Text Generation
Transformers
Safetensors
GGUF
English
llama
tiny-model
from-scratch
conversational
multi-turn
tool-use
agent-harness
retrieval-augmented
attribution
calibrated-honesty
humble-ai
philosophy-of-mind
small-language-model
cpu-trained
muon
text-generation-inference
Instructions to use textilelabs/Loom-Tapestry-3-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textilelabs/Loom-Tapestry-3-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="textilelabs/Loom-Tapestry-3-Flash")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("textilelabs/Loom-Tapestry-3-Flash") model = AutoModelForCausalLM.from_pretrained("textilelabs/Loom-Tapestry-3-Flash", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use textilelabs/Loom-Tapestry-3-Flash 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-3-Flash:F16 # Run inference directly in the terminal: llama cli -hf textilelabs/Loom-Tapestry-3-Flash: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-3-Flash:F16 # Run inference directly in the terminal: llama cli -hf textilelabs/Loom-Tapestry-3-Flash:F16
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf textilelabs/Loom-Tapestry-3-Flash:F16 # Run inference directly in the terminal: ./build/bin/llama cli -hf textilelabs/Loom-Tapestry-3-Flash:F16
Use Docker
docker model run hf.co/textilelabs/Loom-Tapestry-3-Flash:F16
- Llama new
- LM Studio
- Jan
- vLLM
How to use textilelabs/Loom-Tapestry-3-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "textilelabs/Loom-Tapestry-3-Flash" # 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-3-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/textilelabs/Loom-Tapestry-3-Flash:F16
- SGLang
How to use textilelabs/Loom-Tapestry-3-Flash 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-3-Flash" \ --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-3-Flash", "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-3-Flash" \ --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-3-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use textilelabs/Loom-Tapestry-3-Flash with Ollama:
ollama run hf.co/textilelabs/Loom-Tapestry-3-Flash:F16
- Unsloth Desktop
- Docker Model Runner
How to use textilelabs/Loom-Tapestry-3-Flash with Docker Model Runner:
docker model run hf.co/textilelabs/Loom-Tapestry-3-Flash:F16
- Lemonade
How to use textilelabs/Loom-Tapestry-3-Flash with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull textilelabs/Loom-Tapestry-3-Flash:F16
Run and chat with the model
lemonade run user.Loom-Tapestry-3-Flash-F16
List all available models
lemonade list
- Atomic Chat
Download .gitattributes from textilelabs/Loom-Tapestry-3-Flash: direct link, hf CLI and curl.
- Browser
- Download file 1.84 kB
-
https://huggingface.co/textilelabs/Loom-Tapestry-3-Flash/resolve/main/.gitattributes
- Command line
-
hf download hf://textilelabs/Loom-Tapestry-3-Flash/.gitattributes
-
curl -L -o .gitattributes https://huggingface.co/textilelabs/Loom-Tapestry-3-Flash/resolve/main/.gitattributes
1.84 kB
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