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ALT: A C Library for Interpretable Machine Learning Models

Status

ALT is an open-source C library for building interpretable machine learning models, specifically transformer models for natural language processing tasks.

Features

  • Transformer models for natural language processing tasks such as completion, chat completion, and infill operations.
  • Built from scratch in C for portability and efficiency.
  • Support for various features of transformer models, such as byte-pair encoding, continuous bag of words, skip-gram modeling, multi-layer perceptrons, sliding window attention, grouped query attention, forward, upward, and downward projections, single precision and half-precision, 8-bit and 4-bit quantization for digital signal processing, and more.

Requirements

  • Any Linux distribution should work, but officially supports Arch Linux.
  • POSIX for CPU multi-threading.
  • Vulkan for GPU multi-threading.

Installation

Dependencies

  • cmake: Tool for managing source code building
  • libc: Standard C library
  • pthread: POSIX multi-threading library for portable CPUs
  • vulkan: Portable multi-threading library for GPUs
  • libpcre2: Implements Perl 5-style regular expressions
  • libuuid: DCE compatible Universally Unique Identifier library
  • libutf8proc: C library for Unicode handling
  • stb: Image loading/decoding library

Installation (Arch Linux)

sudo pacman -S gcc gdb cmake util-linux-libs pcre2 libutf8proc stb vulkan-headers vulkan-tools

Python Dependencies

sudo pacman -S python-pip python-virtualenv python-isort python-black flake8

Clone the Repository

Clone the repository to your local machine and navigate into it:

git clone https://github.com/teleprint-me/alt.c.git alt
cd alt

Python Virtual Environment

Set up a Python virtual environment to manage project dependencies:

virtualenv .venv
source .venv/bin/activate

Important Note: Arch Linux has recently upgraded Python from 3.12.x to 3.13.x. Some critical libraries, such as SentencePiece and PyTorch, are not fully compatible with Python 3.13.x. This causes issues with the development environment.

To resolve this, I have created a Knowledge Base article that provides a detailed guide on building and using a local Python 3.12.x environment without interfering with the system-wide Python installation. Refer to docs/kb/python.md for step-by-step instructions.

Install CPU Torch

Install the CPU-only version of PyTorch, to avoid GPU dependency issues (GPU support is not required):

pip install torch --index-url https://download.pytorch.org/whl/cpu --upgrade

Install Python Requirements

Install the additional Python dependencies listed in requirements.txt. It is recommended to install these after PyTorch to avoid potential conflicts:

pip install -r requirements.txt

Building the Project

cmake -B build -DCMAKE_BUILD_TYPE=Debug
cmake --build build --config Debug -j $(nproc)

Converting Mistral 7B v0.1

Convert tokenizer model to alt

python -m alt.convert.spm models/mistralai/Mistral-7B-Instruct-v0.1

Convert tensors to alt

TODO: Tensor conversion coming soon.

Examples

Run an Example

./build/examples/models/perceptron

Sample Output

Epoch 0: Average Error: 0.50862
Epoch 1000: Average Error: 0.15645
Epoch 2000: Average Error: 0.10852
Epoch 3000: Average Error: 0.08668
Epoch 4000: Average Error: 0.07379
Epoch 5000: Average Error: 0.06513
Epoch 6000: Average Error: 0.05883
Epoch 7000: Average Error: 0.05400
Epoch 8000: Average Error: 0.05015
Epoch 9000: Average Error: 0.04699
Trained Weights: 5.48, 5.48 | Bias: -8.32
Input: 0, 0 -> Prediction: 0.000
Input: 0, 1 -> Prediction: 0.056
Input: 1, 0 -> Prediction: 0.056
Input: 1, 1 -> Prediction: 0.934

License

This project is licensed under the AGPL License. See the LICENSE file for details.

Note: Everything is still a work in progress—expect changes and refinements as development continues.