--- title: "LlamaFactory vs LLM-FineTuning-Large-Language-Models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-rohan-paul-llm-finetuning-large-language-models" tools: ["hiyouga-llamafactory", "rohan-paul-llm-finetuning-large-language-models"] --- # LlamaFactory vs LLM-FineTuning-Large-Language-Models *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when llamaFactory is primarily Python; LLM-FineTuning-Large-Language-Models is Jupyter Notebook; pick LLM-FineTuning-Large-Language-Models when lLM-FineTuning-Large-Language-Models is primarily Jupyter Notebook; LlamaFactory is Python. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [LLM-FineTuning-Large-Language-Models](https://github.com/rohan-paul/LLM-FineTuning-Large-Language-Models) has 576 stars, 140 forks, and 2 open issues, last pushed Apr 1, 2025. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [LLM-FineTuning-Large-Language-Models's repository](https://github.com/rohan-paul/LLM-FineTuning-Large-Language-Models). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [LLM-FineTuning-Large-Language-Models](/tools/rohan-paul-llm-finetuning-large-language-models.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | LLM (Large Language Model) FineTuning | | Stars | 73,157 | 576 | | Forks | 8,937 | 140 | | Open issues | 1,067 | 2 | | Language | Python | Jupyter Notebook | | 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 | - | | Categories | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [LLM-FineTuning-Large-Language-Models](/tools/rohan-paul-llm-finetuning-large-language-models.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 465d | | Open issues (now) | 1.1k | 2 | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/rohan-paul-llm-finetuning-large-language-models/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 LlamaFactory if… - LlamaFactory is primarily Python; LLM-FineTuning-Large-Language-Models is Jupyter Notebook. - Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose LLM-FineTuning-Large-Language-Models if… - LLM-FineTuning-Large-Language-Models is primarily Jupyter Notebook; LlamaFactory is Python. - Tags unique to LLM-FineTuning-Large-Language-Models: gpt-3, gpt3-turbo, llama2, llm. - Also covers Inference & Serving. ## 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 LLM-FineTuning-Large-Language-Models - Last GitHub push was 466 days ago (dormant maintenance, Apr 1, 2025). Validate activity before betting a new project on LLM-FineTuning-Large-Language-Models. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - 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. ## Common questions ### What is the difference between LlamaFactory and LLM-FineTuning-Large-Language-Models? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. LLM-FineTuning-Large-Language-Models: LLM (Large Language Model) FineTuning. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over LLM-FineTuning-Large-Language-Models? Choose LlamaFactory over LLM-FineTuning-Large-Language-Models when LlamaFactory is primarily Python; LLM-FineTuning-Large-Language-Models is Jupyter Notebook; Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose LLM-FineTuning-Large-Language-Models over LlamaFactory? Choose LLM-FineTuning-Large-Language-Models over LlamaFactory when LLM-FineTuning-Large-Language-Models is primarily Jupyter Notebook; LlamaFactory is Python; Tags unique to LLM-FineTuning-Large-Language-Models: gpt-3, gpt3-turbo, llama2, llm; Also covers Inference & Serving. ### 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 LLM-FineTuning-Large-Language-Models? Last GitHub push was 466 days ago (dormant maintenance, Apr 1, 2025). Validate activity before betting a new project on LLM-FineTuning-Large-Language-Models. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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. ### Is LlamaFactory or LLM-FineTuning-Large-Language-Models more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 576). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and LLM-FineTuning-Large-Language-Models open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LlamaFactory or LLM-FineTuning-Large-Language-Models? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [LLM-FineTuning-Large-Language-Models alternatives](/tools/rohan-paul-llm-finetuning-large-language-models/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [LLM-FineTuning-Large-Language-Models markdown twin](/tools/rohan-paul-llm-finetuning-large-language-models/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-rohan-paul-llm-finetuning-large-language-models.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or LLM-FineTuning-Large-Language-Models? LlamaFactory: Very active. LLM-FineTuning-Large-Language-Models: Dormant. 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 LLM-FineTuning-Large-Language-Models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [LLM-FineTuning-Large-Language-Models trust report](/tools/rohan-paul-llm-finetuning-large-language-models/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/_