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.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+banner.jpg filter=lfs diff=lfs merge=lfs -text
+logo.jpg filter=lfs diff=lfs merge=lfs -text
+loom-weave-2-f16.gguf filter=lfs diff=lfs merge=lfs -text
+loom-weave-2-q8_0.gguf filter=lfs diff=lfs merge=lfs -text

LICENSE

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+MIT License
+
+Copyright (c) 2026 Textile Labs
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Loom-Weave-2/config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "float32",
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 640,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 10,
+  "num_hidden_layers": 10,
+  "num_key_value_heads": 2,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_cache": false,
+  "vocab_size": 16384
+}

Loom-Weave-2/generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": 0,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 0,
+  "transformers_version": "5.0.0",
+  "use_cache": false
+}

Loom-Weave-2/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c79c663642572f33d1d2ad8833407fb55af46d86e08a334bc11849240968ab0f
+size 238614960

Loom-Weave-2/tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+  "backend": "tokenizers",
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": [
+    "<user>",
+    "<loom>"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "TokenizersBackend"
+}

Modelfile

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+FROM ./loom-weave-2-q8_0.gguf
+TEMPLATE """<user> {{ .Prompt }}
+<loom>"""
+PARAMETER stop "<|endoftext|>"
+PARAMETER stop "<user>"
+PARAMETER temperature 0.8
+PARAMETER top_k 50

README.md

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 ---
 license: mit
+language: en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- tiny-model
+- llama
+- from-scratch
+- tool-use
+- calibrated-honesty
+- humble-ai
+- gguf
+widget:
+- text: "<user> who are you\n<loom>"
+  example_title: "Identity"
+- text: "<user> what time is it in berlin\n<loom>"
+  example_title: "Tool call"
+- text: "<user> what's my sister's name\n<loom>"
+  example_title: "Calibration"
 ---
+
+<div align="center">
+<img src="banner.jpg" alt="Loom Weave 2" width="480">
+</div>
+
+# Loom Weave 2
+
+<img src="logo.jpg" alt="" width="20" height="20" style="border-radius:4px;vertical-align:middle;margin-right:6px;"> **~60M parameters · Textile Labs**
+
+A small model built on a simple bet: at this size, honesty and judgment are more achievable than broad knowledge — and more useful than a model that guesses confidently. Loom Weave 2 doesn't know much. It knows what it doesn't know, and it knows when to reach for a tool instead of inventing an answer.
+
+## Honest limitations — read this first
+
+This is a ~60M parameter model. It does not have broad world knowledge, cannot do multi-step reasoning, cannot reliably do arithmetic itself (it routes arithmetic to a calculator tool instead — see below), and will get plain factual questions wrong if no tool is available to check them.
+
+Measured on an internal probe set, out of the four things this model is trained to do:
+
+| | measured |
+|---|---|
+| States its identity correctly | 93% |
+| Admits it can't know something about you, rather than inventing an answer | 69% |
+| Routes to the correct tool — phrasing close to training | 87% |
+| Routes to the correct tool — phrasing and numbers it has never seen | 77% |
+| Ends its own reply without needing a stop token | 91% |
+
+Read the calibration number carefully: **it still invents an answer to unanswerable personal questions roughly 3 times in 10.** It is meaningfully better at this than guessing, not solved.
+
+**Important — this model is a tool *selector*, not a tool *executor*.** The routing numbers above measure whether it picks the right tool (`search` vs `calc` vs `time`, etc.) — they do not measure whether the query it writes inside the tag is accurate. In particular, the `calc` tool sometimes rewrites the numbers you gave it rather than preserving them exactly. **Your harness should re-derive the tool's arguments from the original user message, not trust the text the model puts inside `<tool>...</tool>`.** Picking the right tool for the job is the reliable part; extracting exact arguments is not.
+
+## The tool protocol
+
+Loom Weave 2 doesn't call tools directly — it emits a request, and whatever runs it (your code) is responsible for actually performing the lookup and continuing the conversation.
+
+When it needs information it doesn't have, it replies with:
+
+```
+<tool>NAME: query</tool>
+```
+
+where `NAME` is one of five fixed tools:
+
+| tool | for |
+|---|---|
+| `search` | general facts, current information, anything lookupable |
+| `calc` | arithmetic |
+| `time` | current time/date in a place |
+| `doc` | a document already in context |
+| `note` | saving something for later |
+
+Your harness performs the actual lookup and continues the conversation with:
+
+```
+<result>the answer goes here</result>
+<loom>
+```
+
+so the model can read the result and reply from it. Without a harness wired up, the model will still emit the tool call — it just won't get an answer back.
+
+## Prompt format
+
+```
+<user> your message here
+<loom>
+```
+
+No trailing space after `<loom>`. Multi-turn conversations just continue the same pattern, appending each new `<user>` / `<loom>` pair.
+
+## Usage — transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tok = AutoTokenizer.from_pretrained("textilelabs/Loom-Weave-2")
+model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Weave-2")
+
+prompt = "<user> who are you\n<loom>"
+ids = tok(prompt, return_tensors="pt", add_special_tokens=False).input_ids
+out = model.generate(ids, max_new_tokens=80, 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=True))
+```
+
+## Usage — Ollama
+
+```bash
+ollama run hf.co/textilelabs/Loom-Weave-2
+```
+
+That pulls the GGUF straight from this repo. Ollama applies the `template` and `params` files also included in this repo — no local setup needed. For raw-mode / API use (`POST /api/generate` with `"raw": true`), format the prompt yourself exactly as shown above in **Prompt format**.
+
+Two GGUF files are provided: `loom-weave-2-q8_0.gguf` (64MB, quantized, recommended for everyday use) and `loom-weave-2-f16.gguf` (120MB, full precision).
+
+## What it's for
+
+- A **tool router** in front of a larger model — cheap, instant, and correctly picks search/calc/time/doc/note without needing an API call itself
+- An **offline or embedded assistant** where the host application supplies the actual tools
+- A **base for fine-tuning** — a small model that already has tool structure and calibrated refusal trained in, rather than a raw base
+
+## What it's not for
+
+General chat, factual Q&A without tools connected, anything needing real reasoning or arithmetic it has to do itself.
+
+## Files
+
+```
+config.json / model.safetensors          the model (transformers format)
+tokenizer.json / tokenizer_config.json   custom BPE tokenizer, 16,384 tokens
+generation_config.json
+loom-weave-2-q8_0.gguf                   quantized GGUF, for Ollama / llama.cpp
+loom-weave-2-f16.gguf                    full-precision GGUF
+template / params                        read automatically by `ollama run hf.co/...`
+Modelfile                                for building locally: ollama create loom-weave-2 -f Modelfile
+```
+
+## License
+
+MIT. See `LICENSE`.

loom-weave-2-f16.gguf

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+oid sha256:4d3210bf984373cc0dbcc9eeb924c73aac372cba5614e883f77458b8def1af46
+size 119909056

loom-weave-2-q8_0.gguf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ee6d79a3930599310edb99ff5fc1fb4fd349dedf4cb3e20593f31cd0ee4d245
+size 63998656

params

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+{
+  "stop": ["<|endoftext|>", "<user>"],
+  "temperature": 0.8,
+  "top_k": 50
+}

template

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+<user> {{ .Prompt }}
+<loom>