--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: codellama/CodeLlama-7b-Instruct-hf model-index: - name: Codellama-7b-lora-rps-adapter results: [] --- # Codellama-7b-lora-rps-adapter 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. It achieves the following results on the evaluation set: - Loss: 0.2878 ## 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.2463 | 1.65 | 8000 | 0.2803 | | 0.2618 | 1.66 | 8050 | 0.2798 | | 0.2549 | 1.67 | 8100 | 0.2802 | | 0.2396 | 1.68 | 8150 | 0.2786 | | 0.2349 | 1.69 | 8200 | 0.2778 | | 0.2312 | 1.7 | 8250 | 0.2787 | | 0.2541 | 1.71 | 8300 | 0.2779 | | 0.2596 | 1.72 | 8350 | 0.2765 | | 0.2341 | 1.73 | 8400 | 0.2763 | | 0.2706 | 1.74 | 8450 | 0.2760 | | 0.2453 | 1.75 | 8500 | 0.2765 | | 0.2324 | 1.76 | 8550 | 0.2758 | | 0.276 | 1.77 | 8600 | 0.2740 | | 0.2688 | 1.78 | 8650 | 0.2739 | | 0.2462 | 1.79 | 8700 | 0.2766 | | 0.2741 | 1.8 | 8750 | 0.2740 | | 0.2457 | 1.81 | 8800 | 0.2740 | | 0.265 | 1.82 | 8850 | 0.2732 | | 0.2451 | 1.83 | 8900 | 0.2733 | | 0.2169 | 1.84 | 8950 | 0.2728 | | 0.2415 | 1.85 | 9000 | 0.2728 | | 0.2338 | 1.86 | 9050 | 0.2737 | | 0.2293 | 1.87 | 9100 | 0.2740 | | 0.2235 | 1.88 | 9150 | 0.2722 | | 0.2773 | 1.89 | 9200 | 0.2721 | | 0.2544 | 1.9 | 9250 | 0.2709 | | 0.2474 | 1.91 | 9300 | 0.2706 | | 0.2461 | 1.92 | 9350 | 0.2704 | | 0.2716 | 1.93 | 9400 | 0.2693 | | 0.2773 | 1.94 | 9450 | 0.2695 | | 0.2282 | 1.96 | 9500 | 0.2700 | | 0.2511 | 1.97 | 9550 | 0.2701 | | 0.2652 | 1.98 | 9600 | 0.2697 | | 0.2615 | 1.99 | 9650 | 0.2699 | | 0.2263 | 2.0 | 9700 | 0.2691 | | 0.2136 | 2.01 | 9750 | 0.2807 | | 0.1751 | 2.02 | 9800 | 0.2778 | | 0.1891 | 2.03 | 9850 | 0.2796 | | 0.1758 | 2.04 | 9900 | 0.2822 | | 0.1901 | 2.05 | 9950 | 0.2780 | | 0.2105 | 2.06 | 10000 | 0.2800 | | 0.1901 | 2.07 | 10050 | 0.2794 | | 0.1891 | 2.08 | 10100 | 0.2787 | | 0.1772 | 2.09 | 10150 | 0.2834 | | 0.1761 | 2.1 | 10200 | 0.2805 | | 0.1792 | 2.11 | 10250 | 0.2805 | | 0.1785 | 2.12 | 10300 | 0.2810 | | 0.1899 | 2.13 | 10350 | 0.2821 | | 0.174 | 2.14 | 10400 | 0.2813 | | 0.1796 | 2.15 | 10450 | 0.2818 | | 0.1658 | 2.16 | 10500 | 0.2816 | | 0.1832 | 2.17 | 10550 | 0.2787 | | 0.2024 | 2.18 | 10600 | 0.2795 | | 0.1943 | 2.19 | 10650 | 0.2801 | | 0.1772 | 2.2 | 10700 | 0.2798 | | 0.1744 | 2.21 | 10750 | 0.2811 | | 0.1929 | 2.22 | 10800 | 0.2792 | | 0.1684 | 2.23 | 10850 | 0.2793 | | 0.1672 | 2.24 | 10900 | 0.2783 | | 0.1922 | 2.25 | 10950 | 0.2766 | | 0.1735 | 2.26 | 11000 | 0.2782 | | 0.1773 | 2.27 | 11050 | 0.2788 | | 0.1736 | 2.28 | 11100 | 0.2788 | | 0.1746 | 2.29 | 11150 | 0.2786 | | 0.1769 | 2.31 | 11200 | 0.2785 | | 0.1797 | 2.32 | 11250 | 0.2769 | | 0.197 | 2.33 | 11300 | 0.2757 | | 0.1734 | 2.34 | 11350 | 0.2767 | | 0.1941 | 2.35 | 11400 | 0.2774 | | 0.1742 | 2.36 | 11450 | 0.2783 | | 0.186 | 2.37 | 11500 | 0.2779 | | 0.1743 | 2.38 | 11550 | 0.2759 | | 0.1836 | 2.39 | 11600 | 0.2772 | | 0.1876 | 2.4 | 11650 | 0.2768 | | 0.1912 | 2.41 | 11700 | 0.2748 | | 0.2035 | 2.42 | 11750 | 0.2781 | | 0.1742 | 2.43 | 11800 | 0.2780 | | 0.1612 | 2.44 | 11850 | 0.2759 | | 0.1674 | 2.45 | 11900 | 0.2775 | | 0.1725 | 2.46 | 11950 | 0.2745 | | 0.1877 | 2.47 | 12000 | 0.2756 | | 0.1869 | 2.48 | 12050 | 0.2753 | | 0.1803 | 2.49 | 12100 | 0.2749 | | 0.1747 | 2.5 | 12150 | 0.2761 | | 0.18 | 2.51 | 12200 | 0.2754 | | 0.181 | 2.52 | 12250 | 0.2746 | | 0.1707 | 2.53 | 12300 | 0.2746 | | 0.1875 | 2.54 | 12350 | 0.2749 | | 0.1781 | 2.55 | 12400 | 0.2758 | | 0.1644 | 2.56 | 12450 | 0.2753 | | 0.1995 | 2.57 | 12500 | 0.2727 | | 0.1662 | 2.58 | 12550 | 0.2744 | | 0.1847 | 2.59 | 12600 | 0.2738 | | 0.1771 | 2.6 | 12650 | 0.2731 | | 0.1708 | 2.61 | 12700 | 0.2743 | | 0.1759 | 2.62 | 12750 | 0.2748 | | 0.1899 | 2.63 | 12800 | 0.2743 | | 0.2015 | 2.64 | 12850 | 0.2744 | | 0.1803 | 2.65 | 12900 | 0.2726 | | 0.1588 | 2.67 | 12950 | 0.2750 | | 0.1564 | 2.68 | 13000 | 0.2728 | | 0.1773 | 2.69 | 13050 | 0.2715 | | 0.1707 | 2.7 | 13100 | 0.2736 | | 0.1746 | 2.71 | 13150 | 0.2738 | | 0.1946 | 2.72 | 13200 | 0.2704 | | 0.1669 | 2.73 | 13250 | 0.2740 | | 0.1925 | 2.74 | 13300 | 0.2721 | | 0.1622 | 2.75 | 13350 | 0.2728 | | 0.1741 | 2.76 | 13400 | 0.2705 | | 0.1838 | 2.77 | 13450 | 0.2730 | | 0.1755 | 2.78 | 13500 | 0.2712 | | 0.1718 | 2.79 | 13550 | 0.2719 | | 0.1723 | 2.8 | 13600 | 0.2717 | | 0.164 | 2.81 | 13650 | 0.2720 | | 0.1881 | 2.82 | 13700 | 0.2727 | | 0.1639 | 2.83 | 13750 | 0.2690 | | 0.1703 | 2.84 | 13800 | 0.2716 | | 0.1678 | 2.85 | 13850 | 0.2719 | | 0.1737 | 2.86 | 13900 | 0.2719 | | 0.1836 | 2.87 | 13950 | 0.2707 | | 0.1988 | 2.88 | 14000 | 0.2707 | | 0.1779 | 2.89 | 14050 | 0.2697 | | 0.1679 | 2.9 | 14100 | 0.2706 | | 0.1837 | 2.91 | 14150 | 0.2679 | | 0.1561 | 2.92 | 14200 | 0.2683 | | 0.1693 | 2.93 | 14250 | 0.2683 | | 0.2069 | 2.94 | 14300 | 0.2690 | | 0.1703 | 2.95 | 14350 | 0.2692 | | 0.1909 | 2.96 | 14400 | 0.2675 | | 0.2116 | 2.97 | 14450 | 0.2687 | | 0.1787 | 2.98 | 14500 | 0.2683 | | 0.1569 | 2.99 | 14550 | 0.2683 | | 0.1433 | 3.0 | 14600 | 0.2770 | | 0.126 | 3.02 | 14650 | 0.2872 | | 0.1211 | 3.03 | 14700 | 0.2918 | | 0.1178 | 3.04 | 14750 | 0.2927 | | 0.117 | 3.05 | 14800 | 0.2891 | | 0.1362 | 3.06 | 14850 | 0.2891 | | 0.1128 | 3.07 | 14900 | 0.2904 | | 0.1162 | 3.08 | 14950 | 0.2897 | | 0.1189 | 3.09 | 15000 | 0.2930 | | 0.1267 | 3.1 | 15050 | 0.2895 | | 0.1234 | 3.11 | 15100 | 0.2902 | | 0.1241 | 3.12 | 15150 | 0.2895 | | 0.1302 | 3.13 | 15200 | 0.2918 | | 0.1066 | 3.14 | 15250 | 0.2930 | | 0.1194 | 3.15 | 15300 | 0.2938 | | 0.1209 | 3.16 | 15350 | 0.2931 | | 0.1148 | 3.17 | 15400 | 0.2926 | | 0.1344 | 3.18 | 15450 | 0.2918 | | 0.1145 | 3.19 | 15500 | 0.2894 | | 0.1171 | 3.2 | 15550 | 0.2902 | | 0.1319 | 3.21 | 15600 | 0.2894 | | 0.1196 | 3.22 | 15650 | 0.2889 | | 0.1161 | 3.23 | 15700 | 0.2887 | | 0.124 | 3.24 | 15750 | 0.2900 | | 0.1188 | 3.25 | 15800 | 0.2910 | | 0.1222 | 3.26 | 15850 | 0.2901 | | 0.1044 | 3.27 | 15900 | 0.2905 | | 0.1262 | 3.28 | 15950 | 0.2888 | | 0.1107 | 3.29 | 16000 | 0.2903 | | 0.1169 | 3.3 | 16050 | 0.2905 | | 0.1067 | 3.31 | 16100 | 0.2892 | | 0.1299 | 3.32 | 16150 | 0.2892 | | 0.1353 | 3.33 | 16200 | 0.2865 | | 0.1305 | 3.34 | 16250 | 0.2903 | | 0.1262 | 3.35 | 16300 | 0.2894 | | 0.1363 | 3.36 | 16350 | 0.2878 | | 0.1202 | 3.38 | 16400 | 0.2877 | | 0.1208 | 3.39 | 16450 | 0.2876 | | 0.1234 | 3.4 | 16500 | 0.2880 | | 0.1186 | 3.41 | 16550 | 0.2870 | | 0.1171 | 3.42 | 16600 | 0.2875 | | 0.1225 | 3.43 | 16650 | 0.2883 | | 0.1095 | 3.44 | 16700 | 0.2877 | | 0.1269 | 3.45 | 16750 | 0.2888 | | 0.1212 | 3.46 | 16800 | 0.2894 | | 0.1434 | 3.47 | 16850 | 0.2886 | | 0.1349 | 3.48 | 16900 | 0.2888 | | 0.1166 | 3.49 | 16950 | 0.2887 | | 0.1094 | 3.5 | 17000 | 0.2875 | | 0.1188 | 3.51 | 17050 | 0.2901 | | 0.1227 | 3.52 | 17100 | 0.2865 | | 0.1204 | 3.53 | 17150 | 0.2871 | | 0.13 | 3.54 | 17200 | 0.2872 | | 0.1197 | 3.55 | 17250 | 0.2885 | | 0.1269 | 3.56 | 17300 | 0.2885 | | 0.1144 | 3.57 | 17350 | 0.2887 | | 0.1133 | 3.58 | 17400 | 0.2897 | | 0.1209 | 3.59 | 17450 | 0.2886 | | 0.1216 | 3.6 | 17500 | 0.2887 | | 0.118 | 3.61 | 17550 | 0.2884 | | 0.1245 | 3.62 | 17600 | 0.2879 | | 0.1222 | 3.63 | 17650 | 0.2887 | | 0.1231 | 3.64 | 17700 | 0.2892 | | 0.1229 | 3.65 | 17750 | 0.2881 | | 0.1301 | 3.66 | 17800 | 0.2879 | | 0.1201 | 3.67 | 17850 | 0.2878 | | 0.1146 | 3.68 | 17900 | 0.2898 | | 0.1176 | 3.69 | 17950 | 0.2897 | | 0.1157 | 3.7 | 18000 | 0.2896 | | 0.1283 | 3.71 | 18050 | 0.2894 | | 0.1144 | 3.73 | 18100 | 0.2888 | | 0.1106 | 3.74 | 18150 | 0.2901 | | 0.1224 | 3.75 | 18200 | 0.2891 | | 0.1252 | 3.76 | 18250 | 0.2895 | | 0.1163 | 3.77 | 18300 | 0.2886 | | 0.1213 | 3.78 | 18350 | 0.2883 | | 0.1177 | 3.79 | 18400 | 0.2883 | | 0.1254 | 3.8 | 18450 | 0.2893 | | 0.136 | 3.81 | 18500 | 0.2892 | | 0.1239 | 3.82 | 18550 | 0.2879 | | 0.1229 | 3.83 | 18600 | 0.2882 | | 0.1164 | 3.84 | 18650 | 0.2882 | | 0.1354 | 3.85 | 18700 | 0.2873 | | 0.1167 | 3.86 | 18750 | 0.2873 | | 0.1161 | 3.87 | 18800 | 0.2876 | | 0.112 | 3.88 | 18850 | 0.2883 | | 0.1053 | 3.89 | 18900 | 0.2882 | | 0.1114 | 3.9 | 18950 | 0.2879 | | 0.1104 | 3.91 | 19000 | 0.2877 | | 0.1095 | 3.92 | 19050 | 0.2874 | | 0.123 | 3.93 | 19100 | 0.2881 | | 0.1163 | 3.94 | 19150 | 0.2879 | | 0.1261 | 3.95 | 19200 | 0.2881 | | 0.1151 | 3.96 | 19250 | 0.2880 | | 0.133 | 3.97 | 19300 | 0.2879 | | 0.1182 | 3.98 | 19350 | 0.2878 | | 0.1144 | 3.99 | 19400 | 0.2878 | ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2