README.md

---
license: llama2
library_name: transformers
datasets:
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
pipeline_tag: text-generation
model-index:
- name: Magicoder-S-CL-7B
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 43.34
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ise-uiuc/Magicoder-S-CL-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 67.01
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ise-uiuc/Magicoder-S-CL-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 36.87
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ise-uiuc/Magicoder-S-CL-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 38.67
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ise-uiuc/Magicoder-S-CL-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 62.19
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ise-uiuc/Magicoder-S-CL-7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 14.33
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ise-uiuc/Magicoder-S-CL-7B
      name: Open LLM Leaderboard
---
# 🎩 Magicoder: Source Code Is All You Need

> Refer to our GitHub repo [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/) for an up-to-date introduction to the Magicoder family!

* 🎩**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.
* 🪄**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.

![Overview of OSS-Instruct](assets/overview.svg)
![Overview of Result](assets/result.png)

## Model Details

### Model Description

* **Developed by:**
[Yuxiang Wei](https://yuxiang.cs.illinois.edu),
[Zhe Wang](https://github.com/zhewang2001),
[Jiawei Liu](https://jiawei-site.github.io),
[Yifeng Ding](https://yifeng-ding.com),
[Lingming Zhang](https://lingming.cs.illinois.edu)
* **License:** [Llama 2](https://ai.meta.com/llama/license/)
* **Finetuned from model:** [CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)

### Model Sources

* **Repository:** <https://github.com/ise-uiuc/magicoder>
* **Paper:** <https://arxiv.org/abs/2312.02120>
* **Demo (powered by [Gradio](https://www.gradio.app)):**
<https://github.com/ise-uiuc/magicoder/tree/main/demo>

### Training Data

* [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.
* [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.

## Uses

### Direct Use

Magicoders are designed and best suited for **coding tasks**.

### Out-of-Scope Use

Magicoders may not work well in non-coding tasks.

## Bias, Risks, and Limitations

Magicoders may sometimes make errors, producing misleading contents, or struggle to manage tasks that are not related to coding.

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

## How to Get Started with the Model

Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library.

```python
from transformers import pipeline
import torch

MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.

@@ Instruction
{instruction}

@@ Response
"""

instruction = <Your code instruction here>

prompt = MAGICODER_PROMPT.format(instruction=instruction)
generator = pipeline(
    model="ise-uiuc/Magicoder-S-CL-7B",
    task="text-generation",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0)
print(result[0]["generated_text"])
```

## Technical Details

Refer to our GitHub repo: [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/).

## Citation

```bibtex
@misc{magicoder,
    title={Magicoder: Source Code Is All You Need}, 
    author={Yuxiang Wei and Zhe Wang and Jiawei Liu and Yifeng Ding and Lingming Zhang},
    year={2023},
    eprint={2312.02120},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

## Acknowledgements

* [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder): Evol-Instruct
* [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder): Base model for Magicoder-DS
* [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/): Base model for Magicoder-CL
* [StarCoder](https://arxiv.org/abs/2305.06161): Data decontamination

## Important Note

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.

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ise-uiuc__Magicoder-S-CL-7B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |43.73|
|AI2 Reasoning Challenge (25-Shot)|43.34|
|HellaSwag (10-Shot)              |67.01|
|MMLU (5-Shot)                    |36.87|
|TruthfulQA (0-shot)              |38.67|
|Winogrande (5-shot)              |62.19|
|GSM8k (5-shot)                   |14.33|