Comparison
LlamaFactory vs Awesome-LLMSecOps
Verdict
Pick LlamaFactory when llamaFactory is primarily Python; Awesome-LLMSecOps is HTML; pick Awesome-LLMSecOps when awesome-LLMSecOps is primarily HTML; LlamaFactory is Python.
Markdown twin · LlamaFactory alternatives · Awesome-LLMSecOps alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | LlamaFactory | Awesome-LLMSecOps |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4d · github_public_v1 | Very active (1d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4d · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 4d · osv@v1 | No lockfile (source not queried) As of today · 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
- Awesome-LLMSecOps
- LLM | Agentic | Security | Operations in one github repo with good links and pictures.
Stars
- LlamaFactory
- 73k
- Awesome-LLMSecOps
- 144
Forks
- LlamaFactory
- 8.9k
- Awesome-LLMSecOps
- 51
Open issues
- LlamaFactory
- 1.1k
- Awesome-LLMSecOps
- 8
Language
- LlamaFactory
- Python
- Awesome-LLMSecOps
- HTML
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.
- Awesome-LLMSecOps
- -
Persona
- LlamaFactory
- -
- Awesome-LLMSecOps
- -
Runtime
- LlamaFactory
- -
- Awesome-LLMSecOps
- -
License
- LlamaFactory
- Apache-2.0
- Awesome-LLMSecOps
- -
Last pushed
- LlamaFactory
- Jul 10, 2026
- Awesome-LLMSecOps
- Jul 13, 2026
Categories
- LlamaFactory
- LLM Frameworks, Model Training
- Awesome-LLMSecOps
- AI Agents, LLM Frameworks, Model Training
Trust and health
Days since push
- LlamaFactory
- 0d
- Awesome-LLMSecOps
- 1d
Open issues (now)
- LlamaFactory
- 1.1k
- Awesome-LLMSecOps
- 8
Full report
- LlamaFactory
- Trust report
- Awesome-LLMSecOps
- Trust report
Choose LlamaFactory if…
- LlamaFactory is primarily Python; Awesome-LLMSecOps is HTML.
- Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning.
- When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.
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 Awesome-LLMSecOps if…
- Awesome-LLMSecOps is primarily HTML; LlamaFactory is Python.
- Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, ai-red-team, ai-safety-supply-chain-security.
- Also covers AI Agents.
When NOT to use Awesome-LLMSecOps
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- 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.
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 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 (wearetyomsmnv/Awesome-LLMSecOps) · observed Jul 15, 2026
- GitHub forks (wearetyomsmnv/Awesome-LLMSecOps) · observed Jul 15, 2026
- Last push (wearetyomsmnv/Awesome-LLMSecOps) · observed Jul 13, 2026
- License file (unknown) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: LlamaFactory 73k · Awesome-LLMSecOps 144 (synced Jul 11, 2026).
Common questions
- What is the difference between LlamaFactory and Awesome-LLMSecOps?
- LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. Awesome-LLMSecOps: LLM | Agentic | Security | Operations in one github repo with good links and pictures.. See the comparison table for live GitHub stats and shared categories.
- When should I choose LlamaFactory over Awesome-LLMSecOps?
- Choose LlamaFactory over Awesome-LLMSecOps when LlamaFactory is primarily Python; Awesome-LLMSecOps is HTML; Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.
- When should I choose Awesome-LLMSecOps over LlamaFactory?
- Choose Awesome-LLMSecOps over LlamaFactory when Awesome-LLMSecOps is primarily HTML; LlamaFactory is Python; Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, ai-red-team, ai-safety-supply-chain-security; Also covers AI Agents.
- 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 Awesome-LLMSecOps?
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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.
- Is LlamaFactory or Awesome-LLMSecOps more popular on GitHub?
- LlamaFactory has more GitHub stars (73,157 vs 144). Stars measure visibility, not whether either tool fits your constraints.
- Are LlamaFactory and Awesome-LLMSecOps open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LlamaFactory or Awesome-LLMSecOps?
- GraphCanon lists graph-backed alternatives at LlamaFactory alternatives and Awesome-LLMSecOps alternatives (LlamaFactory markdown twin, Awesome-LLMSecOps 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 Awesome-LLMSecOps?
- LlamaFactory: Very active. Awesome-LLMSecOps: 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 Awesome-LLMSecOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LlamaFactory trust report; Awesome-LLMSecOps trust report.