metadata
license: mit
datasets:
- UCLA-AGI/SPIN_iter0
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-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
- 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. | 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}
}