#Fine-Tuning

Fine-Tuning with Unsloth & QLoRA: Training 70B LLMs on Consumer GPUs (2026)

Distillation in Practice: Turning a Frontier Model Into a Cheap Specialist

A walkthrough of taking a narrow task off an expensive frontier model and onto a small fine-tuned model that costs a fraction as much to run, using the frontier model as the teacher.

Context Caching in LLM Fine-Tuning: Accelerating Iterative Training Runs

Discover how context caching changes language model fine-tuning pipelines. Learn how to cache structural data tokens during backpropagation cycles.

Synthetic Data Generation for Fine-Tuning Specialist Models

Real labeled data for a narrow task is often scarce, expensive, or both. Generating it synthetically works — but only if you take the quality filtering and diversity steps as seriously as the generation itself.

When to Use a Reasoning Model vs a Fine-Tuned Small Model

Two completely different ways to get a hard task right: make the model think harder at inference time, or make a smaller model already know the answer shape. Neither is universally correct.

Fine-Tuning vs RAG vs Prompting in 2026: The Architectural Decision Framework