tinymemorylm
A newer version of this model is available: CompactAI-O/Glint-1.3

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Glint-0.4

It speaks. It actually speaks. Mostly.

We came so far. From the dark ages of couldcouldoldbloodblood to actual, coherent sentences. This is Glint-0.4. 1 million parameters. Small. Trying its best. Unlike its ancestors, it usually succeeds.

Quick Stats

  • Parameters: 1,000,000 (yes, really)
  • Training Tokens: 10 Billion
  • Context Window: 2048 tokens
  • Vibe: Chaotic good, but mostly good

What Is This?

Glint-0.4 is the latest of the old guard before we started the Glint line proper. It builds on Glint-0.3 by adding SPIN (Self-Play Fine-Tuning) to the training loop. This model represents a 3x improvement in combined score over the original Glint-0.1. Coherence jumped from 1.99 to 6.03. Relevance is no longer zero. It is a miracle.

The journey

Model Era Typical Output Combined
Glint-0.1 The Dark Ages couldcouldoldbloodbloodbodybody 1.62
Glint-0.2 Pipe Character Incident |fdish|||||!@| 1.21
Glint-0.3 The Awakening It is about **competent development**... 3.87
Glint-0.4 (SPIN) Current Era The artificial intelligence is a problem... 4.84

Expected output:

"The simple terms arrived in simulant explorers and honey are specific or forecasters. They allow the structure of their similar..."

Disclaimer

This is a 1 million parameter model.

  • It is not GPT-5.
  • It is not GPT-2.
  • It is a tiny neural network running on a prayer and a GPU.
  • It might still output chuamliamce. If it does, try again. It is shy.
  • For best results, use temperature around 0.7. At 2.0, you are on your own.

Benchmarks

We benchmarked Glint-0.4 against all previous versions using a standard 7-question suite. Yes, 7 questions. We kept it small because we were running on a laptop.

Metric Glint-0.1 Glint-0.2 Glint-0.3 Glint-0.4 (SPIN)
Fluency 0.50 1.69 8.35 8.78
Coherence 1.99 1.56 5.72 6.03
Relevance 1.22 0.00 0.00 2.25
Format 3.29 3.29 3.29 3.29
Combined 1.62 1.21 3.87 4.84

Related Models

Acknowledgments

Built with curiosity over compute. Trained on FineWeb-Edu. SPIN optimized. A lot of hope.


Built by CompactAI. If you like tiny models that try their best, give us a follow.

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