trl_rm_tldr_gptj

• Loss: 0.6624
• Accuracy: 0.6857

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 1.41e-05
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Framework versions

• PEFT 0.7.1.dev0
• Transformers 4.36.2
• Pytorch 2.1.2
• Datasets 2.15.0
• Tokenizers 0.15.0

BibTex Citation

@article{sun2024supervised,
  title={Supervised Fine-Tuning as Inverse Reinforcement Learning},
  author={Sun, Hao},
  journal={arXiv preprint arXiv:2403.12017},
  year={2024}
}