Instructions to use ModernoTech/Nova-Core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ModernoTech/Nova-Core 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-Core:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ModernoTech/Nova-Core: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-Core:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ModernoTech/Nova-Core: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-Core:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf ModernoTech/Nova-Core:Q4_K_M

Use Docker

docker model run hf.co/ModernoTech/Nova-Core:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use ModernoTech/Nova-Core with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ModernoTech/Nova-Core"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ModernoTech/Nova-Core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ModernoTech/Nova-Core:Q4_K_M

Ollama
How to use ModernoTech/Nova-Core with Ollama:
```
ollama run hf.co/ModernoTech/Nova-Core:Q4_K_M
```
Unsloth Desktop

How to use ModernoTech/Nova-Core 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-Core: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-Core:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ModernoTech/Nova-Core with Docker Model Runner:
```
docker model run hf.co/ModernoTech/Nova-Core:Q4_K_M
```

Lemonade

How to use ModernoTech/Nova-Core with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ModernoTech/Nova-Core:Q4_K_M

Run and chat with the model

lemonade run user.Nova-Core-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ModernoTech/Nova-Core 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-Core: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-Core:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ModernoTech/Nova-Core 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-Core: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-Core: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"

ModernoTech commited on 5 days ago

Commit

c813382

verified ·

1 Parent(s): d2587d7

Nova Core 2.0 hardware reference and owner-reported result only

Browse files

Files changed (1) hide show

performance/hardware-reference.md +11 -16

performance/hardware-reference.md CHANGED Viewed

@@ -1,22 +1,17 @@
 # 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.

 # Hardware y ancho de banda de referencia
+| Equipo | RAM (GB) | Ancho (GiB/s) | Estado |
 |---|---:|---:|---|
 | MacBook Air 2014 | 4 | 25.6 | Estimación teórica dual-channel LPDDR3-1600 |
+| Lenovo Yoga 720 | 8 | 34.1 | Estimación teórica DDR4-2400/2666 |
+| MacBook Neo A18 Pro | 8 | 60.0 | Valor institucional no validado |
+| MacBook Air M4 | 16 | 120.0 | Valor institucional no validado |
+## Referencia de propietario
+- MacBook Air M4, 16 GB memoria unificada: **225 tok/s**.
+- Clase: `OWNER_REPORTED_RESULT`.
+- Runtime, contexto, batch, ubatch, Metal y método de medición: no documentados en este repositorio.
+## SSD para MNE
+- Ancho efectivo asumido: 2.0 GiB/s.
+- Latencia, cola y prefetch: no medidos.