metadata

base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
license: gemma
datasets:
  - openbmb/UltraFeedback
language:
  - en
pipeline_tag: text-generation
quantized_by: harborwater

This is a 4bit quantized version of UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3

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

Gemma-2-9B-It-SPPO-Iter3

This model was developed using Self-Play Preference Optimization at iteration 3, based on the google/gemma-2-9b-it 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.

Terms of Use: Terms

Links to Other Models

Model Description

Model type: A 8B parameter GPT-like model fine-tuned on synthetic datasets.
Language(s) (NLP): Primarily English
License: Apache-2.0
Finetuned from model: google/gemma-2-9b-it

AlpacaEval Leaderboard Evaluation Results

Model	LC. Win Rate	Win Rate	Avg. Length
Gemma-2-9B-SPPO Iter1	48.70	40.76	1669
Gemma-2-9B-SPPO Iter2	50.93	44.64	1759
Gemma-2-9B-SPPO Iter3	53.27	47.74	1803

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: 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}
}