---
title: "lora vs alpaca-lora"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/cloneofsimo-lora-vs-tloen-alpaca-lora"
tools: ["cloneofsimo-lora", "tloen-alpaca-lora"]
---

# lora vs alpaca-lora

*GraphCanon updated Aug 23, 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 alpaca-lora if alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.

[lora](https://arxiv.org/abs/2106.09685) reports 7.5k GitHub stars, 495 forks, and 88 open issues, last pushed Mar 22, 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 [lora's repository](https://github.com/cloneofsimo/lora) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora).

| | [lora](/tools/cloneofsimo-lora.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) |
| --- | --- | --- |
| Tagline | Jupyter Notebook repository for fine-tuning diffusion models using Low-Rank Adaptation. | Instruct-tune LLaMA on consumer hardware |
| Stars | 7,549 | 18,912 |
| Forks | 495 | 2,180 |
| Open issues | 88 | 365 |
| Language | Jupyter Notebook | Jupyter Notebook |
| 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. | 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 | The project is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution. | 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 | Model Training | Inference & Serving, LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [lora](/tools/cloneofsimo-lora.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) |
| --- | --- | --- |
| Days since push | 884d | 734d |
| Open issues (now) | 88 | 365 |
| Stars delta | +4 (30d) | Unknown |
| Open issues delta | 0 (30d) | Unknown |
| Full report | [trust report](/tools/cloneofsimo-lora/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) |

## 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: 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 lora if…

- Requirements: Suitable for developers and researchers looking to experiment with diffusion models in an interactive Jupyter Notebook environment..
- Tags unique to lora: diffusion, fine-tuning, stable-diffusion.
- Use Lora when you need to fine-tune existing diffusion models quickly with interactive experimentation capabilities available via Jupyter Notebooks.

### Choose alpaca-lora if…

- 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, LLM Frameworks.
- 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 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 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 lora and alpaca-lora?

lora: Jupyter Notebook repository for fine-tuning diffusion models using Low-Rank Adaptation.. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories.

### When should I choose lora over alpaca-lora?

Choose lora over alpaca-lora when Requirements: Suitable for developers and researchers looking to experiment with diffusion models in an interactive Jupyter Notebook environment.; Tags unique to lora: diffusion, fine-tuning, 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 alpaca-lora over lora?

Choose alpaca-lora over lora when 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, LLM Frameworks; 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 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 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 lora or alpaca-lora more popular on GitHub?

alpaca-lora has more GitHub stars (18,912 vs 7,549). Stars measure visibility, not whether either tool fits your constraints.

### Are lora and alpaca-lora open source?

Yes - both are open-source projects on GitHub (lora: Apache-2.0, alpaca-lora: Apache-2.0).

### Where can I find alternatives to lora or alpaca-lora?

GraphCanon lists graph-backed alternatives at [lora alternatives](/tools/cloneofsimo-lora/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([lora markdown twin](/tools/cloneofsimo-lora/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/cloneofsimo-lora-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, lora or alpaca-lora?

lora: 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 lora and alpaca-lora?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lora trust report](/tools/cloneofsimo-lora/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/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/_