Glint-0.3

A 1M-parameter language model that speaks English, technically.

WARNING: This model was trained on a shoestring budget and a prayer.
It does not answer questions correctly. It does not follow instructions well.
It does, however, occasionally produce output that sounds profound
until you read it twice.

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Overview

Glint-0.3 is a tiny autoregressive language model with about one million parameters. That is not a typo. While everyone else is measuring models in billions, we went the other direction. Mostly because we could.

It was trained on 100 tokens per parameter. The math: 1M params x 100 tokens = 100M total. Two thirds went to pretraining (66.7M tokens), then we used the rest to fine-tune on instruction data.

This does not make the model smart. It just makes it slightly less confused than it would have been otherwise.

Funny thing: the pretrained checkpoint sounds more fluent than the instruction-tuned one. Turns out teaching a model to follow directions can make it worse at saying things. Who knew.

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The one claim we are allowed to make

At 100 tokens per parameter, Glint-0.3 is one of the most efficient models at this scale. Against another 1M model trained on fewer tokens, these weights would likely win.

We are not saying state of the art. We are saying we got more out of the data than most would. Modest victory. We will take it.

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Sample outputs

Instruction-tuned

User: Is food safe to eat?
Model: As an Age, in relation (a rush-rict), a decisios is the **exactly* estimate**
       (each labeling at * *Crise*).

       When you cannot find all faces (doinins one way tea t become flawed) are no
       longer rartints like awarded, n but wheret dogs control crashed ttega y

Pretrained

User: A cat, commonly known as the
Model: primary current burden of transportation wasnt impossible. A recence includes
       a personal crisis for participated populatably, to make sure technology
       systems change from an illustrious phase orbit oncold, rock areas, along
       with localizators outside again

If you find meaning in these, that says more about you than the model.

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Specs

Thing	Value
Parameters	~1,000,000
Language	English
Tokenization	Word-level
Architecture	Lightweight Transformer
Total Tokens	~100M (100 tokens/param)
Pretraining Tokens	~66.7M
Instruction Tokens	~33.3M
Throughput	~1M tokens/sec

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "CompactAI-O/Glint-0.3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "A cat, commonly known as the"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.9)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Try temperature between 0.8 and 1.2 for peak confusion.

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What is this actually for?

• Generating writing prompts nobody asked for
• Studying how small models fail in charming ways
• Populating game worlds with NPCs that speak in riddles
• Teaching that bigger is not always better
• Entertaining yourself during long training runs

What is it not for

• Facts. Any facts.
• Customer support
• Medical, legal, or financial advice (oh hell no)
• Replacing a search engine
• Expecting it to know what it is talking about

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Why does this exist?

We wondered what would happen if you trained a very small model on a very large dataset and then asked it to talk. The answer, as you can see, is complicated.

We put two thirds of the token budget into pretraining and used the rest to nudge it toward instruction following. This does not produce a capable assistant. It produces a model that learned as much as it could, given the constraints.

This is part of CompactAI, an ongoing exploration of language modeling at the edge of feasibility. Interesting things happen when you remove the safety net of scale. Sometimes those things are useful. Sometimes they are just funny.

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Contributing

We welcome:

• Bug reports, especially if the failure case is entertaining
• Prompts that coax unexpectedly poetic output from this thing
• Research collaborations on ultra-small model dynamics
• Ideas for making a 1M parameter model slightly less confused

Please do not submit PRs that add more parameters. That defeats the purpose.

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Citation

@misc{glint03,
  title={Glint-0.3: A 1M-Parameter English Language Model for Experimental Use},
  author={CompactAI},
  year={2026},
  howpublished={\url{https://huggingface.co/CompactAI-O/Glint-0.3}},
  note={Trained with hope. Deploy with caution.}
}

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The model generates text. Whether that text means anything is a question for philosophers.

Train small. Expect less. Laugh anyway.

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Built by CompactAI.