118 lines
4.4 KiB
Markdown
118 lines
4.4 KiB
Markdown
---
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license: llama2
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datasets:
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- ise-uiuc/Magicoder-OSS-Instruct-75K
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- ise-uiuc/Magicoder-Evol-Instruct-110K
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library_name: transformers
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pipeline_tag: text-generation
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---
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# 🎩 Magicoder: Source Code Is All You Need
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> Refer to our GitHub repo [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/) for an up-to-date introduction to the Magicoder family!
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* 🎩**Magicoder** is a model family empowered by 🪄**OSS-Instruct**, a novel approach to enlightening LLMs with open-source code snippets for generating *low-bias* and *high-quality* instruction data for code.
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* 🪄**OSS-Instruct** mitigates the *inherent bias* of the LLM-synthesized instruction data by empowering them with *a wealth of open-source references* to produce more diverse, realistic, and controllable data.
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## Model Details
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### Model Description
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* **Developed by:**
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[Yuxiang Wei](https://yuxiang.cs.illinois.edu),
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[Zhe Wang](https://github.com/zhewang2001),
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[Jiawei Liu](https://jiawei-site.github.io),
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[Yifeng Ding](https://yifeng-ding.com),
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[Lingming Zhang](https://lingming.cs.illinois.edu)
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* **License:** [Llama 2](https://ai.meta.com/llama/license/)
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* **Finetuned from model:** [CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)
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### Model Sources
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* **Repository:** <https://github.com/ise-uiuc/magicoder>
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* **Paper:** <https://arxiv.org/abs/2312.02120>
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* **Demo (powered by [Gradio](https://www.gradio.app)):**
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<https://github.com/ise-uiuc/magicoder/tree/main/demo>
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### Training Data
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* [Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder_oss_instruct_75k): generated through **OSS-Instruct** using `gpt-3.5-turbo-1106` and used to train both Magicoder and Magicoder-S series.
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* [Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder_evol_instruct_110k): decontaminated and redistributed from [theblackcat102/evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1), used to further finetune Magicoder series and obtain Magicoder-S models.
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## Uses
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### Direct Use
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Magicoders are designed and best suited for **coding tasks**.
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### Out-of-Scope Use
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Magicoders may not work well in non-coding tasks.
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## Bias, Risks, and Limitations
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Magicoders may sometimes make errors, producing misleading contents, or struggle to manage tasks that are not related to coding.
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
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## How to Get Started with the Model
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Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library.
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```python
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from transformers import pipeline
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import torch
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MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
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@@ Instruction
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{instruction}
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@@ Response
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"""
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instruction = <Your code instruction here>
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prompt = MAGICODER_PROMPT.format(instruction=instruction)
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generator = pipeline(
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model="ise-uiuc/Magicoder-S-CL-7B",
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task="text-generation",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0)
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print(result[0]["generated_text"])
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```
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## Technical Details
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Refer to our GitHub repo: [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/).
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## Citation
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```bibtex
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@misc{magicoder,
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title={Magicoder: Source Code Is All You Need},
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author={Yuxiang Wei and Zhe Wang and Jiawei Liu and Yifeng Ding and Lingming Zhang},
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year={2023},
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eprint={2312.02120},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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## Acknowledgements
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* [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder): Evol-Instruct
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* [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder): Base model for Magicoder-DS
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* [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/): Base model for Magicoder-CL
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* [StarCoder](https://arxiv.org/abs/2305.06161): Data decontamination
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## Important Note
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Magicoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets. Magicoders will not compete with OpenAI's commercial products.
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