Loom Swift Micro
1,159,120 parameters, trained from scratch in 4 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 Micro 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 | 1,159,120 |
| Architecture | Llama-style — 12 layers x 80 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 | 4 minutes 42 seconds, 164 optimizer steps, 1.74 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 Micro | |
|---|---|
| Acceptance battery (133 points) | 104/133 |
| 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 | 25/36 |
| Declines facts it wasn't taught, tools off | 5/20 |
| Holds a 10-12 turn conversation | 28/44 |
Ignores a <tools:on> typed in a message |
8/12 |
| 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 (8/12). - Little warmth, and little personality of its own.
How to run it
ollama run hf.co/textilelabs/Loom-Swift-Micro
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 Mini is the larger model in the tier.
Licence
MIT.
Textile Labs. Small models, trained honestly, on hardware you already own.