LLaMA-Factory
FreeUnified efficient fine-tuning of 100+ LLMs & VLMs with zero-code CLI and Web UI.
By Tanmay Verma, Founder · Last verified 04 Jun 2026
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Added 13d ago
72/100Safe BetIn short
Unified efficient fine-tuning of 100+ LLMs & VLMs with zero-code CLI and Web UI.
By Tanmay Verma, Founder · Last verified 04 Jun 2026
In short
LLaMA-Factory — Unified efficient fine-tuning of 100+ LLMs & VLMs with zero-code CLI and Web UI. Best for Developers fine-tuning LLMs for custom applications like chatbots, role-playing, or domain adaptation., Researchers experimenting with multiple fine-tuning methods (DPO, PPO, ORPO) and advanced optimizers., Organizations deploying fine-tuned models with OpenAI-compatible API and vLLM for production inference.. Free to use.
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A must-try for anyone fine-tuning LLMs/VLMs: supports 100+ models, multiple training methods, and advanced optimizations. The zero-code UI lowers the barrier, while the CLI and API suit power users. It's free and open-source, with active community support.
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Last verified: June 2026
LLaMA-Factory is currently the most comprehensive open-source fine-tuning framework for LLMs and VLMs. Its support for 100+ models, including the latest Qwen3, DeepSeek, and Llama 4, ensures you can fine-tune almost any popular model. The variety of training methods (SFT, DPO, PPO, ORPO, etc.) and advanced algorithms (GaLore, LoRA+, DoRA) cater to both beginners and researchers. The zero-code Web UI (LLaMA Board) makes it easy for non-programmers to start training, while the CLI and API integration with vLLM provide production-grade deployment. However, the framework is Python-based and requires GPU resources for training (though Colab free tier is available). It may have a learning curve for configuring custom datasets and hyperparameters. Compared to other fine-tuning tools like Axolotl or Unsloth, LLaMA-Factory's main advantage is its unified interface and broader model support. If you need to fine-tune multiple model types or experiment with different algorithms, LLaMA-Factory is the go-to choice. If you only need simple LoRA for a single model, a lighter tool might be easier. The active GitHub community and frequent updates (changelog shows daily support for new models) are strong positives. Overall, it's a powerful, free, and well-supported tool for any LLM fine-tuning task.
Skip LLaMA-Factory if Skip LLaMA-Factory if you need a fully managed, hosted fine-tuning service with guaranteed uptime and support, or if you are a complete beginner with no ML background.
How likely is LLaMA-Factory to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
LLaMA-Factory is an open-source framework that enables unified efficient fine-tuning of over 100 large language models (LLMs) and vision-language models (VLMs). It offers a zero-code CLI and Web UI, making fine-tuning accessible to developers, researchers, and organizations. Supported models include LLaMA, LLaVA, Mistral, Qwen3, DeepSeek, Gemma, GLM, Phi, and more. Integrated training methods cover (continuous) pre-training, supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, and more. Advanced algorithms such as GaLore, BAdam, APOLLO, DoRA, LoRA+, and QLoRA via AQLM/AWQ/GPTQ are supported. Practical tricks include FlashAttention-2, Unsloth, Liger Kernel, RoPE scaling, NEFTune, and rsLoRA. The framework supports multi-turn dialogue, tool use, image understanding, video recognition, and audio understanding. Experiment monitoring integrates with LlamaBoard, TensorBoard, Wandb, MLflow, and SwanLab. Inference is accelerated via OpenAI-style API, Gradio UI, and CLI with vLLM or SGLang workers. LLaMA-Factory is used by Amazon, NVIDIA, and Aliyun, and is released under Apache-2.0 license.
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Concrete scenarios for the personas LLaMA-Factory actually fits — and what changes day-one when you adopt it.
You want to evaluate LoRA vs. DoRA on a text classification dataset using a Qwen model.
Outcome: Clone the repo, launch the Web UI, load the dataset, select LoRA and DoRA in two separate runs, compare validation metrics in TensorBoard.
You have a single RTX 3060 GPU and want to fine-tune Mistral 7B for a personal chatbot.
Outcome: Use QLoRA with 4-bit quantization, train for a few hours, export to vLLM, and deploy an OpenAI-compatible API locally.
Your team needs a reproducible fine-tuning pipeline for a domain-specific Llama 3 model.
Outcome: Write a bash script using the CLI with fixed hyperparameters, integrate with Weights & Biases for logging, and export the model to Hugging Face Hub for downstream deployment.
LLaMA-Factory is a free, open-source project without official paid support or SLAs. While community issues are addressed on GitHub, response times vary. The Web UI is designed for single-node experiments; multi-node distributed training requires manual configuration. Some advanced features like model parallelism are not yet fully integrated. Beginners without ML experience will find the setup and configuration challenging.
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
For each published LLaMA-Factory tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Solo researchers, hobbyists, and teams who want to fine-tune models on their own hardware without paying for software licenses.
What this tier adds
This is the starting tier: fully featured, open-source, no paid support. You only pay for your own compute.
The company stage and team size where LLaMA-Factory's pricing actually pencils out — and where peers do it cheaper.
LLaMA-Factory is free and open-source, making it ideal for budget-constrained teams and individuals. For those needing managed infrastructure, cloud GPU rentals (e.g., from AWS, GCP, or specialist providers like Lambda Labs) are an additional cost. Competing services like Together AI or Anyscale charge per-token or per-hour, making LLaMA-Factory cheaper for heavy experimentation.
How long it actually takes to get something useful out of LLaMA-Factory — broken out by persona, not the marketing-page minute.
Setup takes 10-20 minutes: git clone the repo, install dependencies with pip, and launch the Web UI with a single command. For first-time users, reading the README and preparing a dataset may add 30-60 minutes. Cloud GPU users can use the provided Colab notebook for instant start.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
Common stack mates teams adopt alongside LLaMA-Factory, with the specific reason each pairing earns its keep.
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Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024) - hiyouga/LlamaFactory
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