Fine-Tuning Guides
Practical guides to training models you own: supervised fine-tuning, parameter-efficient adapters like LoRA and QLoRA, continued pre-training, vision-language models, dataset preparation and what GPU time actually costs.
How to Know Your Fine-Tune Worked
Training loss always falls. That is not the question. Held-out sets, overfitting signatures, metrics that lie and how to know your fine-tune actually worked.
How Much Data Do You Need to Fine-Tune?
How much data do you need to fine-tune? No magic number, but a shape: what decides your count, where more data stops helping and how to find out cheaply.
Fine-Tune, Prompt or RAG? A Decision That Deserves Better Than a Default
Prompting changes behavior, RAG supplies facts, fine-tuning locks in behavior at scale. How to pick the right lever for the problem you actually have.
Complete Guide to Supervised Fine-Tuning (SFT) for LLMs
Supervised fine-tuning for LLMs, end to end: dataset format, adapters, hyperparameters, evaluation and the mistakes that ruin runs.
LoRA vs QLoRA: Parameter-Efficient Fine-Tuning Explained
LoRA vs QLoRA for parameter-efficient fine-tuning: the memory math, the quality trade-offs and when to pick each.
Full Fine-Tuning: When and Why to Train Every Parameter
When does full fine-tuning beat LoRA and QLoRA? VRAM requirements, dataset size, cost and the cases where adapters lose.
Continued Pre-Training: Domain Adaptation for Large Language Models
When and how to use continued pre-training to adapt LLMs to specialized domains, and when retrieval or SFT is the better tool.
VLM Fine-Tuning: How to Train Vision-Language Models
How to fine-tune vision-language models (InternVL, Qwen-VL, LLaVA): image-text datasets, frozen vision encoders and memory planning.
LLM vs VLM Fine-Tuning: Key Differences and When to Choose Each
Compare text-only LLM fine-tuning with vision-language model (VLM) fine-tuning. Understand dataset formats, training differences, memory needs and use cases.
Dataset Preparation for AI Fine-Tuning: Formats and Best Practices
Preparing datasets for LLM and VLM fine-tuning: JSONL, Parquet, CSV formats, SFT and preference structures and the quality bar that matters.
Adapter Types Compared: LoRA, QLoRA, Full Fine-Tune and Beyond
Compare all adapter types for LLM fine-tuning: LoRA, QLoRA, full fine-tune, AdaLoRA, LoHa, BOFT and ReFT. Learn which adapter fits your use case and budget.
Per-Second GPU Billing: How to Optimize AI Training Costs
Learn how per-second billing works on Run BiOS and how to optimize training costs. Compare with hourly billing, estimate costs and choose the right GPU tier.
Distillation: Teaching Small Models From Big Ones
Distillation teaches a small model what a big model knows. What transfers, what does not and when the student is worth the tuition.
Synthetic Data for Fine-Tuning: Good Enough?
Synthetic data fills the fine-tuning shortage, if it is validated. The generation loop, the failure modes and when generated examples are good enough.
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