--- title: "qlora vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/artidoro-qlora-vs-tloen-alpaca-lora" tools: ["artidoro-qlora", "tloen-alpaca-lora"] --- # qlora vs alpaca-lora *GraphCanon updated Aug 3, 2026* ## Verdict Pick qlora if qLoRA specializes in accelerating the fine-tuning process of quantized large language models like those in the Guanaco family; 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. [qlora](https://arxiv.org/abs/2305.14314) reports 11k GitHub stars, 876 forks, and 206 open issues, last pushed Jun 10, 2024. [alpaca-lora](https://github.com/tloen/alpaca-lora) has 19k stars, 2.2k forks, and 365 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [qlora's repository](https://github.com/artidoro/qlora) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [qlora](/tools/artidoro-qlora.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | QLoRA finetuning of quantized LLMs | Instruct-tune LLaMA on consumer hardware | | Stars | 10,979 | 18,912 | | Forks | 876 | 2,180 | | Open issues | 206 | 365 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | QLoRA specializes in accelerating the fine-tuning process of quantized large language models like those in the Guanaco family. | 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 | MIT License; open-source tool for QLoRA fine-tuning process; LLaMA base models must be obtained legally as per their license terms | 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._ | | [qlora](/tools/artidoro-qlora.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Days since push | 783d | 734d | | Open issues (now) | 206 | 365 | | Full report | [trust report](/tools/artidoro-qlora/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Decision facts: qlora - **Pricing:** freemium - Open source under MIT License; requires access to LLaMA base models - **Requirements:** Installation involves installing PyTorch and specific packages from source; Works with model sizes ranging from 7B to 65B, includes recommendations for tuning different sizes - **Adopt for:** QLoRA specializes in accelerating the fine-tuning process of quantized large language models like those in the Guanaco family. - **License detail:** MIT License; open-source tool for QLoRA fine-tuning process; LLaMA base models must be obtained legally as per their license terms ## 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 qlora if… - License: qlora is MIT, alpaca-lora is Apache-2.0. - Pricing: Open source under MIT License; requires access to LLaMA base models. - Requirements: Installation involves installing PyTorch and specific packages from source; Works with model sizes ranging from 7B to 65B, includes recommendations for tuning different sizes. - Tags unique to qlora: fine-tuning, guanaco, llama models, quantization. - Need efficient fine-tuning for quantized LLaMA-based models ### Choose alpaca-lora if… - License: alpaca-lora is Apache-2.0, qlora is MIT. - 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, llama. - 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 qlora - Require native full-precision model tuning without efficiency constraints - Focusing on non-LLaMA-based language models where specific adaptations may not apply ## 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 qlora and alpaca-lora? qlora: QLoRA finetuning of quantized LLMs. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose qlora over alpaca-lora? Choose qlora over alpaca-lora when License: qlora is MIT, alpaca-lora is Apache-2.0; Pricing: Open source under MIT License; requires access to LLaMA base models; Requirements: Installation involves installing PyTorch and specific packages from source; Works with model sizes ranging from 7B to 65B, includes recommendations for tuning different sizes; Tags unique to qlora: fine-tuning, guanaco, llama models, quantization; Need efficient fine-tuning for quantized LLaMA-based models. ### When should I choose alpaca-lora over qlora? Choose alpaca-lora over qlora when License: alpaca-lora is Apache-2.0, qlora is MIT; 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, llama; 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 qlora? Require native full-precision model tuning without efficiency constraints Focusing on non-LLaMA-based language models where specific adaptations may not apply ### 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 qlora or alpaca-lora more popular on GitHub? alpaca-lora has more GitHub stars (18,912 vs 10,979). Stars measure visibility, not whether either tool fits your constraints. ### Are qlora and alpaca-lora open source? Yes - both are open-source projects on GitHub (qlora: MIT, alpaca-lora: Apache-2.0). ### Where can I find alternatives to qlora or alpaca-lora? GraphCanon lists graph-backed alternatives at [qlora alternatives](/tools/artidoro-qlora/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([qlora markdown twin](/tools/artidoro-qlora/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/artidoro-qlora-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, qlora or alpaca-lora? qlora: Dormant. 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 qlora and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [qlora trust report](/tools/artidoro-qlora/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=artidoro-qlora`](/api/graphcanon/graph?tool=artidoro-qlora) - 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/_