LLaMA-Factory
Open-source framework for efficient fine-tuning of 100+ LLMs and VLMs
LLaMA-Factory remains the most versatile open-source fine-tuning framework, supporting 100+ models and 20+ algorithms. It's ideal for rapid prototyping but requires additional infrastructure for production deployment. For researchers and developers iterating quickly, it's hard to beat.
Verified 17d ago · liveness 69/100 · cite: rightaichoice.com/tools/llama-factory
- Rapid prototyping of LLM fine-tuning with zero-code interface
- Researchers exploring advanced fine-tuning algorithms (LoRA, GaLore, APOLLO, Muon, etc.)
- Developers needing fast iteration on multi-modal models (image, video, audio)
- Experimenting with preference optimization (DPO, PPO, KTO, ORPO)
- Production deployment without setting up infrastructure and inference servers
- Fully managed fine-tuning service — you handle data, compute, and scaling
- Non-technical users wanting a no-code turnkey solution — basic command-line or config editing required
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Skip LLaMA-Factory if you need a fully managed fine-tuning service with SLAs, or if you prefer a commercial product with one-click deployment.
Cloud compute costs for GPU training not included
LLaMA-Factory is free and open-source (Apache-2.0). You only pay for compute (e.g., Colab GPUs or cloud instances). Compared to managed services like Hugging Face AutoTrain or AWS SageMaker, you avoid per-credit or per-hour fees but trade off convenience and support.
In short
LLaMA-Factory — Open-source framework for efficient fine-tuning of 100+ LLMs and VLMs. Best for Rapid prototyping of LLM fine-tuning with zero-code interface, Researchers exploring advanced fine-tuning algorithms (LoRA, GaLore, APOLLO, Muon, etc.), Developers needing fast iteration on multi-modal models (image, video, audio). Free to use.
Viability Score
How likely is LLaMA-Factory to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Zero-code CLI and Web UI for fine-tuning
- Support for 100+ LLMs and VLMs (Qwen3, DeepSeek, Gemma 3, GLM-4.1V, InternLM 3, Llama 4, etc.)
- LoRA, QLoRA, and 2/3/4/5/6/8-bit quantized training via AQLM/AWQ/GPTQ/HQQ
- Advanced optimizers: GaLore, BAdam, APOLLO, Muon, Adam-mini
- DoRA, LongLoRA, LoftQ, PiSSA, OFT, OFTv2 algorithms
- FlashAttention-2, Unsloth, Liger Kernel, KTransformers acceleration
- RoPE scaling, NEFTune, rsLoRA tricks
- Multi-modal understanding: image, vision, video, audio
- Preference optimization: PPO, DPO, KTO, ORPO
- Continuous pre-training and supervised fine-tuning
- Reward modeling and multi-turn dialogue training
- OpenAI-style API with vLLM or SGLang inference
- Experiment monitoring: LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab
- Megatron-core training backend support
- Multi-backend support: AMD GPU, Ascend NPU
About LLaMA-Factory
LLaMA-Factory is a free, open-source framework that unifies efficient fine-tuning of over 100 large language models (LLMs) and vision-language models (VLMs). Aimed at researchers, developers, and machine learning engineers, it provides a zero-code CLI and Web UI to streamline the fine-tuning process. The platform supports a wide range of models including Qwen3, Qwen2.5-VL, DeepSeek, Gemma 3, GLM-4.1V, InternLM 3, MiniCPM-o-2.6, and Llama 4, with advanced algorithms like LoRA, QLoRA, GaLore, APOLLO, Muon, and DoRA. It also offers practical tricks such as FlashAttention-2, Unsloth, Liger Kernel, KTransformers, and RoPE scaling for enhanced performance. With integrated experiment monitoring via LlamaBoard, TensorBoard, Wandb, MLflow, and SwanLab, LLaMA-Factory is a comprehensive tool for scalable fine-tuning across various tasks including multi-turn dialogue, tool use, image understanding, video recognition, and audio understanding. Compared to other fine-tuning frameworks like Axolotl or Unsloth, LLaMA-Factory stands out for its extensive model coverage, seamless multi-modal support, and active community with 73k stars on GitHub.
Behind the Verdict
LLaMA-Factory is the Swiss Army knife of open-source fine-tuning. With support for over 100 models and continuous pre-training, supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, and more, it covers nearly every fine-tuning paradigm you'd need. The zero-code CLI and Web UI lower the barrier significantly, letting you start training with minimal setup. We'd reach for this when we need to experiment with a new algorithm or model quickly — the integration of advanced optimizers like GaLore, APOLLO, and Muon, plus tricks like FlashAttention-2 and Liger Kernel, saves hours of manual implementation. Where it bites: you're on your own for production. There's no managed service, no SLA, and you'll need to set up inference servers (vLLM or SGLang) and handle scaling yourself. Compared to Unsloth, which focuses on speed-optimized LoRA for a smaller set of models, LLaMA-Factory offers broader model support and more training algorithms but may have slightly higher learning curve for newcomers. In practice, the community is extremely active (73k stars, 8.9k forks), and the documentation is improving, but some advanced features like Megatron-core are still experimental. Best for researchers and engineers who want to rapidly prototype fine-tuning on diverse models and aren't afraid of the command line.
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Real-world workflow fit
Concrete scenarios for the personas LLaMA-Factory actually fits — and what changes day-one when you adopt it.
Benchmark LoRA, DoRA, and GaLore on a common dataset using the Web UI
Outcome: Comparative accuracy and memory metrics, enabling selection of best algorithm
Fine-tune Qwen3 on customer support chat logs via CLI, then export to vLLM
Outcome: Deployable model endpoint with domain knowledge, improved response accuracy
Use LLaMA-Factory's multi-modal SFT to fine-tune GLM-4.1V on custom image-caption pairs
Outcome: Vision-language model specialized for niche visual recognition tasks
Use Cases
- Fine-tune LLaMA 3 on custom instruction data for a chatbot
- Apply QLoRA to adapt Mistral on a consumer GPU
- Benchmark LoRA vs DoRA on a classification task
- Export fine-tuned model for inference with vLLM
- Create domain-adapted VLM using vision-language datasets
- Experiment with GaLore or BAdam to reduce memory
- Align model using DPO or PPO for helpfulness
- Continuous pre-train on domain-specific corpus
Models Under the Hood
as of 2026-07-06
Limitations
- 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.
as of 2026-06-24
12-month cost
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.
Plans compared
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
Any individual or organization wanting to fine-tune models without licensing costs
What this tier adds
Starting tier: all features free, unlimited use, open-source community support
Where the pricing makes sense
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 (Apache-2.0). You only pay for compute (e.g., Colab GPUs or cloud instances). Compared to managed services like Hugging Face AutoTrain or AWS SageMaker, you avoid per-credit or per-hour fees but trade off convenience and support.
Setup time & first value
How long it actually takes to get something useful out of LLaMA-Factory — broken out by persona, not the marketing-page minute.
For experienced ML engineers, setup takes about 30 minutes: install via pip, prepare dataset in recommended format, and launch CLI or Web UI. Beginners may take 2-4 hours reading documentation and troubleshooting dependencies. Cloud setups (Colab) are instant.
Switching to or from LLaMA-Factory
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Hugging Face Transformers: reformat dataset to JSON, use provided data_utils
- ↗To Hugging Face Hub: export via output_dir and push_to_hub script
- ↗To vLLM/SGLang: convert to Safetensors and use the exported model
Integrations
Resources & Guides
- Resourcellamafactory.readthedocs.io
LLaMA Factory
Helpful link from llamafactory.readthedocs.io
- Resourceblog.llamafactory.net
LlamaFactory Blog
LlamaFactory 官方博客
- Resourcegithub.com
LlamaFactory/README.md at main · hiyouga/LlamaFactory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024) - hiyouga/LlamaFactory
- Resourcelab4ai.cn
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- Resourcecolab.research.google.com
Google Colab
Helpful link from colab.research.google.com
Tutorials & Learning
Official links
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