README.md

metadata

language:
  - en
license: apache-2.0
library_name: transformers
tags:
  - mergekit
  - merge
base_model:
  - chargoddard/prometheus-llama-3-8b-preference
  - chargoddard/prometheus-llama-3-8b-absolute
datasets:
  - prometheus-eval/Preference-Collection
  - prometheus-eval/Feedback-Collection
model-index:
  - name: prometheus-2-llama-3-8b
    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: 52.89
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chargoddard/prometheus-2-llama-3-8b
          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: 27.8
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chargoddard/prometheus-2-llama-3-8b
          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: 7.25
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chargoddard/prometheus-2-llama-3-8b
          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: 3.02
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chargoddard/prometheus-2-llama-3-8b
          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: 0.78
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chargoddard/prometheus-2-llama-3-8b
          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: 23.19
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chargoddard/prometheus-2-llama-3-8b
          name: Open LLM Leaderboard

prometheus-2-llama-3-8b

Replication of prometheus-7b-v2.0 using Llama 3 8B Instruct as a base model.

As in their paper, two different models were trained on their preference and feedback datasets then linearly merged at equal weight.

Training hyperparameters:

• 1 epoch
• Learning rate 1e-5
• Effective batch size 128
• Cosine annealing
• ~5% warmup

Uses Llama 3 Instruct prompt format and the same prompts as prometheus-7b-v2.0. See that readme for info.

Citations

@misc{kim2023prometheus,
    title={Prometheus: Inducing Fine-grained Evaluation Capability in Language Models},
    author={Seungone Kim and Jamin Shin and Yejin Cho and Joel Jang and Shayne Longpre and Hwaran Lee and Sangdoo Yun and Seongjin Shin and Sungdong Kim and James Thorne and Minjoon Seo},
    year={2023},
    eprint={2310.08491},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{kim2024prometheus,
    title={Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models},
    author={Seungone Kim and Juyoung Suk and Shayne Longpre and Bill Yuchen Lin and Jamin Shin and Sean Welleck and Graham Neubig and Moontae Lee and Kyungjae Lee and Minjoon Seo},
    year={2024},
    eprint={2405.01535},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	19.16
IFEval (0-Shot)	52.89
BBH (3-Shot)	27.80
MATH Lvl 5 (4-Shot)	7.25
GPQA (0-shot)	3.02
MuSR (0-shot)	0.78
MMLU-PRO (5-shot)	23.19