Home/Compare/LlamaFactory vs kubeflow

Comparison

LlamaFactory vs kubeflow

Verdict

Pick LlamaFactory when tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai; pick kubeflow when tags unique to kubeflow: ml, machine-learning, jupyter, minikube.

Markdown twin · LlamaFactory alternatives · kubeflow alternatives

GraphCanon updated today

LlamaFactory logo

LlamaFactory

hiyouga/LlamaFactory

73kpushed Jul 10, 2026
vs
kubeflow logo

kubeflow

kubeflow/kubeflow

16kpushed Jul 10, 2026

Trust & integrity

SignalLlamaFactorykubeflow
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
kubeflow
Machine Learning Toolkit for Kubernetes

Stars

LlamaFactory
73k
kubeflow
16k

Forks

LlamaFactory
8.9k
kubeflow
2.7k

Open issues

LlamaFactory
1.1k
kubeflow
0

Language

LlamaFactory
Python
kubeflow
-

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.
kubeflow
-

Persona

LlamaFactory
-
kubeflow
-

Runtime

LlamaFactory
-
kubeflow
-

License

LlamaFactory
Apache-2.0
kubeflow
Apache-2.0

Last pushed

LlamaFactory
Jul 10, 2026
kubeflow
Jul 10, 2026

Categories

LlamaFactory
Model Training, LLM Frameworks
kubeflow
LLM Frameworks, Model Training, Inference & Serving

Trust and health

Days since push

LlamaFactory
0d
kubeflow
1d

Open issues (now)

LlamaFactory
1.1k
kubeflow
0

Owner type

LlamaFactory
User
kubeflow
Organization

Full report

LlamaFactory
Trust report
kubeflow
Trust report

Choose LlamaFactory if…

  • Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai.
  • When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.
  • More GitHub stars (73k vs 16k) - visibility, not fit.

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 kubeflow if…

  • Tags unique to kubeflow: ml, machine-learning, jupyter, minikube.
  • Also covers Inference & Serving.
  • Leaner open-issue backlog (0).

When NOT to use kubeflow

  • 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.
  • Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

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 · kubeflow 16k (synced Jul 11, 2026).

Common questions

What is the difference between LlamaFactory and kubeflow?
LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. kubeflow: Machine Learning Toolkit for Kubernetes. See the comparison table for live GitHub stats and shared categories.
When should I choose LlamaFactory over kubeflow?
Choose LlamaFactory over kubeflow when Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 16k) - visibility, not fit.
When should I choose kubeflow over LlamaFactory?
Choose kubeflow over LlamaFactory when Tags unique to kubeflow: ml, machine-learning, jupyter, minikube; Also covers Inference & Serving; Leaner open-issue backlog (0).
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 kubeflow?
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. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Is LlamaFactory or kubeflow more popular on GitHub?
LlamaFactory has more GitHub stars (73,157 vs 15,770). Stars measure visibility, not whether either tool fits your constraints.
Are LlamaFactory and kubeflow open source?
Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, kubeflow: Apache-2.0).
Where can I find alternatives to LlamaFactory or kubeflow?
GraphCanon lists graph-backed alternatives at LlamaFactory alternatives and kubeflow alternatives (LlamaFactory markdown twin, kubeflow 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 kubeflow?
LlamaFactory: Very active. kubeflow: 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 kubeflow?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LlamaFactory trust report; kubeflow trust report.