Skip to main content

This repository provides tools to generate a linear algebra dataset and code to train an open-source pre-trained model. Our goal is to explore the model's potential for emergent reasoning, inspired by the Deepseek-R1 paper.

Project description

linalg-zero

Release Build status Commit activity License

This repository offers tools for generating a linear algebra problem dataset and training an open-source base model, aiming to explore its potential for emergent reasoning as inspired by the Deepseek-R1 paper.

Installation

PyTorch Configuration

This project is configured with PyTorch defaults that work for most users:

  • Linux: CUDA 12.8 builds (for GPU acceleration)
  • macOS/Windows: CPU builds

For Different CUDA Versions

If you need a different CUDA version, run the following commands:

# To automatically detect and install dependencies:
UV_TORCH_BACKEND=auto uv sync

# Alternatively, to install Pytorch with a specific CUDA version:
nvidia-smi                      # check your CUDA version
UV_TORCH_BACKEND=cu121 uv sync  # for CUDA 12.1
UV_TORCH_BACKEND=cu124 uv sync  # for CUDA 12.4
UV_TORCH_BACKEND=cpu uv sync    # for CPU-only

For the available CUDA versions see the official documentation.

Installation

uv venv --python 3.11
source .venv/bin/activate.fish
make install

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

linalg_zero-0.3.0.tar.gz (417.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

linalg_zero-0.3.0-py3-none-any.whl (165.3 kB view details)

Uploaded Python 3

File details

Details for the file linalg_zero-0.3.0.tar.gz.

File metadata

  • Download URL: linalg_zero-0.3.0.tar.gz
  • Upload date:
  • Size: 417.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.6.14

File hashes

Hashes for linalg_zero-0.3.0.tar.gz
Algorithm Hash digest
SHA256 3535515d877eddb137283dbd868cbb1baa2f40a0e3fb2c39845a9c576b7d55e0
MD5 fce7e043ecadb206b99a43e881c0e143
BLAKE2b-256 90efc2d89bbf23014cd2d0ff8290bec521ad7a545a281a9bae9314af4aceb9f0

See more details on using hashes here.

File details

Details for the file linalg_zero-0.3.0-py3-none-any.whl.

File metadata

File hashes

Hashes for linalg_zero-0.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 cc8a98732fb4b4d83797d595826c11b96d1c024e699d4de28badf9199ffed658
MD5 1cce7b7a1f094a875f3506585191914e
BLAKE2b-256 c30c8065cbbfe3ca1b20465387a08abdc29cd3a4b0f72838efbe26b6b902179a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page