Comparison
LlamaFactory vs AI-Compass
Verdict
Pick LlamaFactory if 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; pick AI-Compass if aI-Compass offers comprehensive guidance on AI concepts and technologies for developers at all levels, focusing on practical application from theory to implementation.
Markdown twin · LlamaFactory alternatives · AI-Compass alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | LlamaFactory | AI-Compass |
|---|---|---|
| Maintenance | Very active (2d since push) As of 1w · github_public_v1 | Very active (1d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1w · github_public_v1 | Not a fork · Personal account As of 2d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- LlamaFactory
- Unified Efficient Fine-Tuning of 100+ LLMs & VLMs
- AI-Compass
- Guides developers through AI concepts, technologies, and practical applications
Stars
- LlamaFactory
- 74k
- AI-Compass
- 914
Forks
- LlamaFactory
- 9.1k
- AI-Compass
- 122
Open issues
- LlamaFactory
- 1.1k
- AI-Compass
- 1
Language
- LlamaFactory
- Python
- AI-Compass
- 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.
- AI-Compass
- AI-Compass offers comprehensive guidance on AI concepts and technologies for developers at all levels, focusing on practical application from theory to implementation.
Persona
- LlamaFactory
- -
- AI-Compass
- -
Runtime
- LlamaFactory
- -
- AI-Compass
- -
License
- LlamaFactory
- Apache-2.0
- AI-Compass
- -
Last pushed
- LlamaFactory
- Aug 13, 2026
- AI-Compass
- Aug 24, 2026
Categories
- LlamaFactory
- LLM Frameworks, Model Training
- AI-Compass
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Days since push
- LlamaFactory
- 2d
- AI-Compass
- 1d
Open issues (now)
- LlamaFactory
- 1.1k
- AI-Compass
- 1
Stars delta
- LlamaFactory
- +803 (30d)
- AI-Compass
- +37 (30d)
Open issues delta
- LlamaFactory
- +39 (30d)
- AI-Compass
- -3 (30d)
Full report
- LlamaFactory
- Trust report
- AI-Compass
- Trust report
Shared compatibility
- Python · LlamaFactory: Python runtime · AI-Compass: Python runtime
Choose LlamaFactory if…
- Tags unique to LlamaFactory: deepseek, fine-tuning, gemma, gpt.
- When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.
- More GitHub stars (74k vs 914) - 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 AI-Compass if…
- Pricing: The pricing model for AI-Compass is not specified in the repository data provided..
- Requirements: Developers will need a basic understanding of Python to take full advantage of the resources offered by AI-Compass.; The exact system requirements are not provided in the repository data..
- Tags unique to AI-Compass: llm, nlp, rl, rlhf.
- Also covers Inference & Serving.
- When you require detailed, systematic learning resources that cover both basic and advanced AI topics.
When NOT to use AI-Compass
- Avoid if you are looking for a tool that focuses solely on hands-on project work without an emphasis on understanding underlying concepts.
- Not recommended if you prefer more specialized tools that cater strictly either to beginners or advanced developers, rather than serving both audiences cohesively.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hiyouga/LlamaFactory) · observed Aug 16, 2026
- GitHub forks (hiyouga/LlamaFactory) · observed Aug 16, 2026
- Last push (hiyouga/LlamaFactory) · observed Aug 13, 2026
- License file (Apache-2.0) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tingaicompass/AI-Compass) · observed Aug 25, 2026
- GitHub forks (tingaicompass/AI-Compass) · observed Aug 25, 2026
- Last push (tingaicompass/AI-Compass) · observed Aug 24, 2026
- License file (unknown) · observed Aug 25, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LlamaFactory 74k · AI-Compass 914 (synced Aug 16, 2026).
Common questions
- What is the difference between LlamaFactory and AI-Compass?
- LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. AI-Compass: Guides developers through AI concepts, technologies, and practical applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose LlamaFactory over AI-Compass?
- Choose LlamaFactory over AI-Compass when Tags unique to LlamaFactory: deepseek, fine-tuning, gemma, gpt; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (74k vs 914) - visibility, not fit.
- When should I choose AI-Compass over LlamaFactory?
- Choose AI-Compass over LlamaFactory when Pricing: The pricing model for AI-Compass is not specified in the repository data provided.; Requirements: Developers will need a basic understanding of Python to take full advantage of the resources offered by AI-Compass.; The exact system requirements are not provided in the repository data.; Tags unique to AI-Compass: llm, nlp, rl, rlhf; Also covers Inference & Serving; When you require detailed, systematic learning resources that cover both basic and advanced AI topics.
- 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 AI-Compass?
- Avoid if you are looking for a tool that focuses solely on hands-on project work without an emphasis on understanding underlying concepts. Not recommended if you prefer more specialized tools that cater strictly either to beginners or advanced developers, rather than serving both audiences cohesively.
- Is LlamaFactory or AI-Compass more popular on GitHub?
- LlamaFactory has more GitHub stars (74,132 vs 914). Stars measure visibility, not whether either tool fits your constraints.
- Are LlamaFactory and AI-Compass open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LlamaFactory or AI-Compass?
- GraphCanon lists graph-backed alternatives at LlamaFactory alternatives and AI-Compass alternatives (LlamaFactory markdown twin, AI-Compass 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 AI-Compass?
- LlamaFactory: Very active. AI-Compass: 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 AI-Compass?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LlamaFactory trust report; AI-Compass trust report.