--- title: "IndustryBench vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaba-multimodal-industrial-ai-industrybench-vs-hiyouga-llamafactory" tools: ["alibaba-multimodal-industrial-ai-industrybench", "hiyouga-llamafactory"] --- # IndustryBench vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick IndustryBench when license: IndustryBench is MIT, LlamaFactory is Apache-2.0; pick LlamaFactory when license: LlamaFactory is Apache-2.0, IndustryBench is MIT. [IndustryBench](https://github.com/alibaba-multimodal-industrial-ai/IndustryBench) reports 155 GitHub stars, 10 forks, and 1 open issues, last pushed Jun 15, 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 [IndustryBench's repository](https://github.com/alibaba-multimodal-industrial-ai/IndustryBench) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [IndustryBench](/tools/alibaba-multimodal-industrial-ai-industrybench.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | A multi-lingual benchmark for evaluating industrial domain knowledge of LLMs. | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 155 | 73,157 | | Forks | 10 | 8,937 | | Open issues | 1 | 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 | MIT | Apache-2.0 | | Categories | Data & Retrieval, LLM Frameworks, Model Training | Model Training, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [IndustryBench](/tools/alibaba-multimodal-industrial-ai-industrybench.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 26d | 0d | | Open issues (now) | 1 | 1.1k | | Owner type | Organization | User | | Security scan | 4 medium, 3 low (4 medium, 3 low) | No lockfile | | Full report | [trust report](/tools/alibaba-multimodal-industrial-ai-industrybench/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 IndustryBench if… - License: IndustryBench is MIT, LlamaFactory is Apache-2.0. - Tags unique to IndustryBench: python, industry-benchmark, llm evaluation. - Also covers Data & Retrieval. ### Choose LlamaFactory if… - License: LlamaFactory is Apache-2.0, IndustryBench is MIT. - 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 IndustryBench - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - 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 IndustryBench and LlamaFactory? IndustryBench: A multi-lingual benchmark for evaluating industrial domain knowledge of LLMs.. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose IndustryBench over LlamaFactory? Choose IndustryBench over LlamaFactory when License: IndustryBench is MIT, LlamaFactory is Apache-2.0; Tags unique to IndustryBench: python, industry-benchmark, llm evaluation; Also covers Data & Retrieval. ### When should I choose LlamaFactory over IndustryBench? Choose LlamaFactory over IndustryBench when License: LlamaFactory is Apache-2.0, IndustryBench is MIT; 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 IndustryBench? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. 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 IndustryBench or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 155). Stars measure visibility, not whether either tool fits your constraints. ### Are IndustryBench and LlamaFactory open source? Yes - both are open-source projects on GitHub (IndustryBench: MIT, LlamaFactory: Apache-2.0). ### Where can I find alternatives to IndustryBench or LlamaFactory? GraphCanon lists graph-backed alternatives at [IndustryBench alternatives](/tools/alibaba-multimodal-industrial-ai-industrybench/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([IndustryBench markdown twin](/tools/alibaba-multimodal-industrial-ai-industrybench/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/alibaba-multimodal-industrial-ai-industrybench-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, IndustryBench or LlamaFactory? IndustryBench: 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 IndustryBench and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [IndustryBench trust report](/tools/alibaba-multimodal-industrial-ai-industrybench/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alibaba-multimodal-industrial-ai-industrybench`](/api/graphcanon/graph?tool=alibaba-multimodal-industrial-ai-industrybench) - 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/_