--- title: "llm_note vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/harleyszhang-llm-note-vs-hiyouga-llamafactory" tools: ["harleyszhang-llm-note", "hiyouga-llamafactory"] --- # llm_note vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm_note when tags unique to llm_note: cuda-programming, transformer-models, triton-kernels, llm; pick LlamaFactory when tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai. [llm_note](https://github.com/harleyszhang/llm_note) reports 882 GitHub stars, 88 forks, and 0 open issues, last pushed Jul 2, 2026. [LlamaFactory](https://llamafactory.readthedocs.io) has 73k stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [llm_note's repository](https://github.com/harleyszhang/llm_note) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [llm_note](/tools/harleyszhang-llm-note.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | LLM notes, including model inference, transformer model structure, and llm framework code analysis notes. | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 882 | 73,157 | | Forks | 88 | 8,937 | | Open issues | 0 | 1,067 | | Language | Python | Python | | 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, Inference & Serving | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm_note](/tools/harleyszhang-llm-note.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 8d | 0d | | Open issues (now) | 0 | 1.1k | | Full report | [trust report](/tools/harleyszhang-llm-note/trust.md) | [trust report](/tools/hiyouga-llamafactory/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 llm_note if… - Tags unique to llm_note: cuda-programming, transformer-models, triton-kernels, llm. - Also covers Inference & Serving. - Leaner open-issue backlog (0). ### Choose LlamaFactory if… - Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (73k vs 882) - visibility, not fit. ## When NOT to use llm_note - 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. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## 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 ## Common questions ### What is the difference between llm_note and LlamaFactory? llm_note: LLM notes, including model inference, transformer model structure, and llm framework code analysis notes.. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm_note over LlamaFactory? Choose llm_note over LlamaFactory when Tags unique to llm_note: cuda-programming, transformer-models, triton-kernels, llm; Also covers Inference & Serving; Leaner open-issue backlog (0). ### When should I choose LlamaFactory over llm_note? Choose LlamaFactory over llm_note when Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 882) - visibility, not fit. ### When should I avoid llm_note? 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. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### 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 llm_note or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 882). Stars measure visibility, not whether either tool fits your constraints. ### Are llm_note and LlamaFactory open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm_note or LlamaFactory? GraphCanon lists graph-backed alternatives at [llm_note alternatives](/tools/harleyszhang-llm-note/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([llm_note markdown twin](/tools/harleyszhang-llm-note/alternatives.md), [LlamaFactory markdown twin](/tools/hiyouga-llamafactory/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/harleyszhang-llm-note-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm_note or LlamaFactory? llm_note: Active. 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 llm_note and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm_note trust report](/tools/harleyszhang-llm-note/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=harleyszhang-llm-note`](/api/graphcanon/graph?tool=harleyszhang-llm-note) - 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/_