--- datasets: - openbmb/UltraFeedback language: - en license: apache-2.0 pipeline_tag: text-generation tags: - autoquant - UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 - gptq model-index: - name: Llama-3-Instruct-8B-SPPO-Iter3 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 68.28 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 29.74 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 7.33 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 2.01 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 3.09 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 29.38 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 name: Open LLM Leaderboard --- Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675) # Llama-3-Instruct-8B-SPPO-Iter3 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. ## Links to Other Models - [Llama-3-Instruct-8B-SPPO-Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1) - [Llama-3-Instruct-8B-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2) - [Llama-3-Instruct-8B-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-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: meta-llama/Meta-Llama-3-8B-Instruct ## [AlpacaEval Leaderboard Evaluation Results](https://tatsu-lab.github.io/alpaca_eval/) | Model | LC. Win Rate | Win Rate | Avg. Length | |-------------------------------------------|:------------:|:--------:|:-----------:| |[Llama-3-8B-SPPO Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1) |31.73 |31.74 | 1962 |[Llama-3-8B-SPPO Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2) |35.15 |35.98 | 2021 |[Llama-3-8B-SPPO Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) |**38.77** |**39.85** | 2066 ## [Open LLM Leaderboard Evaluation Results](https://github.com/EleutherAI/lm-evaluation-harness) Results are reported by using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.1 | | arc_challenge | truthfulqa_mc2 | winogrande | gsm8k | hellaswag | mmlu | average | |--------|---------------|----------------|------------|-------|-----------|-------|---------| |[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 |[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 |[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** # [Open LLM Leaderboard 2 Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/UCLA-AGI__Llama-3-Instruct-8B-SPPO-Iter3-details) | Metric |Value| |-------------------|----:| |Avg. |23.68| |IFEval (0-Shot) |68.28| |BBH (3-Shot) |29.74| |MATH Lvl 5 (4-Shot)| 7.33| |GPQA (0-shot) | 2.01| |MuSR (0-shot) | 3.09| |MMLU-PRO (5-shot) |29.38| ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - eta: 1000 - 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 ``` @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} } ```