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We Open-Sourced THX-01: A 322M AI Decision Model Small Enough to Run on a Smartphone

#ai #programming #opensource #news

Today, we're introducing THX-01, an open-source, non-autoregressive decision model developed by HAL-X AI in Baku, Azerbaijan.

With just 322 million parameters, THX-01 is lightweight enough for deployment on CPUs and potentially smartphones, without requiring expensive GPU infrastructure.

Unlike traditional large language models, THX-01 doesn't generate text token by token. Instead, it processes inputs and returns structured decisions with calibrated confidence scores in a single forward pass.

Key Highlights

  • 322M parameters, built on mmBERT with a specialized decision head.
  • ~10 ms inference per request on a GPU.
  • CPU-compatible, with a compact architecture suitable for mobile and edge deployment.
  • 98.4% accuracy on our multilingual support-ticket classification benchmark, compared to 98.5% for Claude Sonnet 5.5.
  • 18 languages, including Azerbaijani, English, Russian, and Turkish.
  • Calibrated confidence scores, allowing applications to make decisions based on estimated uncertainty.
  • Apache 2.0 license, with publicly available model weights.

Why We Built It

Not every AI task requires a massive generative model.

Classifying messages, routing customer requests, detecting spam, extracting document values, and making structured decisions can often be handled by significantly smaller models.

THX-01 was designed specifically for these workloads, prioritizing speed, efficiency, and reliability.

Our goal is to make intelligent decision-making accessible across environments, from production servers to resource-constrained edge devices.

Get Started

Install THX-01 using pip:

pip install thx01
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Load the model:

import thx01

agent = thx01.load("doofz/THX-01")
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Open Source

THX-01 is available under the Apache 2.0 license.

Model and documentation: https://huggingface.co/doofz/THX-01

Built by HAL-X AI, Baku, Azerbaijan.

We believe the future of AI isn't only about building larger models. It's also about building smaller, specialized models that make powerful capabilities accessible to everyone.

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