Instructions to use distil-labs/distil-email-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distil-labs/distil-email-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="distil-labs/distil-email-classifier") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("distil-labs/distil-email-classifier") model = AutoModelForCausalLM.from_pretrained("distil-labs/distil-email-classifier") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use distil-labs/distil-email-classifier with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="distil-labs/distil-email-classifier", filename="model.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use distil-labs/distil-email-classifier 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 distil-labs/distil-email-classifier # Run inference directly in the terminal: llama cli -hf distil-labs/distil-email-classifier
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf distil-labs/distil-email-classifier # Run inference directly in the terminal: llama cli -hf distil-labs/distil-email-classifier
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 distil-labs/distil-email-classifier # Run inference directly in the terminal: ./llama-cli -hf distil-labs/distil-email-classifier
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 distil-labs/distil-email-classifier # Run inference directly in the terminal: ./build/bin/llama-cli -hf distil-labs/distil-email-classifier
Use Docker
docker model run hf.co/distil-labs/distil-email-classifier
- LM Studio
- Jan
- vLLM
How to use distil-labs/distil-email-classifier with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "distil-labs/distil-email-classifier" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "distil-labs/distil-email-classifier", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/distil-labs/distil-email-classifier
- SGLang
How to use distil-labs/distil-email-classifier 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 "distil-labs/distil-email-classifier" \ --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": "distil-labs/distil-email-classifier", "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 "distil-labs/distil-email-classifier" \ --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": "distil-labs/distil-email-classifier", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use distil-labs/distil-email-classifier with Ollama:
ollama run hf.co/distil-labs/distil-email-classifier
- Unsloth Studio
How to use distil-labs/distil-email-classifier 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 distil-labs/distil-email-classifier 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 distil-labs/distil-email-classifier to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for distil-labs/distil-email-classifier to start chatting
- Pi
How to use distil-labs/distil-email-classifier with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf distil-labs/distil-email-classifier
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "distil-labs/distil-email-classifier" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use distil-labs/distil-email-classifier with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf distil-labs/distil-email-classifier
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default distil-labs/distil-email-classifier
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use distil-labs/distil-email-classifier with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf distil-labs/distil-email-classifier
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "distil-labs/distil-email-classifier" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use distil-labs/distil-email-classifier with Docker Model Runner:
docker model run hf.co/distil-labs/distil-email-classifier
- Lemonade
How to use distil-labs/distil-email-classifier with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull distil-labs/distil-email-classifier
Run and chat with the model
lemonade run user.distil-email-classifier-{{QUANT_TAG}}List all available models
lemonade list
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