RichardErkhov/UCLA-AGI_-_Gemma-2-9B-It-SPPO-Iter2-gguf

Quantization made by Richard Erkhov.

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Gemma-2-9B-It-SPPO-Iter2 - GGUF

• —Model creator: https://huggingface.co/UCLA-AGI/
• —Original model: https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2/

Original model description: --- license: gemma datasets:

• —openbmb/UltraFeedback language:
• —en pipeline_tag: text-generation --- Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)

Gemma-2-9B-It-SPPO-Iter2

This model was developed using Self-Play Preference Optimization at iteration 2, 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

• —Gemma-2-9B-It-SPPO-Iter1
• —Gemma-2-9B-It-SPPO-Iter2
• —Gemma-2-9B-It-SPPO-Iter3

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

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-07
• —eta: 1000
• —perdevicetrainbatchsize: 8
• —gradientaccumulationsteps: 1
• —seed: 42
• —distributedtype: deepspeedzero3
• —num_devices: 8
• —optimizer: RMSProp
• —lrschedulertype: linear
• —lrschedulerwarmup_ratio: 0.1
• —numtrainepochs: 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}
}