---
title: "LLM-Adapters"
type: "tool"
slug: "agi-edgerunners-llm-adapters"
canonical_url: "https://www.graphcanon.com/tools/agi-edgerunners-llm-adapters"
github_url: "https://github.com/AGI-Edgerunners/LLM-Adapters"
homepage_url: "https://arxiv.org/abs/2304.01933"
stars: 1233
forks: 119
primary_language: "Python"
license: "Apache-2.0"
archived: false
categories: ["llm-frameworks", "model-training"]
tags: ["adapters", "fine-tuning", "large-language-models", "parameter-efficient"]
updated_at: "2026-07-12T06:14:46.203971+00:00"
---

# LLM-Adapters

> Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning using Adapters

Provides code and methodologies for efficient adjustment of pre-existing large language models without extensive retraining, focusing on the use of adapters.

## Facts

- Repository: https://github.com/AGI-Edgerunners/LLM-Adapters
- Homepage: https://arxiv.org/abs/2304.01933
- Stars: 1,233 · Forks: 119 · Open issues: 55 · Watchers: 10
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2024-03-10T08:20:15+00:00

## Trust & health

_Signals computed from public GitHub metadata. Not a security guarantee._

- Maintenance: Dormant (computed 2026-07-11T11:39:22.413Z)
- Security scan: No lockfile (0 critical, 0 high, 0 medium, 0 low) · last scan 2026-07-11T11:39:23.047Z
- Full report: [trust report](/tools/agi-edgerunners-llm-adapters/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/agi-edgerunners-llm-adapters/trust)

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)
- [Model Training](/categories/model-training.md)

## Tags

adapters, fine-tuning, large-language-models, parameter-efficient

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_+ 2 more not listed._

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/agi-edgerunners-llm-adapters`](/api/graphcanon/tools/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/_