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Llama-31-8B_task-3_60-samples_config-3

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metadata
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
  - GaetanMichelet/chat-60_ft_task-3
library_name: peft
license: llama3.1
tags:
  - alignment-handbook
  - trl
  - sft
  - generated_from_trainer
model-index:
  - name: Llama-31-8B_task-3_60-samples_config-3
    results: []

Llama-31-8B_task-3_60-samples_config-3

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the GaetanMichelet/chat-60_ft_task-3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4657

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: 1e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 150

Training results

Training Loss Epoch Step Validation Loss
2.4489 0.8696 5 2.4703
2.4891 1.9130 11 2.4545
2.4697 2.9565 17 2.4172
2.5196 4.0 23 2.3543
2.4481 4.8696 28 2.2785
2.2925 5.9130 34 2.1509
2.0169 6.9565 40 1.9728
1.6364 8.0 46 1.7441
1.6047 8.8696 51 1.5192
1.3126 9.9130 57 1.2353
1.0406 10.9565 63 0.9674
0.8254 12.0 69 0.7402
0.7723 12.8696 74 0.6372
0.4327 13.9130 80 0.5750
0.279 14.9565 86 0.5313
0.4039 16.0 92 0.5047
0.5227 16.8696 97 0.4883
0.3704 17.9130 103 0.4936
0.497 18.9565 109 0.4907
0.4428 20.0 115 0.4828
0.3477 20.8696 120 0.4825
0.4628 21.9130 126 0.4857
0.367 22.9565 132 0.4827
0.2597 24.0 138 0.4768
0.3473 24.8696 143 0.4684
0.3795 25.9130 149 0.4657
0.437 26.9565 155 0.4706
0.3478 28.0 161 0.4760
0.254 28.8696 166 0.4745
0.3934 29.9130 172 0.4813
0.3074 30.9565 178 0.4815
0.3447 32.0 184 0.4823
0.225 32.8696 189 0.4865

Framework versions

  • PEFT 0.12.0
  • Transformers 4.44.0
  • Pytorch 2.1.2+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1