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Microgpt

microGPT is a lightweight GPT language-model implementation written in Python with PyTorch. Its repository includes scripts for tokenizer generation, dataset preparation, model training, and text generation with configurable sampling parameters.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Microgpt — 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 microGPT when you want hands-on access to a compact GPT implementation and its training or inference settings, especially for learning and small-scale experiments.

Who it is for

  • Learners exploring how GPT language models are constructed.
  • Developers experimenting with small-scale language-model training and text generation.
  • Python users who want to modify model or sampling parameters directly.

Documented uses

  • Prepare tokenizer and dataset files for a training run.
  • Train the included GPT model using the documented scripts.
  • Generate text from a supplied context and tune token count, temperature, top-k, or top-p.
  • Modify the model configuration and training parameters for experiments.
Before you choose

Requirements & limits

The project is a public GitHub repository with an MIT license. The documented workflow runs from a local repository using Python scripts and installed requirements.

  • The project is self-hosted code rather than a documented hosted generation service.
  • The README describes an 82-million-parameter model and a 6 GB training dataset.
  • Generation settings, including temperature, top-k, and top-p, are edited in inference.py.
  • The repository points to microGPT-deploy as a separate project for web-app deployment.

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

Set up the documented training workflow

Prepare the tokenizer and dataset files, then start model training.

Before you start

  • A local copy of the microGPT repository
  • Python package dependencies installed with pip install -r requirements.txt

Steps

  1. Clone the repository and install its requirements.

  2. Run tokenizer/train_tokenizer.py to generate the tokenizer file.

  3. Run datasets/prepare_dataset.py to generate dataset files.

  4. Run train.py to start training.

Expected outcome

The documented training script starts after tokenizer and dataset files have been prepared.

Guide limitations

  • The README describes an RTX 4060 with 8 GB of memory as a target consumer GPU, but does not guarantee performance on other hardware.
  • Training completion time and results are not specified.
Documentation 1 ↗
Source-based practical guide

Configure text generation in inference.py

Set the context and sampling parameters used by the documented inference script.

Before you start

  • A local copy of the microGPT repository
  • A model file at the path configured in inference.py

Steps

  1. Open inference.py and locate its generation parameters.

  2. Set the token count, temperature, top-k, top-p, and model path.

  3. Edit the context string that will prompt generation.

Expected outcome

The inference script is configured with the chosen context and generation settings.

Guide limitations

  • The README documents parameter editing but does not provide a complete command for running inference.
  • The configured model path must correspond to the model file used.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
LeeSinLiang/microGPT · Official homepage title · retained in its source language
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