--- title: "LLM-Finetuning vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ashishpatel26-llm-finetuning-vs-hiyouga-llamafactory" tools: ["ashishpatel26-llm-finetuning", "hiyouga-llamafactory"] --- # LLM-Finetuning vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick LLM-Finetuning when lLM-Finetuning is primarily Jupyter Notebook; LlamaFactory is Python; pick LlamaFactory when llamaFactory is primarily Python; LLM-Finetuning is Jupyter Notebook. [LLM-Finetuning](https://github.com/ashishpatel26/LLM-Finetuning) reports 3.0k GitHub stars, 769 forks, and 3 open issues, last pushed Aug 1, 2025. [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 [LLM-Finetuning's repository](https://github.com/ashishpatel26/LLM-Finetuning) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [LLM-Finetuning](/tools/ashishpatel26-llm-finetuning.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | LLM Finetuning with peft | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 2,956 | 73,157 | | Forks | 769 | 8,937 | | Open issues | 3 | 1,067 | | Language | Jupyter Notebook | 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 | | Categories | Model Training, LLM Frameworks | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM-Finetuning](/tools/ashishpatel26-llm-finetuning.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 343d | 0d | | Open issues (now) | 3 | 1.1k | | Full report | [trust report](/tools/ashishpatel26-llm-finetuning/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 LLM-Finetuning if… - LLM-Finetuning is primarily Jupyter Notebook; LlamaFactory is Python. - Tags unique to LLM-Finetuning: llms, llama, lora, llm. - Leaner open-issue backlog (3). ### Choose LlamaFactory if… - LlamaFactory is primarily Python; LLM-Finetuning is Jupyter Notebook. - Tags unique to LlamaFactory: gemma, deepseek, ai, instruction-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ## When NOT to use LLM-Finetuning - Last GitHub push was 344 days ago (slowing maintenance, Aug 1, 2025). Validate activity before betting a new project on LLM-Finetuning. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## 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 LLM-Finetuning and LlamaFactory? LLM-Finetuning: LLM Finetuning with peft. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLM-Finetuning over LlamaFactory? Choose LLM-Finetuning over LlamaFactory when LLM-Finetuning is primarily Jupyter Notebook; LlamaFactory is Python; Tags unique to LLM-Finetuning: llms, llama, lora, llm; Leaner open-issue backlog (3). ### When should I choose LlamaFactory over LLM-Finetuning? Choose LlamaFactory over LLM-Finetuning when LlamaFactory is primarily Python; LLM-Finetuning is Jupyter Notebook; Tags unique to LlamaFactory: gemma, deepseek, ai, instruction-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I avoid LLM-Finetuning? Last GitHub push was 344 days ago (slowing maintenance, Aug 1, 2025). Validate activity before betting a new project on LLM-Finetuning. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### 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 LLM-Finetuning or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 2,956). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM-Finetuning and LlamaFactory open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLM-Finetuning or LlamaFactory? GraphCanon lists graph-backed alternatives at [LLM-Finetuning alternatives](/tools/ashishpatel26-llm-finetuning/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([LLM-Finetuning markdown twin](/tools/ashishpatel26-llm-finetuning/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/ashishpatel26-llm-finetuning-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLM-Finetuning or LlamaFactory? LLM-Finetuning: Slowing. 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 LLM-Finetuning and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Finetuning trust report](/tools/ashishpatel26-llm-finetuning/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ashishpatel26-llm-finetuning`](/api/graphcanon/graph?tool=ashishpatel26-llm-finetuning) - 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/_