ToolDix study guide · Hands-on lab
LlamaFactory Getting Started
LlamaFactory
A practical entry point for supervised fine-tuning, preference optimization, multimodal models, dataset configuration, evaluation, inference, and export across many model families.
Start with the source
The original publisher hosts the complete material and the current terms of use. This button leaves ToolDix and opens github.com in a new tab.
How to use this resource
Why it matters
Fine-tuning quality depends on data rights, formatting, evaluation, compute budgets, and reproducibility; the project exposes those operational choices instead of hiding them behind one API call.
First practical move
Use a small licensed dataset and the lightest supported training method, record the base-model baseline, and stop unless the tuned model improves a held-out evaluation set.
Good fit for
ML engineers with a controlled dataset and explicit evaluation goal
Source and publishing context
This page is an original ToolDix editorial guide. We do not reproduce the source's full article, course media, figures, or book pages. Official LlamaFactory repository; source code is Apache-2.0, while every model, dataset, and generated artifact must be reviewed under its own license and terms.
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