Loom Spark 1.8

Third of the Loom models · Textile Labs · successor to Loom Spark v1 and Loom Spark 1.5 (both remain available, unchanged)

⚠️ Read this before testing in Ollama

Loom is a base-style model with a custom format, not a chat model. Ollama's default template will make it talk to itself — it will answer you, then invent your next message and answer that too. This is not a fault in the weights; nothing tells it where your turn ends except stop tokens. Use the Modelfile in this repo:

ollama create loom-spark-1.8 -f ollama/Modelfile
ollama run loom-spark-1.8

ollama run hf.co/textilelabs/Loom-Spark-1.8 on its own does not pick up the Modelfile — Ollama only pulls the GGUF and applies its own template.

Loom Spark 1.8 is an 18.85M parameter language model trained entirely from scratch on a CPU-only 2013 Dell OptiPlex 9020 (i5-4690, 4 cores, no GPU). It is the third generation of the Loom family, after Loom Spark (7.6M) and Loom Spark 1.5 (12.3M).

It is built on an unusual premise: trade knowledge for wisdom. Loom Spark knows very little on purpose. What it is trained to do instead is know what it is — small, temporary, harness-dependent — and to be honest about the edge of its own knowledge, warm under pressure, and good at exactly one real skill: forming clean search queries that an external agent harness executes for it.

"Computer, technically. Companion, preferably." — Loom Spark 1.8, asked whether it was a computer

What's new in 1.8

1.8 keeps 1.5's data format and curriculum design, grown ~53% in capacity, plus a curriculum fix: earlier generations contained stale self-size statements inherited from forking the previous package — 1.5 would variously claim to be "seven million" or "twelve million" parameters. 1.8's curriculum states its size consistently, and this is verified in evaluation (see evaluation/RESULTS.md).

Measured behaviour

Evaluated on a 30-question offline battery, 12 identity probes, 16 self-size probes and 10 online lookup probes, against the raw checkpoint with no harness filtering — the same battery run against shipped v1.5 for comparison. Full logs in evaluation/.

1.8 improves on 1.5 across every axis measured.

Prompt format (important)

Loom Spark 1.8 is a base-style model with a custom format. It is highly sensitive to getting this exactly right. The mode marker rides on every user turn:

<tools:off>
<tools:off><user> who are you
<loom>

Note there is no trailing space after <loom>. Adding one pushes the model out of distribution and measurably degrades output quality.

Offline (<tools:off>) it answers from its own small store or admits it does not know. Online (<tools:on>) it may emit a search query and end its turn immediately:

<tools:on>
<tools:on><user> what year did the titanic sink
<loom> Not in my head, checking: <lookup>titanic sinking date</lookup><|endoftext|>
<result>The Titanic sank on 15 April 1912.</result>
<loom> April 1912 — ...

<result> blocks are injected by your harness after executing the search. The model never generates them (verified: 0 self-generated results across all probes). Stop generation at <|endoftext|> or <user>.

Usage — transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("textilelabs/Loom-Spark-1.8")
model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Spark-1.8")

prompt = "<tools:off>\n<tools:off><user> who are you\n<loom>"   # no trailing space
ids = tok(prompt, return_tensors="pt", add_special_tokens=False).input_ids
out = model.generate(ids, max_new_tokens=100, do_sample=True,
                     temperature=0.8, top_k=50, pad_token_id=tok.eos_token_id)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=False))

Usage — Ollama

A ready-made Modelfile is in ollama/. It encodes the trained format and stop tokens — do not use Ollama's default chat template, which is out of distribution for this model and produces incoherent output.

ollama create loom-spark-1.8 -f ollama/Modelfile
ollama run loom-spark-1.8

Usage — the agent harness

The harness/ folder contains loomspark-harness (v0.2.1), which implements the full lookup protocol with real web search, a CLI (loom-chat) and a small web UI (loom-web). It auto-detects 1.5/1.8-format models and bans lookup tokens at the logits level when tools are off.

pip install ./harness
loom-chat --model textilelabs/Loom-Spark-1.8

Limitations — please read

Loom Spark 1.8 knows almost nothing, and that is the design. Specifically:

• Do not use it for facts, medicine, law, finance, or anything where being wrong costs more than company.
• It retrieves adjacent facts. Asked the language spoken in Brazil it may answer "Brasília"; asked Germany's currency it may answer "Berlin". Its fact-core is tiny and it pattern-matches to a nearby entry.
• Offline lookup leakage. With tools off, the raw model still emits <lookup> tags on roughly half of fact-style questions (16/30 measured; shipped v1.5 measures 18/30 on the same test). The included harness suppresses this at the logits level, so harness users never see it — but users driving the raw model through llama.cpp or Ollama will. This is a curriculum characteristic of the whole Loom line, not specific to 1.8.
• 256-token context. It has no long-term memory and only a short working window.
• It is a base-style model, not instruction-tuned or RLHF'd.

Training

• Hardware: CPU-only Dell OptiPlex 9020, i5-4690, 4 cores, 16GB RAM, no GPU
• ~5,084 optimizer steps, batch 32 × 256 tokens, ~8 hours wall clock
• Corpus: 70MB / 14.9M tokens of procedurally generated curriculum, 100% owned
• AdamW (β 0.9/0.95), peak LR 6e-4, cosine decay, warmup 100, grad clip 1.0
• Dropout 0.1 resid/mlp, tied embeddings, pre-LN, GELU, learned positions
• Final val loss 0.3279

The curriculum is procedurally generated and fully owned by Textile Labs — no scraped web text, no third-party datasets.

Files

config.json / model.safetensors      transformers weights (GPT2LMHeadModel-compatible)
tokenizer.json / tokenizer_config.json   4096-token custom BPE
loom-spark-1.8-f32.gguf              GGUF for llama.cpp / Ollama
ollama/Modelfile                     correct template + stop tokens
harness/                             agent harness with web search (v0.2.1)
evaluation/                          full acceptance logs, comparison vs v1.5, train log

License

MIT. See LICENSE.

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Loom Spark 1.8 · Textile Labs · trained on a thirteen-year-old office computer.