Ise-uiuc
Models by this creator
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Magicoder-S-DS-6.7B
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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.
Updated 5/28/2024
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Magicoder-S-CL-7B
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The Magicoder-S-CL-7B model is part of the Magicoder family of models developed by Intelligent Software Engineering (iSE). It is powered by the novel OSS-Instruct approach, which empowers large language models (LLMs) with open-source code snippets to generate low-bias and high-quality instruction data for coding tasks. This helps mitigate the inherent bias of LLM-synthesized data by providing a wealth of diverse, realistic, and controllable references. The Magicoder-S-CL-7B model was fine-tuned from the CodeLlama-7b-Python-hf model. It was trained on two datasets: the Magicoder-OSS-Instruct-75K dataset generated through OSS-Instruct, and the Magicoder-Evol-Instruct-110K dataset, which was decontaminated and redistributed from the evol-codealpaca-v1 dataset. Model inputs and outputs Inputs Coding instructions**: Prompts or requests for the model to generate code or complete coding tasks. Outputs Generated code**: The model's response in the form of source code that aims to complete the provided coding instruction. Capabilities The Magicoder-S-CL-7B model is designed and best suited for coding tasks. It can generate code to solve a wide variety of programming problems, from simple tasks to more complex challenges. The model's capabilities include writing functions, implementing algorithms, and solving coding challenges across different programming languages and domains. What can I use it for? The Magicoder-S-CL-7B model can be used for a range of coding-related applications, such as: Code generation**: Automatically generating code to complete programming tasks or solve coding challenges. Code assistance**: Providing suggestions and completing partial code snippets to help developers write more efficient and effective code. Learning and education**: Using the model as a learning tool to help students and beginners understand programming concepts and syntax. Prototyping and experimentation**: Quickly generating code prototypes to test ideas and explore new approaches. Things to try One interesting thing to try with the Magicoder-S-CL-7B model is to provide it with open-ended coding challenges or prompts that require creative problem-solving. Observe how the model approaches and attempts to solve these more complex tasks, and how the generated code compares to what a human programmer might produce. This can provide valuable insights into the model's capabilities and limitations when it comes to more nuanced and open-ended coding problems.
Updated 9/6/2024