--- title: "LlamaFactory vs Awesome-LLMSecOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-wearetyomsmnv-awesome-llmsecops" tools: ["hiyouga-llamafactory", "wearetyomsmnv-awesome-llmsecops"] --- # LlamaFactory vs Awesome-LLMSecOps *GraphCanon updated Jul 15, 2026* ## Verdict Pick LlamaFactory when llamaFactory is primarily Python; Awesome-LLMSecOps is HTML; pick Awesome-LLMSecOps when awesome-LLMSecOps is primarily HTML; LlamaFactory is Python. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [Awesome-LLMSecOps](https://github.com/wearetyomsmnv/Awesome-LLMSecOps) has 144 stars, 51 forks, and 8 open issues, last pushed Jul 13, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [Awesome-LLMSecOps's repository](https://github.com/wearetyomsmnv/Awesome-LLMSecOps). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | LLM | Agentic | Security | Operations in one github repo with good links and pictures. | | Stars | 73,157 | 144 | | Forks | 8,937 | 51 | | Open issues | 1,067 | 8 | | Language | Python | HTML | | Adopt for | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | LLM Frameworks, Model Training | AI Agents, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 1.1k | 8 | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/wearetyomsmnv-awesome-llmsecops/trust.md) | ## Decision facts: LlamaFactory - **Adopt for:** 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. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/hiyouga-llamafactory/alternatives) and [Awesome-LLMSecOps alternatives](/tools/wearetyomsmnv-awesome-llmsecops/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [Awesome-LLMSecOps markdown twin](/tools/wearetyomsmnv-awesome-llmsecops/alternatives.md)), 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](/compare/hiyouga-llamafactory-vs-wearetyomsmnv-awesome-llmsecops.md) 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](/tools/hiyouga-llamafactory/trust); [Awesome-LLMSecOps trust report](/tools/wearetyomsmnv-awesome-llmsecops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hiyouga-llamafactory`](/api/graphcanon/graph?tool=hiyouga-llamafactory) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_