--- title: "Fun-ASR vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/funaudiollm-fun-asr-vs-hiyouga-llamafactory" tools: ["funaudiollm-fun-asr", "hiyouga-llamafactory"] --- # Fun-ASR vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick Fun-ASR when fun-ASR is primarily C; LlamaFactory is Python; pick LlamaFactory when llamaFactory is primarily Python; Fun-ASR is C. [Fun-ASR](https://www.funasr.com) reports 1.4k GitHub stars, 136 forks, and 0 open issues, last pushed Jul 7, 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 [Fun-ASR's repository](https://github.com/FunAudioLLM/Fun-ASR) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [Fun-ASR](/tools/funaudiollm-fun-asr.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Fun-ASR-Nano LLM-ASR model: 31 languages, dialects, accents, lyrics, hotwords, timestamps, and speaker diarization. | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 1,381 | 73,157 | | Forks | 136 | 8,937 | | Open issues | 0 | 1,067 | | Language | C | 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, Inference & Serving | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Fun-ASR](/tools/funaudiollm-fun-asr.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Days since push | 4d | 0d | | Open issues (now) | 0 | 1.1k | | Owner type | Organization | User | | Security scan | 26 low (26 low) | No lockfile | | Full report | [trust report](/tools/funaudiollm-fun-asr/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 Fun-ASR if… - Fun-ASR is primarily C; LlamaFactory is Python. - Tags unique to Fun-ASR: asr, audio-language-model, fun-asr, audio. - Also covers Inference & Serving. ### Choose LlamaFactory if… - LlamaFactory is primarily Python; Fun-ASR is C. - 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. ## When NOT to use Fun-ASR - 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 Fun-ASR and LlamaFactory? Fun-ASR: Fun-ASR-Nano LLM-ASR model: 31 languages, dialects, accents, lyrics, hotwords, timestamps, and speaker diarization.. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Fun-ASR over LlamaFactory? Choose Fun-ASR over LlamaFactory when Fun-ASR is primarily C; LlamaFactory is Python; Tags unique to Fun-ASR: asr, audio-language-model, fun-asr, audio; Also covers Inference & Serving. ### When should I choose LlamaFactory over Fun-ASR? Choose LlamaFactory over Fun-ASR when LlamaFactory is primarily Python; Fun-ASR is C; 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. ### When should I avoid Fun-ASR? 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 Fun-ASR or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 1,381). Stars measure visibility, not whether either tool fits your constraints. ### Are Fun-ASR and LlamaFactory open source? Yes - both are open-source projects on GitHub (Fun-ASR: Apache-2.0, LlamaFactory: Apache-2.0). ### Where can I find alternatives to Fun-ASR or LlamaFactory? GraphCanon lists graph-backed alternatives at [Fun-ASR alternatives](/tools/funaudiollm-fun-asr/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([Fun-ASR markdown twin](/tools/funaudiollm-fun-asr/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/funaudiollm-fun-asr-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Fun-ASR or LlamaFactory? Fun-ASR: 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 Fun-ASR and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Fun-ASR trust report](/tools/funaudiollm-fun-asr/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=funaudiollm-fun-asr`](/api/graphcanon/graph?tool=funaudiollm-fun-asr) - 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/_