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Fine-Tune the Llama Model

prerequisites: Basic Python • Basics of LLMs
skills learned: Develop data set of finetuning • Finetune an LLM

Michael Yuan and Tony Yuan

1 week · 4-6 hours per week · BEGINNER

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Your local school district has an LLM chatbot specialized in chemistry. But it sometimes answers questions entirely unrelated to chemistry! To improve the safety of this LLM application, you are asked to come up with a new LLM that would classify student questions and return machine-readable (JSON) messages to the application frontend so that the application can decide whether to answer this question. To accomplish that, you need fine-tuning to "teach" the model how to classify questions through many examples, which themselves are generated by a Llama-based LLM, and to always respond with JSON.

This project is a part of the series Open Source LLMs on Your Own Computer.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project authors

Michael Yuan

Dr. Michael Yuan is the maintainer of the open-source WasmEdge project, the most popular WebAssembly (Wasm) sandboxes for edge applications and AI agents. He is a Docker Captain and the creator of the VibeKeys series of agentic devices. Dr. Yuan holds a PhD in Astrophysics from the University of Texas at Austin.

Tony Yuan

Tony Yuan is a student at Liberal Arts and Science Academy in Austin, Texas, who loves technology and AI. He serves as the VP for the Student Council and represents the school in national Quiz Bowl and History Bowl competitions. He leads the Map Wars club to teach geopolitics to high school students. He enjoys listening to rock music and reading history books when he's not programming or studying.

prerequisites

This liveProject is for software developers and IT professionals who are interested in building LLM applications in their own domains. To begin this liveProject, you will need to know the following:

TOOLS

Intermediate Linux
Basics CMAKE and Linux dev tools
Basics of Git
Basics of Hugging Face
Basics of Python

TECHNIQUES

Command line tools
Basics of LLMs
Basics of model training and inference
Deploying and testing web services

features

Self-paced: You choose the schedule and decide how much time to invest as you build your project.
Project roadmap: Each project is divided into several achievable steps.
Get Help: While within the liveProject platform, get help from fellow participants and even more help with paid sessions with our expert mentors.
Compare with others: For each step, compare your deliverable to the solutions by the author and other participants.
books included as resources: Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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