skills.rest
tadod12
tadod12COMMUNITY
Explore all skills by this author →
GitHub

llama-factory

Guides users to fine-tune LLaMA-Factory models via a WebUI no-code workflow.

Updated Mar 18, 2026
Search Tags:#llm#fine-tuning#multimodal#lora#webui#qlora#llama-factory
One-click install
npx skills add https://github.com/tadod12/fraud-detection-research --skill llama-factory-tadod12
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-factory
Source: https://github.com/tadod12/fraud-detection-research/tree/main/.agent/skills/03-fine-tuning/llama-factory
Command: npx skills add https://github.com/tadod12/fraud-detection-research --skill llama-factory-tadod12

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires llmtuner, torch, transformers, datasets, peft, accelerate, gradio, and includes references (resource) components.

What problem does it solve?

This Skill provides expert guidance for fine-tuning LLaMA-Factory models, including WebUI no-code workflows and support for 100+ models, multiple quantization and LoRA options.

Core Features & Use Cases

  • Comprehensive guidance for llama-factory development, covering setup, reference docs, and best practices.
  • Supports multimodal workflows, QLoRA/LoRA/quantization, and integration with HuggingFace models for research experimentation.
  • Real-world use: fine-tune a base LLaMA model on a niche dataset using WebUI and export a ready-to-deploy model.

Quick Start

Configure a LLaMA-Factory training project and start a LoRA-based fine-tuning session with your dataset.

Frequently Asked Questions about llama-factory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a LLaMA model without writing code?▼

Fine-tune LLaMA models without code by using the WebUI workflow, which provides a graphical interface to configure training parameters, load datasets, and start LoRA-based fine-tuning sessions. This simplifies the entire process from setup to export.

What adapter methods are supported for LLM fine-tuning?▼

Supported adapter methods for LLM fine-tuning include LoRA, PiSSA, and GaLore. These options allow you to efficiently update model weights during training while reducing memory consumption through parameter-efficient techniques.

Can I use QLoRA and quantization for multimodal models?▼

Yes, you can use QLoRA and quantization options with multimodal workflows. The system supports integration with HuggingFace models, enabling research experimentation across 100+ models while optimizing memory usage during training.

Does this workflow support distributed training across multiple GPUs?▼

Yes, the workflow supports distributed training across both single and multi-GPU setups. It leverages dependencies like accelerate and torch to manage distributed training configurations for research and production environments.

What is the best way to prepare datasets for LLaMA-Factory fine-tuning?▼

The best way to prepare datasets is to follow the provided practical configuration examples and official documentation. These resources guide you through formatting requirements and setup needed for LoRA-based fine-tuning sessions.

Related Skills

Explore More Education & Research Skills→
ever-oliever-oli
3

fine-tuning-with-trl

Fine-tune HuggingFace Transformers with TRL for RLHF alignment.

Community
Advanced
ScientiaCapitalScientiaCapital
1

model-deployment

Export fine-tuned models to GGUF and deploy with vLLM or Ollama.

Community
Advanced
kngender5kngender5

gradio-app-builder

Build and deploy interactive web applications with Gradio.

Community
Intermediate
Tyler-R-KendrickTyler-R-Kendrick
1

trl-training

Train transformer language models with SFT, DPO, GRPO, and RLOO via TRL.

Community
Advanced