--- title: "LlamaFactory vs ludwig" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-ludwig-ai-ludwig" tools: ["hiyouga-llamafactory", "ludwig-ai-ludwig"] --- # LlamaFactory vs ludwig *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory if 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; pick ludwig if ludwig is a low-code framework that simplifies the process of training deep learning models including custom LLMs and neural networks using Python. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [ludwig](http://ludwig.ai) has 12k stars, 1.2k forks, and 1 open issues, last pushed Jul 4, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [ludwig's repository](https://github.com/ludwig-ai/ludwig). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [ludwig](/tools/ludwig-ai-ludwig.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Low-code framework for building custom LLMs, neural networks, and other AI models | | Stars | 73,157 | 11,734 | | Forks | 8,937 | 1,218 | | Open issues | 1,067 | 1 | | 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. | Ludwig is a low-code framework that simplifies the process of training deep learning models including custom LLMs and neural networks using Python. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0: Permissive open-source license allowing free use in both community and commercial projects. | | Categories | LLM Frameworks, Model Training | Computer Vision, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [ludwig](/tools/ludwig-ai-ludwig.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 7d | | Open issues (now) | 1.1k | 1 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/ludwig-ai-ludwig/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. ## Decision facts: ludwig - **Requirements:** Min 4 GB RAM; Requires Python and is compatible with popular deep learning libraries like PyTorch. - **Adopt for:** Ludwig is a low-code framework that simplifies the process of training deep learning models including custom LLMs and neural networks using Python. - **License detail:** Apache-2.0: Permissive open-source license allowing free use in both community and commercial projects. ## Choose when ### Choose LlamaFactory if… - Tags unique to LlamaFactory: agent, ai, deepseek, gemma. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (73k vs 12k) - visibility, not fit. ### Choose ludwig if… - Requirements: Min 4 GB RAM; Requires Python and is compatible with popular deep learning libraries like PyTorch.. - Tags unique to ludwig: computer-vision, data-centric, data-science, deep. - Also covers Computer Vision. - When you need to build custom language models (LLMs) or other AI models with minimal configuration in Python. ## 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 ludwig - If you require direct access and extensive customization of the model architecture, as Ludwig abstracts some of these details away under its low-code interface. - When your team prefers a high-level of control over all aspects of the training process, including architectural decisions; Ludwig streamlines this process which may limit flexible adjustments. ## Common questions ### What is the difference between LlamaFactory and ludwig? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. ludwig: Low-code framework for building custom LLMs, neural networks, and other AI models. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over ludwig? Choose LlamaFactory over ludwig when Tags unique to LlamaFactory: agent, ai, deepseek, gemma; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 12k) - visibility, not fit. ### When should I choose ludwig over LlamaFactory? Choose ludwig over LlamaFactory when Requirements: Min 4 GB RAM; Requires Python and is compatible with popular deep learning libraries like PyTorch.; Tags unique to ludwig: computer-vision, data-centric, data-science, deep; Also covers Computer Vision; When you need to build custom language models (LLMs) or other AI models with minimal configuration in Python. ### 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 ludwig? If you require direct access and extensive customization of the model architecture, as Ludwig abstracts some of these details away under its low-code interface. When your team prefers a high-level of control over all aspects of the training process, including architectural decisions; Ludwig streamlines this process which may limit flexible adjustments. ### Is LlamaFactory or ludwig more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 11,734). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and ludwig open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, ludwig: Apache-2.0). ### Where can I find alternatives to LlamaFactory or ludwig? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [ludwig alternatives](/tools/ludwig-ai-ludwig/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [ludwig markdown twin](/tools/ludwig-ai-ludwig/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-ludwig-ai-ludwig.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or ludwig? LlamaFactory: Very active. ludwig: 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 LlamaFactory and ludwig? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [ludwig trust report](/tools/ludwig-ai-ludwig/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/_