--- base_model: codellama/CodeLlama-7b-Instruct-hf library_name: peft license: llama2 tags: - trl - sft - generated_from_trainer 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: 0.3065 ## 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 | |:-------------:|:------:|:-----:|:---------------:| | 0.189 | 2.1545 | 13000 | 0.2986 | | 0.2052 | 2.1627 | 13050 | 0.2999 | | 0.2068 | 2.1710 | 13100 | 0.2962 | | 0.1851 | 2.1793 | 13150 | 0.2998 | | 0.2012 | 2.1876 | 13200 | 0.2979 | | 0.182 | 2.1959 | 13250 | 0.2975 | | 0.2076 | 2.2042 | 13300 | 0.2975 | | 0.1883 | 2.2125 | 13350 | 0.2966 | | 0.1748 | 2.2207 | 13400 | 0.2962 | | 0.1783 | 2.2290 | 13450 | 0.2982 | | 0.1898 | 2.2373 | 13500 | 0.2976 | | 0.2092 | 2.2456 | 13550 | 0.2966 | | 0.1828 | 2.2539 | 13600 | 0.2955 | | 0.1997 | 2.2622 | 13650 | 0.2974 | | 0.1966 | 2.2705 | 13700 | 0.2975 | | 0.2008 | 2.2788 | 13750 | 0.2955 | | 0.2011 | 2.2870 | 13800 | 0.2946 | | 0.1937 | 2.2953 | 13850 | 0.2979 | | 0.2004 | 2.3036 | 13900 | 0.2956 | | 0.1816 | 2.3119 | 13950 | 0.2971 | | 0.1935 | 2.3202 | 14000 | 0.2957 | | 0.1864 | 2.3285 | 14050 | 0.2962 | | 0.1914 | 2.3368 | 14100 | 0.2945 | | 0.1939 | 2.3450 | 14150 | 0.2948 | | 0.1912 | 2.3533 | 14200 | 0.2954 | | 0.1862 | 2.3616 | 14250 | 0.2956 | | 0.1951 | 2.3699 | 14300 | 0.2950 | | 0.1958 | 2.3782 | 14350 | 0.2942 | | 0.1838 | 2.3865 | 14400 | 0.2940 | | 0.2191 | 2.3948 | 14450 | 0.2941 | | 0.1995 | 2.4030 | 14500 | 0.2922 | | 0.1954 | 2.4113 | 14550 | 0.2952 | | 0.1959 | 2.4196 | 14600 | 0.2969 | | 0.1849 | 2.4279 | 14650 | 0.2934 | | 0.1842 | 2.4362 | 14700 | 0.2938 | | 0.1858 | 2.4445 | 14750 | 0.2943 | | 0.1827 | 2.4528 | 14800 | 0.2942 | | 0.198 | 2.4611 | 14850 | 0.2921 | | 0.2311 | 2.4693 | 14900 | 0.2951 | | 0.2 | 2.4776 | 14950 | 0.2932 | | 0.1871 | 2.4859 | 15000 | 0.2935 | | 0.1833 | 2.4942 | 15050 | 0.2947 | | 0.2216 | 2.5025 | 15100 | 0.2921 | | 0.1829 | 2.5108 | 15150 | 0.2924 | | 0.1772 | 2.5191 | 15200 | 0.2916 | | 0.1806 | 2.5273 | 15250 | 0.2930 | | 0.2089 | 2.5356 | 15300 | 0.2920 | | 0.216 | 2.5439 | 15350 | 0.2922 | | 0.1763 | 2.5522 | 15400 | 0.2908 | | 0.1835 | 2.5605 | 15450 | 0.2924 | | 0.1928 | 2.5688 | 15500 | 0.2925 | | 0.1982 | 2.5771 | 15550 | 0.2895 | | 0.195 | 2.5853 | 15600 | 0.2907 | | 0.1791 | 2.5936 | 15650 | 0.2904 | | 0.1782 | 2.6019 | 15700 | 0.2915 | | 0.1871 | 2.6102 | 15750 | 0.2898 | | 0.1892 | 2.6185 | 15800 | 0.2916 | | 0.1921 | 2.6268 | 15850 | 0.2914 | | 0.1799 | 2.6351 | 15900 | 0.2890 | | 0.1923 | 2.6434 | 15950 | 0.2870 | | 0.2109 | 2.6516 | 16000 | 0.2910 | | 0.2088 | 2.6599 | 16050 | 0.2890 | | 0.1968 | 2.6682 | 16100 | 0.2880 | | 0.1938 | 2.6765 | 16150 | 0.2904 | | 0.1839 | 2.6848 | 16200 | 0.2921 | | 0.1764 | 2.6931 | 16250 | 0.2907 | | 0.1696 | 2.7014 | 16300 | 0.2897 | | 0.2276 | 2.7096 | 16350 | 0.2892 | | 0.1968 | 2.7179 | 16400 | 0.2875 | | 0.1991 | 2.7262 | 16450 | 0.2884 | | 0.1683 | 2.7345 | 16500 | 0.2883 | | 0.1765 | 2.7428 | 16550 | 0.2879 | | 0.1988 | 2.7511 | 16600 | 0.2883 | | 0.1921 | 2.7594 | 16650 | 0.2887 | | 0.1799 | 2.7676 | 16700 | 0.2894 | | 0.1907 | 2.7759 | 16750 | 0.2895 | | 0.1805 | 2.7842 | 16800 | 0.2894 | | 0.1595 | 2.7925 | 16850 | 0.2884 | | 0.1758 | 2.8008 | 16900 | 0.2870 | | 0.1768 | 2.8091 | 16950 | 0.2868 | | 0.2019 | 2.8174 | 17000 | 0.2859 | | 0.1985 | 2.8257 | 17050 | 0.2868 | | 0.2022 | 2.8339 | 17100 | 0.2865 | | 0.213 | 2.8422 | 17150 | 0.2858 | | 0.1809 | 2.8505 | 17200 | 0.2859 | | 0.1735 | 2.8588 | 17250 | 0.2868 | | 0.1929 | 2.8671 | 17300 | 0.2866 | | 0.1908 | 2.8754 | 17350 | 0.2862 | | 0.2051 | 2.8837 | 17400 | 0.2857 | | 0.1711 | 2.8919 | 17450 | 0.2863 | | 0.1926 | 2.9002 | 17500 | 0.2863 | | 0.1923 | 2.9085 | 17550 | 0.2847 | | 0.198 | 2.9168 | 17600 | 0.2870 | | 0.1882 | 2.9251 | 17650 | 0.2872 | | 0.1932 | 2.9334 | 17700 | 0.2846 | | 0.1839 | 2.9417 | 17750 | 0.2852 | | 0.2221 | 2.9500 | 17800 | 0.2836 | | 0.1874 | 2.9582 | 17850 | 0.2844 | | 0.1677 | 2.9665 | 17900 | 0.2851 | | 0.1802 | 2.9748 | 17950 | 0.2832 | | 0.1873 | 2.9831 | 18000 | 0.2846 | | 0.187 | 2.9914 | 18050 | 0.2856 | | 0.1837 | 2.9997 | 18100 | 0.2866 | | 0.1303 | 3.0080 | 18150 | 0.3016 | | 0.125 | 3.0162 | 18200 | 0.3052 | | 0.1264 | 3.0245 | 18250 | 0.3051 | | 0.1199 | 3.0328 | 18300 | 0.3092 | | 0.1403 | 3.0411 | 18350 | 0.3044 | | 0.128 | 3.0494 | 18400 | 0.3089 | | 0.1466 | 3.0577 | 18450 | 0.3043 | | 0.1307 | 3.0660 | 18500 | 0.3046 | | 0.135 | 3.0742 | 18550 | 0.3071 | | 0.1282 | 3.0825 | 18600 | 0.3053 | | 0.1343 | 3.0908 | 18650 | 0.3073 | | 0.1211 | 3.0991 | 18700 | 0.3069 | | 0.1382 | 3.1074 | 18750 | 0.3058 | | 0.1347 | 3.1157 | 18800 | 0.3064 | | 0.1246 | 3.1240 | 18850 | 0.3087 | | 0.1278 | 3.1323 | 18900 | 0.3075 | | 0.1233 | 3.1405 | 18950 | 0.3083 | | 0.1393 | 3.1488 | 19000 | 0.3069 | | 0.124 | 3.1571 | 19050 | 0.3049 | | 0.138 | 3.1654 | 19100 | 0.3064 | | 0.1355 | 3.1737 | 19150 | 0.3073 | | 0.1401 | 3.1820 | 19200 | 0.3084 | | 0.1196 | 3.1903 | 19250 | 0.3107 | | 0.1248 | 3.1985 | 19300 | 0.3088 | | 0.1342 | 3.2068 | 19350 | 0.3077 | | 0.1436 | 3.2151 | 19400 | 0.3062 | | 0.1467 | 3.2234 | 19450 | 0.3079 | | 0.1246 | 3.2317 | 19500 | 0.3095 | | 0.1293 | 3.2400 | 19550 | 0.3068 | | 0.1236 | 3.2483 | 19600 | 0.3100 | | 0.1385 | 3.2565 | 19650 | 0.3074 | | 0.1194 | 3.2648 | 19700 | 0.3068 | | 0.1283 | 3.2731 | 19750 | 0.3077 | | 0.1412 | 3.2814 | 19800 | 0.3064 | | 0.1209 | 3.2897 | 19850 | 0.3070 | | 0.145 | 3.2980 | 19900 | 0.3068 | | 0.1416 | 3.3063 | 19950 | 0.3052 | | 0.1138 | 3.3146 | 20000 | 0.3057 | | 0.1296 | 3.3228 | 20050 | 0.3076 | | 0.1419 | 3.3311 | 20100 | 0.3093 | | 0.1243 | 3.3394 | 20150 | 0.3083 | | 0.1206 | 3.3477 | 20200 | 0.3082 | | 0.1279 | 3.3560 | 20250 | 0.3070 | | 0.13 | 3.3643 | 20300 | 0.3070 | | 0.1284 | 3.3726 | 20350 | 0.3064 | | 0.1259 | 3.3808 | 20400 | 0.3074 | | 0.1255 | 3.3891 | 20450 | 0.3052 | | 0.1227 | 3.3974 | 20500 | 0.3062 | | 0.1381 | 3.4057 | 20550 | 0.3066 | | 0.1304 | 3.4140 | 20600 | 0.3065 | | 0.1388 | 3.4223 | 20650 | 0.3071 | | 0.1227 | 3.4306 | 20700 | 0.3065 | | 0.1185 | 3.4388 | 20750 | 0.3062 | | 0.1289 | 3.4471 | 20800 | 0.3083 | | 0.1367 | 3.4554 | 20850 | 0.3089 | | 0.1241 | 3.4637 | 20900 | 0.3070 | | 0.1137 | 3.4720 | 20950 | 0.3092 | | 0.1177 | 3.4803 | 21000 | 0.3080 | | 0.1369 | 3.4886 | 21050 | 0.3073 | | 0.126 | 3.4969 | 21100 | 0.3072 | | 0.1174 | 3.5051 | 21150 | 0.3074 | | 0.1235 | 3.5134 | 21200 | 0.3077 | | 0.1297 | 3.5217 | 21250 | 0.3068 | | 0.1377 | 3.5300 | 21300 | 0.3078 | | 0.1215 | 3.5383 | 21350 | 0.3062 | | 0.1358 | 3.5466 | 21400 | 0.3069 | | 0.1123 | 3.5549 | 21450 | 0.3076 | | 0.1358 | 3.5631 | 21500 | 0.3080 | | 0.1396 | 3.5714 | 21550 | 0.3061 | | 0.1216 | 3.5797 | 21600 | 0.3075 | | 0.1162 | 3.5880 | 21650 | 0.3081 | | 0.128 | 3.5963 | 21700 | 0.3061 | | 0.1173 | 3.6046 | 21750 | 0.3070 | | 0.125 | 3.6129 | 21800 | 0.3065 | | 0.1262 | 3.6211 | 21850 | 0.3077 | | 0.1249 | 3.6294 | 21900 | 0.3073 | | 0.1212 | 3.6377 | 21950 | 0.3071 | | 0.1188 | 3.6460 | 22000 | 0.3065 | | 0.1241 | 3.6543 | 22050 | 0.3054 | | 0.1302 | 3.6626 | 22100 | 0.3064 | | 0.1329 | 3.6709 | 22150 | 0.3055 | | 0.1276 | 3.6792 | 22200 | 0.3059 | | 0.1336 | 3.6874 | 22250 | 0.3082 | | 0.1173 | 3.6957 | 22300 | 0.3091 | | 0.1205 | 3.7040 | 22350 | 0.3075 | | 0.1196 | 3.7123 | 22400 | 0.3080 | | 0.1128 | 3.7206 | 22450 | 0.3066 | | 0.1188 | 3.7289 | 22500 | 0.3079 | | 0.1154 | 3.7372 | 22550 | 0.3076 | | 0.135 | 3.7454 | 22600 | 0.3076 | | 0.1341 | 3.7537 | 22650 | 0.3067 | | 0.1396 | 3.7620 | 22700 | 0.3062 | | 0.1336 | 3.7703 | 22750 | 0.3059 | | 0.1295 | 3.7786 | 22800 | 0.3066 | | 0.1226 | 3.7869 | 22850 | 0.3068 | | 0.1299 | 3.7952 | 22900 | 0.3067 | | 0.1355 | 3.8034 | 22950 | 0.3068 | | 0.1197 | 3.8117 | 23000 | 0.3069 | | 0.1196 | 3.8200 | 23050 | 0.3075 | | 0.1413 | 3.8283 | 23100 | 0.3066 | | 0.1377 | 3.8366 | 23150 | 0.3064 | | 0.1082 | 3.8449 | 23200 | 0.3074 | | 0.129 | 3.8532 | 23250 | 0.3074 | | 0.1279 | 3.8615 | 23300 | 0.3073 | | 0.1296 | 3.8697 | 23350 | 0.3064 | | 0.121 | 3.8780 | 23400 | 0.3072 | | 0.1267 | 3.8863 | 23450 | 0.3069 | | 0.1224 | 3.8946 | 23500 | 0.3071 | | 0.1187 | 3.9029 | 23550 | 0.3073 | | 0.1264 | 3.9112 | 23600 | 0.3074 | | 0.1252 | 3.9195 | 23650 | 0.3073 | | 0.1279 | 3.9277 | 23700 | 0.3072 | | 0.1262 | 3.9360 | 23750 | 0.3070 | | 0.1255 | 3.9443 | 23800 | 0.3068 | | 0.1227 | 3.9526 | 23850 | 0.3066 | | 0.1285 | 3.9609 | 23900 | 0.3068 | | 0.1225 | 3.9692 | 23950 | 0.3068 | | 0.1302 | 3.9775 | 24000 | 0.3066 | | 0.1501 | 3.9857 | 24050 | 0.3065 | | 0.126 | 3.9940 | 24100 | 0.3065 | ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1