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Do you want an AI coding assistant that perfectly understands your personal coding style, variable naming, and repository structure? Discover how to fine-tune a local Large Language Model (LLM) on your own codebase for free, using nothing but Unsloth and a consumer GPU (or a free Google Colab). 🔔 Subscribe: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w?sub_confirmation=1 💙 Become a Member: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w/join 🐦 Twitter/X: https://x.com/cloud_codes 💬 Discord: https://discord.gg/4kJqEBMMf In this video, Cloud Codes breaks down the system design of LLM Fine-Tuning. We explore the critical difference between RAG (Retrieval-Augmented Generation) and Fine-Tuning: why RAG is for "facts" and Fine-Tuning is for "form/style." We walk you through the entire Unsloth pipeline: downloading a base model in 4-bit, configuring a LoRA adapter (QLoRA), generating a JSONL dataset directly from your git commits, and running Supervised Fine-Tuning (SFT). Finally, we show you how to merge your custom LoRA adapter back into the base model, export it as a GGUF file, and run it completely offline using Ollama. We also cover the honest catches: how to avoid "overfitting" and catastrophic forgetting by keeping your epochs low. ⏱️ TIMESTAMPS: 0:00 - The Custom AI Coding Assistant 1:10 - RAG vs Fine-Tuning (Facts vs Form) 2:35 - The Toolkit: Unsloth & Small Open Models (Qwen3) 4:15 - Step 1: Building a Dataset from Your Codebase 5:09 - Step 2: Loading Unsloth & QLoRA Adapters 5:53 - Step 3: Supervised Fine-Tuning (Learning Rate & Epochs) 6:37 - Step 4: Exporting to GGUF & Running in Ollama 7:37 - The Receipts: Jumping from 1% to 90% Accuracy 8:16 - The Catch: Overfitting & When You SHOULDN'T Fine-Tune 9:06 - Summary: Your Model, Your Code, Offline Forever #finetuning #localai #unsloth #softwareengineering #machinelearning #cloudcodes #lora #qlora #ollama #artificialintelligence #python User Queries: how to fine tune a local llm on your own data unsloth qlora fine tuning tutorial train ai on my codebase local rag vs fine tuning explained how to use unsloth to train an llm export lora to gguf ollama train qwen 3 local machine how to prevent overfitting in llm fine tuning system design fine tuning vs retrieval augmented generation stop paying for github copilot build your own

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