--- title: "LlamaFactory vs VideoPipe" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-sherlockchou86-videopipe" tools: ["hiyouga-llamafactory", "sherlockchou86-videopipe"] --- # LlamaFactory vs VideoPipe *GraphCanon updated Jul 15, 2026* ## Verdict Pick LlamaFactory when llamaFactory is primarily Python; VideoPipe is C++; pick VideoPipe when videoPipe is primarily C++; 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. [VideoPipe](http://www.videopipe.cool) has 2.9k stars, 449 forks, and 4 open issues, last pushed Feb 25, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [VideoPipe's repository](https://github.com/sherlockchou86/VideoPipe). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [VideoPipe](/tools/sherlockchou86-videopipe.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | A cross-platform video structuring (video analysis) framework. If you find it helpful, please give it a star: ) 跨平台的视频结构化（视频分析）框架，觉得有帮助的请给个星星 : ) | | Stars | 73,157 | 2,870 | | Forks | 8,937 | 449 | | Open issues | 1,067 | 4 | | Language | Python | C++ | | 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 | Apache-2.0 | | 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) | [VideoPipe](/tools/sherlockchou86-videopipe.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 140d | | Open issues (now) | 1.1k | 4 | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/sherlockchou86-videopipe/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; VideoPipe is C++. - Tags unique to LlamaFactory: agent, deepseek, fine-tuning, gemma. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose VideoPipe if… - VideoPipe is primarily C++; LlamaFactory is Python. - Tags unique to VideoPipe: behaviour-analysis, cv, deep-learning, deepstream. - Also covers Inference & Serving. ## 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 VideoPipe - Last GitHub push was 140 days ago (slowing maintenance, Feb 25, 2026). Validate activity before betting a new project on VideoPipe. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - 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 VideoPipe? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. VideoPipe: A cross-platform video structuring (video analysis) framework. If you find it helpful, please give it a star: ) 跨平台的视频结构化（视频分析）框架，觉得有帮助的请给个星星 : ). See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over VideoPipe? Choose LlamaFactory over VideoPipe when LlamaFactory is primarily Python; VideoPipe is C++; Tags unique to LlamaFactory: agent, deepseek, fine-tuning, gemma; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose VideoPipe over LlamaFactory? Choose VideoPipe over LlamaFactory when VideoPipe is primarily C++; LlamaFactory is Python; Tags unique to VideoPipe: behaviour-analysis, cv, deep-learning, deepstream; Also covers Inference & Serving. ### 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 VideoPipe? Last GitHub push was 140 days ago (slowing maintenance, Feb 25, 2026). Validate activity before betting a new project on VideoPipe. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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 VideoPipe more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 2,870). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and VideoPipe open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, VideoPipe: Apache-2.0). ### Where can I find alternatives to LlamaFactory or VideoPipe? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [VideoPipe alternatives](/tools/sherlockchou86-videopipe/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [VideoPipe markdown twin](/tools/sherlockchou86-videopipe/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-sherlockchou86-videopipe.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or VideoPipe? LlamaFactory: Very active. VideoPipe: Slowing. 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 VideoPipe? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [VideoPipe trust report](/tools/sherlockchou86-videopipe/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/_