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textilelabs
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Same probe structure 1.8 published, so this is like-for-like rather than cherry-picked.
| Spark 1.8 | Spark 2 | |
|---|---|---|
offline <lookup> leak | 16/30 · 53% | 0/30 · 0% |
identity probes leaking <lookup> | 0/12 | 0/12 |
| clean single online lookup | 8/10 | 10/10 |
| identity correct | — | 12/12 |
| identity under CAPS / typos / filler | not trained for | 6/6 |
| self-termination without a Modelfile | needed one | 12/12 |
| parameters · context | 18.85M · 256 | 19.87M · 512 |
The offline leak result is the one that matters most in practice. 1.8 reached for a lookup on more than half of all offline questions; Spark 2 did not do it once in thirty.
It can be given a tool result and asked to answer from it. That part is weak:
| score | |
|---|---|
answers correctly from a supplied <result> | 2/5 |
| says the result doesn't contain the answer | 0/4 |
| admits an unknowable personal fact | 4/8 |
It does not reliably say "the result doesn't say" — it fabricates instead. If you feed it results, validate the output; do not treat a grounded answer as trustworthy.
It also has almost no world knowledge. With tools off it will decline factual questions, which is the intended behaviour, not a bug.
Why: one hour of training gives ~809 optimiser steps, and validation accuracy was still climbing steeply (0.47 → 0.54 over the final 200 steps) when the clock ran out. The model is under-trained rather than under-sized.
<tools:off> (the default) — conversational. Identity, limits, warmth, brevity. No
harness needed.
<tools:on> — it emits <lookup>query</lookup> and stops. Your harness runs the
lookup and continues with a <result> block:
markdown
<tools:on><user>what is the capital of Peru<|eot|><loom><lookup>what is the capital of Peru</lookup><|eot|><result>Lima is the capital and largest city of Peru.<|eot|><loom>
Note the persona slice was trained entirely under tools:off, so identity questions asked
with tools on will often be turned into a lookup. Keep tools off for chat.
harness.py in this repo is a working harness: it runs the lookup Loom asks for and
feeds the result back. Wikipedia is used because it is free and needs no key — swap the
search() function for anything else; the contract is just text in, text out.
bash
python3 harness.py "who wrote Dracula" # with lookupspython3 harness.py # interactivepython3 harness.py --no-tools "who are you" # chat only
markdown
you > who wrote Dracula[loom wants: 'who wrote Dracula'][result: Dracula is an 1897 Gothic horror novel by Irish author Bram Stoker...]
Three things any harness for this model needs, learned the hard way:
<result>. The model will earnestly try to
answer from the error text. Fail loudly instead — harness.py does.User-Agent.And the honest warning: with the grounded score at 2/5, the final answer is frequently
wrong even when the lookup and the result are both perfect. The example above returns
"Scrucula" from that passage. Treat the retrieved <result> as the trustworthy part and
the model's summary of it as unreliable.
bash
ollama run hf.co/textilelabs/Loom-Spark-2 "who are you"# Loom, a small model from Textile Labs.
Ollama reads the template and params files in this repo — nothing to set up. The
template defaults to tools:off. To build locally: ollama create loom-spark-2 -f Modelfile.
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMtok = AutoTokenizer.from_pretrained("textilelabs/Loom-Spark-2")model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Spark-2").eval()eot = tok.convert_tokens_to_ids("<|eot|>")def ask(message, tools=False):p = f"<tools:{'on' if tools else 'off'}>\n<user>\n{message}\n<|eot|>\n<loom>\n"ids = tok(p, return_tensors="pt", add_special_tokens=False).input_idswith torch.no_grad():out = model.generate(ids, max_new_tokens=64, do_sample=False,eos_token_id=eot,pad_token_id=tok.convert_tokens_to_ids("<|pad|>"))[0]return tok.decode(out[ids.shape[1]:], skip_special_tokens=True).strip()ask("who are you") # -> 'Loom, a small model from Textile Labs.'ask("what is the capital of Peru", True) # -> '<lookup>what is the capital of Peru</lookup>'
Prompt format is exact: <tools:off>\n<user>\n{message}\n<|eot|>\n<loom>\n.
markdown
config.json / model.safetensors the modeltokenizer.json / tokenizer_config.json custom BPE tokenizer, 4,096 tokensloom-spark-2-f16.gguf 40MB, for Ollama / llama.cppharness.py runnable harness — runs lookups, feeds results backtemplate / params read automatically by `ollama run hf.co/...`Modelfile for building locallyATTRIBUTION.md required credits for the training corpora
Built from openly licensed corpora of real human text, plus a persona curriculum written
for Loom. See ATTRIBUTION.md — several of these licences require credit.
| slice | source |
|---|---|
| grounded reading, and "the result doesn't say" | SQuAD 2.0 (CC BY-SA 4.0) |
| when to reach for a tool | MASSIVE (CC BY 4.0) · CLINC150 (CC BY 3.0) |
| instruction following | databricks-dolly-15k (CC BY-SA 3.0) |
| multi-turn dialogue structure | OpenAssistant OASST1 (Apache 2.0) |
| identity, limits, warmth, brevity | Textile Labs — written for Loom |
~11.4M tokens, 43% multi-turn. Validation is a held-out split of the same corpora.
Model: MIT. Training data retains its original licences and attribution.