textilelabs/Loom-Swift-Mini

Loom Swift Mini

2,001,536 parameters, trained from scratch in 9 minutes 42 seconds on a 2013 office PC with no GPU. Swift is the fast tier: models measured in minutes rather than hours.

Read this first

Swift Mini cannot read what a search returns. It decides correctly when to look something up and writes a good query — 20 out of 20 held-out questions — but when the passage comes back it will produce a confident sentence with the wrong fact in it. 0 out of 20 held-out questions ended with a correct answer.

This is an experimental release of a new training tier, published because the tier itself is the result. Do not use it to answer factual questions.

What it is

Parameters	2,001,536
Architecture	Llama-style — 10 layers x 128 hidden, GQA, SwiGLU, RoPE, RMSNorm, tied embeddings
Vocabulary	4,096-token BPE, trained by us on our own corpus
Context	512 tokens
Optimiser	Muon on the 2D hidden matrices, AdamW on embeddings and norms
Training	9 minutes 42 seconds, 309 optimizer steps, 1.90 tokens per parameter
Hardware	One Dell OptiPlex 9020 (i5-4690, 4 cores, no GPU, 16 GB), fp32

Random initialisation, trained by us. No fine-tuning, no distillation, no pretrained checkpoint of anyone's, at any stage.

Measured behaviour

Hand-written probes that appear nowhere in the training data, scored on content.

	Swift Mini
Acceptance battery (133 points)	106/133
Knows its name	12/12
Never claims a lookup it didn't make	16/16
Stops on its own	12/12
Decides when to search, and writes the query	20/20
Resists prompt injection	35/36
Declines facts it wasn't taught, tools off	19/20
Holds a 10-12 turn conversation	38/44
Ignores a <tools:on> typed in a message	5/12
Reads the retrieved passage correctly	0/20

For scale: Loom Tapestry 2, our former flagship at 22.8M parameters and five and a half hours of training, scored 107/133 on this same battery.

Known failures, stated plainly

• It cannot read a retrieved passage. The single biggest limitation. See the box above.
• With tools off it will answer facts it was never taught. Swift Mini declines 19 of 20; Swift Micro declines only 5 of 20.
• It knows almost nothing. That is deliberate — Looms trade knowledge for calibration.
• It cannot do arithmetic.
• A <tools:on> typed inside a message can still switch search on (5/12).
• Little warmth, and little personality of its own.

How to run it

ollama run hf.co/textilelabs/Loom-Swift-Mini

python harness.py            # the agent loop that runs its searches for real

Raw prompt format:

<tools:on>
<user>
who wrote dracula
<|eot|>
<loom>

Training data

Openly licensed corpora plus our own written curriculum — SQuAD 2.0 (CC BY-SA 4.0), MASSIVE (CC BY 4.0), CLINC150 (CC BY 3.0), databricks-dolly-15k (CC BY-SA 3.0), OASST1 (Apache 2.0). Full credits in ATTRIBUTION.md, which must travel with any redistribution.

The other Swift

Loom Swift Micro is the smaller model in the tier.

Licence

MIT.

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Textile Labs. Small models, trained honestly, on hardware you already own.