Fine tune an LLM

2026-07-09 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

Victor Yeo successfully fine-tuned the Qwen 2.5 (7B) large language model using Supervised Fine-Tuning (SFT) to incorporate custom company policies, creating a standalone, locally deployable LLM. The end-to-end instruction-tuning pipeline involved several critical steps: initially quantizing the base model to 4-bit using BitsAndBytesConfig (NF4/double quant) to manage VRAM on consumer hardware. Subsequently, LoRA fine-tuning was applied to both attention projections and MLP layers with TRL's SFTTrainer in native bfloat16. The LoRA weights were then permanently merged into the unquantized base architecture, yielding a fully independent model. Finally, the model was exported to GGUF format via llama.cpp and deployed locally using Ollama. The process encountered and resolved issues related to TRL data parsing, PyTorch mixed-precision handling with bfloat16 gradients, and tokenizer formatting discrepancies between Hugging Face and llama.cpp.

Key takeaway

For AI Engineers aiming to deploy custom LLMs on consumer-grade hardware, this pipeline offers a robust approach. You should consider 4-bit quantization and LoRA fine-tuning to manage VRAM, then merge weights for a standalone model. Exporting to GGUF via llama.cpp and deploying with Ollama provides full local control. Be prepared to troubleshoot mixed-precision and tokenizer formatting issues for successful implementation.

Key insights

The article demonstrates an end-to-end pipeline for fine-tuning and deploying a custom LLM locally, overcoming common technical hurdles.

Principles

• Quantization enables LLM fine-tuning on consumer GPUs.
• Merging LoRA weights creates standalone, deployable models.
• Troubleshooting mixed-precision and format issues is key.

Method

The method involves 4-bit quantization, LoRA fine-tuning with TRL's SFTTrainer, merging LoRA weights, and GGUF export via llama.cpp for local deployment.

In practice

• Fine-tune LLMs for specific company policies.
• Deploy custom LLMs locally with Ollama.
• Optimize LLM memory footprint with 4-bit quantization.

Topics

• LLM Fine-tuning
• LoRA
• Quantization
• GGUF
• llama.cpp
• Local LLM Deployment

Best for: AI Engineer, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.