190 lines
6.6 KiB
Markdown
190 lines
6.6 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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datasets:
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- openbmb/UltraFeedback
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pipeline_tag: text-generation
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model-index:
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- name: Llama-3-Instruct-8B-SPPO-Iter3
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 68.28
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 29.74
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 7.33
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 2.01
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 3.09
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 29.38
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
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name: Open LLM Leaderboard
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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-Iter3
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This model was developed using [Self-Play Preference Optimization](https://arxiv.org/abs/2405.00675) at iteration 3, 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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# [Open LLM Leaderboard 2 Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/UCLA-AGI__Llama-3-Instruct-8B-SPPO-Iter3-details)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |23.68|
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|IFEval (0-Shot) |68.28|
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|BBH (3-Shot) |29.74|
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|MATH Lvl 5 (4-Shot)| 7.33|
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|GPQA (0-shot) | 2.01|
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|MuSR (0-shot) | 3.09|
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|MMLU-PRO (5-shot) |29.38|
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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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```
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