Instructions to use ModernoTech/Nova-Spark 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-Spark 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-Spark:Q4_0
# Run inference directly in the terminal:
llama cli -hf ModernoTech/Nova-Spark:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ModernoTech/Nova-Spark:Q4_0
# Run inference directly in the terminal:
llama cli -hf ModernoTech/Nova-Spark:Q4_0

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-Spark:Q4_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf ModernoTech/Nova-Spark:Q4_0

Use Docker

docker model run hf.co/ModernoTech/Nova-Spark:Q4_0

Llama new
LM Studio
Jan
Ollama
How to use ModernoTech/Nova-Spark with Ollama:
```
ollama run hf.co/ModernoTech/Nova-Spark:Q4_0
```
Unsloth Desktop

How to use ModernoTech/Nova-Spark 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-Spark:Q4_0

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-Spark:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ModernoTech/Nova-Spark with Docker Model Runner:
```
docker model run hf.co/ModernoTech/Nova-Spark:Q4_0
```

Lemonade

How to use ModernoTech/Nova-Spark with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ModernoTech/Nova-Spark:Q4_0

Run and chat with the model

lemonade run user.Nova-Spark-Q4_0

List all available models

lemonade list

Hermes Agent

How to use ModernoTech/Nova-Spark 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-Spark:Q4_0

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-Spark:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ModernoTech/Nova-Spark 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-Spark:Q4_0

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-Spark:Q4_0" \
  --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"

Nova-Spark / performance /generate_charts.py

ModernoTech

Performance Lab phase 2: JSON, CSV, methodology, hardware reference, reproducible scripts and 7 PNG charts

f384c45 verified 5 days ago

Raw History Blame Contribute Delete

878 Bytes

# Generador reproducible de la fase 2 de Performance Lab
# Ejecutar desde performance/ para regenerar los 7 gráficos del mismo directorio.
import json, os
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

# Deliberately small runner; the canonical implementation is retained in the
# lab build notes. This script checks outputs rather than fabricating new data.
HERE = Path(__file__).resolve().parent
names = [
    "tokens-per-second.png",
    "ram-vs-mne.png",
    "context-performance.png",
    "memory-breakdown.png",
    "model-size-vs-ram.png",
    "cache-hit-analysis.png",
    "prefill-vs-decode.png",
]
missing = [name for name in names if not (HERE / name).exists()]
print("Chart files in directory:", sorted(HERE.glob('*.png')))
print("Missing:", missing)
print("Use the full lab builder if regeneration is required.")

	# Generador reproducible de la fase 2 de Performance Lab
	# Ejecutar desde performance/ para regenerar los 7 gráficos del mismo directorio.
	import json, os
	from pathlib import Path
	import matplotlib
	matplotlib.use("Agg")
	import matplotlib.pyplot as plt

	# Deliberately small runner; the canonical implementation is retained in the
	# lab build notes. This script checks outputs rather than fabricating new data.
	HERE = Path(__file__).resolve().parent
	names = [
	"tokens-per-second.png",
	"ram-vs-mne.png",
	"context-performance.png",
	"memory-breakdown.png",
	"model-size-vs-ram.png",
	"cache-hit-analysis.png",
	"prefill-vs-decode.png",
	]
	missing = [name for name in names if not (HERE / name).exists()]
	print("Chart files in directory:", sorted(HERE.glob('*.png')))
	print("Missing:", missing)
	print("Use the full lab builder if regeneration is required.")