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MatMul-Free LM

MatMul-Free LM is a Python repository implementing a language-model architecture that eliminates matrix-multiplication operations. Its code is compatible with Hugging Face Transformers and includes model initialization examples, generation guidance, and links to pretrained model artifacts.

Explore the practical guide ↓

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MatMul-Free LM — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use the repository when exploring or adapting a language-model implementation designed to avoid matrix multiplication, or when working with its linked pretrained models through Transformers-compatible interfaces.

Who it is for

  • Researchers exploring matrix-multiplication-free or linear-transformer language models.
  • Developers who want to initialize or adapt the implementation through Hugging Face Transformers.
  • Users investigating the repository's linked pretrained model artifacts.

Documented uses

  • Install the implementation with its listed Python dependencies.
  • Initialize a model from the default configuration using Transformers AutoModel.
  • Use a pretrained model and tokenizer with Transformers text-generation APIs.
  • Compare the documented 370M, 1.3B and 2.7B model configurations and their reported training-token totals.
Before you choose

Requirements & limits

The repository is public and released under Apache License 2.0.

  • The README requires PyTorch 2.0 or later, Triton 2.2 or later, and einops.
  • The documented pretrained model sizes are 370M, 1.3B and 2.7B parameters.
  • The repository describes its implementation as adapted from flash-linear-attention.
  • The generation example loads a model and tokenizer through Transformers and uses CUDA tensors.

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

Install the MatMul-Free LM package

Install the repository's Python package after meeting its listed dependencies.

Before you start

  • Python environment
  • PyTorch 2.0 or later
  • Triton 2.2 or later
  • einops

Steps

  1. Install PyTorch 2.0 or later, Triton 2.2 or later, and einops.

  2. Run the command below.

    Documented command
    pip install -U git+https://github.com/ridgerchu/matmulfreellm

Expected outcome

The MatMul-Free LM package is installed from the GitHub repository.

Guide limitations

  • The installation instructions do not document hardware requirements or a verification procedure.
Documentation 1 ↗
Source-based practical guide

Initialize a model with Hugging Face Transformers

Create a model from the repository's default HGRNBit configuration using the Transformers AutoModel interface.

Before you start

  • The MatMul-Free LM package installed
  • Hugging Face Transformers available in the Python environment

Steps

  1. Import HGRNBitConfig from mmfreelm.models.

  2. Import AutoModel from transformers.

  3. Create the configuration with HGRNBitConfig().

  4. Pass the configuration to AutoModel.from_config(config).

Expected outcome

The documented example initializes an HGRNBitModel from its default configuration.

Guide limitations

  • This recipe covers model initialization only; it does not document training or text generation.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
10 October 2026
Original website title
ridgerchu/matmulfreellm · Official homepage title · retained in its source language
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