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LLM-Adapters

AGI-Edgerunners/LLM-Adapters

Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs

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GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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1.2k stars116 forksLast push Mar 10, 2024 Python Apache-2.0

Decision brief

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

Good fit when

Optimizing resource usage when you need to fine-tune large language models without altering their core parameters
Your project involves parameter-efficient updates, following the latest research trends outlined in EMNLP 2023

Avoid when

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

Observed Jul 12, 2026 · Source: enrich:decision_facts

LLM-Adapters alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Dormant (923d since push); As of Sep 19, 2026
Provenance: Not a fork · Organization account; As of Sep 19, 2026
Security (OSV): No lockfile; As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install LLM-Adapters

PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Repository contains code and resources related to the research paper on developing an adapter family for parameter-efficient fine-tuning of large language models.

Capability facts

Languages: python
Source: github.language+pyproject.toml · Sep 19, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 19, 2026)

pip install -r requirements.txt

Source link

Works with ChatGPTChatGPT

Source: README excerpt (regex_v1, Sep 19, 2026)

ased commonsense170k dataset and the The LLaMA-13B-Parallel model outformances ChatGPT on 8 commonsense benchmarks.

Source link

README

<h1 align="center" <img src="picture.jpg" width="73" height="114" <p LLM Adapters</p </h1 <h3 align="center" <p LLM Adapters: An Adapter Family for Parameter Efficient Fine Tuning of Large Language Models </p </h3 LLM Adapters is an easy to use framework that integrates various a...

For agents

This page has a .md twin and JSON over the API.

/tools/agi-edgerunners-llm-adapters.md GET /api/graphcanon/tools/agi-edgerunners-llm-adapters GET /api/graphcanon/graph?tool=agi-edgerunners-llm-adapters