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General Preference Modeling with Preference Representations for Aligning Language Models (https://arxiv.org/abs/2410.02197)

GPO-Llama-3-8B-Instruct-GPM-2B

This model was developed using General Preference Optimization (GPO) at iteration 3 and the General Preference representation Model (GPM) (specifically, using GPM-Gemma-2B), based on the meta-llama/Meta-Llama-3-8B-Instruct 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.

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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: meta-llama/Meta-Llama-3-8B-Instruct

AlpacaEval Leaderboard Evaluation Results

Model LC. Win Rate Win Rate Avg. Length
GPO-Llama-3-8B-Instruct-GPM-2B 38.43 48.87 2613

Open LLM Leaderboard Evaluation Results

Results are reported by using lm-evaluation-harness v0.4.1

arc_challenge truthfulqa_mc2 winogrande gsm8k hellaswag mmlu average
GPO-Llama-3-8B-Instruct-GPM-2B 61.43 53.54 75.22 76.12 78.06 65.65 68.34

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-07
  • beta: 0.001
  • 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: 6.0 (stop at epoch=1.0)

Citation

@article{zhang2024general,
  title={General Preference Modeling with Preference Representations for Aligning Language Models},
  author={Zhang, Yifan and Zhang, Ge and Wu, Yue and Xu, Kangping and Gu, Quanquan},
  journal={arXiv preprint arXiv:2410.02197},
  year={2024}
}
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Dataset used to train general-preference/GPO-Llama-3-8B-Instruct-GPM-2B