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KAM-Llama3.2-3B

This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct on the None dataset.

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: 0.0002
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 3407
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 20
  • training_steps: 1200
  • mixed_precision_training: Native AMP

Training results

Step    Training Loss

  • 50     2.436700
  • 100     2.103400
  • 150     2.048900
  • 200     2.041700
  • 250     2.002900
  • 300     1.991700
  • 350     1.977400
  • 400     1.974500
  • 450     1.945000
  • 500     1.951100
  • 550     1.950700
  • 600     1.943000
  • 650     1.927900
  • 700     1.920900
  • 750     1.903400
  • 800     1.896000
  • 850     1.910800
  • 900     1.904600
  • 950     1.918100
  • 1000     1.911500
  • 1050     1.909100
  • 1100     1.928900
  • 1150     1.896100
  • 1200     1.876700

Framework versions

  • PEFT 0.13.2
  • Transformers 4.44.2
  • Pytorch 2.5.0+cu121
  • Datasets 3.0.2
  • Tokenizers 0.19.1
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