Token Classification
PyTorch
Safetensors
GGUF
MLX
English
customs
span-tagging
pii
pii-detection
privacy
data-minimisation
on-device
Synthetic
Instructions to use NagaYu/customs-decomposer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use NagaYu/customs-decomposer with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir customs-decomposer NagaYu/customs-decomposer
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use NagaYu/customs-decomposer 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 NagaYu/customs-decomposer # Run inference directly in the terminal: llama cli -hf NagaYu/customs-decomposer
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf NagaYu/customs-decomposer # Run inference directly in the terminal: llama cli -hf NagaYu/customs-decomposer
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 NagaYu/customs-decomposer # Run inference directly in the terminal: ./llama-cli -hf NagaYu/customs-decomposer
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 NagaYu/customs-decomposer # Run inference directly in the terminal: ./build/bin/llama-cli -hf NagaYu/customs-decomposer
Use Docker
docker model run hf.co/NagaYu/customs-decomposer
- LM Studio
- Jan
- Ollama
How to use NagaYu/customs-decomposer with Ollama:
ollama run hf.co/NagaYu/customs-decomposer
- Unsloth Studio
How to use NagaYu/customs-decomposer 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 NagaYu/customs-decomposer 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 NagaYu/customs-decomposer to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for NagaYu/customs-decomposer to start chatting
- Docker Model Runner
How to use NagaYu/customs-decomposer with Docker Model Runner:
docker model run hf.co/NagaYu/customs-decomposer
- Lemonade
How to use NagaYu/customs-decomposer with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NagaYu/customs-decomposer
Run and chat with the model
lemonade run user.customs-decomposer-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| { | |
| "vocab_size": 481, | |
| "tags": [ | |
| "O", | |
| "B-person_name", | |
| "I-person_name", | |
| "B-email", | |
| "I-email", | |
| "B-phone", | |
| "I-phone", | |
| "B-account_id", | |
| "I-account_id", | |
| "B-device_id", | |
| "I-device_id", | |
| "B-street_address", | |
| "I-street_address", | |
| "B-health_condition", | |
| "I-health_condition", | |
| "B-medication", | |
| "I-medication", | |
| "B-financial_amount", | |
| "I-financial_amount", | |
| "B-employer", | |
| "I-employer", | |
| "B-relationship", | |
| "I-relationship", | |
| "B-home_city", | |
| "I-home_city", | |
| "B-birth_date", | |
| "I-birth_date", | |
| "B-calendar_event", | |
| "I-calendar_event", | |
| "B-travel_date", | |
| "I-travel_date" | |
| ], | |
| "d_model": 128, | |
| "n_heads": 4, | |
| "n_layers": 2, | |
| "d_ff": 256, | |
| "max_len": 128, | |
| "dropout": 0.1, | |
| "architecture": "customs-span-tagger" | |
| } |