Model reference · open weights
Loom-Swift-Micro is an open-weight language model from textilelabs. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | textilelabs |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 1M |
| Context | 512 tokens |
| Runs with | transformers |
| Released | 2026-09-13 |
| Popularity | 567 downloads / month |
| Licence | Open weights |
About
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.
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.
| 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.
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 `` 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.
ollama run hf.co/textilelabs/Loom-Swift-Micro
python harness.py # the agent loop that runs its searches for real
Raw prompt format:
who wrote dracula
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.
Loom Swift Mini is the larger model in the tier.
MIT.
Textile Labs. Small models, trained honestly, on hardware you already own.
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys loom-swift-micro for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (loom-swift-micro below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"loom-swift-micro","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.