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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: ECS-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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# ECS-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: 0.2955 |
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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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| 0.1784 | 2.6210 | 15000 | 0.2849 | |
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| 0.2039 | 2.6297 | 15050 | 0.2825 | |
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| 0.194 | 2.6385 | 15100 | 0.2842 | |
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| 0.2073 | 2.6472 | 15150 | 0.2844 | |
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| 0.1818 | 2.6559 | 15200 | 0.2841 | |
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| 0.1858 | 2.6647 | 15250 | 0.2837 | |
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| 0.191 | 2.6734 | 15300 | 0.2821 | |
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| 0.2024 | 2.6822 | 15350 | 0.2814 | |
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| 0.1699 | 2.6909 | 15400 | 0.2832 | |
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| 0.1782 | 2.6996 | 15450 | 0.2813 | |
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| 0.1971 | 2.7084 | 15500 | 0.2818 | |
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| 0.1974 | 2.7171 | 15550 | 0.2811 | |
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| 0.1867 | 2.7258 | 15600 | 0.2818 | |
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| 0.1843 | 2.7346 | 15650 | 0.2836 | |
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| 0.192 | 2.7433 | 15700 | 0.2834 | |
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| 0.2191 | 2.7521 | 15750 | 0.2800 | |
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| 0.1797 | 2.7608 | 15800 | 0.2797 | |
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| 0.1871 | 2.7695 | 15850 | 0.2817 | |
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| 0.1893 | 2.7783 | 15900 | 0.2817 | |
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| 0.1845 | 2.7870 | 15950 | 0.2824 | |
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| 0.1954 | 2.7957 | 16000 | 0.2828 | |
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| 0.1752 | 2.8045 | 16050 | 0.2824 | |
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| 0.213 | 2.8132 | 16100 | 0.2803 | |
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| 0.1953 | 2.8219 | 16150 | 0.2818 | |
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| 0.1959 | 2.8307 | 16200 | 0.2807 | |
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| 0.1904 | 2.8394 | 16250 | 0.2814 | |
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| 0.191 | 2.8482 | 16300 | 0.2806 | |
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| 0.1783 | 2.8569 | 16350 | 0.2803 | |
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| 0.1997 | 2.8656 | 16400 | 0.2802 | |
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| 0.2195 | 2.8744 | 16450 | 0.2787 | |
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| 0.189 | 2.8831 | 16500 | 0.2800 | |
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| 0.1951 | 2.8918 | 16550 | 0.2788 | |
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| 0.1985 | 2.9006 | 16600 | 0.2789 | |
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| 0.2169 | 2.9093 | 16650 | 0.2785 | |
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| 0.195 | 2.9180 | 16700 | 0.2788 | |
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| 0.1744 | 2.9268 | 16750 | 0.2800 | |
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| 0.1635 | 2.9355 | 16800 | 0.2800 | |
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| 0.1877 | 2.9443 | 16850 | 0.2782 | |
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| 0.1977 | 2.9530 | 16900 | 0.2770 | |
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| 0.1808 | 2.9617 | 16950 | 0.2781 | |
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| 0.1824 | 2.9705 | 17000 | 0.2784 | |
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| 0.1947 | 2.9792 | 17050 | 0.2781 | |
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| 0.1946 | 2.9879 | 17100 | 0.2767 | |
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| 0.1742 | 2.9967 | 17150 | 0.2770 | |
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| 0.1527 | 3.0054 | 17200 | 0.2886 | |
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| 0.1205 | 3.0142 | 17250 | 0.2929 | |
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| 0.1261 | 3.0229 | 17300 | 0.2981 | |
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| 0.1122 | 3.0316 | 17350 | 0.2997 | |
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| 0.1441 | 3.0404 | 17400 | 0.2979 | |
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| 0.1202 | 3.0491 | 17450 | 0.3007 | |
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| 0.1285 | 3.0578 | 17500 | 0.2983 | |
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| 0.149 | 3.0666 | 17550 | 0.3007 | |
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| 0.1369 | 3.0753 | 17600 | 0.2968 | |
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| 0.1225 | 3.0840 | 17650 | 0.2994 | |
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| 0.132 | 3.0928 | 17700 | 0.3007 | |
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| 0.1296 | 3.1015 | 17750 | 0.3006 | |
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| 0.1207 | 3.1103 | 17800 | 0.3000 | |
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| 0.1385 | 3.1190 | 17850 | 0.2981 | |
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| 0.1347 | 3.1277 | 17900 | 0.3000 | |
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| 0.114 | 3.1365 | 17950 | 0.2994 | |
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| 0.1233 | 3.1452 | 18000 | 0.2991 | |
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| 0.1284 | 3.1539 | 18050 | 0.2991 | |
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| 0.1222 | 3.1627 | 18100 | 0.3005 | |
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| 0.1367 | 3.1714 | 18150 | 0.2988 | |
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| 0.1308 | 3.1802 | 18200 | 0.2992 | |
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| 0.1138 | 3.1889 | 18250 | 0.3001 | |
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| 0.1259 | 3.1976 | 18300 | 0.2979 | |
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| 0.1383 | 3.2064 | 18350 | 0.2993 | |
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| 0.1288 | 3.2151 | 18400 | 0.2989 | |
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| 0.1364 | 3.2238 | 18450 | 0.2974 | |
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| 0.1232 | 3.2326 | 18500 | 0.2989 | |
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| 0.1348 | 3.2413 | 18550 | 0.3012 | |
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| 0.1168 | 3.2500 | 18600 | 0.2998 | |
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| 0.1342 | 3.2588 | 18650 | 0.3026 | |
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| 0.1385 | 3.2675 | 18700 | 0.2979 | |
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| 0.1298 | 3.2763 | 18750 | 0.2962 | |
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| 0.1373 | 3.2850 | 18800 | 0.2950 | |
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| 0.1292 | 3.2937 | 18850 | 0.2986 | |
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| 0.1329 | 3.3025 | 18900 | 0.2965 | |
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| 0.1324 | 3.3112 | 18950 | 0.3016 | |
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| 0.1176 | 3.3199 | 19000 | 0.2991 | |
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| 0.1444 | 3.3287 | 19050 | 0.2940 | |
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| 0.1395 | 3.3374 | 19100 | 0.2960 | |
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| 0.1247 | 3.3461 | 19150 | 0.2975 | |
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| 0.1313 | 3.3549 | 19200 | 0.2976 | |
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| 0.1299 | 3.3636 | 19250 | 0.2967 | |
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| 0.1339 | 3.3724 | 19300 | 0.2969 | |
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| 0.128 | 3.3811 | 19350 | 0.2949 | |
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| 0.1296 | 3.3898 | 19400 | 0.2978 | |
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| 0.1346 | 3.3986 | 19450 | 0.2961 | |
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| 0.1388 | 3.4073 | 19500 | 0.2960 | |
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| 0.1236 | 3.4160 | 19550 | 0.2951 | |
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| 0.1203 | 3.4248 | 19600 | 0.2952 | |
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| 0.1161 | 3.4335 | 19650 | 0.2977 | |
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| 0.1158 | 3.4423 | 19700 | 0.2955 | |
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| 0.1292 | 3.4510 | 19750 | 0.2979 | |
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| 0.1224 | 3.4597 | 19800 | 0.2976 | |
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| 0.1241 | 3.4685 | 19850 | 0.2979 | |
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| 0.1411 | 3.4772 | 19900 | 0.2953 | |
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| 0.1337 | 3.4859 | 19950 | 0.2966 | |
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| 0.1298 | 3.4947 | 20000 | 0.2964 | |
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| 0.1176 | 3.5034 | 20050 | 0.2958 | |
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| 0.1175 | 3.5121 | 20100 | 0.2966 | |
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| 0.1409 | 3.5209 | 20150 | 0.2952 | |
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| 0.1339 | 3.5296 | 20200 | 0.2951 | |
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| 0.1348 | 3.5384 | 20250 | 0.2956 | |
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| 0.1281 | 3.5471 | 20300 | 0.2956 | |
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| 0.1293 | 3.5558 | 20350 | 0.2981 | |
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| 0.1257 | 3.5646 | 20400 | 0.2969 | |
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| 0.1152 | 3.5733 | 20450 | 0.2955 | |
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| 0.1276 | 3.5820 | 20500 | 0.2960 | |
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| 0.1366 | 3.5908 | 20550 | 0.2977 | |
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| 0.1364 | 3.5995 | 20600 | 0.2982 | |
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| 0.134 | 3.6082 | 20650 | 0.2967 | |
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| 0.1266 | 3.6170 | 20700 | 0.2965 | |
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| 0.1215 | 3.6257 | 20750 | 0.2970 | |
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| 0.1253 | 3.6345 | 20800 | 0.2991 | |
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| 0.116 | 3.6432 | 20850 | 0.2976 | |
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| 0.1255 | 3.6519 | 20900 | 0.2972 | |
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| 0.1271 | 3.6607 | 20950 | 0.2969 | |
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| 0.1155 | 3.6694 | 21000 | 0.2970 | |
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| 0.1223 | 3.6781 | 21050 | 0.2968 | |
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| 0.1317 | 3.6869 | 21100 | 0.2956 | |
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| 0.1257 | 3.6956 | 21150 | 0.2957 | |
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| 0.1262 | 3.7044 | 21200 | 0.2952 | |
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| 0.1215 | 3.7131 | 21250 | 0.2957 | |
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| 0.1285 | 3.7218 | 21300 | 0.2955 | |
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| 0.1264 | 3.7306 | 21350 | 0.2956 | |
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| 0.1364 | 3.7393 | 21400 | 0.2967 | |
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| 0.1213 | 3.7480 | 21450 | 0.2966 | |
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| 0.1316 | 3.7568 | 21500 | 0.2972 | |
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| 0.1174 | 3.7655 | 21550 | 0.2991 | |
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| 0.1167 | 3.7742 | 21600 | 0.2982 | |
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| 0.1274 | 3.7830 | 21650 | 0.2974 | |
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| 0.1302 | 3.7917 | 21700 | 0.2960 | |
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| 0.118 | 3.8005 | 21750 | 0.2958 | |
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| 0.1264 | 3.8092 | 21800 | 0.2977 | |
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| 0.1115 | 3.8179 | 21850 | 0.2971 | |
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| 0.1128 | 3.8267 | 21900 | 0.2973 | |
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| 0.1186 | 3.8354 | 21950 | 0.2965 | |
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| 0.1173 | 3.8441 | 22000 | 0.2965 | |
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| 0.1293 | 3.8529 | 22050 | 0.2963 | |
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| 0.1226 | 3.8616 | 22100 | 0.2964 | |
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| 0.1173 | 3.8703 | 22150 | 0.2964 | |
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| 0.1343 | 3.8791 | 22200 | 0.2966 | |
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| 0.1365 | 3.8878 | 22250 | 0.2962 | |
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| 0.1187 | 3.8966 | 22300 | 0.2963 | |
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| 0.1132 | 3.9053 | 22350 | 0.2963 | |
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| 0.1328 | 3.9140 | 22400 | 0.2961 | |
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| 0.1394 | 3.9228 | 22450 | 0.2956 | |
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| 0.1312 | 3.9315 | 22500 | 0.2959 | |
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| 0.1256 | 3.9402 | 22550 | 0.2958 | |
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| 0.1272 | 3.9490 | 22600 | 0.2955 | |
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| 0.1128 | 3.9577 | 22650 | 0.2954 | |
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| 0.1193 | 3.9665 | 22700 | 0.2955 | |
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| 0.1169 | 3.9752 | 22750 | 0.2954 | |
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| 0.1308 | 3.9839 | 22800 | 0.2954 | |
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| 0.1185 | 3.9927 | 22850 | 0.2955 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.43.3 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
