Skip to main content

Train your own AI on your computer — by Inscribe AI

Project description

Inscribe AI

Train your own AI model on your computer — no coding needed.

Install

pip install inscribe-ai

Run

inscribe-ai

That's it! Your browser will open automatically at http://localhost:5050.

What it does

  • Fine-tunes AI models (GPT-2, TinyLlama, and more) on your own data
  • Runs entirely on your computer using your own GPU/CPU
  • Sign in with your Inscribe AI account at inscribeai.netlify.app

Requirements

  • Python 3.9+
  • For GPU training: CUDA-compatible GPU recommended (CPU works too, just slower)

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

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

inscribe_ai-5.1.0-py3-none-any.whl (42.6 kB view details)

Uploaded Python 3

File details

Details for the file inscribe_ai-5.1.0-py3-none-any.whl.

File metadata

  • Download URL: inscribe_ai-5.1.0-py3-none-any.whl
  • Upload date:
  • Size: 42.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for inscribe_ai-5.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 5123891c72182c06ece65bc174793b4317e4174a8962448c85d06b2e747a737b
MD5 d8ca20c5ef541e06419bc274d2d28eb6
BLAKE2b-256 7c89952015cf612ac179ddd7bd0c757366333922827626ec75e01342d97b5fdb

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