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metadata
base_model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
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 is a GPTQ of the SPPO model developed using Self-Play Preference Optimization at iteration 3, based on the meta-llama/Meta-Llama-3-8B-Instruct 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.

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

Model LC. Win Rate Win Rate Avg. Length
Llama-3-8B-SPPO Iter1 31.73 31.74 1962
Llama-3-8B-SPPO Iter2 35.15 35.98 2021
Llama-3-8B-SPPO Iter3 38.77 39.85 2066

Open LLM Leaderboard Evaluation Results

Results are reported by using lm-evaluation-harness v0.4.1

arc_challenge truthfulqa_mc2 winogrande gsm8k hellaswag mmlu average
Llama-3-8B-SPPO Iter1 63.82 54.96 76.40 75.44 79.80 65.65 69.35
Llama-3-8B-SPPO Iter2 64.93 56.48 76.87 75.13 80.39 65.67 69.91
Llama-3-8B-SPPO Iter3 65.19 58.04 77.11 74.91 80.86 65.60 70.29

Open LLM Leaderboard 2 Evaluation Results

Detailed results can be found here

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}
}