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llama-factory

Fine-tune and deploy LLMs using the LLaMA-Factory framework.

1|Updated Feb 10, 2026
Search Tags:#ai development#llm#machine learning#fine-tuning#model deployment#llama factory
One-click install
npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill llama-factory-informatico-madrid
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-factory
Source: https://github.com/informatico-madrid/Architect-Expert-Gap-Forge/tree/main/.github/skills/llama-factory
Command: npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill llama-factory-informatico-madrid

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the complex process of fine-tuning and deploying Large Language Models (LLMs), making advanced AI capabilities accessible without extensive coding.

Core Features & Use Cases

  • No-Code Fine-Tuning: Train over 100 LLMs using a user-friendly WebUI.
  • Advanced Training Options: Supports various fine-tuning techniques like QLoRA, multimodal training, and distributed training across multiple GPUs and machines.
  • Efficient Inference: Offers optimized inference engines for fast and scalable model deployment.
  • Use Case: A researcher wants to fine-tune a Llama 3 model on a custom dataset for a specific domain. They can use this Skill's WebUI to upload their data, configure training parameters, and train the model without writing any Python code.

Quick Start

Use the llama-factory skill to fine-tune a Llama 3 model with the provided 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 large language model without writing Python code?▼

You can fine-tune large language models without coding by using a WebUI to upload datasets, configure training parameters, and initiate training. This no-code interface supports over 100 models, simplifying the LLM fine-tuning process.

What training techniques are supported for fine-tuning LLMs with LLaMA-Factory?▼

LLM fine-tuning supports advanced techniques including QLoRA, multimodal training, and distributed training across multiple GPUs. It covers training stages like pre-training, SFT, RLHF, DPO, and KTO for comprehensive model customization.

Can I use distributed training across multiple GPUs for LLM fine-tuning?▼

Yes, LLM fine-tuning supports distributed training across multiple GPUs and machines. This allows you to scale training operations efficiently when working with large language models and extensive custom datasets.

What is the best way to deploy a custom Llama 3 model after training?▼

The best way to deploy a custom Llama 3 model is using optimized inference engines provided by the framework. These engines offer fast and scalable deployment for models fine-tuned on your specific domain data.

Does LLaMA-Factory support multimodal training and model merging?▼

Yes, LLaMA-Factory supports multimodal training and model merging. These features allow you to handle diverse data types and combine fine-tuned models effectively during the LLM development lifecycle.

What dependencies do I need to install for LLM fine-tuning and deployment?▼

You need dependencies including torch, transformers, datasets, peft, accelerate, and gradio. These libraries provide the foundational environment for executing training scripts, handling datasets, and launching the WebUI.

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