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 the fine-tune actually worked.
How Much Data Do You Need to Fine-Tune?
Fine-tuning dataset size: no magic number — the variables that decide yours, the knee where more data stops paying, and how to find it 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.
Supervised Fine-Tuning (SFT) for LLMs: A Complete Guide
Supervised fine-tuning end to end — dataset shape, adapter choice, the hyperparameters that matter, reading a training run, and the mistakes that ruin one.
LoRA vs QLoRA: Parameter-Efficient Fine-Tuning Explained
LoRA and QLoRA in practice: the memory ladder from 56 GB to 12, the rank-and-alpha defaults that hold up, and the failures each method is not for.
Full Fine-Tuning: When and Why to Train Every Parameter
When full fine-tuning beats adapters: the VRAM ladder to 560 GB, the dataset thresholds that justify it, and the cost controls that keep a run sane.
Continued Pre-Training: Domain Adaptation for Large Language Models
When continued pre-training earns its place for domain adaptation — and when retrieval or SFT is the sharper tool.
VLM Fine-Tuning: How to Train Vision-Language Models
Fine-tuning VLMs — InternVL, Qwen-VL, LLaVA: the image-text dataset shape, the freeze-the-encoder rule, and VRAM planning for multimodal runs.
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 fine-tuning datasets: the four formats, SFT and preference shapes, the quality bar that beats quantity, and validation before a run starts.
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 Vidman AI 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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