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v3c_mistral_lora

This model is a fine-tuned version of peiyi9979/math-shepherd-mistral-7b-prm on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2939
  • Accuracy: 0.8636
  • Precision: 0.8421
  • Recall: 0.6324
  • F1: 0.7223

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: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 765837
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • total_eval_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
No log 0 0 0.6026 0.7339 0.6 0.1542 0.2453
0.6391 0.0248 20 0.5954 0.7361 0.6119 0.1621 0.2562
0.5891 0.0495 40 0.5570 0.7550 0.6176 0.3320 0.4319
0.4606 0.0743 60 0.4962 0.7794 0.6667 0.4269 0.5205
0.4229 0.0990 80 0.4433 0.7905 0.6649 0.5099 0.5772
0.3836 0.1238 100 0.4297 0.8160 0.7605 0.5020 0.6048
0.3363 0.1485 120 0.3676 0.8381 0.7892 0.5771 0.6667
0.2483 0.1733 140 0.3537 0.8404 0.8225 0.5494 0.6588
0.2803 0.1980 160 0.3468 0.8415 0.8481 0.5296 0.6521
0.2782 0.2228 180 0.3493 0.8237 0.8310 0.4664 0.5975
0.2174 0.2475 200 0.3329 0.8492 0.8232 0.5889 0.6866
0.2965 0.2723 220 0.3314 0.8448 0.8343 0.5573 0.6682
0.2379 0.2970 240 0.3736 0.8149 0.8468 0.4150 0.5570
0.1587 0.3218 260 0.3315 0.8404 0.8609 0.5138 0.6436
0.1769 0.3465 280 0.3329 0.8370 0.8313 0.5257 0.6441
0.1984 0.3713 300 0.3211 0.8537 0.8712 0.5613 0.6827
0.2109 0.3960 320 0.3064 0.8570 0.8333 0.6126 0.7062
0.1961 0.4208 340 0.3035 0.8625 0.8413 0.6285 0.7195
0.2369 0.4455 360 0.2959 0.8747 0.8365 0.6877 0.7549
0.2355 0.4703 380 0.3176 0.8537 0.8380 0.5929 0.6944
0.1538 0.4950 400 0.3098 0.8503 0.8554 0.5613 0.6778
0.2261 0.5198 420 0.2964 0.8659 0.8235 0.6640 0.7352
0.1894 0.5446 440 0.3085 0.8625 0.8772 0.5929 0.7075
0.2089 0.5693 460 0.3103 0.8592 0.8621 0.5929 0.7026
0.225 0.5941 480 0.2933 0.8670 0.8519 0.6364 0.7285
0.2837 0.6188 500 0.2955 0.8636 0.8283 0.6482 0.7273
0.2046 0.6436 520 0.2943 0.8647 0.8429 0.6364 0.7252
0.1548 0.6683 540 0.3003 0.8636 0.8421 0.6324 0.7223
0.1626 0.6931 560 0.2982 0.8625 0.8603 0.6087 0.7130
0.2065 0.7178 580 0.2877 0.8636 0.8186 0.6601 0.7309
0.1423 0.7426 600 0.3031 0.8603 0.8757 0.5850 0.7014
0.1743 0.7673 620 0.2920 0.8659 0.8511 0.6324 0.7256
0.1281 0.7921 640 0.2912 0.8659 0.8474 0.6364 0.7269
0.1879 0.8168 660 0.2938 0.8625 0.8449 0.6245 0.7182
0.1741 0.8416 680 0.2965 0.8625 0.8486 0.6206 0.7169
0.1429 0.8663 700 0.2911 0.8647 0.8359 0.6443 0.7277
0.2218 0.8911 720 0.2950 0.8625 0.8449 0.6245 0.7182
0.1608 0.9158 740 0.2995 0.8603 0.8508 0.6087 0.7097
0.2056 0.9406 760 0.2967 0.8592 0.8424 0.6126 0.7094
0.2127 0.9653 780 0.2944 0.8625 0.8413 0.6285 0.7195
0.2252 0.9901 800 0.2939 0.8636 0.8421 0.6324 0.7223

Framework versions

  • PEFT 0.13.2
  • Transformers 4.46.0
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.20.3
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