Home/Compare/LlamaFactory vs trainer

Comparison

LlamaFactory vs trainer

Verdict

Pick LlamaFactory when llamaFactory is primarily Python; trainer is Go; pick trainer when trainer is primarily Go; LlamaFactory is Python.

Markdown twin · LlamaFactory alternatives · trainer alternatives

GraphCanon updated today

LlamaFactory logo

LlamaFactory

hiyouga/LlamaFactory

73kpushed Jul 10, 2026
vs
trainer logo

trainer

kubeflow/trainer

2.1kpushed Jul 10, 2026

Trust & integrity

SignalLlamaFactorytrainer
Maintenance
Very active (0d since push)
As of today · github_public_v1
Very active (1d since push)
As of today · github_public_v1
Provenance
Not a fork · Personal account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No lockfile
As of today · none

Tagline

LlamaFactory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs
trainer
Distributed AI Model Training and LLM Fine-Tuning on Kubernetes

Stars

LlamaFactory
73k
trainer
2.1k

Forks

LlamaFactory
8.9k
trainer
983

Open issues

LlamaFactory
1.1k
trainer
144

Language

LlamaFactory
Python
trainer
Go

Adopt for

LlamaFactory
LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization.
trainer
-

Persona

LlamaFactory
-
trainer
-

Runtime

LlamaFactory
-
trainer
-

License

LlamaFactory
Apache-2.0
trainer
Apache-2.0

Last pushed

LlamaFactory
Jul 10, 2026
trainer
Jul 10, 2026

Categories

LlamaFactory
Model Training, LLM Frameworks
trainer
LLM Frameworks, Model Training

Trust and health

Days since push

LlamaFactory
0d
trainer
1d

Open issues (now)

LlamaFactory
1.1k
trainer
144

Owner type

LlamaFactory
User
trainer
Organization

Full report

LlamaFactory
Trust report

Choose LlamaFactory if…

  • LlamaFactory is primarily Python; trainer is Go.
  • Tags unique to LlamaFactory: gemma, deepseek, instruction-tuning, large-language-models.
  • When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.

When NOT to use LlamaFactory

  • When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory.
  • If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa

Choose trainer if…

  • trainer is primarily Go; LlamaFactory is Python.
  • Tags unique to trainer: gpu, distributed, kubeflow, huggingface.
  • Leaner open-issue backlog (144).

When NOT to use trainer

  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: LlamaFactory 73k · trainer 2.1k (synced Jul 11, 2026).

Common questions

What is the difference between LlamaFactory and trainer?
LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. trainer: Distributed AI Model Training and LLM Fine-Tuning on Kubernetes. See the comparison table for live GitHub stats and shared categories.
When should I choose LlamaFactory over trainer?
Choose LlamaFactory over trainer when LlamaFactory is primarily Python; trainer is Go; Tags unique to LlamaFactory: gemma, deepseek, instruction-tuning, large-language-models; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.
When should I choose trainer over LlamaFactory?
Choose trainer over LlamaFactory when trainer is primarily Go; LlamaFactory is Python; Tags unique to trainer: gpu, distributed, kubeflow, huggingface; Leaner open-issue backlog (144).
When should I avoid LlamaFactory?
When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa
When should I avoid trainer?
LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Is LlamaFactory or trainer more popular on GitHub?
LlamaFactory has more GitHub stars (73,157 vs 2,135). Stars measure visibility, not whether either tool fits your constraints.
Are LlamaFactory and trainer open source?
Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, trainer: Apache-2.0).
Where can I find alternatives to LlamaFactory or trainer?
GraphCanon lists graph-backed alternatives at LlamaFactory alternatives and trainer alternatives (LlamaFactory markdown twin, trainer markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, LlamaFactory or trainer?
LlamaFactory: Very active. trainer: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for LlamaFactory and trainer?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LlamaFactory trust report; trainer trust report.