metadata

language:
  - en
inference: false
pipeline_tag: text-generation
model-index:
  - name: WizardMath-7B-V1.1
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 61.86
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WizardLM/WizardMath-7B-V1.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 84.5
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WizardLM/WizardMath-7B-V1.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 61.53
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WizardLM/WizardMath-7B-V1.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 47.04
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WizardLM/WizardMath-7B-V1.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 77.35
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WizardLM/WizardMath-7B-V1.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 67.4
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=WizardLM/WizardMath-7B-V1.1
          name: Open LLM Leaderboard

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (RLEIF)

🏠 Home Page

🤗 HF Repo •🐱 Github Repo • 🐦 Twitter

📃 [WizardLM] • 📃 [WizardCoder] • 📃 [WizardMath]

👋 Join our Discord

News

[12/19/2023] 🔥 We released WizardMath-7B-V1.1 trained from Mistral-7B, the SOTA 7B math LLM, achieves 83.2 pass@1 on GSM8k, and 33.0 pass@1 on MATH. Use this [Demo] to chat with it.

[12/19/2023] 🔥 WizardMath-7B-V1.1 outperforms ChatGPT 3.5, Gemini Pro, Mixtral MOE, and Claude Instant on GSM8K pass@1.

[12/19/2023] 🔥 WizardMath-7B-V1.1 is comparable with ChatGPT 3.5, Gemini Pro, and surpasses Mixtral MOE on MATH pass@1.

Model	Checkpoint	Paper	GSM8k	MATH	Demo
WizardMath-7B-V1.1	🤗 HF Link	📃 [WizardMath]	83.2	33.0	[Demo]
WizardMath-70B-V1.0	🤗 HF Link	📃 [WizardMath]	81.6	22.7
WizardMath-13B-V1.0	🤗 HF Link	📃 [WizardMath]	63.9	14.0
WizardMath-7B-V1.0	🤗 HF Link	📃 [WizardMath]	54.9	10.7

[12/19/2023] Comparing WizardMath-7B-V1.1 with other open source 7B size math LLMs.

Model	GSM8k Pass@1	MATH Pass@1
MPT-7B	6.8	3.0
Llama 1-7B	11.0	2.9
Llama 2-7B	12.3	2.8
Yi-6b	32.6	5.8
Mistral-7B	37.8	9.1
Qwen-7b	47.8	9.3
RFT-7B	50.3	--
MAmmoTH-7B (COT)	50.5	10.4
WizardMath-7B-V1.0	54.9	10.7
Abel-7B-001	59.7	13
MetaMath-7B	66.5	19.8
Arithmo-Mistral-7B	74.7	25.3
MetaMath-Mistral-7B	77.7	28.2
Abel-7B-002	80.4	29.5
WizardMath-7B-V1.1	83.2	33.0

[12/19/2023] Comparing WizardMath-7B-V1.1 with large open source (30B~70B) LLMs.

Model	GSM8k Pass@1	MATH Pass@1
Llemma-34B	51.5	25.0
Minerva-62B	52.4	27.6
Llama 2-70B	56.8	13.5
DeepSeek 67B	63.4	--
Gork 33B	62.9	23.9
MAmmoTH-70B	72.4	21.1
Yi-34B	67.9	15.9
Mixtral 8x7B	74.4	28.4
MetaMath-70B	82.3	26.6
WizardMath-7B-V1.1	83.2	33.0

❗ Data Contamination Check:

Before model training, we carefully and rigorously checked all the training data, and used multiple deduplication methods to verify and prevent data leakage on GSM8k and MATH test set.

🔥 ❗Note for model system prompts usage:

Please use the same systems prompts strictly with us, and we do not guarantee the accuracy of the quantified versions.

Default version:

"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"

CoT Version: （❗For the simple math questions, we do NOT recommend to use the CoT prompt.）

"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step."

Inference WizardMath Demo Script

We provide the WizardMath inference demo code here.

Citation

Please cite the repo if you use the data, method or code in this repo.

@article{luo2023wizardmath,
  title={WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct},
  author={Luo, Haipeng and Sun, Qingfeng and Xu, Can and Zhao, Pu and Lou, Jianguang and Tao, Chongyang and Geng, Xiubo and Lin, Qingwei and Chen, Shifeng and Zhang, Dongmei},
  journal={arXiv preprint arXiv:2308.09583},
  year={2023}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	66.61
AI2 Reasoning Challenge (25-Shot)	61.86
HellaSwag (10-Shot)	84.50
MMLU (5-Shot)	61.53
TruthfulQA (0-shot)	47.04
Winogrande (5-shot)	77.35
GSM8k (5-shot)	67.40