 | 0.1725 |
| 0.151 | 6.0017 | 21600 | 0.1710 |
| 0.1444 | 6.2017 | 22320 | 0.1706 |
| 0.1447 | 6.4018 | 23040 | 0.1719 |
| 0.1429 | 6.6018 | 23760 | 0.1716 |
| 0.1428 | 6.8019 | 24480 | 0.1673 |
| 0.1407 | 7.0019 | 25200 | 0.1669 |
| 0.1335 | 7.2020 | 25920 | 0.1650 |
| 0.131 | 7.4021 | 26640 | 0.1670 |
| 0.1314 | 7.6021 | 27360 | 0.1617 |
| 0.1293 | 7.8022 | 28080 | 0.1615 |
| 0.1309 | 8.0022 | 28800 | 0.1615 |
| 0.1192 | 8.2023 | 29520 | 0.1590 |
| 0.1186 | 8.4023 | 30240 | 0.1567 |
| 0.1191 | 8.6024 | 30960 | 0.1597 |
| 0.1181 | 8.8024 | 31680 | 0.1533 |
| 0.1181 | 9.0025 | 32400 | 0.1540 |
| 0.1068 | 9.2026 | 33120 | 0.1538 |
| 0.1066 | 9.4026 | 33840 | 0.1517 |
| 0.1071 | 9.6027 | 34560 | 0.1535 |
| 0.107 | 9.8027 | 35280 | 0.1484 |
| 0.1038 | 10.0028 | 36000 | 0.1516 |
| 0.0952 | 10.2028 | 36720 | 0.1565 |
| 0.094 | 10.4029 | 37440 | 0.1533 |
| 0.0943 | 10.6029 | 38160 | 0.1556 |
| 0.094 | 10.8030 | 38880 | 0.1504 |
| 0.0942 | 11.0031 | 39600 | 0.1492 |
| 0.0856 | 11.2031 | 40320 | 0.1560 |
| 0.085 | 11.4032 | 41040 | 0.1553 |
| 0.0832 | 11.6032 | 41760 | 0.1560 |
| 0.0833 | 11.8033 | 42480 | 0.1544 |