Instructions to use ModernoTech/Nova-Nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ModernoTech/Nova-Nano 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 ModernoTech/Nova-Nano:Q4_K_M # Run inference directly in the terminal: llama cli -hf ModernoTech/Nova-Nano:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ModernoTech/Nova-Nano:Q4_K_M # Run inference directly in the terminal: llama cli -hf ModernoTech/Nova-Nano:Q4_K_M
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 ModernoTech/Nova-Nano:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf ModernoTech/Nova-Nano:Q4_K_M
Use Docker
docker model run hf.co/ModernoTech/Nova-Nano:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use ModernoTech/Nova-Nano with Ollama:
ollama run hf.co/ModernoTech/Nova-Nano:Q4_K_M
- Unsloth Desktop
- Pi
How to use ModernoTech/Nova-Nano with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ModernoTech/Nova-Nano:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ModernoTech/Nova-Nano:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ModernoTech/Nova-Nano with Docker Model Runner:
docker model run hf.co/ModernoTech/Nova-Nano:Q4_K_M
- Lemonade
How to use ModernoTech/Nova-Nano with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ModernoTech/Nova-Nano:Q4_K_M
Run and chat with the model
lemonade run user.Nova-Nano-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use ModernoTech/Nova-Nano with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ModernoTech/Nova-Nano: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 ModernoTech/Nova-Nano:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ModernoTech/Nova-Nano with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ModernoTech/Nova-Nano:Q4_K_M
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 "ModernoTech/Nova-Nano:Q4_K_M" \ --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"
Performance Lab phase 2: JSON, CSV, methodology, hardware reference, reproducible scripts and 7 PNG charts
440128a verified |
Download performance/hardware-reference.md from ModernoTech/Nova-Nano: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/ModernoTech/Nova-Nano/resolve/main/performance/hardware-reference.md
- Command line
-
hf download hf://ModernoTech/Nova-Nano/performance/hardware-reference.md
-
curl -L -o hardware-reference.md https://huggingface.co/ModernoTech/Nova-Nano/resolve/main/performance/hardware-reference.md
1.03 kB
| # Hardware y ancho de banda de referencia | |
| | Equipo | RAM (GB) | Ancho de banda (GiB/s) | Estado | | |
| |---|---:|---:|---| | |
| | MacBook Air 2014 | 4 | 25.6 | Estimación teórica dual-channel LPDDR3-1600 | | |
| | Lenovo Yoga 720 (i7-7700HQ) | 8 | 34.1 | Estimación teórica DDR4-2400 / 2666 | | |
| | MacBook Neo A18 Pro | 8 | 60.0 | Valor institucional (no validado por benchmark HF) | | |
| | MacBook Air M4 | 16 | 120.0 | Valor institucional (no validado por benchmark de memoria) | | |
| ## Disco SSD de MNE | |
| - Ancho de banda efectivo asumido: 2.0 GiB/s. | |
| - Latencia efectiva, cola SDS y prefetch no se miden. | |
| - Necesario un benchmark secuencial por partícula antes de prometer tiempos de inferencia. | |
| ## Hardware real probado | |
| Solo está documentada la siguiente ejecución: | |
| - Macbook Air M4, 16 GB, llama-cli, archivo Core GGUF, contexto 512. | |
| - load observed: 3.16 s. | |
| - prefill: 49.5 tok/s en el log local. | |
| - decode: 34.5 tok/s en el log local. | |
| - config flags: `-ngl 0`, `--ctx-size 512`, `--temp 0.7`. | |
| - Otras plataformas: no hay datos de esta fase. | |