Overview
This series shows how LlamaFactory keeps dataset meaning, model templates, adaptation scope, hardware, objectives, artifacts, and serving paths aligned. It moves from data-to-token contracts through memory and distributed choices to evaluation and deployment, while treating compatibility, performance estimates, and support as conditional evidence rather than guarantees.
Learning outcomes
- LlamaFactory's useful unit is a compatible workflow path, not a feature list: model, data, method, hardware, artifact, and backend must agree.
- A structurally valid dataset can still teach the wrong behavior when schema, template, labels, reasoning settings, or media alignment are wrong.
- Choose full, freeze, LoRA, or OFT by required behavioral scope and artifact needs; add memory optimizations only for measured bottlenecks.
- A successful launch, loss curve, checkpoint, or service response is incomplete evidence; objective-aligned and external task checks remain necessary.
Keywords
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PlansFAQ
What does LlamaFactory actually coordinate?
LlamaFactory coordinates the path from model and dataset inputs through configuration, data and template processing, adaptation, training, evaluation, export, and serving.
Why can a LlamaFactory training run succeed but teach the wrong behavior?
A run can complete while schema mapping, conversation formatting, label masks, reasoning settings, or media alignment gives the model a different training signal from the one intended.
How should you choose a LlamaFactory fine-tuning method?
Choose full tuning, freeze tuning, LoRA, or OFT according to the breadth of the desired behavior, available memory, and whether the result must remain an adapter or become a merged artifact.
Does a completed LlamaFactory run prove the model is ready to serve?
No. A completed run still needs objective-aligned evaluation, external task checks, artifact verification, and testing with the exact serving backend and prompt construction.