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--- |
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library_name: peft |
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base_model: peiyi9979/math-shepherd-mistral-7b-prm |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: v3c_mistral_lora |
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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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# v3c_mistral_lora |
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This model is a fine-tuned version of [peiyi9979/math-shepherd-mistral-7b-prm](https://huggingface.co/peiyi9979/math-shepherd-mistral-7b-prm) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2939 |
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- Accuracy: 0.8636 |
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- Precision: 0.8421 |
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- Recall: 0.6324 |
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- F1: 0.7223 |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 765837 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 32 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 0 | 0 | 0.6026 | 0.7339 | 0.6 | 0.1542 | 0.2453 | |
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| 0.6391 | 0.0248 | 20 | 0.5954 | 0.7361 | 0.6119 | 0.1621 | 0.2562 | |
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| 0.5891 | 0.0495 | 40 | 0.5570 | 0.7550 | 0.6176 | 0.3320 | 0.4319 | |
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| 0.4606 | 0.0743 | 60 | 0.4962 | 0.7794 | 0.6667 | 0.4269 | 0.5205 | |
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| 0.4229 | 0.0990 | 80 | 0.4433 | 0.7905 | 0.6649 | 0.5099 | 0.5772 | |
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| 0.3836 | 0.1238 | 100 | 0.4297 | 0.8160 | 0.7605 | 0.5020 | 0.6048 | |
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| 0.3363 | 0.1485 | 120 | 0.3676 | 0.8381 | 0.7892 | 0.5771 | 0.6667 | |
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| 0.2483 | 0.1733 | 140 | 0.3537 | 0.8404 | 0.8225 | 0.5494 | 0.6588 | |
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| 0.2803 | 0.1980 | 160 | 0.3468 | 0.8415 | 0.8481 | 0.5296 | 0.6521 | |
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| 0.2782 | 0.2228 | 180 | 0.3493 | 0.8237 | 0.8310 | 0.4664 | 0.5975 | |
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| 0.2174 | 0.2475 | 200 | 0.3329 | 0.8492 | 0.8232 | 0.5889 | 0.6866 | |
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| 0.2965 | 0.2723 | 220 | 0.3314 | 0.8448 | 0.8343 | 0.5573 | 0.6682 | |
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| 0.2379 | 0.2970 | 240 | 0.3736 | 0.8149 | 0.8468 | 0.4150 | 0.5570 | |
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| 0.1587 | 0.3218 | 260 | 0.3315 | 0.8404 | 0.8609 | 0.5138 | 0.6436 | |
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| 0.1769 | 0.3465 | 280 | 0.3329 | 0.8370 | 0.8313 | 0.5257 | 0.6441 | |
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| 0.1984 | 0.3713 | 300 | 0.3211 | 0.8537 | 0.8712 | 0.5613 | 0.6827 | |
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| 0.2109 | 0.3960 | 320 | 0.3064 | 0.8570 | 0.8333 | 0.6126 | 0.7062 | |
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| 0.1961 | 0.4208 | 340 | 0.3035 | 0.8625 | 0.8413 | 0.6285 | 0.7195 | |
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| 0.2369 | 0.4455 | 360 | 0.2959 | 0.8747 | 0.8365 | 0.6877 | 0.7549 | |
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| 0.2355 | 0.4703 | 380 | 0.3176 | 0.8537 | 0.8380 | 0.5929 | 0.6944 | |
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| 0.1538 | 0.4950 | 400 | 0.3098 | 0.8503 | 0.8554 | 0.5613 | 0.6778 | |
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| 0.2261 | 0.5198 | 420 | 0.2964 | 0.8659 | 0.8235 | 0.6640 | 0.7352 | |
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| 0.1894 | 0.5446 | 440 | 0.3085 | 0.8625 | 0.8772 | 0.5929 | 0.7075 | |
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| 0.2089 | 0.5693 | 460 | 0.3103 | 0.8592 | 0.8621 | 0.5929 | 0.7026 | |
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| 0.225 | 0.5941 | 480 | 0.2933 | 0.8670 | 0.8519 | 0.6364 | 0.7285 | |
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| 0.2837 | 0.6188 | 500 | 0.2955 | 0.8636 | 0.8283 | 0.6482 | 0.7273 | |
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| 0.2046 | 0.6436 | 520 | 0.2943 | 0.8647 | 0.8429 | 0.6364 | 0.7252 | |
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| 0.1548 | 0.6683 | 540 | 0.3003 | 0.8636 | 0.8421 | 0.6324 | 0.7223 | |
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| 0.1626 | 0.6931 | 560 | 0.2982 | 0.8625 | 0.8603 | 0.6087 | 0.7130 | |
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| 0.2065 | 0.7178 | 580 | 0.2877 | 0.8636 | 0.8186 | 0.6601 | 0.7309 | |
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| 0.1423 | 0.7426 | 600 | 0.3031 | 0.8603 | 0.8757 | 0.5850 | 0.7014 | |
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| 0.1743 | 0.7673 | 620 | 0.2920 | 0.8659 | 0.8511 | 0.6324 | 0.7256 | |
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| 0.1281 | 0.7921 | 640 | 0.2912 | 0.8659 | 0.8474 | 0.6364 | 0.7269 | |
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| 0.1879 | 0.8168 | 660 | 0.2938 | 0.8625 | 0.8449 | 0.6245 | 0.7182 | |
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| 0.1741 | 0.8416 | 680 | 0.2965 | 0.8625 | 0.8486 | 0.6206 | 0.7169 | |
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| 0.1429 | 0.8663 | 700 | 0.2911 | 0.8647 | 0.8359 | 0.6443 | 0.7277 | |
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| 0.2218 | 0.8911 | 720 | 0.2950 | 0.8625 | 0.8449 | 0.6245 | 0.7182 | |
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| 0.1608 | 0.9158 | 740 | 0.2995 | 0.8603 | 0.8508 | 0.6087 | 0.7097 | |
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| 0.2056 | 0.9406 | 760 | 0.2967 | 0.8592 | 0.8424 | 0.6126 | 0.7094 | |
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| 0.2127 | 0.9653 | 780 | 0.2944 | 0.8625 | 0.8413 | 0.6285 | 0.7195 | |
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| 0.2252 | 0.9901 | 800 | 0.2939 | 0.8636 | 0.8421 | 0.6324 | 0.7223 | |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.46.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
