Fine Tune LLaMA 3.2 for Python Code Generation – Step-by-Step Guide
In this video, I'll walk through the steps of fine tuning LLaMA 3.2 (1B model) for Python code generation! 🔥
The key steps covered in the video include:
1. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 – Formatting the dataset to align with LLaMA's processing requirements.
2. 𝗤𝗟𝗼𝗥𝗔 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 – Setting optimal parameters for efficient fine-tuning.
3. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗦𝗲𝘁𝘂𝗽 – Defining training arguments and execution parameters.
This model can be fine-tuned and run on a single GPU with 16GB of memory.
Now let's dive in! 🚀💡
Source code: https://github.com/dtdo90/Llama3.2_py...
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