Magicoder-S-DS-6.7B
Maintainer: ise-uiuc
198
🛠️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
Magicoder-S-DS-6.7B is a model in the Magicoder family, developed by researchers at the University of Illinois Urbana-Champaign (UIUC). The model is empowered by a novel approach called "OSS-Instruct", which enlightens large language models (LLMs) with open-source code snippets to generate high-quality and low-bias instructional data for coding tasks. This mitigates the inherent bias of LLM-synthesized instruction data by providing a wealth of open-source references to produce more diverse, realistic, and controllable data.
The Magicoder models are designed and best suited for coding tasks, and may not work as well for non-coding tasks. Similar models include codellama-13b-instruct from Meta, chatglm3-6b from nomagick, and other Llama-based models fine-tuned for coding by Meta and others.
Model Inputs and Outputs
Inputs
- Text prompts for coding-related tasks, such as code generation, code explanation, or code translation.
Outputs
- Generated code, code explanations, or code translations, depending on the specific task.
Capabilities
The Magicoder-S-DS-6.7B model is capable of generating high-quality code and providing explanations for code snippets. It can be used for a variety of coding-related tasks, such as code generation, code translation, and code understanding.
What Can I Use It For?
The Magicoder-S-DS-6.7B model can be used for a variety of coding-related projects, such as developing intelligent code assistants, automating code generation, or enhancing code understanding. It could be particularly useful for companies looking to improve their software development workflows or for individual developers seeking to boost their coding productivity.
Things to Try
One interesting thing to try with the Magicoder-S-DS-6.7B model is to provide it with a coding prompt and observe how it generates code that is both syntactically correct and semantically meaningful. You could also try providing the model with a code snippet and asking it to explain the purpose and functionality of the code.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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