--- title: "lora vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/cloneofsimo-lora-vs-hiyouga-llamafactory" tools: ["cloneofsimo-lora", "hiyouga-llamafactory"] --- # lora vs LlamaFactory *GraphCanon updated Sep 20, 2026* ## Verdict Pick lora if lora is designed for quick model customization through low-rank adaptation of pre-existing diffusion models like Stable Diffusion using Jupyter Notebooks in an interactive learning environment; pick LlamaFactory if llamaFactory is a Python-based tool for efficient fine-tuning of over 100 large language models and vision-language models, supporting various methods like LoRA, QLoRA, and quantization. [lora](https://arxiv.org/abs/2106.09685) reports 7.6k GitHub stars, 497 forks, and 88 open issues, last pushed Mar 22, 2024. [LlamaFactory](https://llamafactory.readthedocs.io) has 75k stars, 9.2k forks, and 1.2k open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [lora's repository](https://github.com/cloneofsimo/lora) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [lora](/tools/cloneofsimo-lora.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Jupyter Notebook repository for fine-tuning diffusion models using Low-Rank Adaptation. | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 7,556 | 74,849 | | Forks | 497 | 9,170 | | Open issues | 88 | 1,156 | | Language | Jupyter Notebook | Python | | Adopt for | Lora is designed for quick model customization through low-rank adaptation of pre-existing diffusion models like Stable Diffusion using Jupyter Notebooks in an interactive learning environment. | LlamaFactory is a Python-based tool for efficient fine-tuning of over 100 large language models and vision-language models, supporting various methods like LoRA, QLoRA, and quantization. | | Persona | - | - | | Runtime | - | - | | License | The project is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution. | Apache-2.0 | | Categories | Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lora](/tools/cloneofsimo-lora.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 911d | 4d | | Open issues (now) | 88 | 1.2k | | Stars delta | +11 (30d) | +717 (30d) | | Open issues delta | 0 (30d) | +43 (30d) | | Full report | [trust report](/tools/cloneofsimo-lora/trust.md) | [trust report](/tools/hiyouga-llamafactory/trust.md) | ## Shared compatibility - **Python**: [lora](/tools/cloneofsimo-lora.md) - Python runtime; [LlamaFactory](/tools/hiyouga-llamafactory.md) - Python runtime ## Decision facts: lora - **Requirements:** Suitable for developers and researchers looking to experiment with diffusion models in an interactive Jupyter Notebook environment. - **Adopt for:** Lora is designed for quick model customization through low-rank adaptation of pre-existing diffusion models like Stable Diffusion using Jupyter Notebooks in an interactive learning environment. - **License detail:** The project is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution. ## Decision facts: LlamaFactory - **Requirements:** Min 4 GB RAM; Requires Docker; Hardware requirements vary based on the method and model size. For example, full fine-tuning of a 7B model requires 120GB of memory, while QLoRA with 2-bit can ; Installation is mandatory and can be done from source or via Docker. Additional dependencies like `metrics` and `deepspeed` are optional.; For Windows users, manual installation of PyTorch with CUDA support is required, and bitsandbytes must be installed for QLoRA support. - **Adopt for:** LlamaFactory is a Python-based tool for efficient fine-tuning of over 100 large language models and vision-language models, supporting various methods like LoRA, QLoRA, and quantization. ## Choose when ### Choose lora if… - lora is primarily Jupyter Notebook; LlamaFactory is Python. - Requirements: Suitable for developers and researchers looking to experiment with diffusion models in an interactive Jupyter Notebook environment.. - Tags unique to lora: diffusion, lora, stable-diffusion. - Use Lora when you need to fine-tune existing diffusion models quickly with interactive experimentation capabilities available via Jupyter Notebooks. ### Choose LlamaFactory if… - LlamaFactory is primarily Python; lora is Jupyter Notebook. - Requirements: Min 4 GB RAM; Requires Docker; Hardware requirements vary based on the method and model size. For example, full fine-tuning of a 7B model requires 120GB of memory, while QLoRA with 2-bit can ; Installation is mandatory and can be done from source or via Docker. Additional dependencies like `metrics` and `deepspeed` are optional.; For Windows users, manual installation of PyTorch with CUDA support is required, and bitsandbytes must be installed for QLoRA support.. - Tags unique to LlamaFactory: agent, ai, deepseek, gpt. - Also covers LLM Frameworks. - Use LlamaFactory when you need to fine-tune a wide range of large language models and vision-language models with methods like LoRA, QLoRA, and quantization. ## When NOT to use lora - Do not use Lora when you require a more thorough or comprehensive fine-tuning approach that goes beyond quick customization through low-rank adaptation. - Avoid Lora if your development workflow is not compatible with Jupyter Notebooks, as this is the primary environment provided by the repository. ## When NOT to use LlamaFactory - Avoid LlamaFactory if you are working with models not included in its supported list of over 100 models, as it may not provide the necessary fine-tuning capabilities. - Do not use LlamaFactory if you are working in an environment where Python or specific dependencies like PyTorch and bitsandbytes cannot be installed or are not supported. - LlamaFactory may not be suitable if you require real-time or near-real-time fine-tuning capabilities, as the setup and installation process can be complex and time-consuming. ## Common questions ### What is the difference between lora and LlamaFactory? lora: Jupyter Notebook repository for fine-tuning diffusion models using Low-Rank Adaptation.. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose lora over LlamaFactory? Choose lora over LlamaFactory when lora is primarily Jupyter Notebook; LlamaFactory is Python; Requirements: Suitable for developers and researchers looking to experiment with diffusion models in an interactive Jupyter Notebook environment.; Tags unique to lora: diffusion, lora, stable-diffusion; Use Lora when you need to fine-tune existing diffusion models quickly with interactive experimentation capabilities available via Jupyter Notebooks. ### When should I choose LlamaFactory over lora? Choose LlamaFactory over lora when LlamaFactory is primarily Python; lora is Jupyter Notebook; Requirements: Min 4 GB RAM; Requires Docker; Hardware requirements vary based on the method and model size. For example, full fine-tuning of a 7B model requires 120GB of memory, while QLoRA with 2-bit can ; Installation is mandatory and can be done from source or via Docker. Additional dependencies like `metrics` and `deepspeed` are optional.; For Windows users, manual installation of PyTorch with CUDA support is required, and bitsandbytes must be installed for QLoRA support.; Tags unique to LlamaFactory: agent, ai, deepseek, gpt; Also covers LLM Frameworks; Use LlamaFactory when you need to fine-tune a wide range of large language models and vision-language models with methods like LoRA, QLoRA, and quantization. ### When should I avoid lora? Do not use Lora when you require a more thorough or comprehensive fine-tuning approach that goes beyond quick customization through low-rank adaptation. Avoid Lora if your development workflow is not compatible with Jupyter Notebooks, as this is the primary environment provided by the repository. ### When should I avoid LlamaFactory? Avoid LlamaFactory if you are working with models not included in its supported list of over 100 models, as it may not provide the necessary fine-tuning capabilities. Do not use LlamaFactory if you are working in an environment where Python or specific dependencies like PyTorch and bitsandbytes cannot be installed or are not supported. LlamaFactory may not be suitable if you require real-time or near-real-time fine-tuning capabilities, as the setup and installation process can be complex and time-consuming. ### Is lora or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (74,849 vs 7,556). Stars measure visibility, not whether either tool fits your constraints. ### Are lora and LlamaFactory open source? Yes - both are open-source projects on GitHub (lora: Apache-2.0, LlamaFactory: Apache-2.0). ### Where can I find alternatives to lora or LlamaFactory? GraphCanon lists graph-backed alternatives at [lora alternatives](/tools/cloneofsimo-lora/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([lora markdown twin](/tools/cloneofsimo-lora/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/cloneofsimo-lora-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lora or LlamaFactory? lora: Dormant. 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 lora and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lora trust report](/tools/cloneofsimo-lora/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=cloneofsimo-lora`](/api/graphcanon/graph?tool=cloneofsimo-lora) - 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/_