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--- |
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license: apache-2.0 |
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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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# Llama-3-Instruct-8B-SPPO-Iter1 |
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This model was developed using [Self-Play Preference Optimization](https://arxiv.org/abs/2405.00675) at iteration 1, based on the [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 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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## Links to Other Models |
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- [Llama-3-Instruct-8B-SPPO-Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1) |
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- [Llama-3-Instruct-8B-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2) |
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- [Llama-3-Instruct-8B-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-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: meta-llama/Meta-Llama-3-8B-Instruct |
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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/Llama-3-Instruct-8B-SPPO-Iter1) |31.73 |31.74 | 1962 |
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|[Llama-3-8B-SPPO Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2) |35.15 |35.98 | 2021 |
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|[Llama-3-8B-SPPO Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) |**38.77** |**39.85** | 2066 |
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## [Open LLM Leaderboard Evaluation Results](https://github.com/EleutherAI/lm-evaluation-harness) |
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Results are reported by using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.1 |
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| | arc_challenge | truthfulqa_mc2 | winogrande | gsm8k | hellaswag | mmlu | average | |
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|--------|---------------|----------------|------------|-------|-----------|-------|---------| |
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|[Llama-3-8B-SPPO Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1) | 63.82 | 54.96 | 76.40 | 75.44 | 79.80 | 65.65 | 69.35 |
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|[Llama-3-8B-SPPO Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2) | 64.93 | 56.48 | 76.87 | 75.13 | 80.39 | 65.67 | 69.91 |
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|[Llama-3-8B-SPPO Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) | 65.19 | 58.04 | 77.11 | 74.91 | 80.86 | 65.60 | **70.29** |
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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: 6.0 (stop at epoch=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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``` |
