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--- |
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base_model: codellama/CodeLlama-7b-Instruct-hf |
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library_name: peft |
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license: llama2 |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: Codellama-7b-lora-rps-adapter |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Codellama-7b-lora-rps-adapter |
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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: 0.3001 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 0.1811 | 2.3788 | 13000 | 0.2900 | |
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| 0.2081 | 2.3879 | 13050 | 0.2917 | |
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| 0.1862 | 2.3971 | 13100 | 0.2925 | |
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| 0.1806 | 2.4062 | 13150 | 0.2905 | |
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| 0.1844 | 2.4154 | 13200 | 0.2887 | |
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| 0.1862 | 2.4245 | 13250 | 0.2879 | |
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| 0.1783 | 2.4337 | 13300 | 0.2897 | |
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| 0.2143 | 2.4428 | 13350 | 0.2871 | |
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| 0.2068 | 2.4520 | 13400 | 0.2877 | |
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| 0.1815 | 2.4611 | 13450 | 0.2877 | |
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| 0.1657 | 2.4703 | 13500 | 0.2871 | |
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| 0.2198 | 2.4794 | 13550 | 0.2857 | |
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| 0.1709 | 2.4886 | 13600 | 0.2872 | |
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| 0.1741 | 2.4977 | 13650 | 0.2857 | |
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| 0.217 | 2.5069 | 13700 | 0.2847 | |
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| 0.2064 | 2.5160 | 13750 | 0.2861 | |
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| 0.1813 | 2.5252 | 13800 | 0.2856 | |
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| 0.1895 | 2.5343 | 13850 | 0.2867 | |
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| 0.1881 | 2.5435 | 13900 | 0.2856 | |
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| 0.1815 | 2.5526 | 13950 | 0.2872 | |
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| 0.1772 | 2.5618 | 14000 | 0.2849 | |
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| 0.1808 | 2.5709 | 14050 | 0.2858 | |
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| 0.1808 | 2.5801 | 14100 | 0.2851 | |
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| 0.1949 | 2.5892 | 14150 | 0.2865 | |
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| 0.1894 | 2.5984 | 14200 | 0.2859 | |
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| 0.18 | 2.6075 | 14250 | 0.2846 | |
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| 0.1765 | 2.6167 | 14300 | 0.2842 | |
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| 0.178 | 2.6258 | 14350 | 0.2841 | |
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| 0.2042 | 2.6349 | 14400 | 0.2844 | |
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| 0.2073 | 2.6441 | 14450 | 0.2854 | |
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| 0.1748 | 2.6532 | 14500 | 0.2842 | |
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| 0.2015 | 2.6624 | 14550 | 0.2838 | |
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| 0.1968 | 2.6715 | 14600 | 0.2836 | |
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| 0.1919 | 2.6807 | 14650 | 0.2846 | |
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| 0.1811 | 2.6898 | 14700 | 0.2834 | |
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| 0.1751 | 2.6990 | 14750 | 0.2852 | |
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| 0.1889 | 2.7081 | 14800 | 0.2818 | |
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| 0.184 | 2.7173 | 14850 | 0.2833 | |
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| 0.1861 | 2.7264 | 14900 | 0.2852 | |
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| 0.1997 | 2.7356 | 14950 | 0.2835 | |
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| 0.1638 | 2.7447 | 15000 | 0.2839 | |
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| 0.1941 | 2.7539 | 15050 | 0.2830 | |
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| 0.1759 | 2.7630 | 15100 | 0.2833 | |
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| 0.1803 | 2.7722 | 15150 | 0.2837 | |
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| 0.1727 | 2.7813 | 15200 | 0.2834 | |
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| 0.2242 | 2.7905 | 15250 | 0.2816 | |
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| 0.1911 | 2.7996 | 15300 | 0.2849 | |
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| 0.196 | 2.8088 | 15350 | 0.2825 | |
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| 0.196 | 2.8179 | 15400 | 0.2815 | |
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| 0.1701 | 2.8271 | 15450 | 0.2800 | |
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| 0.1771 | 2.8362 | 15500 | 0.2823 | |
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| 0.187 | 2.8454 | 15550 | 0.2816 | |
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| 0.1804 | 2.8545 | 15600 | 0.2823 | |
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| 0.1722 | 2.8637 | 15650 | 0.2805 | |
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| 0.1957 | 2.8728 | 15700 | 0.2777 | |
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| 0.1725 | 2.8820 | 15750 | 0.2794 | |
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| 0.1945 | 2.8911 | 15800 | 0.2793 | |
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| 0.1884 | 2.9003 | 15850 | 0.2781 | |
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| 0.1687 | 2.9094 | 15900 | 0.2797 | |
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| 0.1965 | 2.9186 | 15950 | 0.2783 | |
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| 0.1669 | 2.9277 | 16000 | 0.2789 | |
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| 0.1827 | 2.9369 | 16050 | 0.2781 | |
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| 0.1663 | 2.9460 | 16100 | 0.2779 | |
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| 0.1665 | 2.9552 | 16150 | 0.2807 | |
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| 0.1812 | 2.9643 | 16200 | 0.2796 | |
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| 0.1995 | 2.9735 | 16250 | 0.2784 | |
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| 0.1792 | 2.9826 | 16300 | 0.2786 | |
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| 0.184 | 2.9918 | 16350 | 0.2786 | |
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| 0.1727 | 3.0009 | 16400 | 0.2781 | |
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| 0.1239 | 3.0101 | 16450 | 0.3034 | |
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| 0.1256 | 3.0192 | 16500 | 0.2975 | |
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| 0.1732 | 3.0284 | 16550 | 0.2993 | |
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| 0.1216 | 3.0375 | 16600 | 0.2999 | |
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| 0.1283 | 3.0467 | 16650 | 0.3015 | |
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| 0.1225 | 3.0558 | 16700 | 0.3011 | |
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| 0.1284 | 3.0650 | 16750 | 0.3038 | |
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| 0.1246 | 3.0741 | 16800 | 0.2990 | |
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| 0.1277 | 3.0833 | 16850 | 0.3011 | |
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| 0.1237 | 3.0924 | 16900 | 0.3023 | |
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| 0.1174 | 3.1016 | 16950 | 0.3053 | |
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| 0.1202 | 3.1107 | 17000 | 0.3037 | |
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| 0.121 | 3.1199 | 17050 | 0.3013 | |
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| 0.126 | 3.1290 | 17100 | 0.3034 | |
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| 0.1188 | 3.1382 | 17150 | 0.3008 | |
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| 0.1346 | 3.1473 | 17200 | 0.2997 | |
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| 0.1428 | 3.1565 | 17250 | 0.3011 | |
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| 0.1263 | 3.1656 | 17300 | 0.3038 | |
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| 0.1391 | 3.1747 | 17350 | 0.3044 | |
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| 0.128 | 3.1839 | 17400 | 0.3026 | |
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| 0.1367 | 3.1930 | 17450 | 0.3040 | |
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| 0.1214 | 3.2022 | 17500 | 0.3046 | |
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| 0.1253 | 3.2113 | 17550 | 0.3049 | |
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| 0.1157 | 3.2205 | 17600 | 0.3048 | |
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| 0.1354 | 3.2296 | 17650 | 0.3033 | |
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| 0.1233 | 3.2388 | 17700 | 0.3001 | |
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| 0.1139 | 3.2479 | 17750 | 0.3028 | |
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| 0.1297 | 3.2571 | 17800 | 0.3026 | |
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| 0.1339 | 3.2662 | 17850 | 0.3014 | |
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| 0.1244 | 3.2754 | 17900 | 0.3041 | |
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| 0.1351 | 3.2845 | 17950 | 0.3051 | |
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| 0.1147 | 3.2937 | 18000 | 0.3036 | |
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| 0.1251 | 3.3028 | 18050 | 0.3031 | |
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| 0.1158 | 3.3120 | 18100 | 0.3030 | |
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| 0.1192 | 3.3211 | 18150 | 0.3043 | |
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| 0.1293 | 3.3303 | 18200 | 0.3048 | |
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| 0.1255 | 3.3394 | 18250 | 0.3006 | |
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| 0.1306 | 3.3486 | 18300 | 0.3016 | |
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| 0.1195 | 3.3577 | 18350 | 0.3029 | |
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| 0.1273 | 3.3669 | 18400 | 0.3043 | |
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| 0.1127 | 3.3760 | 18450 | 0.3044 | |
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| 0.1315 | 3.3852 | 18500 | 0.3040 | |
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| 0.1138 | 3.3943 | 18550 | 0.3018 | |
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| 0.1277 | 3.4035 | 18600 | 0.3039 | |
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| 0.1139 | 3.4126 | 18650 | 0.3031 | |
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| 0.1244 | 3.4218 | 18700 | 0.3019 | |
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| 0.1323 | 3.4309 | 18750 | 0.3013 | |
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| 0.128 | 3.4401 | 18800 | 0.3015 | |
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| 0.1232 | 3.4492 | 18850 | 0.3035 | |
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| 0.1276 | 3.4584 | 18900 | 0.3032 | |
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| 0.1258 | 3.4675 | 18950 | 0.3037 | |
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| 0.1376 | 3.4767 | 19000 | 0.3022 | |
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| 0.1337 | 3.4858 | 19050 | 0.3019 | |
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| 0.1146 | 3.4950 | 19100 | 0.3028 | |
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| 0.115 | 3.5041 | 19150 | 0.3020 | |
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| 0.1229 | 3.5133 | 19200 | 0.3014 | |
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| 0.1324 | 3.5224 | 19250 | 0.3015 | |
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| 0.1294 | 3.5316 | 19300 | 0.3026 | |
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| 0.1284 | 3.5407 | 19350 | 0.3004 | |
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| 0.1247 | 3.5499 | 19400 | 0.3029 | |
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| 0.1227 | 3.5590 | 19450 | 0.3018 | |
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| 0.1192 | 3.5682 | 19500 | 0.3007 | |
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| 0.1204 | 3.5773 | 19550 | 0.3021 | |
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| 0.1124 | 3.5865 | 19600 | 0.3035 | |
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| 0.1254 | 3.5956 | 19650 | 0.3028 | |
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| 0.1225 | 3.6048 | 19700 | 0.3018 | |
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| 0.1198 | 3.6139 | 19750 | 0.3027 | |
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| 0.1245 | 3.6231 | 19800 | 0.3023 | |
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| 0.1175 | 3.6322 | 19850 | 0.3031 | |
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| 0.113 | 3.6414 | 19900 | 0.3034 | |
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| 0.1241 | 3.6505 | 19950 | 0.3028 | |
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| 0.1333 | 3.6597 | 20000 | 0.3028 | |
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| 0.1159 | 3.6688 | 20050 | 0.3016 | |
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| 0.1208 | 3.6780 | 20100 | 0.3015 | |
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| 0.1327 | 3.6871 | 20150 | 0.3002 | |
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| 0.1176 | 3.6962 | 20200 | 0.3020 | |
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| 0.1149 | 3.7054 | 20250 | 0.3020 | |
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| 0.1126 | 3.7145 | 20300 | 0.3013 | |
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| 0.1238 | 3.7237 | 20350 | 0.3001 | |
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| 0.1129 | 3.7328 | 20400 | 0.3006 | |
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| 0.1259 | 3.7420 | 20450 | 0.3004 | |
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| 0.1186 | 3.7511 | 20500 | 0.3004 | |
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| 0.1257 | 3.7603 | 20550 | 0.3006 | |
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| 0.1265 | 3.7694 | 20600 | 0.3006 | |
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| 0.1257 | 3.7786 | 20650 | 0.3005 | |
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| 0.1167 | 3.7877 | 20700 | 0.3000 | |
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| 0.1121 | 3.7969 | 20750 | 0.3010 | |
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| 0.1201 | 3.8060 | 20800 | 0.3002 | |
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| 0.1299 | 3.8152 | 20850 | 0.3004 | |
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| 0.1325 | 3.8243 | 20900 | 0.2993 | |
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| 0.1221 | 3.8335 | 20950 | 0.2999 | |
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| 0.1166 | 3.8426 | 21000 | 0.3004 | |
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| 0.1149 | 3.8518 | 21050 | 0.3009 | |
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| 0.1094 | 3.8609 | 21100 | 0.3007 | |
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| 0.1267 | 3.8701 | 21150 | 0.2994 | |
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| 0.1246 | 3.8792 | 21200 | 0.2995 | |
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| 0.1217 | 3.8884 | 21250 | 0.2994 | |
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| 0.1133 | 3.8975 | 21300 | 0.2993 | |
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| 0.1106 | 3.9067 | 21350 | 0.2998 | |
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| 0.116 | 3.9158 | 21400 | 0.3001 | |
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| 0.1155 | 3.9250 | 21450 | 0.2998 | |
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| 0.1249 | 3.9341 | 21500 | 0.3002 | |
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| 0.1203 | 3.9433 | 21550 | 0.3003 | |
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| 0.1307 | 3.9524 | 21600 | 0.3001 | |
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| 0.1328 | 3.9616 | 21650 | 0.3001 | |
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| 0.1202 | 3.9707 | 21700 | 0.3001 | |
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| 0.1283 | 3.9799 | 21750 | 0.3001 | |
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| 0.1133 | 3.9890 | 21800 | 0.3001 | |
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| 0.1198 | 3.9982 | 21850 | 0.3001 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
