NuminaMath-7B-CoT / README.md
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metadata
license: other
tags:
  - alignment-handbook
  - generated_from_trainer
base_model: deepseek-ai/deepseek-math-7b-base
datasets:
  - AI-MO/numina-problems-sft-v1.7-preproc
model-index:
  - name: sft_deepseek-math-7b_aimo_v31.24
    results: []

sft_deepseek-math-7b_aimo_v31.24

This model is a fine-tuned version of deepseek-ai/deepseek-math-7b-base on the AI-MO/numina-problems-sft-v1.7-preproc dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4538

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: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 32
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 3.0

Training results

Training Loss Epoch Step Validation Loss
0.4649 1.0 6954 0.4518
0.4026 2.0 13908 0.4403
0.3461 3.0 20862 0.4538

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.19.1