--- title: "LlamaFactory vs llm-action" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-liguodongiot-llm-action" tools: ["hiyouga-llamafactory", "liguodongiot-llm-action"] --- # LlamaFactory vs llm-action *GraphCanon updated Aug 16, 2026* ## 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 llm-action if llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training. [LlamaFactory](https://llamafactory.readthedocs.io) reports 74k GitHub stars, 9.1k forks, and 1.1k open issues, last pushed Aug 13, 2026. [llm-action](https://www.zhihu.com/column/c_1456193767213043713) has 25k stars, 2.8k forks, and 19 open issues, last pushed Jul 19, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [llm-action's repository](https://github.com/liguodongiot/llm-action). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [llm-action](/tools/liguodongiot-llm-action.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Aims to share large model technology principles and practical experience (large model engineering, application implementation) | | Stars | 74,132 | 24,898 | | Forks | 9,071 | 2,842 | | Open issues | 1,113 | 19 | | 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. | llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | llm-action is open-source under the Apache-2.0 license. | | Categories | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [llm-action](/tools/liguodongiot-llm-action.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 2d | 28d | | Open issues (now) | 1.1k | 19 | | Stars delta | +803 (30d) | +162 (30d) | | Open issues delta | +39 (30d) | +1 (30d) | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/liguodongiot-llm-action/trust.md) | **Typed relationship:** LlamaFactory _(related)_ llm-action ## 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. ## Decision facts: llm-action - **Adopt for:** llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training. - **License detail:** llm-action is open-source under the Apache-2.0 license. ## Choose when ### Choose LlamaFactory if… - LlamaFactory is primarily Python; llm-action is HTML. - Graph edge: LlamaFactory is a typed related of llm-action - see the relationship row above. - 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 llm-action if… - llm-action is primarily HTML; LlamaFactory is Python. - Graph edge: llm-action is a typed related of LlamaFactory - see the relationship row above. - Tags unique to llm-action: deployment, engineering, inference, large model. - Also covers Inference & Serving. - - When you need detailed examples and best practices of implementing large language models (LLMs) in real-world applications, llm-action provides insights into the challenges faced during LLM's actual ## 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 llm-action - - If your focus is narrowly on cutting-edge research rather than practical implementation details, llm-action may not be the best resource as it focuses more on deployment processes. - - When looking for a full-stack solution that includes detailed code implementations and libraries for each phase (training, serving), llm-action might fall short. It shines in sharing knowledge but ## Common questions ### What is the difference between LlamaFactory and llm-action? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. llm-action: Aims to share large model technology principles and practical experience (large model engineering, application implementation). See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over llm-action? Choose LlamaFactory over llm-action when LlamaFactory is primarily Python; llm-action is HTML; Graph edge: LlamaFactory is a typed related of llm-action - see the relationship row above; 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 llm-action over LlamaFactory? Choose llm-action over LlamaFactory when llm-action is primarily HTML; LlamaFactory is Python; Graph edge: llm-action is a typed related of LlamaFactory - see the relationship row above; Tags unique to llm-action: deployment, engineering, inference, large model; Also covers Inference & Serving; - When you need detailed examples and best practices of implementing large language models (LLMs) in real-world applications, llm-action provides insights into the challenges faced during LLM's actual. ### 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 llm-action? - If your focus is narrowly on cutting-edge research rather than practical implementation details, llm-action may not be the best resource as it focuses more on deployment processes. - When looking for a full-stack solution that includes detailed code implementations and libraries for each phase (training, serving), llm-action might fall short. It shines in sharing knowledge but ### Is LlamaFactory or llm-action more popular on GitHub? LlamaFactory has more GitHub stars (74,132 vs 24,898). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and llm-action open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, llm-action: Apache-2.0). ### Where can I find alternatives to LlamaFactory or llm-action? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [llm-action alternatives](/tools/liguodongiot-llm-action/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [llm-action markdown twin](/tools/liguodongiot-llm-action/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-liguodongiot-llm-action.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or llm-action? LlamaFactory: Very active. llm-action: 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 llm-action? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [llm-action trust report](/tools/liguodongiot-llm-action/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/_