--- title: "h2o-llmstudio vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/h2oai-h2o-llmstudio-vs-hiyouga-llamafactory" tools: ["h2oai-h2o-llmstudio", "hiyouga-llamafactory"] --- # h2o-llmstudio vs LlamaFactory *GraphCanon updated Jul 12, 2026* ## Verdict Pick h2o-llmstudio when tags unique to h2o-llmstudio: chatbot, chatgpt, fedramp, finetuning; pick LlamaFactory when tags unique to LlamaFactory: agent, deepseek, gemma, gpt. [h2o-llmstudio](https://h2o.ai) reports 5.0k GitHub stars, 538 forks, and 40 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 [h2o-llmstudio's repository](https://github.com/h2oai/h2o-llmstudio) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [h2o-llmstudio](/tools/h2oai-h2o-llmstudio.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/ | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 5,039 | 73,157 | | Forks | 538 | 8,937 | | Open issues | 40 | 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 | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [h2o-llmstudio](/tools/h2oai-h2o-llmstudio.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 40 | 1.1k | | Owner type | Organization | User | | Full report | [trust report](/tools/h2oai-h2o-llmstudio/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 h2o-llmstudio if… - Tags unique to h2o-llmstudio: chatbot, chatgpt, fedramp, finetuning. - Leaner open-issue backlog (40). ### Choose LlamaFactory if… - Tags unique to LlamaFactory: agent, deepseek, gemma, gpt. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (73k vs 5.0k) - visibility, not fit. ## When NOT to use h2o-llmstudio - 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 h2o-llmstudio and LlamaFactory? h2o-llmstudio: H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose h2o-llmstudio over LlamaFactory? Choose h2o-llmstudio over LlamaFactory when Tags unique to h2o-llmstudio: chatbot, chatgpt, fedramp, finetuning; Leaner open-issue backlog (40). ### When should I choose LlamaFactory over h2o-llmstudio? Choose LlamaFactory over h2o-llmstudio when Tags unique to LlamaFactory: agent, deepseek, gemma, gpt; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 5.0k) - visibility, not fit. ### When should I avoid h2o-llmstudio? 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 h2o-llmstudio or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 5,039). Stars measure visibility, not whether either tool fits your constraints. ### Are h2o-llmstudio and LlamaFactory open source? Yes - both are open-source projects on GitHub (h2o-llmstudio: Apache-2.0, LlamaFactory: Apache-2.0). ### Where can I find alternatives to h2o-llmstudio or LlamaFactory? GraphCanon lists graph-backed alternatives at [h2o-llmstudio alternatives](/tools/h2oai-h2o-llmstudio/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([h2o-llmstudio markdown twin](/tools/h2oai-h2o-llmstudio/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/h2oai-h2o-llmstudio-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, h2o-llmstudio or LlamaFactory? h2o-llmstudio: 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 h2o-llmstudio and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [h2o-llmstudio trust report](/tools/h2oai-h2o-llmstudio/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=h2oai-h2o-llmstudio`](/api/graphcanon/graph?tool=h2oai-h2o-llmstudio) - 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/_