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WizardCoder-Python-13B-V1.0

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metadata
license: llama2
metrics:
  - code_eval
library_name: transformers
tags:
  - code
model-index:
  - name: WizardCoder-Python-13B-V1.0
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.64
            verified: false

🤗 HF Repo •🐱 Github Repo • 🐦 Twitter • 📃 [WizardLM] • 📃 [WizardCoder] • 📃 [WizardMath]

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News

  • 🔥🔥🔥[2023/08/26] We released WizardCoder-Python-34B-V1.0 , which achieves the 73.2 pass@1 and surpasses GPT4 (2023/03/15), ChatGPT-3.5, and Claude2 on the HumanEval Benchmarks.
  • [2023/06/16] We released WizardCoder-15B-V1.0 , which achieves the 57.3 pass@1 and surpasses Claude-Plus (+6.8), Bard (+15.3) and InstructCodeT5+ (+22.3) on the HumanEval Benchmarks.

❗Note: There are two HumanEval results of GPT4 and ChatGPT-3.5. The 67.0 and 48.1 are reported by the official GPT4 Report (2023/03/15) of OpenAI. The 82.0 and 72.5 are tested by ourselves with the latest API (2023/08/26).

Model Checkpoint Paper HumanEval MBPP Demo License
WizardCoder-Python-34B-V1.0 🤗 HF Link 📃 [WizardCoder] 73.2 61.2 Demo Llama2
WizardCoder-15B-V1.0 🤗 HF Link 📃 [WizardCoder] 59.8 50.6 -- OpenRAIL-M
WizardCoder-Python-13B-V1.0 🤗 HF Link 📃 [WizardCoder] 64.0 55.6 -- Llama2
WizardCoder-3B-V1.0 🤗 HF Link 📃 [WizardCoder] 34.8 37.4 Demo OpenRAIL-M
WizardCoder-1B-V1.0 🤗 HF Link 📃 [WizardCoder] 23.8 28.6 -- OpenRAIL-M
  • Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
  • Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM, and achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points higher than the SOTA open-source LLM.
Model Checkpoint Paper GSM8k MATH Online Demo License
WizardMath-70B-V1.0 🤗 HF Link 📃 [WizardMath] 81.6 22.7 Demo Llama 2
WizardMath-13B-V1.0 🤗 HF Link 📃 [WizardMath] 63.9 14.0 Demo Llama 2
WizardMath-7B-V1.0 🤗 HF Link 📃 [WizardMath] 54.9 10.7 Demo Llama 2
Model Checkpoint Paper MT-Bench AlpacaEval GSM8k HumanEval License
WizardLM-70B-V1.0 🤗 HF Link 📃Coming Soon 7.78 92.91% 77.6% 50.6 Llama 2 License
WizardLM-13B-V1.2 🤗 HF Link 7.06 89.17% 55.3% 36.6 Llama 2 License
WizardLM-13B-V1.1 🤗 HF Link 6.76 86.32% 25.0 Non-commercial
WizardLM-30B-V1.0 🤗 HF Link 7.01 37.8 Non-commercial
WizardLM-13B-V1.0 🤗 HF Link 6.35 75.31% 24.0 Non-commercial
WizardLM-7B-V1.0 🤗 HF Link 📃 [WizardLM] 19.1 Non-commercial

Comparing WizardCoder-Python-34B-V1.0 with Other LLMs.

🔥 The following figure shows that our WizardCoder-Python-34B-V1.0 attains the second position in this benchmark, surpassing GPT4 (2023/03/15, 73.2 vs. 67.0), ChatGPT-3.5 (73.2 vs. 72.5) and Claude2 (73.2 vs. 71.2).

WizardCoder

Prompt Format 

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

Inference Demo Script

We provide the inference demo code here.

Note: This script supports WizardLM/WizardCoder-Python-34B/13B/7B-V1.0. If you want to inference with WizardLM/WizardCoder-15B/3B/1B-V1.0, please change the stop_tokens = ['</s>'] to stop_tokens = ['<|endoftext|>'] in the script.

Citation

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

@misc{luo2023wizardcoder,
      title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, 
      author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang},
      year={2023},
}