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Quantization made by Richard Erkhov.

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Llama-360M-RUN2 - AWQ

Original model description:

tags: - generated_from_trainer model-index: - name: Llama-360M results: []

Llama-360M

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 5.1685

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: 0.0003
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
8.4647 0.98 7 8.5154
7.2112 1.96 14 7.6819
6.3283 2.95 21 6.9987
5.5163 3.93 28 6.4019
4.7022 4.91 35 5.8715
3.7692 5.89 42 5.4877
3.2137 6.88 49 5.2686
2.6388 8.0 57 5.1854
2.0768 8.98 64 5.1622
1.715 9.82 70 5.1685

Framework versions

  • Transformers 4.39.1
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0
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