WizardLMTeam/WizardCoder-Python-34B-V1.0

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

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

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News

[2024/01/04] 🔥 We released WizardCoder-33B-V1.1 trained from deepseek-coder-33b-base, the SOTA OSS Code LLM on EvalPlus Leaderboard, achieves 79.9 pass@1 on HumanEval, 73.2 pass@1 on HumanEval-Plus, 78.9 pass@1 on MBPP, and 66.9 pass@1 on MBPP-Plus.

[2024/01/04] 🔥 WizardCoder-33B-V1.1 outperforms ChatGPT 3.5, Gemini Pro, and DeepSeek-Coder-33B-instruct on HumanEval and HumanEval-Plus pass@1.

[2024/01/04] 🔥 WizardCoder-33B-V1.1 is comparable with ChatGPT 3.5, and surpasses Gemini Pro on MBPP and MBPP-Plus pass@1.

Model	Checkpoint	Paper	HumanEval	HumanEval+	MBPP	MBPP+	License
GPT-4-Turbo (Nov 2023)	-	-	85.4	81.7	83.0	70.7	-
GPT-4 (May 2023)	-	-	88.4	76.8	-	-	-
GPT-3.5-Turbo (Nov 2023)	-	-	72.6	65.9	81.7	69.4	-
Gemini Pro	-	-	63.4	55.5	72.9	57.9	-
DeepSeek-Coder-33B-instruct	-	-	78.7	72.6	78.7	66.7	-
WizardCoder-33B-V1.1	🤗 HF Link	📃 [WizardCoder]	79.9	73.2	78.9	66.9	MSFTResearch
WizardCoder-Python-34B-V1.0	🤗 HF Link	📃 [WizardCoder]	73.2	64.6	73.2	59.9	Llama2
WizardCoder-15B-V1.0	🤗 HF Link	📃 [WizardCoder]	59.8	52.4	--	--	OpenRAIL-M
WizardCoder-Python-13B-V1.0	🤗 HF Link	📃 [WizardCoder]	64.0	--	--	--	Llama2
WizardCoder-Python-7B-V1.0	🤗 HF Link	📃 [WizardCoder]	55.5	--	--	--	Llama2
WizardCoder-3B-V1.0	🤗 HF Link	📃 [WizardCoder]	34.8	--	--	--	OpenRAIL-M
WizardCoder-1B-V1.0	🤗 HF Link	📃 [WizardCoder]	23.8	--	--	--	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

[08/09/2023] We released WizardLM-70B-V1.0 model. Here is Full Model Weight.

^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).

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.

Citation

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

@article{luo2023wizardcoder,
  title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
  author={Luo, Ziyang and Xu, Can and Zhao, Pu and Sun, Qingfeng and Geng, Xiubo and Hu, Wenxiang and Tao, Chongyang and Ma, Jing and Lin, Qingwei and Jiang, Daxin},
  journal={arXiv preprint arXiv:2306.08568},
  year={2023}
}

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WizardCoder-Python-34B-V1.0