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metadata
license: mit
datasets:
  - HuggingFaceH4/ultrachat_200k
language:
  - en
pipeline_tag: text-generation

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models (https://arxiv.org/abs/2401.01335)

zephyr-7b-sft-full-spin-iter3

This model is a self-play fine-tuned model at iteration 3 from alignment-handbook/zephyr-7b-sft-full using synthetic data based on on the HuggingFaceH4/ultrachat_200k dataset.

Model Details

Model Description

  • Model type: A 7B parameter GPT-like model fine-tuned on synthetic datasets.
  • Language(s) (NLP): Primarily English
  • License: MIT
  • Finetuned from model: alignment-handbook/zephyr-7b-sft-full (based on mistralai/Mistral-7B-v0.1)

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-07
  • train_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 64
  • optimizer: RMSProp
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 2.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 63.70
ARC (25-shot) 66.13
HellaSwag (10-shot) 85.85
MMLU (5-shot) 61.51
TruthfulQA (0-shot) 57.89
Winogrande (5-shot) 76.64
GSM8K (5-shot) 34.19

Citation

@misc{chen2024selfplay,
      title={Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models}, 
      author={Zixiang Chen and Yihe Deng and Huizhuo Yuan and Kaixuan Ji and Quanquan Gu},
      year={2024},
      eprint={2401.01335},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}