| --- |
| license: mit |
| language: en |
| library_name: transformers |
| pipeline_tag: text-classification |
| tags: |
| - tiny-model |
| - llama |
| - from-scratch |
| - router |
| - tool-use |
| - agentic |
| - gguf |
| widget: |
| - text: "<user>\nwhats the weather in leeds\n<|eot|>\n<loom>\n" |
| example_title: "Needs a tool" |
| - text: "<user>\nwho are you\n<|eot|>\n<loom>\n" |
| example_title: "No tool needed" |
| --- |
| |
| <div align="center"> |
| <img src="banner.jpg" alt="Loom Atom" width="520"> |
| </div> |
|
|
| # Loom Atom |
|
|
| **22,392 parameters · 57 KB · Textile Labs** |
|
|
| **One question, one bit: does this need a tool?** |
|
|
| ``` |
| whats the weather in leeds → <tool> |
| remind me to call mum at 6 → <tool> |
| convert 30 miles to km → <tool> |
| |
| who are you → <none> |
| i had a rough day → <none> |
| sort it out → <none> |
| ``` |
|
|
| It emits exactly one token. That's the whole model. |
|
|
| Trained **from scratch in five minutes** on a 2013 desktop CPU — randomly initialised |
| weights, nothing fine-tuned from a pretrained base. It is a real causal transformer, not a |
| classifier: 2 layers, 24 hidden dimensions, tied embeddings. |
|
|
| ## Measured |
|
|
| | | Loom Atom | keyword baseline | |
| |---|---|---| |
| | **held-out human utterances** (1,860) | **96.0%** | 64.9% | |
| | **SNIPS** — never seen in training (700) | **96.6%** | 59.0% | |
|
|
| The second row is the one that matters. **SNIPS played no part in training** and the score |
| does not drop — so this is not memorised phrasings. The keyword baseline is a hand-written |
| list of ~75 tool-ish words scored on the identical splits. |
|
|
| Both test sets are balanced, so chance is 50%. |
|
|
| ## How small is 22,392 parameters? |
|
|
| | | parameters | ratio | |
| |---|---:|---| |
| | Loom Weave 2 | 59,650,000 | 2,664× | |
| | Loom Spark 2 | 19,867,008 | 887× | |
| | Loom Router 1 | 1,435,040 | 64× | |
| | **Loom Atom** | **22,392** | **1×** | |
|
|
| 57 KB. Small enough to embed as a byte array in a header file, and it runs in well under a |
| millisecond on a CPU. |
|
|
| ## What it's for |
|
|
| The cheapest useful decision in an agent stack: **should this request touch a tool at all?** |
|
|
| Put it in front of everything. If it says `<none>`, you have saved a retrieval call, a |
| router call, and possibly a large-model call — for the cost of a 57 KB matrix multiply. If |
| it says `<tool>`, hand off to something that decides *which* tool |
| ([Loom Router 1](https://huggingface.co/textilelabs/Loom-Router-1) does that in one token |
| across 17 routes). |
|
|
| It is **not** a chat model, a router, or a classifier of intent. It answers one binary |
| question and nothing else. |
|
|
| ## Where the floor is |
|
|
| A full ladder was trained, four minutes per rung, identical data: |
|
|
| | params | dim | layers | held-out | SNIPS | |
| |---:|---:|---:|---:|---:| |
| | 86,640 | 48 | 3 | 93.0% | 95.9% | |
| | 26,976 | 32 | 2 | 95.6% | 93.6% | |
| | **22,392** | **24** | **2** | **96.0%** | **96.6%** | |
| | 9,392 | 16 | 2 | 93.4% | 94.1% | |
| | 6,800 | 16 | **1** | 83.0% | 84.4% | |
| | 4,812 | 12 | **1** | 82.2% | 81.9% | |
|
|
| **Depth matters more than width.** Narrowing from 24 to 16 dimensions cost about 3 points. |
| Dropping from **two layers to one cost ten**. One attention layer can notice keywords; two |
| can combine a keyword with its context. The floor is a layer count, not a parameter count. |
|
|
| Every rung beats the keyword baseline — even 4,812 parameters, by 17 points. |
|
|
| One honest note: the 86,640-parameter model scores *lowest* on held-out data because every |
| rung got the same four minutes, and it completed 3,006 optimiser steps against 24d2L's |
| 10,809. It is under-trained, not worse. Do not read this table as "smaller is better". |
|
|
| ## Known weakness |
|
|
| Questions about **the user personally** — "what is my sister's name", "what did I have for |
| breakfast" — are the hard case. They need no tool (no tool can answer them), but they look |
| like lookups. Atom gets some right and some wrong; treat `<tool>` on a first-person |
| question as unreliable. |
|
|
| ## Usage — Ollama |
|
|
| ```bash |
| ollama run hf.co/textilelabs/Loom-Atom "whats the weather in leeds" |
| # <tool> |
| ``` |
|
|
| The `template` and `params` files in this repo are read automatically. `params` pins |
| `temperature: 0` and `num_predict: 1` — one token, deterministic. |
|
|
| ## Usage — transformers |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| tok = AutoTokenizer.from_pretrained("textilelabs/Loom-Atom") |
| model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Atom").eval() |
| pair = torch.tensor([tok.convert_tokens_to_ids("<tool>"), |
| tok.convert_tokens_to_ids("<none>")]) |
| |
| def needs_tool(message: str) -> bool: |
| p = f"<user>\n{message.strip()}\n<|eot|>\n<loom>\n" |
| ids = tok(p, return_tensors="pt", add_special_tokens=False).input_ids |
| with torch.no_grad(): |
| logits = model(input_ids=ids).logits[0, -1] |
| # decide only between the two legal answers |
| return bool(logits[pair].argmax() == 0) |
| |
| needs_tool("whats the weather in leeds") # True |
| needs_tool("i had a rough day") # False |
| ``` |
|
|
| Prompt format is exact: `<user>\n{message}\n<|eot|>\n<loom>\n`. |
|
|
| ## Files |
|
|
| ``` |
| config.json / model.safetensors the model — 67 KB |
| tokenizer.json / tokenizer_config.json custom BPE tokenizer, 512 tokens |
| loom-atom-f16.gguf 57 KB, for Ollama / llama.cpp |
| template / params read automatically by `ollama run hf.co/...` |
| Modelfile for building locally |
| ``` |
|
|
| ## Training data |
|
|
| Real human utterances from two openly licensed corpora, relabelled to a single bit: |
|
|
| - **MASSIVE** — Amazon (CC BY 4.0), derived from **SLURP** (CC BY 4.0) |
| - **CLINC150** — `clinc/oos-eval` (CC BY 3.0) |
|
|
| 15,502 utterances, balanced 50/50 by downsampling the majority class. A small procedurally |
| generated slice written by Textile Labs covers "no tool needed" cases that public assistant |
| corpora do not contain — chit-chat, ambiguity, and questions only the user can answer. |
|
|
| Both licences require attribution; this section satisfies that and must be kept with any |
| redistribution. |
|
|
| ## License |
|
|
| Model: MIT. Training data retains its original licences and attribution as above. |
|
|
