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.
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.
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
LLaMA-Factory alternatives
Other fine tuning platforms we track, ranked by the same independent score.
Compare LLaMA-Factory
Side by side against other fine tuning platforms, attribute by attribute, with a source on every value.
The Vioscale 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 | 6.8 | ✓ |
| Release cadence | 86 | 0.05 | 4.5 | ✓ |
| Development activity | 37 | 0.09 | 3.5 | ✓ |
| Stars | 92 | 0.03 | 2.4 | ✓ |
| Integrations | 26 | 0.07 | 1.8 | ✓ |
| Dependent projects | 0 | 0.06 | 0.0 | - |
| Security posture | 0 | 0.07 | 0.0 | - |
| Package downloads | 0 | 0.14 | 0.0 | - |
| Security score | 0 | 0.04 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 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 | 14 | mediumsource · 2026-08-26 · 65% |
Adoption
| Attribute | Value | Evidence |
|---|---|---|
| Github stars | 74,382 | highsource · 2026-08-26 · 90% |
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 · 2026-08-26 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-08-26 · 95% |