Mistral7B-PairRM-SPPO-GGUF

Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)

Mistral7B-PairRM-SPPO

This model was developed using Self-Play Preference Optimization at iteration 3, based on the mistralai/Mistral-7B-Instruct-v0.2 architecture as starting point. We utilized the prompt sets from the openbmb/UltraFeedback dataset, splited to 3 parts for 3 iterations by snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset. All responses used are synthetic.

While K = 5 (generate 5 samples per iteration), this model uses 3 samples to estimate the soft probabilities P(y_w > y_l) and P(y_l > y_w). These samples include the winner, the loser, and another random sample. This approach has shown to deliver better performance on AlpacaEval 2.0 than the results reported in the paper, but it might also lead to overfitting the PairRM core.

❗Please refer to the original checkpoint at UCLA-AGI/Mistral7B-PairRM-SPPO-Iter3 as reported in our paper. We anticipate that the version in paper demonstrates a more consistent performance improvement across all benchmark tasks.

Links to Other Models

• Mistral7B-PairRM-SPPO-Iter1
• Mistral7B-PairRM-SPPO-Iter2
• Mistral7B-PairRM-SPPO-Iter3
• Mistral7B-PairRM-SPPO

Model Description

• Model type: A 7B parameter GPT-like model fine-tuned on synthetic datasets.
• Language(s) (NLP): Primarily English
• License: Apache-2.0
• Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2

AlpacaEval Leaderboard Evaluation Results

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-07
• eta: 1000
• per_device_train_batch_size: 8
• gradient_accumulation_steps: 1
• seed: 42
• distributed_type: deepspeed_zero3
• num_devices: 8
• optimizer: RMSProp
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_train_epochs: 18.0 (stop at epoch=1.0)

Citation

@misc{wu2024self,
      title={Self-Play Preference Optimization for Language Model Alignment}, 
      author={Wu, Yue and Sun, Zhiqing and Yuan, Huizhuo and Ji, Kaixuan and Yang, Yiming and Gu, Quanquan},
      year={2024},
      eprint={2405.00675},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}