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vm-llama3-lr4e-5-be5e-4-r8

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the alphamath dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1354
  • Accuracy: 0.4164

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: 4e-05
  • train_batch_size: 26
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 5
  • total_train_batch_size: 1040
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.03
  • num_epochs: 12

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.2619 5.0 500 0.1481 0.3158
0.2206 10.0 1000 0.1354 0.4080

Framework versions

  • PEFT 0.12.0
  • Transformers 4.44.0
  • Pytorch 2.4.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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