LlamaFactory
LlamaFactory is an open-source Python framework for fine-tuning 100+ large language models and vision-language models using methods like LoRA, QLoRA, and full-parameter tuning. It provides CLI and web UI interfaces, supports quantization techniques, and integrates with popular training frameworks and deployment tools.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | hiyouga/LlamaFactory |
| Owner | hiyouga |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 73k |
| Forks | 8.9k |
| Open issues | 1.1k |
| Latest release | v0.9.5 (2026-05-30) |
| Last updated | 2026-07-07 |
| Source | https://github.com/hiyouga/LlamaFactory |
What LlamaFactory is
LlamaFactory abstracts fine-tuning workflows across diverse LLM architectures (LLaMA, Qwen, DeepSeek, Gemma, etc.) via a unified API. Core capabilities include parameter-efficient tuning (LoRA/QLoRA with AQLM/AWQ/GPTQ quantization), advanced optimizers (GaLore, BAdam, Muon), reinforcement learning (PPO/DPO/KTO), and inference via vLLM/SGLang backends. Built on HuggingFace Transformers.
Get the LlamaFactory source
Clone the repository and explore it locally.
git clone https://github.com/hiyouga/LlamaFactory.gitcd LlamaFactory# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires GPU with CUDA 11.8+ or AMD ROCm support; CPU training is very slow. Plan for 20-100 GB storage per model depending on quantization strategy.
- Data preparation must follow the framework's instruction-following format (role-based, multi-turn chat structure). Unstructured or non-dialogue data requires preprocessing pipelines.
- Hyperparameter tuning is manual or via grid search; no built-in Bayesian optimization. Start with provided templates and iterate based on validation metrics.
- Model checkpointing and resume logic are built-in but require careful state management in distributed setups. Single-GPU training is straightforward; multi-node training requires SLURM or cloud orchestration.
- Inference serving via vLLM/SGLang adds operational complexity (separate process, memory overhead). Evaluate managed inference endpoints if operations team is lean.
When to avoid it — and what to weigh
- Pre-training from scratch at scale — LlamaFactory is optimized for supervised fine-tuning and RLHF. Large-scale pre-training (petabyte-scale data, distributed training across 1000+ GPUs) requires specialized frameworks like Megatron-LM or DeepSpeed with lower-level control.
- Proprietary/closed-source model requirements — The framework is tightly coupled to open-source models from HuggingFace Hub and ModelScope. Closed APIs (e.g., OpenAI GPT-4, Anthropic Claude) are not fine-tunable via LlamaFactory; consider vendor-specific SDKs instead.
- Production systems requiring SLA/support contracts — LlamaFactory is community-maintained open-source software with no guaranteed SLA, security patching timeline, or enterprise support. Organizations with strict uptime/compliance requirements should evaluate commercial alternatives or engage consultants.
- Real-time inference with strict latency budgets — While vLLM integration accelerates inference, LlamaFactory is a training-centric tool. Latency-critical applications (sub-100ms p99) may require purpose-built inference engines or managed services (e.g., Replicate, Modal).
License & commercial use
Apache-2.0 (Apache License 2.0). Permissive OSI-approved open-source license allowing commercial use, modification, and distribution with attribution. No copyleft restrictions.
Apache-2.0 permits commercial deployment and closed-source applications. However, verify all transitive dependencies (HuggingFace Transformers, PyTorch, vLLM) also permit your intended use. Dependencies like PyTorch (BSD) are also permissive, but some quantization backends (e.g., GPTQ, AWQ) may have additional licensing constraints—review if distributing compiled binaries. No license grants patent indemnification; consult legal counsel for patent-sensitive applications.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit data provided. Standard considerations: dependency supply-chain risk (PyTorch, Transformers, quantization libraries—audit versions). Model weights downloaded from public hubs; verify integrity if security-critical. Training data is user-managed; LlamaFactory does not enforce input sanitization. No built-in access control; assume single-tenant or air-gapped deployment. Inference endpoint (OpenAI API) has no authentication by default; add reverse proxy/API key layer for production exposure.
Alternatives to consider
HuggingFace TRL (Transformer Reinforcement Learning)
Narrower scope (focused on RLHF/DPO/PPO) but tightly integrated with HuggingFace ecosystem. Better for teams already using transformers trainer; less unified model support than LlamaFactory.
Axolotl
Alternative unified fine-tuning framework with similar multi-model support and QLoRA. Smaller community (fewer GitHub stars), less documentation. Consider if LlamaFactory has blocker issues.
Managed services (Modal, Replicate, LiteLLM proxy)
Eliminate infrastructure management and provide production SLAs. Pay-per-use or subscription; higher cost but lower ops burden. Suitable if you prioritize simplicity over control.
Build on LlamaFactory with DEV.co software developers
LlamaFactory lowers the barrier to custom LLM deployment. Start with a free Colab notebook or discuss a production implementation plan with our AI engineering team.
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LlamaFactory FAQ
Can I fine-tune proprietary models like GPT-4 or Claude with LlamaFactory?
What GPU memory is required?
Can I use LlamaFactory in production?
How do I handle multi-turn dialogue or custom data formats?
Software developers & web developers for hire
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If LlamaFactory is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Fine-Tune Your LLM?
LlamaFactory lowers the barrier to custom LLM deployment. Start with a free Colab notebook or discuss a production implementation plan with our AI engineering team.