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v3c_mistral_lora_lastn

PEFT
Safetensors
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metadata
library_name: peft
base_model: peiyi9979/math-shepherd-mistral-7b-prm
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: v3c_mistral_lora_lastn
    results: []

v3c_mistral_lora_lastn

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: 0.3067
  • Accuracy: 0.8592
  • Precision: 0.8580
  • Recall: 0.5968
  • F1: 0.7040

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: 4
  • total_train_batch_size: 128
  • 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
0.5522 0.0495 20 0.5768 0.7395 0.5682 0.2964 0.3896
0.4818 0.0990 40 0.4859 0.7761 0.6835 0.3755 0.4847
0.3892 0.1485 60 0.4218 0.7982 0.6766 0.5375 0.5991
0.2916 0.1980 80 0.3747 0.8237 0.7701 0.5296 0.6276
0.2191 0.2475 100 0.3538 0.8304 0.7778 0.5534 0.6467
0.2189 0.2970 120 0.3754 0.8248 0.88 0.4348 0.5820
0.1841 0.3465 140 0.3427 0.8415 0.8438 0.5336 0.6538
0.2144 0.3960 160 0.3301 0.8404 0.8303 0.5415 0.6555
0.2638 0.4455 180 0.3202 0.8470 0.8485 0.5534 0.6699
0.2032 0.4950 200 0.3125 0.8570 0.8370 0.6087 0.7048
0.1703 0.5446 220 0.3295 0.8337 0.8552 0.4901 0.6231
0.175 0.5941 240 0.3116 0.8503 0.8471 0.5692 0.6809
0.1927 0.6436 260 0.3218 0.8459 0.8654 0.5336 0.6601
0.1848 0.6931 280 0.3069 0.8647 0.8659 0.6126 0.7176
0.222 0.7426 300 0.3036 0.8581 0.8613 0.5889 0.6995
0.1693 0.7921 320 0.3096 0.8525 0.8614 0.5652 0.6826
0.1752 0.8416 340 0.3108 0.8503 0.8554 0.5613 0.6778
0.2353 0.8911 360 0.3072 0.8592 0.8580 0.5968 0.7040
0.1984 0.9406 380 0.3078 0.8603 0.8629 0.5968 0.7056
0.2194 0.9901 400 0.3067 0.8592 0.8580 0.5968 0.7040

Framework versions

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