#fine-tuning
4 posts tagged with #fine-tuning
Every article below is hand-written, technically reviewed, and focused on fine-tuning. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
Technology How to Run Unsloth on AMD GPUs (ROCm) [2026 Guide]
A late‑2026 compatibility matrix plus known‑good commands for getting Unsloth LoRA fine‑tuning to actually converge on Radeon and MI GPUs with ROCm.
AI and Machine Learning Fine-Tuning vs RAG vs Prompt Engineering: Decision Framework [2026]
Stop guessing which LLM technique to use. A 2026-updated decision matrix with real cost figures, concrete examples, and a clear flowchart for when fine-tuning beats RAG, when RAG beats both, and when prompt engineering alone is the right answer.
![Fine-Tune Open-Source LLMs: LoRA, QLoRA, Gemma 4 [2026]](https://img.kunalganglani.com/images/vzekdneq/production/bbdfb150ddb4280a3411f8cbcf4d7f0f54cfdf63-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning Fine-Tune Open-Source LLMs: LoRA, QLoRA, Gemma 4 [2026]
A practical 2026 guide to fine-tuning open-source LLMs with LoRA and QLoRA using Unsloth + Gemma 4 — including GPU requirements, hyperparameter defaults, evaluation setup, and when to just prompt instead.
AI and Machine Learning Fine-Tuning Gemma 2 for Code Generation: 31 Percentage Points of Accuracy for Under $15 [2026 Guide]
I fine-tuned Google's Gemma 2 9B model for Python code generation using QLoRA on a single GPU. Accuracy jumped from 42% to 73% — here's exactly how, what it cost, and what I learned.