Instructions to use smashingtags/nova-router-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smashingtags/nova-router-1.5b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="smashingtags/nova-router-1.5b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("smashingtags/nova-router-1.5b") model = AutoModelForMultimodalLM.from_pretrained("smashingtags/nova-router-1.5b") 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 smashingtags/nova-router-1.5b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="smashingtags/nova-router-1.5b", filename="gguf/eightly-agent-router-q16-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use smashingtags/nova-router-1.5b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf smashingtags/nova-router-1.5b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf smashingtags/nova-router-1.5b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf smashingtags/nova-router-1.5b:Q4_K_M # Run inference directly in the terminal: llama-cli -hf smashingtags/nova-router-1.5b:Q4_K_M
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 smashingtags/nova-router-1.5b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf smashingtags/nova-router-1.5b:Q4_K_M
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 smashingtags/nova-router-1.5b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf smashingtags/nova-router-1.5b:Q4_K_M
Use Docker
docker model run hf.co/smashingtags/nova-router-1.5b:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use smashingtags/nova-router-1.5b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "smashingtags/nova-router-1.5b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "smashingtags/nova-router-1.5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/smashingtags/nova-router-1.5b:Q4_K_M
- SGLang
How to use smashingtags/nova-router-1.5b 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 "smashingtags/nova-router-1.5b" \ --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": "smashingtags/nova-router-1.5b", "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 "smashingtags/nova-router-1.5b" \ --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": "smashingtags/nova-router-1.5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use smashingtags/nova-router-1.5b with Ollama:
ollama run hf.co/smashingtags/nova-router-1.5b:Q4_K_M
- Unsloth Studio
How to use smashingtags/nova-router-1.5b 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 smashingtags/nova-router-1.5b 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 smashingtags/nova-router-1.5b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for smashingtags/nova-router-1.5b to start chatting
- Pi
How to use smashingtags/nova-router-1.5b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf smashingtags/nova-router-1.5b:Q4_K_M
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": "smashingtags/nova-router-1.5b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use smashingtags/nova-router-1.5b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf smashingtags/nova-router-1.5b:Q4_K_M
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 smashingtags/nova-router-1.5b:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use smashingtags/nova-router-1.5b with Docker Model Runner:
docker model run hf.co/smashingtags/nova-router-1.5b:Q4_K_M
- Lemonade
How to use smashingtags/nova-router-1.5b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull smashingtags/nova-router-1.5b:Q4_K_M
Run and chat with the model
lemonade run user.nova-router-1.5b-Q4_K_M
List all available models
lemonade list
Nova Router 1.5B
Nova Router 1.5B is the tool-routing model for Nova, the local AI assistant built into Eight.ly OS. Given a user request, it decides which NAS management tool to call (and with what arguments) across the full Eight.ly tool catalog — Docker, storage, VMs, LXC, file sharing, and system administration.
It is a fine-tune of Qwen2.5-Coder-1.5B and scores 99% tool-selection accuracy on the Eight.ly evaluation suite.
Role in Nova
Nova is router-in-front: this 1.5B model is the single tool router for every conversation. It runs as a hidden dependency — it is installed automatically alongside whichever talker (conversational model) the user chooses, and is never selected directly. The router picks the tool; the talker writes the reply.
user query → intent classifier → retrieval (nomic-embed-text) → Nova Router 1.5B (picks the tool) → talker (writes the reply)
Files
| File | Quant | Size | Use |
|---|---|---|---|
gguf/eightly-agent-router-q16-Q8_0.gguf |
Q8_0 | ~1.6 GB | Shipped quant (Ollama) |
gguf/eightly-agent-router-q16-Q4_K_M.gguf |
Q4_K_M | ~1.0 GB | Smaller alternative |
*.safetensors |
fp16 | — | Merged weights + LoRA adapter |
Usage
ollama pull hf.co/smashingtags/nova-router-1.5b:Q8_0
Within Eight.ly OS this is handled automatically — installing any Nova talker pulls this router and the retrieval embedder with it.
Training
- Base: Qwen2.5-Coder-1.5B
- Data: the Eight.ly tool-calling dataset (41 tools across 6 domains)
- Eval: 99% tool-selection accuracy, 0 false positives on no-tool queries
License
Apache-2.0.
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Model tree for smashingtags/nova-router-1.5b
Base model
Qwen/Qwen2.5-1.5B