OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Microgpt C

Microgpt C is a public MIT-licensed, dependency-free C implementation of a small character-level GPT with transformer forward passes, backpropagation, Adam optimization, training and sampling in one file.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Microgpt C — 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 it when you need a compact, inspectable C implementation for learning, local experiments or embedding-oriented prototypes rather than a hosted AI service.

Who it is for

  • C developers studying GPT internals
  • Researchers and learners prototyping small character-level models
  • Developers embedding minimal training or inference logic in C applications
  • Engineers exploring CPU inference with NEON or AVX2 build paths

Documented uses

  • Train a small GPT on line-separated text
  • Generate local character-level samples
  • Study forward propagation, backpropagation and Adam optimization in compact C code
  • Prototype transformer training without external machine-learning dependencies
  • Explore host-specific CPU inference paths
Before you choose

Requirements & limits

The repository is public and open source under the MIT license. The README lists macOS, Linux and Windows through MSYS2, with ARM64 NEON and x86-64 AVX2 build paths.

  • The implementation uses only libc and is concentrated in one C file.
  • Input is documented as a corpus with one item per line.
  • The repository separates training and inference forward passes, with the inference logits documented as matching within fp32 rounding.
  • The Makefile selects host flags and lists NEON for ARM64 and AVX2 for x86-64.
  • The example model has 4,192 parameters, making the project focused on compact experiments rather than large-scale language modeling.

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

Train and sample from a line-separated corpus

Build and run Microgpt C with a corpus containing one item per line, then use its documented training and sampling workflow.

Before you start

  • A checkout of the public Microgpt C repository.
  • A corpus formatted as one item per line.
  • A macOS, Linux or Windows environment using MSYS2; the README lists ARM64 NEON and x86-64 AVX2 build paths.

Steps

  1. Place a line-separated corpus where the executable can receive it as the documented input file.

  2. Build and run the project with the documented the command below workflow, or run ./microgpt data/names.txt with the corresponding corpus path.

    Documented command
    make run

  3. Allow the program to train and produce its documented inference samples.

Expected outcome

A locally trained character-level GPT produces generated sequences from the supplied corpus.

Guide limitations

  • The README documents the corpus format and invocation, but does not establish performance or output quality for every dataset.
  • The reported throughput figures are examples for specific NEON and AVX2 machines, not compatibility guarantees.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
3 October 2026
Original website title
vixhal-baraiya/microgpt-c · Official homepage title · retained in its source language
How we review sourcesSuggest a correction