Home/Compare/model_card vs LlamaFactory

Comparison

model_card vs LlamaFactory

Verdict

Pick model_card when also covers Vector Databases; pick LlamaFactory when tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai.

Markdown twin · model_card alternatives · LlamaFactory alternatives

GraphCanon updated today

model_card logo

model_card

bigscience-workshop/model_card

26pushed Jul 11, 2022
vs
LlamaFactory logo

LlamaFactory

hiyouga/LlamaFactory

73kpushed Jul 10, 2026

Trust & integrity

Signalmodel_cardLlamaFactory
Maintenance
Dormant (1461d 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

model_card
model_card
LlamaFactory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs

Stars

model_card
26
LlamaFactory
73k

Forks

model_card
5
LlamaFactory
8.9k

Open issues

model_card
0
LlamaFactory
1.1k

Language

model_card
-
LlamaFactory
Python

Adopt for

model_card
-
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

model_card
-
LlamaFactory
-

Runtime

model_card
-
LlamaFactory
-

License

model_card
Apache-2.0
LlamaFactory
Apache-2.0

Last pushed

model_card
Jul 11, 2022
LlamaFactory
Jul 10, 2026

Categories

model_card
Model Training, LLM Frameworks, Vector Databases
LlamaFactory
Model Training, LLM Frameworks

Trust and health

Maintenance

model_card
Dormant (18%)
LlamaFactory
Very active (96%)

Days since push

model_card
1461d
LlamaFactory
0d

Open issues (now)

model_card
0
LlamaFactory
1.1k

Owner type

model_card
Organization
LlamaFactory
User

Full report

model_card
Trust report
LlamaFactory
Trust report

Choose model_card if…

  • Also covers Vector Databases.
  • Leaner open-issue backlog (0).

When NOT to use model_card

  • Last GitHub push was 1461 days ago (dormant maintenance, Jul 11, 2022). Validate activity before betting a new project on model_card.
  • 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.
  • Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

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 26) - 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: model_card 26 · LlamaFactory 73k (synced Jul 11, 2026).

Common questions

What is the difference between model_card and LlamaFactory?
model_card: model_card. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories.
When should I choose model_card over LlamaFactory?
Choose model_card over LlamaFactory when Also covers Vector Databases; Leaner open-issue backlog (0).
When should I choose LlamaFactory over model_card?
Choose LlamaFactory over model_card 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 26) - visibility, not fit.
When should I avoid model_card?
Last GitHub push was 1461 days ago (dormant maintenance, Jul 11, 2022). Validate activity before betting a new project on model_card. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
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 model_card or LlamaFactory more popular on GitHub?
LlamaFactory has more GitHub stars (73,157 vs 26). Stars measure visibility, not whether either tool fits your constraints.
Are model_card and LlamaFactory open source?
Yes - both are open-source projects on GitHub (model_card: Apache-2.0, LlamaFactory: Apache-2.0).
Where can I find alternatives to model_card or LlamaFactory?
GraphCanon lists graph-backed alternatives at model_card alternatives and LlamaFactory alternatives (model_card 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, model_card or LlamaFactory?
model_card: Dormant. 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 model_card and LlamaFactory?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: model_card trust report; LlamaFactory trust report.