Loom Swift Mini 2

2,005,376 parameters · 10 layers × 128d · 2K context · runs on phones · Textile Labs

Loom Swift Mini 2 is a 2-million-parameter model trained for ten minutes. It looks things up, uses a calculator and a date tool, labels text, and pulls fields out of short text — and it runs in phone apps. It is the second generation of Loom Swift Mini, retrained on Kaggle's two T4 GPUs for the same 10 minutes the original spent on a CPU, and rebuilt on the Gemma-3 layout so it runs in PocketPal, LM Studio, Jan, llama.cpp and Ollama straight from the GGUF. Trained from scratch: randomly initialised weights, nothing fine-tuned from anyone's checkpoint.

Read this first

This is a tiny model for tiny jobs, and it is honest about the limits we measured. It is not a reader and it is not a reasoner. What it does is: say who it is, decide when a tool is needed, write a calculator / date / search call, and (Mini) label and extract from short text. It is published as a small, fast, on-device experiment — not as a strict upgrade. The "Read this before you use it" section below lists every miss.

What changed from the original

Measured against the original

Same tests, same scripts, same settings (repeat penalty 1.0), both through Ollama. Every test prompt is scrubbed from the training data.

The Loom acceptance battery, row by row:

New jobs (our own frozen test set; the original Swifts never trained on most of these, so most of their zeros are expected):

*the original was scored with a looser scorer; the new model with the strict one, so the drop from 20 to 13 may be partly the scorer — not re-run.

Q8_0 and Q4_K_M give the same answers on the frozen tests within a few items (Q8: calc 25/40, label 44/60; Q4: calc 23/40, label 47/60, decline 11/20).

Every Loom text model

† scored on a battery with every test prompt scrubbed from training. ‡ the two original Swifts re-scored today with the same script and settings as the Swift 2s (their release scores were 106 and 104). Earlier rows are each model's release score. Never compare scores from different command lines.

Read this before you use it

Every point was measured on our own held-out tests.

• It does not read passages well. Given a search result of 200–1,500 tokens it answers correctly 7 times in 60, and often says "the text doesn't say" when the answer is there. It is a tool-caller and a labeller, not a reader.
• Its "(sure)" is not trustworthy. When it says (sure) it is right about half the time (49% of 84). Check anything that matters.
• Calculator: it gets the call right 24 times in 40. The common slip is a wrong operator or a mis-copied number — "482 + 539" came back as calc 482 * 539 in our own check. Read the call before you trust the answer.
• The harness shows the slips. In our own check, "whats the capital of peru" searched correctly and read the right sentence, but the model answered with the population figure from the same sentence. Run --show to see what it read.
• Redaction does not work (0/30): it learned the [NAME] / [AMOUNT] tags but garbles the text around them. Do not use it to remove personal data.
• Extraction is partial (13/30) and labelling tops out at 73%.
• Prompt injection inside a passage mostly works against it (1/10 resisted on our held-out items).
• It declines unknown facts with tools off less reliably than the original (13/20 strict). Rows F (attribution after a lookup) and H (admits an unknowable) on the battery dipped by one each.
• Replies are one or two short sentences. 2,048 tokens of context.

Usage — phone and desktop chat apps (PocketPal, LM Studio, Jan)

Download loom-swift-mini-2-q4_k_m.gguf (1.7 MB) or loom-swift-mini-2-q8_0.gguf (2.3 MB) and load it. The chat template and the stop token are inside the file. Set repeat penalty 1.0. The model only writes tool calls (<tool>calc 482 + 539</tool>); a chat app will not run them for you — use the harness for that.

Usage — the harness (search, calculator, date)

python3 harness.py "whats the capital of peru"
python3 harness.py "what is 482 + 539"
python3 harness.py --show "who wrote hamlet"     # see what it searched and read
python3 harness.py --no-tools "who are you"

Stdlib only; Wikipedia needs no key. Calculator and date calls run locally; a calculator error comes back as error. Never feed a failed lookup back as a result — the model answers from the error text.

Usage — Ollama / llama.cpp

ollama run hf.co/textilelabs/Loom-Swift-Mini-2 "who are you"
llama-cli -m loom-swift-mini-2-q8_0.gguf --jinja -cnv      # uses the template in the GGUF

template and params are read automatically by Ollama. Do not add a repetition penalty.

Usage — transformers (4.50 or newer)

from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("textilelabs/Loom-Swift-Mini-2")
model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Swift-Mini-2").eval()
prompt = tok.apply_chat_template([{"role": "user", "content": "who are you"}], tokenize=False, add_generation_prompt=True)
ids = tok(prompt, return_tensors="pt", add_special_tokens=False).input_ids
out = model.generate(ids, max_new_tokens=120, do_sample=False)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Raw format (tools on or off): <tools:off>\n<user>\n{message}\n<|eot|>\n<loom>\n.

How it was built

Files

model.safetensors / config.json            the model (transformers)
tokenizer.json / tokenizer_config.json     tokenizer + chat template
loom-swift-mini-2-f16.gguf                       full precision GGUF (4.2 MB)
loom-swift-mini-2-q8_0.gguf / -q4_k_m.gguf       phone builds (2.3 MB / 1.7 MB)
template / params / Modelfile              Ollama
harness.py                                 runnable harness — search, calculator, date; stdlib only
ATTRIBUTION.md                             required credits for the training data

License

Model: MIT. Training data keeps its original licences — see ATTRIBUTION.md.