llama-factory_skill

Overview

This skill provides expert guidance for fine-tuning large language models using LLaMA-Factory WebUI. It covers no-code workflows, support for 100+ models, quantized training (2/3/4/5/6/8-bit QLoRA), and multimodal configurations. The goal is to help engineers and researchers deploy efficient, reproducible fine-tuning pipelines.

How this skill works

The skill inspects LLaMA-Factory features, documentation references, and common usage patterns to give actionable steps and troubleshooting advice. It explains how to configure WebUI sessions, select quantization and bit-width for QLoRA, and prepare datasets for multimodal training. It also highlights scripts, templates, and automation tips for end-to-end workflows.

When to use it

• You are preparing to fine-tune a model with LLaMA-Factory WebUI.
• You need guidance selecting QLoRA bit-width or performance vs. resource trade-offs.
• You are integrating multimodal inputs (text, images) into a fine-tuning job.
• You want no-code or low-code options to run reproducible experiments.
• You are debugging training, convergence, or quantization-related issues.

Best practices

• Start with the getting_started and tutorials references to understand core flows.
• Choose QLoRA bit-width based on GPU memory and target performance; test 4-bit and 8-bit first for balance.
• Use well-structured, cleaned datasets and consistent preprocessing for multimodal inputs.
• Automate experiment logging and checkpointing; keep reproducible config files for each run.
• Validate models on held-out sets and run lightweight inference checks before full deployment.

Example use cases

• Low-cost fine-tuning of a 7B model using 4-bit QLoRA via the WebUI for domain adaptation.
• Rapid prototyping of multimodal assistants by combining text and image datasets in LLaMA-Factory.
• Comparing inference latency and accuracy across models using 2/3/4/8-bit quantized checkpoints.
• Automating batch runs with provided helper scripts to sweep hyperparameters and collect metrics.
• Debugging training instability by inspecting config, optimizer settings, and dataset examples.