Magicoder: Source Code Is All You Need
Magicoder is an innovative project that introduces a powerful model family designed to work with programming codes. It leverages a groundbreaking approach called OSS-Instruct, which uses open-source code snippets to enhance large language models (LLMs). This process helps in generating instructional data that is low in bias and high in quality, aiming for more realistic and versatile code generation.
Models
Magicoder offers several models, each with specific capabilities and features:
- Magicoder-CL-7B: With a size of 7 billion parameters, this model achieves notable results on HumanEval and MBPP benchmarks.
- Magicoder-S-CL-7B: This variant improves upon the CL model by offering better performance on the same benchmarks.
- Magicoder-DS-6.7B: With 6.7 billion parameters, this model focuses on refining code generation techniques.
- Magicoder-S-DS-6.7B: The standout model, surpassing known models like GPT-3.5-turbo-1106 and Gemini Ultra in specific evaluations.
Key Achievements
- Magicoder models have inspired a host of new projects, such as Google CodeGemma and OpenCodeInterpreter.
- Magicoder-S-DS-6.7B has demonstrated superior performance on the HumanEval benchmark, significantly outscoring some of the leading AI models in its category.
Datasets
Magicoder utilizes two major datasets:
- Magicoder-OSS-Instruct-75K: Produced using the OSS-Instruct framework, this dataset aids in the foundational training of Magicoder series models.
- Magicoder-Evol-Instruct-110K: Originated from evol-codealpaca, this dataset is used for further fine-tuning Magicoder models to enhance their performance and capabilities.
Quick Start Guide
Magicoder offers a seamless integration experience through a Python-based code that demonstrates its functionality. Users can avail themselves of these pre-trained models to generate code solutions and interact with them directly via code snippets. Here’s a simplified walkthrough for using Magicoder to create a basic TODO list API:
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 = "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."
prompt = MAGICODER_PROMPT.format(instruction=instruction)
generator = pipeline(
model="ise-uiuc/Magicoder-S-DS-6.7B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
print(result[0]["generated_text"])
Online and Local Demos
For immediate testing, users can access an online demo powered by Gradio, or set up a local server for more personalized experiments with Magicoder models. Detailed instructions are provided for setting up a local Gradio demo using straightforward command-line instructions.
Acknowledgements & Citation
The Magicoder team appreciates the invaluable input from collaborating projects and individuals that inspired its development. For academic reference or further study, a citation is available to guide users in acknowledging the work formally:
@article{wei2023magicoder,
title={Magicoder: Source Code Is All You Need},
author={Wei, Yuxiang and Wang, Zhe and Liu, Jiawei and Ding, Yifeng and Zhang, Lingming},
journal={arXiv preprint arXiv:2312.02120},
year={2023}
}
Conclusion
Magicoder stands as a substantial advancement in the realm of code generation, drawing upon open-source resources to offer precise, varied, and manageable coding solutions. Its models not only excel in benchmarks but also set a roadmap for future innovations in AI-enhanced coding tools.