--- title: "LlamaFactory vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-tloen-alpaca-lora" tools: ["hiyouga-llamafactory", "tloen-alpaca-lora"] --- # LlamaFactory vs alpaca-lora *GraphCanon updated Sep 26, 2026* ## Verdict 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; pick alpaca-lora if alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. [LlamaFactory](https://llamafactory.readthedocs.io) reports 75k GitHub stars, 9.2k forks, and 1.2k open issues, last pushed Sep 14, 2026. [alpaca-lora](https://github.com/tloen/alpaca-lora) has 19k stars, 2.2k forks, and 364 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Instruct-tune LLaMA on consumer hardware | | Stars | 74,849 | 18,902 | | Forks | 9,170 | 2,170 | | Open issues | 1,156 | 364 | | Language | Python | Jupyter Notebook | | 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. | alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. | | Persona | - | developer harness | | Runtime | - | - | | License | Apache-2.0 | The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables. | | 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) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 4d | 788d | | Open issues (now) | 1.2k | 364 | | Stars delta | +717 (30d) | -10 (30d) | | Open issues delta | +43 (30d) | -1 (30d) | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## 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. ## Decision facts: alpaca-lora - **Pricing:** freemium - The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply. - **Adopt for:** alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. - **License detail:** The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables. - **Persona:** developer harness ## Choose when ### Choose LlamaFactory if… - LlamaFactory is primarily Python; alpaca-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, fine-tuning. - 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. ### Choose alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; LlamaFactory is Python. - Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.. - Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, lora. - Also covers Inference & Serving. - alpaca-lora ships Docker support for self-hosted deployment. - When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker. ## 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. ## When NOT to use alpaca-lora - When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface. - For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware. ## Common questions ### What is the difference between LlamaFactory and alpaca-lora? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over alpaca-lora? Choose LlamaFactory over alpaca-lora when LlamaFactory is primarily Python; alpaca-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, fine-tuning; 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 choose alpaca-lora over LlamaFactory? Choose alpaca-lora over LlamaFactory when alpaca-lora is primarily Jupyter Notebook; LlamaFactory is Python; Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.; Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, lora; Also covers Inference & Serving; alpaca-lora ships Docker support for self-hosted deployment; When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker. ### 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. ### When should I avoid alpaca-lora? When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface. For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware. ### Is LlamaFactory or alpaca-lora more popular on GitHub? LlamaFactory has more GitHub stars (74,849 vs 18,902). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and alpaca-lora open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, alpaca-lora: Apache-2.0). ### Where can I find alternatives to LlamaFactory or alpaca-lora? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [alpaca-lora markdown twin](/tools/tloen-alpaca-lora/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-tloen-alpaca-lora.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or alpaca-lora? LlamaFactory: Very active. alpaca-lora: 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 alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/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/_