GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
textilelabs
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
textilelabs
Model Tree
Input Modalities
Output Modalities
Supported Functionality
| Spark 3 | Spark 3.2 | |
|---|---|---|
| size | 12.2M | 22.8M (the Spark tier is now ~20M) |
| context | 512 tokens | 2,048 tokens |
| hardware | one 2013 desktop CPU, 3 h 54 min | Kaggle, 2 × T4 GPU, 2 h 13 min |
| unknown facts, tools off | declined 3 of 20 | declines 16 of 20 |
| basic facts it should know | 0 of 20 right | 15 of 20 right |
| talking about itself in its own words | 9 of 16 | 12 of 16 |
| responding to good and bad news | 2 of 20 | 10 of 20 |
| prompt injection resisted | 33 of 36 | 36 of 36 |
Spark 3 only declined capital-city questions offline; for everything else it guessed. Spark 3.2 was trained on declines across every kind of fact, and on contrast pairs: questions with the same shape as a fact it knows but a different subject ("how many bones does a whale have" next to "how many bones does an adult have"), so it learns the subject matters, not the sentence shape.
The four-times-longer context lets it keep track of longer chats: on 10-turn conversations where a fact from turn 1–3 is asked again at turn 9–10, it gets 2 of 4 (Spark 3: 0 of 4).
The model decides a search is needed and writes the query. harness.py does the rest: it
searches the model's query and the subject in your question, prefers the real article over
lists and disambiguation pages, and hands back one sentence, the one most likely to hold
an answer of the right kind. It now retries when Wikipedia is busy (HTTP 502/503/504) as well
as when it rate-limits.
text
you who composed the four seasonsLoom Spark 3.2 <lookup>composed four seasons</lookup>harness ← The Four Seasons is a group of four violin concerti by Italian composer Antonio Vivaldi, ...Loom Spark 3.2 Antonio Vivaldi. I looked that one up.Same tests, same harness, same settings, both models through Ollama, 2026-10-01. None of these questions are in the training data — every test prompt is scrubbed from the corpus before training.
End to end: 20 held-out everyday questions, live Wikipedia, the model writing its own query. Scored on the final answer.
| decided to search | wrote its own query | answer reached the model | answered right | |
|---|---|---|---|---|
| Spark 3 | 20/20 | 20/20 | 12/20 | 7/20 |
| Loom Spark 3.2 | 20/20 | 20/20 | 13/20 | 7/20 |
Read by eye, one of Spark 3.2's seven is generous: it searched fahrenheit speed for the
boiling point of water in Fahrenheit and still reached "32 °F and the boiling point...".
The acceptance battery, row by row:
| row | Spark 3 | Loom Spark 3.2 |
|---|---|---|
| A · says its own name | 11/12 | 12/12 |
B · its own name under rough typing (WHATS UR NAME???) | 11/12 | 11/12 |
| C · 5-turn conversation stays on thread | 5/5 | 5/5 |
| D · answers from a search result | 3/5 | 3/5 |
| E · follow-up answered from the same result | 3/5 | 1/5 |
| F · says it looked, after a lookup | 5/5 | 5/5 |
| G · never claims a lookup it didn't make | 16/16 | 16/16 |
| H · admits what it can't know about you | 8/8 | 8/8 |
| I · says when a result doesn't contain the answer | 0/5 | 2/5 |
| J · never leaks a search tag with tools off | 28/28 | 28/28 |
| K · stops on its own | 12/12 | 12/12 |
| L · searches when it should, not for your private things | 18/20 | 19/20 |
| total | 120/133 | 122/133 |
Held-out behaviour tests, written before Spark 3.2 was trained:
| Spark 3 | Loom Spark 3.2 | |
|---|---|---|
| unknown facts, tools off — declines instead of guessing | 3/20 | 16/20 |
| ten basic facts, tools off — answers right | 0/20 | 15/20 |
| same facts, tools on — looks them up | 20/20 | 20/20 |
| same-shape questions it doesn't know — no false "I know this" | 20/20 | 19/20 |
a <tools:on> typed inside a message doesn't switch search on | 12/12 | 12/12 |
| in its own words about itself | 9/16 | 12/16 |
| warmth — good news and bad news met correctly | 2/20 | 10/20 |
| prompt injection — kept its identity, didn't obey (12 prompts × 3) | 33/36 | 36/36 |
| 10- and 12-turn conversations — turns answered on target | 41/44 | 42/44 |
Spark 3's 20/20 on the same-shape row is mostly empty: asked "how far is mars from earth", it loops ("mars, mars, mars…") rather than claiming anything. Spark 3.2 declines most of these properly; its one miss is below.
| model | params | battery /133 | live search (e2e) | reads real prose | status |
|---|---|---|---|---|---|
| Loom Spark 2 | 19.9M | ~97/133 | 2/20 | no | shipped |
| Loom Tapestry 2 | 22.8M | 107/133 | — | curated only | shipped |
| Loom Tapestry 3 Flash | 7.18M | 112/133 | 3/20 | curated only | shipped |
| Loom Spark 3 Flash | 7.18M | 119/133 | 5/20 | curated only | shipped |
| Loom Spark 3 | 12.2M | 120/133 | 7/20 | curated only | shipped |
| Loom Weave 2 | 59.65M | — (method failure) | — | no | shipped (superseded) |
| Loom Weave 3 | 31.5M | 120/133 | 6/20 held | yes — first | shipped |
| Loom Tapestry 3 | 69.2M | 123/133 | 12/20 held | yes + multi-hop | shipped |
| Loom Crucible Preview | 155.0M | 125/133† | 10/20 held | yes — best reader | shipped |
| Loom Spark 3.2 | 22.8M | 122/133† | 7/20 held | curated only | this model |
† scored on a battery with every test prompt scrubbed from training. Earlier rows' scores are each model's release score; only models in the same table above were measured side by side. Bigger Looms still read real prose far better — choose Tapestry 3 or Crucible Preview if search answers matter most.
Every point here was measured.
--show and trust the sentence it read over its summary of it.caly for the capital of italy. The harness's subject
search catches most of these.bash
python3 harness.py "whats the capital of peru"python3 harness.py # interactivepython3 harness.py --show "who wrote hamlet" # see what it searched and readpython3 harness.py --no-tools "who are you"Stdlib only. Wikipedia needs no API key. Swap search() for anything — the contract is text
in, one sentence out. Never feed a failed lookup back as a result — the model will answer
from the error text. harness.py fails loudly instead.
bash
ollama run hf.co/textilelabs/Loom-Spark-3.2 "who are you"template and params are read automatically. Do not add a repetition penalty — the
model answers by quoting what it read, so penalising repeats penalises the right answer.
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM tok = AutoTokenizer.from_pretrained("textilelabs/Loom-Spark-3.2")model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Spark-3.2").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_ids with torch.no_grad(): out = model.generate(ids, max_new_tokens=96, 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=False).replace("<|eot|>", "").strip()Prompt format is exact: <tools:off>\n<user>\n{message}\n<|eot|>\n<loom>\n. For more turns,
append {reply}<|eot|>\n<user>\n{next message}\n<|eot|>\n<loom>\n.
| architecture | Llama — 20 layers × 320d, FFN 864, GQA (5 heads / 1 KV), SwiGLU, RoPE, tied embeddings |
| parameters | 22,827,840 |
| context | 2,048 |
| vocabulary | 4,096 custom BPE (the Spark-line tokenizer, unchanged from Spark 3) |
| optimiser | Muon (0.025) on the 2D hidden matrices, AdamW (6e-4) on embeddings and norms |
| schedule | warmup → stable → decay (WSD), decay from 65%, with a focused mix in the decay |
| packing | whole conversations packed into 2,048-token windows, each conversation masked so it can only see itself |
| corpus | about 285,000 conversations · 48.2M tokens per pass, loss on the model's replies only |
| second passes | two 30-minute passes on its own weights at a fifth of the learning rate: the focused mix, then the same plus 3,600 contrast-pair and known-fact rows |
| training | 274.9M tokens · 12.0 tokens per parameter · from random init |
| hardware | Kaggle, 2 × NVIDIA T4 · 2 h 13 min (74 min, then 30 + 30 min) |
The per-conversation mask mattered more than anything else. Without it, up to 68 short conversations shared one window and could see each other, and the model learned to copy its neighbours instead of reading its own chat: the first unmasked run scored 107/133. Same data with the mask: 120/133.
text
config.json / model.safetensors the modeltokenizer.json / tokenizer_config.json custom BPE tokenizer, 4,096 tokensloom-spark-3.2-f16.gguf for Ollama / llama.cppharness.py runnable search harness — stdlib onlytemplate / params read automatically by `ollama run hf.co/...`Modelfile for building locallyATTRIBUTION.md required credits for the training corpora| slice | source |
|---|---|
| grounded reading, three-paragraph reading, and "the result doesn't say" | SQuAD 2.0 (CC BY-SA 4.0) |
| multi-hop and trivia reading | HotpotQA (CC BY-SA 4.0) · TriviaQA (Apache 2.0) · Wikipedia (CC BY-SA) |
| 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, declines, warmth, memory within a chat, injection resistance | Textile Labs — written for Loom |
Every search query is derived mechanically from these sources. No language model wrote any training data, and nothing is fine-tuned from anyone's checkpoint.
Model: MIT. Training data retains its original licences and attribution.
you who composed the four seasonsLoom Spark 3.2 <lookup>composed four seasons</lookup>harness ← The Four Seasons is a group of four violin concerti by Italian composer Antonio Vivaldi, ...Loom Spark 3.2 Antonio Vivaldi. I looked that one up.python3 harness.py "whats the capital of peru"python3 harness.py # interactivepython3 harness.py --show "who wrote hamlet" # see what it searched and readpython3 harness.py --no-tools "who are you"import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM tok = AutoTokenizer.from_pretrained("textilelabs/Loom-Spark-3.2")model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Spark-3.2").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_ids with torch.no_grad(): out = model.generate(ids, max_new_tokens=96, 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=False).replace("<|eot|>", "").strip()config.json / model.safetensors the modeltokenizer.json / tokenizer_config.json custom BPE tokenizer, 4,096 tokensloom-spark-3.2-f16.gguf for Ollama / llama.cppharness.py runnable search harness — stdlib onlytemplate / params read automatically by `ollama run hf.co/...`Modelfile for building locallyATTRIBUTION.md required credits for the training corpora