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Bitnet

Bitnet is a Python and PyTorch implementation of BitNet's 1-bit Transformer linear methods and model. It provides BitLinear quantized layers, Transformer components and example inference and Hugging Face workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bitnet — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Bitnet when you need a code-based PyTorch starting point for investigating 1-bit Transformer methods or integrating BitLinear-style projections into a research prototype.

Who it is for

  • AI and machine-learning researchers studying quantization.
  • PyTorch developers prototyping lower-precision Transformer architectures.
  • Developers testing BitLinear replacements in Transformer models.

Documented uses

  • Study the sequence of normalization, binarization, quantization and dequantization used by BitLinear.
  • Replace standard Transformer linear projections with BitLinear modules.
  • Prototype BitNet-style Transformer, attention and feed-forward components.
  • Experiment with a Hugging Face model after replacing its linear layers.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. The documented installation uses `pip3 install bitnet`.

  • It is a research and experimentation implementation rather than evidence of a drop-in conversion for an already trained model.
  • The README documents both individual BitLinear layers and higher-level Transformer components.
  • The Hugging Face example demonstrates layer replacement before classification inference.
  • Some newer functionality is described as in progress, and BitLinear 1.5 is reported to have bugs.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Replace linear projections with BitLinear

Experiment with a BitNet-style Transformer implementation by replacing standard linear projections.

Before you start

  • Python with the Bitnet package installed.
  • A PyTorch Transformer or model containing linear projections.

Steps

  1. Install the package with the command below.

    Documented command
    pip3 install bitnet

  2. Import BitLinear or the documented replace_linears_in_hf helper.

  3. Replace the relevant linear projections with BitLinear modules.

  4. Train or fine-tune the resulting model rather than only swapping layers in an already trained model.

Expected outcome

A model configured to use BitNet-style linear methods for experimentation.

Guide limitations

  • The README states that broader compatibility with every model and training setup is not established.
  • The documentation says an already trained model cannot simply have its linear methods changed.
  • BitLinear 1.5 is described as unfinished and having dequantization bugs.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
30 September 2026
Original website title
kyegomez/BitNet · Official homepage title · retained in its source language
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