# OpenGVLab/LLaMA-Adapter

[ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters

Repository: https://github.com/OpenGVLab/LLaMA-Adapter
Canonical: https://ross.abutalabs.com/products/llama-adapter
Language: Python
License: GPL-3.0
License Family: copyleft
Last push: 2024-03-14T08:12:53+00:00

## Health v2 (maintenance only)
Score: 21/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 90
- inputs: {"age_days": 1263, "days_push": 902, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5914, forks 378 (observed 2026-08-28T04:09:32.207678+00:00)

## What it is
Official implementation of LLaMA-Adapter and its V2 successor, a parameter-efficient fine-tuning method that adapts LLaMA models to follow instructions using only 1.2M trainable parameters and zero-init attention. V2 extends this to a lightweight multimodal visual instruction model.

## Use cases
- fine-tune llama on custom instructions cheaply
- parameter-efficient fine-tuning of large language models
- build a multimodal instruction-following chatbot
- reproduce the llama-adapter paper results
- adapt an open-source LLM without full fine-tuning
- train a visual instruction model on limited GPU budget

## When to choose
- you want to instruction-tune LLaMA with minimal trainable parameters and compute
- you need a research-grade reference implementation of the LLaMA-Adapter papers
- you want a lightweight multimodal LLaMA variant

## When to avoid
- you need a maintained general toolkit - the team recommends LLaMA2-Accessory instead
- you want production-ready chatbot deployment out of the box
- you work with non-LLaMA model families unsupported by this codebase

## Facets
- artifact type: library
- maturity: maintenance
- function: llm-training, machine-learning, chatbot, sdk
- domain: large-language-models, deep-learning, artificial-intelligence
- platform: python
- tags: parameter-efficient-fine-tuning, lora-alternative, instruction-tuning, multimodal, llama, zero-init-attention, iclr-2024, research-code, natural-language-processing, gpu, linux, docker

## Member repositories
- OpenGVLab/LLaMA-Adapter (main) score 21

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:32.207678+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:51:15.885923+00:00, confidence not recorded.
  - readme: https://github.com/OpenGVLab/LLaMA-Adapter (fetched 2026-08-28T04:09:32.207678+00:00, sha 87b94b9e0baa)
- Data as of 2026-08-30T08:39:29.467469+00:00.