---
title: "LLM-Adapters vs lora"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/agi-edgerunners-llm-adapters-vs-cloneofsimo-lora"
tools: ["agi-edgerunners-llm-adapters", "cloneofsimo-lora"]
---

# LLM-Adapters vs lora

*GraphCanon updated Aug 24, 2026*

## Verdict

Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; 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.

[LLM-Adapters](https://arxiv.org/abs/2304.01933) reports 1.2k GitHub stars, 115 forks, and 55 open issues, last pushed Mar 10, 2024. [lora](https://arxiv.org/abs/2106.09685) has 7.5k stars, 495 forks, and 88 open issues, last pushed Mar 22, 2024. Figures are from public GitHub metadata via [LLM-Adapters's repository](https://github.com/AGI-Edgerunners/LLM-Adapters) and [lora's repository](https://github.com/cloneofsimo/lora).

| | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [lora](/tools/cloneofsimo-lora.md) |
| --- | --- | --- |
| Tagline | Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs | Jupyter Notebook repository for fine-tuning diffusion models using Low-Rank Adaptation. |
| Stars | 1,233 | 7,549 |
| Forks | 115 | 495 |
| Open issues | 55 | 88 |
| Language | Python | Jupyter Notebook |
| Adopt for | LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. | 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. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | The project is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution. |
| Categories | LLM Frameworks, Model Training | Model Training |

## Trust and health

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

| | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [lora](/tools/cloneofsimo-lora.md) |
| --- | --- | --- |
| Days since push | 896d | 884d |
| Open issues (now) | 55 | 88 |
| Stars delta | -1 (30d) | +4 (30d) |
| Owner type | Organization | User |
| Full report | [trust report](/tools/agi-edgerunners-llm-adapters/trust.md) | [trust report](/tools/cloneofsimo-lora/trust.md) |

## Shared compatibility

- **Python**: [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) - Python runtime; [lora](/tools/cloneofsimo-lora.md) - Python runtime

## Decision facts: LLM-Adapters

- **Adopt for:** LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing.

## 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.

## Choose when

### Choose LLM-Adapters if…

- LLM-Adapters is primarily Python; lora is Jupyter Notebook.
- Tags unique to LLM-Adapters: adapters, large language models, parameter-efficient.
- Also covers LLM Frameworks.
- Optimizing resource usage when you need to fine-tune large language models without altering their core parameters

### Choose lora if…

- lora is primarily Jupyter Notebook; LLM-Adapters 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 NOT to use LLM-Adapters

- You require a full retraining approach that modifies all model weights, not just adapters
- Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023

## 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.

## Common questions

### What is the difference between LLM-Adapters and lora?

LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. lora: Jupyter Notebook repository for fine-tuning diffusion models using Low-Rank Adaptation.. See the comparison table for live GitHub stats and shared categories.

### When should I choose LLM-Adapters over lora?

Choose LLM-Adapters over lora when LLM-Adapters is primarily Python; lora is Jupyter Notebook; Tags unique to LLM-Adapters: adapters, large language models, parameter-efficient; Also covers LLM Frameworks; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters.

### When should I choose lora over LLM-Adapters?

Choose lora over LLM-Adapters when lora is primarily Jupyter Notebook; LLM-Adapters 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 avoid LLM-Adapters?

You require a full retraining approach that modifies all model weights, not just adapters Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023

### 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.

### Is LLM-Adapters or lora more popular on GitHub?

lora has more GitHub stars (7,549 vs 1,233). Stars measure visibility, not whether either tool fits your constraints.

### Are LLM-Adapters and lora open source?

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

### Where can I find alternatives to LLM-Adapters or lora?

GraphCanon lists graph-backed alternatives at [LLM-Adapters alternatives](/tools/agi-edgerunners-llm-adapters/alternatives) and [lora alternatives](/tools/cloneofsimo-lora/alternatives) ([LLM-Adapters markdown twin](/tools/agi-edgerunners-llm-adapters/alternatives.md), [lora markdown twin](/tools/cloneofsimo-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/agi-edgerunners-llm-adapters-vs-cloneofsimo-lora.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, LLM-Adapters or lora?

LLM-Adapters: Dormant. 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 LLM-Adapters and lora?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Adapters trust report](/tools/agi-edgerunners-llm-adapters/trust); [lora trust report](/tools/cloneofsimo-lora/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters`](/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters)
- 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/_