--- 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 --- ## WizardCoder: Empowering Code Large Language Models with Evol-Instruct

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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](https://evalplus.github.io/leaderboard.html), 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](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM, and achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), 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](http://47.103.63.15:50083/)| Llama 2 | | WizardMath-13B-V1.0 | 🤗 HF Link | 📃 [WizardMath]| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| Llama 2 | | WizardMath-7B-V1.0 | 🤗 HF Link | 📃 [WizardMath]| **54.9** | **10.7** | [Demo ](http://47.103.63.15:50080/)| Llama 2 | - [08/09/2023] We released **WizardLM-70B-V1.0** model. Here is [Full Model Weight](https://huggingface.co./WizardLM/WizardLM-70B-V1.0). | ^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](https://github.com/nlpxucan/WizardLM/tree/main/demo). 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 = ['']` to `stop_tokens = ['<|endoftext|>']` in the script. ## 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} } ```