UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0

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

zephyr-7b-sft-full-spin-iter0

This model is a self-play fine-tuned model at iteration 0 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: 5e-07
• —trainbatchsize: 8
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 8
• —totaltrainbatch_size: 64
• —optimizer: RMSProp
• —lrschedulertype: linear
• —lrschedulerwarmup_ratio: 0.1
• —num_epochs: 2.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here | Metric | Value | |-----------------------|---------------------------| | Avg. | 62.37 | | ARC (25-shot) | 63.65 | | HellaSwag (10-shot) | 84.44 | | MMLU (5-shot) | 61.01 | | TruthfulQA (0-shot) | 50.48 | | Winogrande (5-shot) | 77.98 | | GSM8K (5-shot) | 36.69 |

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}
}