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---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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: 0.2965
## 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 |
|:-------------:|:------:|:-----:|:---------------:|
| 0.1823 | 2.4463 | 14000 | 0.2873 |
| 0.1939 | 2.4550 | 14050 | 0.2878 |
| 0.1729 | 2.4637 | 14100 | 0.2896 |
| 0.195 | 2.4725 | 14150 | 0.2871 |
| 0.1865 | 2.4812 | 14200 | 0.2867 |
| 0.1932 | 2.4900 | 14250 | 0.2889 |
| 0.188 | 2.4987 | 14300 | 0.2876 |
| 0.2059 | 2.5074 | 14350 | 0.2861 |
| 0.1771 | 2.5162 | 14400 | 0.2876 |
| 0.1829 | 2.5249 | 14450 | 0.2863 |
| 0.185 | 2.5336 | 14500 | 0.2863 |
| 0.1968 | 2.5424 | 14550 | 0.2844 |
| 0.1806 | 2.5511 | 14600 | 0.2852 |
| 0.1938 | 2.5598 | 14650 | 0.2868 |
| 0.1897 | 2.5686 | 14700 | 0.2855 |
| 0.1934 | 2.5773 | 14750 | 0.2867 |
| 0.1823 | 2.5861 | 14800 | 0.2858 |
| 0.1815 | 2.5948 | 14850 | 0.2858 |
| 0.2005 | 2.6035 | 14900 | 0.2841 |
| 0.1738 | 2.6123 | 14950 | 0.2858 |
| 0.178 | 2.6210 | 15000 | 0.2845 |
| 0.2055 | 2.6297 | 15050 | 0.2834 |
| 0.1926 | 2.6385 | 15100 | 0.2855 |
| 0.207 | 2.6472 | 15150 | 0.2846 |
| 0.1847 | 2.6559 | 15200 | 0.2840 |
| 0.185 | 2.6647 | 15250 | 0.2840 |
| 0.1907 | 2.6734 | 15300 | 0.2817 |
| 0.1997 | 2.6822 | 15350 | 0.2814 |
| 0.1698 | 2.6909 | 15400 | 0.2829 |
| 0.178 | 2.6996 | 15450 | 0.2824 |
| 0.1954 | 2.7084 | 15500 | 0.2823 |
| 0.1974 | 2.7171 | 15550 | 0.2817 |
| 0.1848 | 2.7258 | 15600 | 0.2818 |
| 0.1841 | 2.7346 | 15650 | 0.2830 |
| 0.193 | 2.7433 | 15700 | 0.2825 |
| 0.2192 | 2.7521 | 15750 | 0.2801 |
| 0.1785 | 2.7608 | 15800 | 0.2803 |
| 0.1866 | 2.7695 | 15850 | 0.2815 |
| 0.1888 | 2.7783 | 15900 | 0.2804 |
| 0.1838 | 2.7870 | 15950 | 0.2823 |
| 0.198 | 2.7957 | 16000 | 0.2823 |
| 0.1764 | 2.8045 | 16050 | 0.2812 |
| 0.2103 | 2.8132 | 16100 | 0.2806 |
| 0.1941 | 2.8219 | 16150 | 0.2825 |
| 0.1938 | 2.8307 | 16200 | 0.2811 |
| 0.1902 | 2.8394 | 16250 | 0.2807 |
| 0.1922 | 2.8482 | 16300 | 0.2797 |
| 0.1777 | 2.8569 | 16350 | 0.2798 |
| 0.1994 | 2.8656 | 16400 | 0.2796 |
| 0.2168 | 2.8744 | 16450 | 0.2785 |
| 0.1896 | 2.8831 | 16500 | 0.2798 |
| 0.1954 | 2.8918 | 16550 | 0.2780 |
| 0.1988 | 2.9006 | 16600 | 0.2791 |
| 0.2163 | 2.9093 | 16650 | 0.2781 |
| 0.1937 | 2.9180 | 16700 | 0.2786 |
| 0.1752 | 2.9268 | 16750 | 0.2800 |
| 0.1643 | 2.9355 | 16800 | 0.2799 |
| 0.1872 | 2.9443 | 16850 | 0.2779 |
| 0.1969 | 2.9530 | 16900 | 0.2770 |
| 0.1825 | 2.9617 | 16950 | 0.2784 |
| 0.185 | 2.9705 | 17000 | 0.2780 |
| 0.1943 | 2.9792 | 17050 | 0.2785 |
| 0.1939 | 2.9879 | 17100 | 0.2761 |
| 0.1724 | 2.9967 | 17150 | 0.2781 |
| 0.1541 | 3.0054 | 17200 | 0.2892 |
| 0.1171 | 3.0142 | 17250 | 0.2950 |
| 0.128 | 3.0229 | 17300 | 0.2991 |
| 0.1143 | 3.0316 | 17350 | 0.2984 |
| 0.1453 | 3.0404 | 17400 | 0.2967 |
| 0.1212 | 3.0491 | 17450 | 0.3002 |
| 0.1293 | 3.0578 | 17500 | 0.2962 |
| 0.1504 | 3.0666 | 17550 | 0.2994 |
| 0.1366 | 3.0753 | 17600 | 0.2987 |
| 0.1224 | 3.0840 | 17650 | 0.2992 |
| 0.1347 | 3.0928 | 17700 | 0.2974 |
| 0.1283 | 3.1015 | 17750 | 0.3009 |
| 0.1188 | 3.1103 | 17800 | 0.2991 |
| 0.1382 | 3.1190 | 17850 | 0.3008 |
| 0.1336 | 3.1277 | 17900 | 0.3004 |
| 0.1159 | 3.1365 | 17950 | 0.2976 |
| 0.1237 | 3.1452 | 18000 | 0.2985 |
| 0.1275 | 3.1539 | 18050 | 0.2994 |
| 0.1219 | 3.1627 | 18100 | 0.3012 |
| 0.1351 | 3.1714 | 18150 | 0.3006 |
| 0.13 | 3.1802 | 18200 | 0.2999 |
| 0.1153 | 3.1889 | 18250 | 0.2992 |
| 0.127 | 3.1976 | 18300 | 0.2973 |
| 0.1383 | 3.2064 | 18350 | 0.2970 |
| 0.1321 | 3.2151 | 18400 | 0.2988 |
| 0.135 | 3.2238 | 18450 | 0.2978 |
| 0.1231 | 3.2326 | 18500 | 0.2997 |
| 0.134 | 3.2413 | 18550 | 0.3012 |
| 0.1183 | 3.2500 | 18600 | 0.3012 |
| 0.1353 | 3.2588 | 18650 | 0.3008 |
| 0.1378 | 3.2675 | 18700 | 0.2974 |
| 0.1304 | 3.2763 | 18750 | 0.2966 |
| 0.1379 | 3.2850 | 18800 | 0.2957 |
| 0.129 | 3.2937 | 18850 | 0.2973 |
| 0.133 | 3.3025 | 18900 | 0.2975 |
| 0.134 | 3.3112 | 18950 | 0.2993 |
| 0.1177 | 3.3199 | 19000 | 0.2978 |
| 0.1432 | 3.3287 | 19050 | 0.2951 |
| 0.1379 | 3.3374 | 19100 | 0.2966 |
| 0.1239 | 3.3461 | 19150 | 0.2976 |
| 0.1323 | 3.3549 | 19200 | 0.2976 |
| 0.1291 | 3.3636 | 19250 | 0.2973 |
| 0.1333 | 3.3724 | 19300 | 0.2976 |
| 0.1265 | 3.3811 | 19350 | 0.2960 |
| 0.1303 | 3.3898 | 19400 | 0.2985 |
| 0.1344 | 3.3986 | 19450 | 0.2967 |
| 0.1389 | 3.4073 | 19500 | 0.2956 |
| 0.1246 | 3.4160 | 19550 | 0.2952 |
| 0.1209 | 3.4248 | 19600 | 0.2971 |
| 0.1153 | 3.4335 | 19650 | 0.2998 |
| 0.1139 | 3.4423 | 19700 | 0.2975 |
| 0.1304 | 3.4510 | 19750 | 0.2994 |
| 0.1241 | 3.4597 | 19800 | 0.2983 |
| 0.1235 | 3.4685 | 19850 | 0.2977 |
| 0.1408 | 3.4772 | 19900 | 0.2964 |
| 0.1338 | 3.4859 | 19950 | 0.2978 |
| 0.1296 | 3.4947 | 20000 | 0.2974 |
| 0.1165 | 3.5034 | 20050 | 0.2980 |
| 0.118 | 3.5121 | 20100 | 0.2982 |
| 0.1411 | 3.5209 | 20150 | 0.2962 |
| 0.1327 | 3.5296 | 20200 | 0.2976 |
| 0.1364 | 3.5384 | 20250 | 0.2974 |
| 0.1285 | 3.5471 | 20300 | 0.2961 |
| 0.1293 | 3.5558 | 20350 | 0.2970 |
| 0.1244 | 3.5646 | 20400 | 0.2977 |
| 0.115 | 3.5733 | 20450 | 0.2970 |
| 0.1279 | 3.5820 | 20500 | 0.2970 |
| 0.136 | 3.5908 | 20550 | 0.2986 |
| 0.1366 | 3.5995 | 20600 | 0.2988 |
| 0.1349 | 3.6082 | 20650 | 0.2970 |
| 0.1271 | 3.6170 | 20700 | 0.2967 |
| 0.1213 | 3.6257 | 20750 | 0.2977 |
| 0.1254 | 3.6345 | 20800 | 0.2982 |
| 0.1159 | 3.6432 | 20850 | 0.2976 |
| 0.1259 | 3.6519 | 20900 | 0.2977 |
| 0.1262 | 3.6607 | 20950 | 0.2979 |
| 0.1172 | 3.6694 | 21000 | 0.2974 |
| 0.1218 | 3.6781 | 21050 | 0.2983 |
| 0.1292 | 3.6869 | 21100 | 0.2984 |
| 0.1262 | 3.6956 | 21150 | 0.2976 |
| 0.1266 | 3.7044 | 21200 | 0.2968 |
| 0.1211 | 3.7131 | 21250 | 0.2970 |
| 0.1279 | 3.7218 | 21300 | 0.2971 |
| 0.1263 | 3.7306 | 21350 | 0.2966 |
| 0.1371 | 3.7393 | 21400 | 0.2980 |
| 0.1202 | 3.7480 | 21450 | 0.2974 |
| 0.1301 | 3.7568 | 21500 | 0.2980 |
| 0.1191 | 3.7655 | 21550 | 0.2985 |
| 0.1173 | 3.7742 | 21600 | 0.2984 |
| 0.1246 | 3.7830 | 21650 | 0.2995 |
| 0.1289 | 3.7917 | 21700 | 0.2989 |
| 0.1153 | 3.8005 | 21750 | 0.2981 |
| 0.1267 | 3.8092 | 21800 | 0.2985 |
| 0.1115 | 3.8179 | 21850 | 0.2985 |
| 0.115 | 3.8267 | 21900 | 0.2986 |
| 0.1188 | 3.8354 | 21950 | 0.2979 |
| 0.1177 | 3.8441 | 22000 | 0.2975 |
| 0.1289 | 3.8529 | 22050 | 0.2976 |
| 0.1218 | 3.8616 | 22100 | 0.2980 |
| 0.1176 | 3.8703 | 22150 | 0.2979 |
| 0.1347 | 3.8791 | 22200 | 0.2977 |
| 0.1375 | 3.8878 | 22250 | 0.2968 |
| 0.1191 | 3.8966 | 22300 | 0.2968 |
| 0.1147 | 3.9053 | 22350 | 0.2966 |
| 0.1323 | 3.9140 | 22400 | 0.2969 |
| 0.1382 | 3.9228 | 22450 | 0.2970 |
| 0.1308 | 3.9315 | 22500 | 0.2973 |
| 0.1263 | 3.9402 | 22550 | 0.2970 |
| 0.1266 | 3.9490 | 22600 | 0.2966 |
| 0.1121 | 3.9577 | 22650 | 0.2965 |
| 0.1199 | 3.9665 | 22700 | 0.2966 |
| 0.1159 | 3.9752 | 22750 | 0.2964 |
| 0.1297 | 3.9839 | 22800 | 0.2965 |
| 0.1214 | 3.9927 | 22850 | 0.2965 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |