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metadata
library_name: transformers
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
  - alignment-handbook
  - trl
  - cpo
  - generated_from_trainer
  - trl
  - cpo
  - generated_from_trainer
datasets:
  - princeton-nlp/llama3-ultrafeedback
model-index:
  - name: llama3.1-cpo_j-full-0912
    results: []

llama3.1-cpo_j-full-0912

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the princeton-nlp/llama3-ultrafeedback dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4395
  • Rewards/chosen: -16.1609
  • Rewards/rejected: -16.9344
  • Rewards/accuracies: 0.6326
  • Rewards/margins: 0.7735
  • Logps/rejected: -169.3439
  • Logps/chosen: -161.6093
  • Logits/rejected: -0.3578
  • Logits/chosen: -0.3883
  • Nll Loss: 0.2841

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-06
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen Nll Loss
1.7848 0.2311 100 1.6452 -15.3752 -15.7662 0.5804 0.3910 -157.6625 -153.7521 -0.3516 -0.3794 0.2719
1.5276 0.4623 200 1.5229 -15.8100 -16.4430 0.6043 0.6331 -164.4303 -158.0997 -0.3983 -0.4237 0.2748
1.4811 0.6934 300 1.4640 -16.0706 -16.8001 0.6130 0.7296 -168.0013 -160.7057 -0.4069 -0.4339 0.2804
1.4642 0.9246 400 1.4429 -16.1577 -16.9120 0.6304 0.7544 -169.1204 -161.5765 -0.3509 -0.3812 0.2845

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.3.1
  • Datasets 2.21.0
  • Tokenizers 0.19.1