Instructions to use textilelabs/Loom-Tapestry-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use textilelabs/Loom-Tapestry-2 with Transformers:

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

pipe = pipeline("text-generation", model="textilelabs/Loom-Tapestry-2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("textilelabs/Loom-Tapestry-2")
model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Tapestry-2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use textilelabs/Loom-Tapestry-2 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-Tapestry-2:F16
# Run inference directly in the terminal:
llama cli -hf textilelabs/Loom-Tapestry-2:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf textilelabs/Loom-Tapestry-2:F16
# Run inference directly in the terminal:
llama cli -hf textilelabs/Loom-Tapestry-2:F16

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-Tapestry-2:F16
# Run inference directly in the terminal:
./llama-cli -hf textilelabs/Loom-Tapestry-2:F16

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-Tapestry-2:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf textilelabs/Loom-Tapestry-2:F16

Use Docker

docker model run hf.co/textilelabs/Loom-Tapestry-2:F16

LM Studio
Jan

vLLM

How to use textilelabs/Loom-Tapestry-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "textilelabs/Loom-Tapestry-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textilelabs/Loom-Tapestry-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/textilelabs/Loom-Tapestry-2:F16

SGLang

How to use textilelabs/Loom-Tapestry-2 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-Tapestry-2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textilelabs/Loom-Tapestry-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Tapestry-2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textilelabs/Loom-Tapestry-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use textilelabs/Loom-Tapestry-2 with Ollama:
```
ollama run hf.co/textilelabs/Loom-Tapestry-2:F16
```
Unsloth Desktop
Docker Model Runner
How to use textilelabs/Loom-Tapestry-2 with Docker Model Runner:
```
docker model run hf.co/textilelabs/Loom-Tapestry-2:F16
```

Lemonade

How to use textilelabs/Loom-Tapestry-2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull textilelabs/Loom-Tapestry-2:F16

Run and chat with the model

lemonade run user.Loom-Tapestry-2-F16

List all available models

lemonade list

Atomic Chat

Loom-Tapestry-2 / UPLOAD-STEPS.txt

textilelabs

Upload 17 files

ce0f75c verified about 1 month ago

Raw History Blame

2.1 kB

	LOOM TAPESTRY 2 — UPLOAD STEPS
	==============================

	1. Add your two images to this folder FIRST, named exactly:
	banner.jpg
	logo.jpg
	(prompts are in TAPESTRY-2-ARTWORK-PROMPTS.md in the project folder.
	The name must be .jpg — a .jpeg mismatch broke the images on an earlier release.)

	2. On huggingface.co, create a NEW model repo:
	textilelabs/Loom-Tapestry-2
	Owner: textilelabs · Public · License: MIT

	3. Files and versions -> Add file -> Upload files.
	Drag in ALL the CONTENTS of this folder (not the folder itself).
	Do NOT upload UPLOAD-STEPS.txt — it is for you, not the repo.

	4. Commit. That is it — no settings to change.
	The tags, widget examples and licence all come from the top of README.md.

	5. Verify (takes a minute):
	ollama run hf.co/textilelabs/Loom-Tapestry-2 "who are you"
	Expected: "Loom Tapestry 2, a small model by Textile Labs." or similar.
	If Ollama says it cannot find a template, the template/params files did not
	upload — re-add just those two.

	6. Optional: add it to your "Loom-Spark" collection so it sits with the family.


	WHAT IS IN HERE (14 files + your 2 images = 16)
	-----------------------------------------------
	README.md the model card (tags/widgets are in its header)
	config.json model architecture
	model.safetensors the weights, 87 MB
	generation_config.json
	tokenizer.json custom 4,096-token BPE
	tokenizer_config.json
	special_tokens_map.json
	loom-tapestry-2-f16.gguf 44 MB, for Ollama / llama.cpp
	template Ollama reads this automatically (defaults tools:off)
	params Ollama reads this automatically
	Modelfile only needed for `ollama create` locally
	harness.py runnable harness — does the lookups
	ATTRIBUTION.md licence credits, required by the corpora
	LICENSE MIT
	banner.jpg <- you add
	logo.jpg <- you add