Instructions to use textilelabs/Loom-Spark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use textilelabs/Loom-Spark with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="textilelabs/Loom-Spark")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("textilelabs/Loom-Spark")
model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Spark", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use textilelabs/Loom-Spark with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf textilelabs/Loom-Spark:F32
# Run inference directly in the terminal:
llama cli -hf textilelabs/Loom-Spark:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf textilelabs/Loom-Spark:F32
# Run inference directly in the terminal:
llama cli -hf textilelabs/Loom-Spark:F32

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf textilelabs/Loom-Spark:F32
# Run inference directly in the terminal:
./llama-cli -hf textilelabs/Loom-Spark:F32

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf textilelabs/Loom-Spark:F32
# Run inference directly in the terminal:
./build/bin/llama-cli -hf textilelabs/Loom-Spark:F32

Use Docker

docker model run hf.co/textilelabs/Loom-Spark:F32

LM Studio
Jan

vLLM

How to use textilelabs/Loom-Spark with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "textilelabs/Loom-Spark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textilelabs/Loom-Spark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/textilelabs/Loom-Spark:F32

SGLang

How to use textilelabs/Loom-Spark with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "textilelabs/Loom-Spark" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textilelabs/Loom-Spark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "textilelabs/Loom-Spark" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textilelabs/Loom-Spark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use textilelabs/Loom-Spark with Ollama:
```
ollama run hf.co/textilelabs/Loom-Spark:F32
```

Unsloth Studio

How to use textilelabs/Loom-Spark with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for textilelabs/Loom-Spark to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for textilelabs/Loom-Spark to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for textilelabs/Loom-Spark to start chatting

Docker Model Runner
How to use textilelabs/Loom-Spark with Docker Model Runner:
```
docker model run hf.co/textilelabs/Loom-Spark:F32
```

Lemonade

How to use textilelabs/Loom-Spark with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull textilelabs/Loom-Spark:F32

Run and chat with the model

lemonade run user.Loom-Spark-F32

List all available models

lemonade list

Atomic Chat

Loom-Spark / harness /README.md

textilelabs

Upload 24 files

254754a verified 1 day ago

preview code

Raw

History Blame Contribute Delete

3.83 kB

Loom Spark Harness

The agent harness for Loom Spark (Textile Labs). Loom Spark is a ~7.6M-parameter model trained to be humble, self-aware, and curious instead of encyclopedic. Its one real superpower is forming clean search queries — this harness is what turns that into actual internet access.

It implements the tool protocol the model was trained on:

<tools:on> | <tools:off>     mode header, set by the harness each session
<lookup>query</lookup>       emitted by the model; harness runs a real search
<result>text</result>        injected by the harness; model then summarizes

With tools off, the model never emits lookup tags (banned at the logits level and string-stripped as a safety net). With tools on, the harness owns the <result> slot entirely — the model cannot hallucinate one.

Install

pip install .          # from this folder (or the harness/ folder of the HF repo)

Works on Python 3.9 through 3.13 (CPU torch wheels exist for all of them). Optional: pip install ".[logo]" adds Pillow so the terminal banner renders the Textile Labs avatar in truecolor blocks; without it you get a clean ASCII mark instead.

Terminal chat

loom-chat                          # auto-finds a local export dir or pulls
                                   # TextileLabs/loom-spark from the hub
loom-chat --offline                # start with tools off
loom-chat --backend duckduckgo     # search backend: mock | duckduckgo
loom-chat --model path/or/org-name
loom-chat --plain                  # no colors / artwork

In-chat commands: /online /offline /backend NAME /reset /quit.

Web GUI

loom-web --port 7860               # then open http://localhost:7860

Local-only chat page (stdlib HTTP server) with a tools-on/off switch, token streaming, and a live feed of internet lookups.

Model resolution

--model wins; else $LOOM_MODEL; else an export/loom-spark-hf directory next to the package or in the cwd; else the hub id TextileLabs/loom-spark. Accepted values: HF export directory, .pt training checkpoint (needs the training repo importable), or any org/name hub id.

Search backends

name	internet	notes
`mock`	no	canned curriculum-style results; demos & tests
`duckduckgo`	yes	keyless scrape of DDG's HTML endpoint; stdlib only

A backend maps query → plain text, or None when unreachable — the model was trained to fall back gracefully on empty results. Add your own by subclassing loomspark_harness.search.base.SearchBackend (e.g. Brave/Serper with an API key) and registering it in search/__init__.py.

The web GUI header and the CLI banner use static/logo.png (the Textile Labs founder's avatar). Replace that file to rebrand.

Troubleshooting

Intel (x86_64) Macs: PyTorch stopped shipping Intel-macOS wheels at 2.2.2, and the newest numpy/transformers are incompatible with it. Install era-matched pins instead of plain pip install torch transformers:

pip install "numpy<2" "torch==2.2.2" "transformers<5" tokenizers pillow

Using the agent from Python

from loomspark_harness.loader import load_model_and_tokenizer
from loomspark_harness.agent import LoomAgent
from loomspark_harness.search import get_backend

model, tok, block = load_model_and_tokenizer("TextileLabs/loom-spark")
agent = LoomAgent(model, tok, backend=get_backend("duckduckgo"),
                  online=True, block_size=block)
print(agent.reply("what year did the Titanic sink?")["text"])

reply() returns {"text", "query", "result"}; pass on_event= for a callback stream of token / preamble / lookup / result / done events (the web GUI is built on this).

License

MIT — see LICENSE.