hiyouga/LlamaFactory
LlamaFactory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Usage guide
LlamaFactory is an open-source project around agent, deepseek, fine-tuning with 72,696 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating LlamaFactory for Python AI workflows.
- Comparing a GitHub project with 72,696 stars and current repository activity.
Pros
- LlamaFactory has visible GitHub traction with 72,696 stars. Topics: agent, ai, deepseek.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
LlamaFactory should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
LlamaFactory architecture preview
LlamaFactory's main path starts at the entry surface, runs through Agent orchestration runtime, combines OpenAI / Llama / DeepSeek / Qwen, Runtime context, GitHub / Discord, and returns Assistant response / action result.
Entry
CLI / terminal entry
LlamaFactory is primarily entered through a developer command or terminal workflow.
git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
OpenAI / Llama / DeepSeek / Qwen
Model calls are likely routed through OpenAI, Llama, DeepSeek, Qwen based on README and topic signals.
OpenAI, Llama, DeepSeek, Qwen
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub / Discord
Tool adapters let the runtime act outside the model through GitHub / Discord.
GitHub, Discord
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- Docker Engine with enough disk space for images and volumes
- A clean working directory for the first test run
Check the runtime environment
LlamaFactory has Docker in the setup path. Confirm Docker Engine works and reserve enough disk space for images and volumes.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone --depth 1 https://github.com/hiyouga/LlamaFactory.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ pip install -e .Adoption guidance and sources
Practical use cases
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
This is one of the documented reasons to evaluate LlamaFactory before choosing a stack.
Focus area: agent
This is one of the documented reasons to evaluate LlamaFactory before choosing a stack.
All project comparison
Compare LlamaFactory with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official LlamaFactory setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Check exposed ports, mounted volumes, and environment variables before running the container in a shared environment.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented LlamaFactory example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is LlamaFactory?
LlamaFactory is an open-source all project. Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
How do I install LlamaFactory?
Start with the official README. The first detected setup step is: git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git.
Is LlamaFactory beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can LlamaFactory be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does LlamaFactory need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt LlamaFactory?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.