Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-sft-full-SPIN-iter0 - bnb 8bits - Model creator: https://huggingface.co/UCLA-AGI/ - Original model: https://huggingface.co/UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0/ Original model description: --- 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](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) using synthetic data based on on the [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__test0) | 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} } ```