llama3_help_rm_2ep / README.md
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llama31_help_rm_full
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metadata
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: llama3_rm
    results: []

llama3_rm

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

  • Loss: 0.3742
  • Accuracy: 0.8948

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-06
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 256
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.03
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6626 0.0705 10 0.5923 0.6719
0.5039 0.1410 20 0.5139 0.7568
0.4865 0.2115 30 0.4816 0.7646
0.4814 0.2819 40 0.4566 0.7891
0.4932 0.3524 50 0.4449 0.7979
0.4723 0.4229 60 0.4267 0.8031
0.3906 0.4934 70 0.4042 0.8208
0.3418 0.5639 80 0.3907 0.8245
0.4427 0.6344 90 0.3736 0.8359
0.4022 0.7048 100 0.3578 0.8484
0.3738 0.7753 110 0.3470 0.8542
0.3619 0.8458 120 0.3328 0.8609
0.3266 0.9163 130 0.3256 0.8651
0.2786 0.9868 140 0.3245 0.8693
0.1685 1.0573 150 0.4035 0.8786
0.0421 1.1278 160 0.4395 0.8823
0.0655 1.1982 170 0.3843 0.8828
0.0734 1.2687 180 0.3645 0.8823
0.1441 1.3392 190 0.4277 0.8833
0.1176 1.4097 200 0.4040 0.8896
0.0826 1.4802 210 0.3609 0.8870
0.056 1.5507 220 0.3542 0.8891
0.0487 1.6211 230 0.3668 0.8927
0.0815 1.6916 240 0.3735 0.8938
0.0842 1.7621 250 0.3751 0.8943
0.0868 1.8326 260 0.3758 0.8932
0.0743 1.9031 270 0.3753 0.8938
0.0874 1.9736 280 0.3742 0.8948

Framework versions

  • Transformers 4.43.4
  • Pytorch 2.1.2+cu121
  • Datasets 4.4.1
  • Tokenizers 0.19.1