--- 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 --- ## Note This is a replica of the official repository, intended solely for research purposes to replicate results. If there are any copyright issues, please contact me.

🤗 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](https://github.com/openai/human-eval). - [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](https://github.com/openai/human-eval). ❗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](https://arxiv.org/abs/2303.08774). 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](http://47.103.63.15:50085/) | 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-Python-7B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 55.5 | 51.6 | [Demo](http://47.103.63.15:50088/) | Llama2 | | WizardCoder-3B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 34.8 |37.4 | -- | 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](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} } ```