LLaMA-Factory
A customizable Sphinx documentation theme with clean design and organized navigation
- Also known as
- llama-factory
What is LLaMA-Factory?
Open source Sphinx theme offering customizable styling, sidebar navigation, and clear typography. Built for smaller documentation projects.
LLaMA-Factory pricing
We don't have LLaMA-Factory's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
Free, open source (MIT license)
What LLaMA-Factory does
The capabilities that matter for fine tuning platforms, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Architecture model
- Open source library local GPU
- Peft lora and qlora parameter efficient tuning
- ✓
- Rlhf and dpo preference alignment optimization
- ✓
- Serverless hosting of fine tuned adapters lorax
- -
- Four bit and eight bit memory quantization
- ✓
- Flash attention and xformers compilation
- ✓
- Distributed multi GPU orchestration fsdp deepspeed
- ✓
- Synthetic data generation and evaluation pipeline
- -
- Native huggingface hub push pull integration
- -
- Weights and biases wandb experiment tracking
- ✓
- SOC2 type ii
- -
- Mit or apache permissive oss license
- ✓
- Pricing model
- Free open source
Platform & deployment
Independently observed- CLI
- Self-hosted
Integrations (7)
Independently observed- TensorBoard
- Wandb
- MLflow
- SwanLab
- Transformers
- vLLM
- Hugging Face Hub
Security & compliance
Known vulnerabilities: 0 (0 in the last 12 months) sourcea count reflects scale & disclosure, not quality
LLaMA-Factory FAQ
Common questions about LLaMA-Factory, answered from independent, dated evidence.
What is LLaMA-Factory?
LlamaFactory is a user-friendly training and fine-tuning platform that enables local optimization of hundreds of pre-trained language models without requiring code. It supports diverse training methods including supervised fine-tuning, reinforcement learning approaches, and parameter-efficient techniques. It is indexed under Fine Tuning Platforms.
Is LLaMA-Factory free to use?
LLaMA-Factory is open source, so it can be self-hosted and used at no licence cost. It is released under the Apache-2.0 licence. Pricing changes often, so verify at source before relying on it.
What platforms does LLaMA-Factory support?
LLaMA-Factory supports a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.
Can LLaMA-Factory be self-hosted?
Yes. LLaMA-Factory can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.
What does LLaMA-Factory integrate with?
We have confirmed 7 integrations for LLaMA-Factory, including TensorBoard, Wandb, MLflow, SwanLab, Transformers, vLLM and Hugging Face Hub. This is what we could verify from public sources, so the vendor may support others we have not indexed.
Is LLaMA-Factory open source?
Yes. LLaMA-Factory is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.
LLaMA-Factory alternatives
Other fine tuning platforms we track, ranked by the same independent score.
- UnslothDesktop application for running and training machine learning models locally on your hardwarelow · 39%
- TRLReinforcement learning tools for language model traininglow · 32%
- AxolotlAn open-source framework for training and optimizing large language models efficientlylow · 33%
- Snorkel AIProgrammatic data labeling and curation platform for AI model traininglow · 29%
Compare LLaMA-Factory
Side by side against other fine tuning platforms, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for LLaMA-Factory, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Capabilities | 87 | 0.08 | 7.0 | ✓ |
| Release cadence | 86 | 0.08 | 7.0 | ✓ |
| Stars | 92 | 0.05 | 4.3 | ✓ |
| Development activity | 30 | 0.14 | 4.1 | ✓ |
| Integrations | 26 | 0.05 | 1.4 | ✓ |
| Dependent projects | 0 | 0.05 | 0.0 | ✓ |
| Security posture | 0 | 0.12 | 0.0 | - |
| Package downloads | 0 | 0.04 | 0.0 | - |
| Security score | 0 | 0.05 | 0.0 | - |
| Developer Q&A activity | 0 | 0.14 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 8 | mediumsource · 2026-10-05 · 65% |
Adoption
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 6 items | mediumsource · 2026-09-10 · 66% |
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Pricing model: free_open_source · Architecture model: open_source_library_local_gpu · Mit or apache permissive oss license: Yes · Flash attention and xformers compilation: Yes · Four bit and eight bit memory quantization: Yes · Weights and biases wandb experiment tracking: Yes | mediumsource · 2026-08-21 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 7 | mediumsource · 2026-08-21 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-10-05 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-10-05 · 95% |
Pricing
Release
Security
| Attribute | Value | Evidence |
|---|---|---|
| Vulnerabilities | Count: 0 · Source: https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=go&package_name=github.com%2Fhiyouga%2FLlamaFactory&per_page=100 · Last 12m: 0 | highsource · 2026-10-05 · 90% |
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