Home/Compare/LlamaFactory vs Eagle

Comparison

LlamaFactory vs Eagle

Verdict

Pick LlamaFactory when tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai; pick Eagle when tags unique to Eagle: llama, gpt4, eagle, demo.

Markdown twin · LlamaFactory alternatives · Eagle alternatives

GraphCanon updated today

LlamaFactory logo

LlamaFactory

hiyouga/LlamaFactory

73kpushed Jul 10, 2026
vs
Eagle logo

Eagle

NVlabs/Eagle

3.2kpushed Jun 24, 2026

Trust & integrity

SignalLlamaFactoryEagle
Maintenance
Very active (0d since push)
As of today · github_public_v1
Active (16d 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
Eagle
Eagle: Frontier Vision-Language Models with Data-Centric Strategies

Stars

LlamaFactory
73k
Eagle
3.2k

Forks

LlamaFactory
8.9k
Eagle
301

Open issues

LlamaFactory
1.1k
Eagle
57

Language

LlamaFactory
Python
Eagle
Python

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.
Eagle
-

Persona

LlamaFactory
-
Eagle
-

Runtime

LlamaFactory
-
Eagle
-

License

LlamaFactory
Apache-2.0
Eagle
Apache-2.0

Last pushed

LlamaFactory
Jul 10, 2026
Eagle
Jun 24, 2026

Categories

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

Trust and health

Maintenance

LlamaFactory
Very active (96%)
Eagle
Active (82%)

Days since push

LlamaFactory
0d
Eagle
16d

Open issues (now)

LlamaFactory
1.1k
Eagle
57

Owner type

LlamaFactory
User
Eagle
Organization

Full report

LlamaFactory
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 3.2k) - 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 Eagle if…

  • Tags unique to Eagle: llama, gpt4, eagle, demo.
  • Also covers Computer Vision.
  • Leaner open-issue backlog (57).

When NOT to use Eagle

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

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 · Eagle 3.2k (synced Jul 11, 2026).

Common questions

What is the difference between LlamaFactory and Eagle?
LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. Eagle: Eagle: Frontier Vision-Language Models with Data-Centric Strategies. See the comparison table for live GitHub stats and shared categories.
When should I choose LlamaFactory over Eagle?
Choose LlamaFactory over Eagle 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 3.2k) - visibility, not fit.
When should I choose Eagle over LlamaFactory?
Choose Eagle over LlamaFactory when Tags unique to Eagle: llama, gpt4, eagle, demo; Also covers Computer Vision; Leaner open-issue backlog (57).
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 Eagle?
Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Is LlamaFactory or Eagle more popular on GitHub?
LlamaFactory has more GitHub stars (73,157 vs 3,159). Stars measure visibility, not whether either tool fits your constraints.
Are LlamaFactory and Eagle open source?
Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, Eagle: Apache-2.0).
Where can I find alternatives to LlamaFactory or Eagle?
GraphCanon lists graph-backed alternatives at LlamaFactory alternatives and Eagle alternatives (LlamaFactory markdown twin, Eagle 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 Eagle?
LlamaFactory: Very active. Eagle: 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 Eagle?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LlamaFactory trust report; Eagle trust report.