--- title: "flower vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/flwrlabs-flower-vs-hiyouga-llamafactory" tools: ["flwrlabs-flower", "hiyouga-llamafactory"] --- # flower vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick flower when tags unique to flower: federated-analytics, deep-learning, federated-learning-framework, android; pick LlamaFactory when tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning. [flower](https://flower.ai) reports 7.0k GitHub stars, 1.2k forks, and 329 open issues, last pushed Jul 10, 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 [flower's repository](https://github.com/flwrlabs/flower) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [flower](/tools/flwrlabs-flower.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Flower: A Friendly Federated AI Framework | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 7,027 | 73,157 | | Forks | 1,212 | 8,937 | | Open issues | 329 | 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 | Apache-2.0 | | Categories | LLM Frameworks, Model Training, Computer Vision | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [flower](/tools/flwrlabs-flower.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 329 | 1.1k | | Owner type | Organization | User | | Full report | [trust report](/tools/flwrlabs-flower/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 flower if… - Tags unique to flower: federated-analytics, deep-learning, federated-learning-framework, android. - Also covers Computer Vision. - More recently updated (last pushed Jul 10, 2026). ### Choose LlamaFactory if… - Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (73k vs 7.0k) - visibility, not fit. ## When NOT to use flower - 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. ## 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 flower and LlamaFactory? flower: Flower: A Friendly Federated AI Framework. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose flower over LlamaFactory? Choose flower over LlamaFactory when Tags unique to flower: federated-analytics, deep-learning, federated-learning-framework, android; Also covers Computer Vision; More recently updated (last pushed Jul 10, 2026). ### When should I choose LlamaFactory over flower? Choose LlamaFactory over flower when Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 7.0k) - visibility, not fit. ### When should I avoid flower? 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. ### 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 flower or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 7,027). Stars measure visibility, not whether either tool fits your constraints. ### Are flower and LlamaFactory open source? Yes - both are open-source projects on GitHub (flower: Apache-2.0, LlamaFactory: Apache-2.0). ### Where can I find alternatives to flower or LlamaFactory? GraphCanon lists graph-backed alternatives at [flower alternatives](/tools/flwrlabs-flower/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([flower markdown twin](/tools/flwrlabs-flower/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/flwrlabs-flower-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, flower or LlamaFactory? flower: Very 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 flower and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [flower trust report](/tools/flwrlabs-flower/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=flwrlabs-flower`](/api/graphcanon/graph?tool=flwrlabs-flower) - 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/_