Mastering LoRA — Fine-tune a 7B Model on a Single Notebook
From LoRA theory to hands-on Qwen 2.5 7B fine-tuning. Train only 0.18% of parameters while achieving 98% of full fine-tuning performance. VRAM reduced from 130GB to 18GB.
Mastering LoRA — Fine-tune a 7B Model on a Single Notebook
What if you could fine-tune a 7-billion-parameter model on a single GPU?
Just two years ago, LLM fine-tuning required 8x A100 GPUs and hundreds of gigabytes of memory — a luxury reserved for big tech companies. LoRA (Low-Rank Adaptation) changed the game entirely. For a 7B model, it reduces trainable parameters to 0.1% while achieving performance on par with full fine-tuning.
In this series, we walk through the entire pipeline — LoRA, QLoRA, evaluation, and deployment — using Qwen 2.5 7B as our target model.
- Part 1 (this post): LoRA theory + first fine-tune
Related Posts
Agentic RAG Pipeline — Multi-step Retrieval in Production
Build a full Plan-Retrieve-Evaluate-Synthesize pipeline. Unify vector search, web search, and SQL as agent tools. Add hallucination detection and source grounding.
Self-RAG and Corrective RAG — The Agent Evaluates Its Own Retrieval
Implement Self-RAG reflection tokens and CRAG quality-based fallback. Build retry/fallback logic with LangGraph conditional edges.
Why Agentic RAG? — Query Routing and Adaptive Retrieval
Diagnose naive RAG limitations, classify query intent, and route to the optimal retrieval source with LangGraph. Implement adaptive retrieval that skips unnecessary searches.