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--- |
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base_model: UCLA-AGI/Mistral7B-PairRM-SPPO-Iter3 |
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datasets: |
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- openbmb/UltraFeedback |
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language: |
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- en |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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tags: |
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- autoquant |
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- UCLA-AGI/Mistral7B-PairRM-SPPO-Iter3 |
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- gptq |
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--- |
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Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675) |
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# Mistral7B-PairRM-SPPO-Iter3 |
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This model was developed using [Self-Play Preference Optimization](https://arxiv.org/abs/2405.00675) at iteration 3, based on the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) architecture as starting point. We utilized the prompt sets from the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, splited to 3 parts for 3 iterations by [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset). All responses used are synthetic. |
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**This is the model reported in the paper** , with K=5 (generate 5 responses per iteration). We attached the Arena-Hard eval results in this model page. |
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## Links to Other Models |
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- [Mistral7B-PairRM-SPPO-Iter1](https://huggingface.co/UCLA-AGI/Mistral7B-PairRM-SPPO-Iter1) |
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- [Mistral7B-PairRM-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2) |
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- [Mistral7B-PairRM-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Mistral7B-PairRM-SPPO-Iter3) |
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- [Mistral7B-PairRM-SPPO](https://huggingface.co/UCLA-AGI/Mistral7B-PairRM-SPPO) |
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### Model Description |
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- Model type: A 7B parameter GPT-like model fine-tuned on synthetic datasets. |
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- Language(s) (NLP): Primarily English |
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- License: Apache-2.0 |
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- Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2 |
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## [AlpacaEval Leaderboard Evaluation Results](https://tatsu-lab.github.io/alpaca_eval/) |
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| Model | LC. Win Rate | Win Rate | Avg. Length | |
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|-------------------------------------------|:------------:|:--------:|:-----------:| |
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| Mistral7B-PairRM-SPPO Iter 1 | 24.79 | 23.51 | 1855 | |
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| Mistral7B-PairRM-SPPO Iter 2 | 26.89 | 27.62 | 2019 | |
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| Mistral7B-PairRM-SPPO Iter 3 | 28.53 | 31.02 | 2163 | |
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| Mistral7B-PairRM-SPPO Iter 1 (best-of-16) | 28.71 | 27.77 | 1901 | |
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| Mistral7B-PairRM-SPPO Iter 2 (best-of-16) | 31.23 | 32.12 | 2035 | |
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| Mistral7B-PairRM-SPPO Iter 3 (best-of-16) | 32.13 | 34.94 | 2174 | |
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## [Arena-Hard Evaluation Results](https://github.com/lm-sys/arena-hard) |
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Model | Score | 95% CI | average \# Tokens | |
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|----------|-----------|--------------|-----------| |
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Mistral7B-PairRM-SPPO-Iter3| 23.3 | (-1.8, 1.8)|578| |
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## [Open LLM Leaderboard Evaluation Results](https://github.com/EleutherAI/lm-evaluation-harness) |
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Results are reported by using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.1 |
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| | arc_challenge | truthfulqa_mc2 | winogrande | gsm8k | hellaswag | mmlu | average | |
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|--------|---------------|----------------|------------|-------|-----------|-------|---------| |
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| Mistral7B-PairRM-SPPO Iter 1 | 65.02 | 69.4 | 77.82 | 43.82 | 85.11 | 58.84 | 66.67 | |
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| Mistral7B-PairRM-SPPO Iter 2 | 65.53 | 69.55 | 77.03 | 44.35 | 85.29 | 58.72 | 66.75 | |
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| Mistral7B-PairRM-SPPO Iter 3 | 65.36 | 69.97 | 76.8 | 42.68 | 85.16 | 58.45 | 66.4 | |
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## [MT-Bench Evaluation Results](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) |
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| | 1st Turn | 2nd Turn | Average | |
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|--------|----------|----------|---------| |
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| Mistral7B-PairRM-SPPO Iter 1 | 7.63 | 6.79 | 7.21 | |
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| Mistral7B-PairRM-SPPO Iter 2 | 7.90 | 7.08 | 7.49 | |
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| Mistral7B-PairRM-SPPO Iter 3 | 7.84 | 7.34 | 7.59 | |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-07 |
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- eta: 1000 |
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- per_device_train_batch_size: 8 |
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- gradient_accumulation_steps: 1 |
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- seed: 42 |
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- distributed_type: deepspeed_zero3 |
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- num_devices: 8 |
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- optimizer: RMSProp |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_train_epochs: 18.0 (stop at epoch=1.0) |
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## Citation |
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``` |
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@misc{wu2024self, |
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title={Self-Play Preference Optimization for Language Model Alignment}, |
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author={Wu, Yue and Sun, Zhiqing and Yuan, Huizhuo and Ji, Kaixuan and Yang, Yiming and Gu, Quanquan}, |
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year={2024}, |
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eprint={2405.00675}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
