Author(s): Abhay Aditya

Originally published on Towards AI.

Here is the step-by-step story of how I customized Meta’s Llama 3 8B using Unsloth, LoRA, and a “Silent Coder” approach, all within the RAM limits of a free Google Colab instance.

I’ll be honest with you: looking at the hardware requirements for modern Large Language Models (LLMs) is usually enough to kill any student’s motivation.

After the lead, the author explains how they trained an 8B Llama model for multi-step mathematical “reasoning” on free Colab by breaking the work into phases: understanding the “Silent Coder” mindset for truly reading and internalizing code; speeding fine-tuning with Unsloth; fitting the model into limited GPU memory via 4-bit quantization; using LoRA so only a small fraction of parameters are trained; shaping the dataset and training settings (e.g., penalizing only the solution path) to encourage reasoning instead of memorization; tuning batch/gradient accumulation to simulate larger hardware; and tracking results through training loss convergence and exact-match accuracy on unseen test problems. They then deploy the model outside the notebook by exporting to GGUF and building a custom Gradio streaming UI, publishing the pipeline and emphasizing why this kind of entry-level, architecture-driven approach makes capable fine-tuning accessible to more people.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI