LLaMA-Factory is live · free · last verified 22 Oct 2026 · viability healthy
LLaMA-Factory
LLaMA-Factory fine-tunes 100+ LLMs and VLMs with a zero-code CLI and the LlamaBoard web UI
Still the widest method and optimizer menu you can get without writing a training loop, now with a Megatron-core backend for models too big for one card. Pick it when you want to compare DPO against KTO against ORPO on your own data rather than trust a vendor's default. Budget for compute and for babysitting runs — that part is yours.
Verdict generatedVerified 7d ago · liveness 79/100 · cite: rightaichoice.com/tools/llama-factory
- Researchers comparing GaLore, BAdam or APOLLO against plain AdamW on GPU capacity they already rent
- Engineers fine-tuning multimodal models for image, video or audio tasks through LlamaBoard
- Teams that want DPO, KTO and ORPO tried on the same dataset without writing a training loop
- Developers who need day-0 recipes for a fresh base model like Qwen3, Gemma 3 or GLM-4.1V
- Teams that want a managed fine-tuning service and no infrastructure to run
- Non-technical users expecting push-button setup with no environment debugging
- Production serving that needs built-in autoscaling or monitoring — you assemble those
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Skip LLaMA-Factory if you have no GPU capacity of your own and want a managed fine-tuning service that handles infrastructure, runs, and deployment for you.
The software is $0 under Apache-2.0, but you pay for every GPU-hour yourself — a multi-day QLoRA run on rented A100/H100 time is the real bill.
The framework itself is $0 under Apache-2.0, so the pricing comparison is really a compute comparison. Against a managed fine-tuning platform that bundles GPUs into a per-token or per-hour rate, LLaMA-Factory costs you the raw cloud GPU bill plus engineer hours — cheaper at steady volume with existing hardware, pricier for a one-off run at a small team with no cluster.
In short
LLaMA-Factory — LLaMA-Factory fine-tunes 100+ LLMs and VLMs with a zero-code CLI and the LlamaBoard web UI. Best for Researchers comparing GaLore, BAdam or APOLLO against plain AdamW on GPU capacity they already rent, Engineers fine-tuning multimodal models for image, video or audio tasks through LlamaBoard, Teams that want DPO, KTO and ORPO tried on the same dataset without writing a training loop. Free to use.
No head-to-head comparison for LLaMA-Factory yet — the alternatives below are the closest matches we list.
What is LLaMA-Factory?
LLaMA-Factory — LLaMA-Factory fine-tunes 100+ LLMs and VLMs with a zero-code CLI and the LlamaBoard web UI. Best for Researchers comparing GaLore, BAdam or APOLLO against plain AdamW on GPU capacity they already rent, Engineers fine-tuning multimodal models for image, video or audio tasks through LlamaBoard, Teams that want DPO, KTO and ORPO tried on the same dataset without writing a training loop. Free to use.
LLaMA-Factory is an Apache-2.0 framework for fine-tuning and adapting open-weight large language models and vision-language models without writing a training loop yourself. You point it at a checkpoint, pick a method in the zero-code CLI or the LlamaBoard web interface, and launch the run. The roster covers LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen3, Qwen3-VL, DeepSeek, Gemma, GLM and Phi, with day-0 support listed for Qwen3, Qwen2.5-VL, Gemma 3, GLM-4.1V, InternLM 3 and MiniCPM-o-2.6, and day-1 support for Llama 4. Method coverage runs from continuous pre-training and multimodal supervised fine-tuning through reward modeling and PPO, DPO, KTO and ORPO preference optimization. You can scale from 16-bit full-tuning and freeze-tuning down to LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM, AWQ, GPTQ, LLM.int8, HQQ and EETQ. Memory-saving algorithms include GaLore, BAdam, APOLLO, Adam-mini, Muon, OFT, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA, alongside FlashAttention-2, Unsloth, Liger Kernel, KTransformers, NEFTune and rsLoRA. Task coverage is broad: multi-turn dialogue, tool use, image understanding, visual grounding, video recognition and audio understanding. A Megatron-core training backend arrived via mcore_adapter, so very large models can be trained with a sharded pipeline rather than a single-GPU workaround. Serving lives in the same repo: serve the tuned model through an OpenAI-style API, Gradio UI or CLI on vLLM or SGLang workers, and log runs to LlamaBoard, TensorBoard, WandB, MLflow or SwanLab. Researchers and ML engineers adapting models are the core audience; the README notes use by Amazon, NVIDIA and Aliyun, and the project was published at ACL 2024. Against managed fine-tuning platforms, LLaMA-Factory expects you to bring GPUs and run the pipeline. What you get back is control of every training knob and a method menu managed services rarely ship.
Key features
- Zero-code CLI and LlamaBoard web UI for fine-tuning 100+ LLMs and VLMs
- Day-0 support for Qwen3, Qwen2.5-VL, Gemma 3, GLM-4.1V, InternLM 3 and MiniCPM-o-2.6
- Day-1 support for Llama 4, Llama 3, GLM-4, Mistral Small and PaliGemma2
- Multimodal supervised fine-tuning for image understanding, visual grounding and video recognition
- Audio understanding fine-tuning
- Continuous pre-training and supervised fine-tuning (SFT)
- Reward modeling plus PPO, DPO, KTO and ORPO preference optimization
- LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM, AWQ, GPTQ, LLM.int8, HQQ and EETQ
- 16-bit full-tuning and freeze-tuning modes
- Memory-efficient optimizers: GaLore, BAdam, APOLLO, Adam-mini, Muon, OFT, DoRA, LongLoRA, LoRA+, LoftQ, PiSSA
- Training acceleration with FlashAttention-2, Unsloth, Liger Kernel, KTransformers, NEFTune and rsLoRA
- Megatron-core training backend via mcore_adapter for sharded large-model training
- RoPE scaling, Mixture-of-Depths and LLaMA Pro techniques
- OpenAI-style API, Gradio UI and CLI inference on vLLM or SGLang workers
- Experiment tracking hooks for LlamaBoard, TensorBoard, WandB, MLflow and SwanLab
Behind the verdict
The honest framing: LLaMA-Factory is a training harness, not a service. If your team already has GPU capacity and wants to test four preference-optimization methods against the same dataset, this is the fastest route from checkpoint to comparable runs. The day-0 model table matters more than it looks — when Qwen3 or GLM-4.1V drops, having a working fine-tune recipe that afternoon saves a week of plumbing. Where it bites is the operational tail. You assemble the environment, you watch the loss curves, and you own deployment once training finishes. The vLLM and SGLang workers in the repo get you an OpenAI-style endpoint, but autoscaling and monitoring are on your side of the line. Compare carefully. Axolotl suits teams that think in YAML and want configs as the source of truth. Unsloth wins when raw single-GPU speed is the only metric that counts. A hand-rolled TRL/PEFT script is the right call when you need exactly one method and none of the menu. LLaMA-Factory earns its place in the middle: more methods than either, less infrastructure than a managed platform. The zero-code CLI and LlamaBoard GUI meaningfully lower the entry cost; someone who has never trained a model can get a LoRA run going on a free Colab or PAI-DSW notebook. That is a real on-ramp, though it flattens once you hit multi-node runs or custom data pipelines, where you will be reading docs and source. One caution on the ecosystem: the same maintainers now point at PenguinHarness, a separate local-first multi-agent app platform. It is a sibling project, not a LLaMA-Factory feature, so do not budget for it as part of your fine-tuning stack. Our call: reach for it when method breadth, new-model coverage and staying inside Apache-2.0 matter. Pass if you want a managed fine-tuning service with someone
Models under the hood
as of 2026-10-23
Limitations
- LLaMA-Factory is a free, open-source framework, so you handle your own compute, storage, and inference infrastructure.
- Support comes from the community — GitHub issues, Discussions, and the WeChat and NPU user groups — and there is no paid support contract.
- The zero-code UI still requires editing config files for advanced settings.
- Fine-tuning very large models (100B+ parameters) demands specialized hardware and distributed setup, which is out of reach for most hobbyists.
- There is no built-in autoscaling, monitoring, or deployment SLA: you assemble your own serving stack around the vLLM or SGLang worker.
- You also need to review the datasets you supply yourself; the framework gives you the training loop, not a data-quality process.
as of 2026-10-18
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Is LLaMA-Factory still active?
LLaMA-Factory is live · free · last verified 22 Oct 2026 · viability healthy.
Viability score
How well maintained and how widely used is LLaMA-Factory? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about LLaMA-Factory?
What people actually say about LLaMA-Factory — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
ResearchedDiscussed by users across 3 sources (Hacker News, GitHub, Lemmy) · researched Oct 11, 2026.
Weighted by the 8 posts each of 3 sources contributed.
- +Genuinely spans SFT, pre-training, reward modeling, PPO, DPO, KTO, and ORPO in one framework
- +Zero-code CLI and LlamaBoard web UI reduce setup friction for standard fine-tuning recipes
- +Day-0 support for Qwen3, Llama 4, Gemma 3, and other fresh model releases
- +Broad quantization support (QLoRA 2-8 bit via AQLM, AWQ, GPTQ, HQQ, EETQ) fits constrained hardware
- +Memory optimizers like GaLore, BAdam, and LoRA+ extend what fits on a single GPU
- −Meaningful results realistically require multi-GPU enterprise hardware, not a single consumer card
- −1,183 open GitHub issues indicate uneven maintenance across the long tail of integrations
- −Configuration surface is huge — easy to pick a wrong optimizer or quantization combo
- −Full operational burden lands on the user, unlike managed fine-tuning services
- −Recent MindSpore HyperParallel backend still lacks CP/TP/EP and activation swap support
- • GPU compute — the real cost driver, with community commentary pointing at 8xH200-class hardware for good results
- • Engineering time to configure and operate training runs end-to-end
- • Storage and egress costs for hosting base checkpoints and fine-tuned adapters
- • Potential lock-in to specific quantization or parallelism recipes once tuned
How we verified this
Every listed fact about LLaMA-Factory— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 22 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified LLaMA-Factory 21 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing page
- — changed features, who should skip it, description, summary, our verdict, pricing tiers, who it suits
- — changed switching out, switching in, who it suits, our verdict, hidden costs, who should skip it, setup time, pricing fit, description, models
- — changed description, who it suits, our verdict, features, who should skip it, pricing tiers
- — changed pricing page
- — changed description, summary, who should skip it, who it suits, features, pricing tiers, our verdict
Showing the 6 most recent of 21 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does LLaMA-Factory cost in 2026?
LLaMA-Factory: Free to use. Pricing last verified 22 Oct 2026.
Plans compared
Every published LLaMA-Factory tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free (Apache-2.0 open source) | Free | — | Researchers, ML engineers, and hobbyists with access to GPUs who want the full method and optimizer menu without a license fee.Adds: The only tier — full source under Apache-2.0 at $0, no paid upgrade, with compute billed separately by your GPU provider. |
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.
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.
The framework itself is $0 under Apache-2.0, so the pricing comparison is really a compute comparison. Against a managed fine-tuning platform that bundles GPUs into a per-token or per-hour rate, LLaMA-Factory costs you the raw cloud GPU bill plus engineer hours — cheaper at steady volume with existing hardware, pricier for a one-off run at a small team with no cluster.
Has LLaMA-Factory's pricing changed?
No plan price for LLaMA-Factory has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick LLaMA-Factory?
Pick LLaMA-Factory if you are Researchers comparing GaLore, BAdam or APOLLO against plain AdamW on GPU capacity they already rent, Engineers fine-tuning multimodal models for image, video or audio tasks through LlamaBoard, Teams that want DPO, KTO and ORPO tried on the same dataset without writing a training loop, Developers who need day-0 recipes for a fresh base model like Qwen3, Gemma 3 or GLM-4.1V.
Look elsewhere if you are Teams that want a managed fine-tuning service and no infrastructure to run, Non-technical users expecting push-button setup with no environment debugging, Production serving that needs built-in autoscaling or monitoring — you assemble those, Shops that need exactly one training method and would rather keep a small TRL/PEFT script.
Skip it if Skip LLaMA-Factory if you have no GPU capacity of your own and want a managed fine-tuning service that handles infrastructure, runs, and deployment for you.
GeneratedReal-world workflow fit
Concrete scenarios for the personas LLaMA-Factory actually fits — and what changes day-one when you adopt it.
Load an instruction dataset, pick QLoRA 4-bit with DoRA and FlashAttention-2 in LlamaBoard, and launch an SFT run against a Qwen3 base checkpoint.
Outcome: An adapter you can serve the same day through the built-in vLLM worker as an OpenAI-style endpoint, with the loss curve visible in LlamaBoard.
Run the same preference dataset through DPO, KTO, and ORPO in sequence, swapping only the training-stage argument in the config.
Outcome: Three comparable checkpoints plus WandB or MLflow logs, giving you an evidence-based answer on which objective fit your data.
Continuous pre-train on a domain corpus, then run multimodal SFT on image and video samples covering visual grounding, and export the merged weights.
Outcome: A domain-adapted vision-language model with a documented training config you can rerun when the next base model ships at day-0.
Use cases
- Fine-tune LLaMA on custom instruction data for a multi-turn chatbot
- Apply QLoRA to adapt Mistral on a consumer GPU
- Benchmark LoRA against DoRA on a classification task
- Export a fine-tuned model and serve it with vLLM through an OpenAI-style API
- Build a domain-adapted VLM using vision-language datasets
- Use GaLore or BAdam to cut memory use on a constrained GPU
- Align a model with DPO, KTO, ORPO, or PPO for helpfulness
- Continuous pre-train on a domain-specific corpus
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.
Comfortable engineer with a GPU box: around half a day to install, prepare a dataset in the expected format, and land a first LoRA run. Hobbyist on the free Colab or PAI-DSW notebook: under an hour to a sample run, but expect longer before your own data trains cleanly. Multi-node Megatron runs add days of infrastructure work.
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 a hand-written TRL/PEFT script: map your dataset to the framework's data format, then re-declare your LoRA and optimizer settings as CLI or LlamaBoard arguments.
- →From Axolotl: translate your YAML fields into LLaMA-Factory's CLI flags or LlamaBoard form; the training methods and quant backends overlap.
- →From a managed fine-tuning platform: export your dataset, rent equivalent GPUs, and reproduce the job with the same base checkpoint and method.
- ↗To a custom TRL/PEFT trainer: export your prepared dataset and hyperparameters, then re-implement the single method you actually need.
- ↗To vLLM or SGLang standalone: take the merged or adapter weights LLaMA-Factory exports and serve them directly on your own worker.
- ↗To a managed fine-tuning service: export your dataset and the winning config, then hand both to the vendor's job API.
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
- Resourcecolab.research.google.com
Google Colab
Helpful link from colab.research.google.com
Tutorials & Learning
Official links
What are the best LLaMA-Factory alternatives?
The closest alternatives to LLaMA-Factory we list are Axolotl, Vllm and Vast.ai. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Axolotl | Free | Free | — |
| Vllm | Free | Free | — |
| Vast.ai | Freemium | Pricing not published | — |
Alternatives to LLaMA-Factory
View all
Tools that pair well with LLaMA-Factory
Common stack mates teams adopt alongside LLaMA-Factory, with the specific reason each pairing earns its keep.
Axolotl
Free Apache-2.0 command-line framework for fine-tuning and post-training LLMs, vision-language and MoE models on GPUs you own or rent
Vllm
vLLM is the open-source LLM inference engine for high-throughput, OpenAI-compatible serving on your own GPUs.
Vast.ai
Vast.ai is a GPU rental marketplace where market-set prices and per-second billing let you provision 20,000+ GPUs from code.
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