Comparison
awesome-llms-fine-tuning vs LlamaFactory
Verdict
Pick awesome-llms-fine-tuning when tags unique to awesome-llms-fine-tuning: awesome-list, deep-learning, llms, machine-learning; pick LlamaFactory when tags unique to LlamaFactory: agent, deepseek, gemma, instruction-tuning.
Markdown twin · awesome-llms-fine-tuning alternatives · LlamaFactory alternatives
GraphCanon updated today
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Trust & integrity
| Signal | awesome-llms-fine-tuning | LlamaFactory |
|---|---|---|
| Maintenance | Dormant (585d 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
- awesome-llms-fine-tuning
- Explore a comprehensive collection of resources, tutorials, papers, tools, and best practices for fine-tuning Large Language Models (LLMs). Perfect for ML practitioners and researchers!
- LlamaFactory
- Unified Efficient Fine-Tuning of 100+ LLMs & VLMs
Stars
- awesome-llms-fine-tuning
- 521
- LlamaFactory
- 73k
Forks
- awesome-llms-fine-tuning
- 77
- LlamaFactory
- 8.9k
Open issues
- awesome-llms-fine-tuning
- 8
- LlamaFactory
- 1.1k
Language
- awesome-llms-fine-tuning
- -
- LlamaFactory
- Python
Adopt for
- awesome-llms-fine-tuning
- -
- 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
- awesome-llms-fine-tuning
- -
- LlamaFactory
- -
Runtime
- awesome-llms-fine-tuning
- -
- LlamaFactory
- -
License
- awesome-llms-fine-tuning
- -
- LlamaFactory
- Apache-2.0
Last pushed
- awesome-llms-fine-tuning
- Dec 2, 2024
- LlamaFactory
- Jul 10, 2026
Categories
- awesome-llms-fine-tuning
- LLM Frameworks, Model Training
- LlamaFactory
- LLM Frameworks, Model Training
Trust and health
Maintenance
- awesome-llms-fine-tuning
- Dormant (18%)
- LlamaFactory
- Very active (96%)
Days since push
- awesome-llms-fine-tuning
- 585d
- LlamaFactory
- 0d
Open issues (now)
- awesome-llms-fine-tuning
- 8
- LlamaFactory
- 1.1k
Owner type
- awesome-llms-fine-tuning
- Organization
- LlamaFactory
- User
Full report
- awesome-llms-fine-tuning
- Trust report
- LlamaFactory
- Trust report
Choose awesome-llms-fine-tuning if…
- Tags unique to awesome-llms-fine-tuning: awesome-list, deep-learning, llms, machine-learning.
- Leaner open-issue backlog (8).
When NOT to use awesome-llms-fine-tuning
- Last GitHub push was 586 days ago (dormant maintenance, Dec 2, 2024). Validate activity before betting a new project on awesome-llms-fine-tuning.
- 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: agent, deepseek, gemma, 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 521) - 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 (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Jul 11, 2026
- GitHub forks (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Jul 11, 2026
- Last push (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Dec 2, 2024
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (hiyouga/LlamaFactory) · observed Jul 11, 2026
- GitHub forks (hiyouga/LlamaFactory) · observed Jul 11, 2026
- Last push (hiyouga/LlamaFactory) · observed Jul 10, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-llms-fine-tuning 521 · LlamaFactory 73k (synced Jul 11, 2026).
Common questions
- What is the difference between awesome-llms-fine-tuning and LlamaFactory?
- awesome-llms-fine-tuning: Explore a comprehensive collection of resources, tutorials, papers, tools, and best practices for fine-tuning Large Language Models (LLMs). Perfect for ML practitioners and researchers!. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-llms-fine-tuning over LlamaFactory?
- Choose awesome-llms-fine-tuning over LlamaFactory when Tags unique to awesome-llms-fine-tuning: awesome-list, deep-learning, llms, machine-learning; Leaner open-issue backlog (8).
- When should I choose LlamaFactory over awesome-llms-fine-tuning?
- Choose LlamaFactory over awesome-llms-fine-tuning when Tags unique to LlamaFactory: agent, deepseek, gemma, 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 521) - visibility, not fit.
- When should I avoid awesome-llms-fine-tuning?
- Last GitHub push was 586 days ago (dormant maintenance, Dec 2, 2024). Validate activity before betting a new project on awesome-llms-fine-tuning. 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 awesome-llms-fine-tuning or LlamaFactory more popular on GitHub?
- LlamaFactory has more GitHub stars (73,157 vs 521). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-llms-fine-tuning and LlamaFactory open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-llms-fine-tuning or LlamaFactory?
- GraphCanon lists graph-backed alternatives at awesome-llms-fine-tuning alternatives and LlamaFactory alternatives (awesome-llms-fine-tuning 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, awesome-llms-fine-tuning or LlamaFactory?
- awesome-llms-fine-tuning: 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 awesome-llms-fine-tuning and LlamaFactory?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-llms-fine-tuning trust report; LlamaFactory trust report.