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+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-iter2 - bnb 4bits
+- Model creator: https://huggingface.co/UCLA-AGI/
+- Original model: https://huggingface.co/UCLA-AGI/zephyr-7b-sft-full-SPIN-iter2/
+
+
+
+
+Original model description:
+---
+license: mit
+datasets:
+- UCLA-AGI/SPIN_iter2
+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-iter2
+
+This model is a self-play fine-tuned model at iteration 2 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: 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__test-test)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 63.54   |
+| ARC (25-shot)         | 66.47          |
+| HellaSwag (10-shot)   | 85.82    |
+| MMLU (5-shot)         | 61.48         |
+| TruthfulQA (0-shot)   | 57.75   |
+| Winogrande (5-shot)   | 76.95   |
+| GSM8K (5-shot)        | 32.75        |
+  
+## 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}
+}
+```
+