Home/Compare/flower vs LlamaFactory

Comparison

flower vs LlamaFactory

Verdict

Pick flower when tags unique to flower: federated-analytics, deep-learning, federated-learning-framework, android; pick LlamaFactory when tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning.

Markdown twin · flower alternatives · LlamaFactory alternatives

GraphCanon updated today

flower logo

flower

flwrlabs/flower

7.0kpushed Jul 10, 2026
vs
LlamaFactory logo

LlamaFactory

hiyouga/LlamaFactory

73kpushed Jul 10, 2026

Trust & integrity

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

Tagline

flower
Flower: A Friendly Federated AI Framework
LlamaFactory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs

Stars

flower
7.0k
LlamaFactory
73k

Forks

flower
1.2k
LlamaFactory
8.9k

Open issues

flower
329
LlamaFactory
1.1k

Language

flower
Python
LlamaFactory
Python

Adopt for

flower
-
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.

Persona

flower
-
LlamaFactory
-

Runtime

flower
-
LlamaFactory
-

License

flower
Apache-2.0
LlamaFactory
Apache-2.0

Last pushed

flower
Jul 10, 2026
LlamaFactory
Jul 10, 2026

Categories

flower
LLM Frameworks, Model Training, Computer Vision
LlamaFactory
Model Training, LLM Frameworks

Trust and health

Days since push

flower
1d
LlamaFactory
0d

Open issues (now)

flower
329
LlamaFactory
1.1k

Owner type

flower
Organization
LlamaFactory
User

Full report

LlamaFactory
Trust report

Choose flower if…

  • Tags unique to flower: federated-analytics, deep-learning, federated-learning-framework, android.
  • Also covers Computer Vision.
  • More recently updated (last pushed Jul 10, 2026).

When NOT to use flower

  • 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.

Choose LlamaFactory if…

  • Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning.
  • When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.
  • More GitHub stars (73k vs 7.0k) - 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

Explore

Sources

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

GitHub stars on cards: flower 7.0k · LlamaFactory 73k (synced Jul 11, 2026).

Common questions

What is the difference between flower and LlamaFactory?
flower: Flower: A Friendly Federated AI Framework. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories.
When should I choose flower over LlamaFactory?
Choose flower over LlamaFactory when Tags unique to flower: federated-analytics, deep-learning, federated-learning-framework, android; Also covers Computer Vision; More recently updated (last pushed Jul 10, 2026).
When should I choose LlamaFactory over flower?
Choose LlamaFactory over flower when Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 7.0k) - visibility, not fit.
When should I avoid flower?
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.
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
Is flower or LlamaFactory more popular on GitHub?
LlamaFactory has more GitHub stars (73,157 vs 7,027). Stars measure visibility, not whether either tool fits your constraints.
Are flower and LlamaFactory open source?
Yes - both are open-source projects on GitHub (flower: Apache-2.0, LlamaFactory: Apache-2.0).
Where can I find alternatives to flower or LlamaFactory?
GraphCanon lists graph-backed alternatives at flower alternatives and LlamaFactory alternatives (flower markdown twin, LlamaFactory 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, flower or LlamaFactory?
flower: Very active. LlamaFactory: 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 flower and LlamaFactory?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: flower trust report; LlamaFactory trust report.