adapters

library1.3.0pypypiunverified

A unified library for parameter-efficient and modular transfer learning, supporting adapter methods like LoRA, Bottleneck Adapters, AdapterPlus, DoRA, and ReFT. Built on Hugging Face Transformers. Current version: 1.3.0. Release cadence: irregular, roughly every 2-3 months.

llm-agents ai-ml

pip install adapters

github ↗package ↗

INSTALL

IMPORT

SIG · ADAPTERS

adapters

llm-agentspythonv1.3.0

Install

12.8s avg

Import

—

Disk

251MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.3.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.000s · 248.3MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 12.8s · import 0.000s · 227MB

251MB installed

● package 251MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

AutoAdapterModel

✓ from adapters import AutoAdapterModel

✗ from adapters import AutoAdapterModel

Load a BERT model, add a LoRA adapter, and run a forward pass.

from transformers import AutoTokenizer from adapters import AutoAdapterModel, LoRAConfig tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") model = AutoAdapterModel.from_pretrained("bert-base-uncased") # Add a LoRA adapter with configuration config = LoRAConfig(r=8, alpha=16) model.add_adapter("my_lora_adapter", config=config) model.train_adapter("my_lora_adapter") # Example: encode a sentence inputs = tokenizer("Hello, adapters!", return_tensors="pt") outputs = model(**inputs) print(outputs.last_hidden_state.shape)

Debug

Known issues

breakingEach version of adapters supports a specific range of transformers versions. Using an incompatible transformers version may cause import errors or silent misbehavior. Check the release notes for the supported transformers version.

fix

Pin transformers to the version listed in the adapters release (e.g., for adapters 1.3.0 use transformers >=4.51.0, <4.58.0).

affects: all

gotchaWhen using QLoRA with bnb (bitsandbytes), the quantized model must be loaded with `load_in_4bit=True` and `torch_dtype=torch.bfloat16`. Omitting these may cause runtime errors or incorrect behavior.

fix

Use `model = AutoAdapterModel.from_pretrained(..., load_in_4bit=True, torch_dtype=torch.bfloat16)` and ensure bitsandbytes is installed.

affects: >=0.2.0

gotchaThe `adapter_to()` method is used to move adapter weights to a device and optionally convert dtype. Forgetting to call it after adding an adapter can leave weights on CPU, causing issues when training on GPU.

fix

After adding an adapter, call `model.adapter_to('cuda')` or `model.adapter_to('cuda', dtype=torch.float16)`.

affects: >=0.2.1

Upgrade

Version history

1.3.0latest on PyPI · released Apr 26, 2026

Audit

Dependencies

transformersrequiredCore dependency; adapters is built on top of HF Transformers

Agent activity

58 hits · last 30 days

node

Anthropic

Perplexity

OpenAI (training)

Resources

githubhttps://github.com/adapter-hub/adapters ↗

packagehttps://pypi.org/project/adapters/ ↗