Text Generation
Transformers
Safetensors
GGUF
English
gpt2
tiny-model
from-scratch
tool-use
agent-harness
humble-ai
philosophy-of-mind
text-generation-inference
Instructions to use textilelabs/Loom-Spark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Upload 2 files
Browse files- README.md +12 -5
- ollama/Modelfile +3 -2
README.md
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```bash
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ollama pull hf.co/textilelabs/Loom-Spark
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ollama run loom-spark "hi"
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Note: without a wrapper that executes `<lookup>` calls and splices `<result>`
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blocks back in, GGUF/Ollama runners get the model's honest "I don't know, but
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```bash
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ollama pull hf.co/textilelabs/Loom-Spark
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ollama create loom-spark -f Modelfile
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ollama run loom-spark "hi"
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re-wrapped in `<user>`/`<loom>` markers) and stops generation cleanly. That
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gives the offline persona: greetings, identity, honest deferrals, made-up words.
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Two honest caveats: if a reply ends in a `<lookup>…</lookup>` line, that's the
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model saying *"I'd search for this"* — raw runners can't execute searches, so
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for real internet answers use the harness (Option C). And at temperature 0.85 a
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7M model occasionally misreads intent ("whats your name?" sometimes gets a
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philosophy answer; ask again or drop `--temperature 0.7`). Both quirks shrink
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in Loom Spark v2's curriculum.
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Note: without a wrapper that executes `<lookup>` calls and splices `<result>`
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TEMPLATE """<tools:off>
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<user> {{ .
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<loom>
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FROM hf.co/textilelabs/Loom-Spark
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TEMPLATE """<tools:off>
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{{ range .Messages }}{{ if eq .Role "user" }}<user> {{ .Content }}
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{{ else }}<loom> {{ .Content }}<|endoftext|>
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{{ end }}{{ end }}<loom>"""
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