75 lines
2.7 KiB
Markdown
75 lines
2.7 KiB
Markdown
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---
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license: gemma
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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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pipeline_tag: text-generation
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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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# Gemma-2-9B-It-SPPO-Iter2
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This model was developed using [Self-Play Preference Optimization](https://arxiv.org/abs/2405.00675) at iteration 2, based on the [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) 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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**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-9b-it)
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## Links to Other Models
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- [Gemma-2-9B-It-SPPO-Iter1](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter1)
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- [Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2)
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- [Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3)
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### Model Description
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- Model type: A 8B 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: google/gemma-2-9b-it
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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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|[Llama-3-8B-SPPO Iter1](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter1) |48.70 |40.76 | 1669
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|[Llama-3-8B-SPPO Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) |50.93 | 44.64 | 1759
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|[Llama-3-8B-SPPO Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3) |**53.27** |**47.74** | 1803
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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: 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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```
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