Instructions to use Noid3a-Labs/Sparky-4B-V1 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 Noid3a-Labs/Sparky-4B-V1 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 Noid3a-Labs/Sparky-4B-V1:Q4_K_M # Run inference directly in the terminal: llama cli -hf Noid3a-Labs/Sparky-4B-V1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Noid3a-Labs/Sparky-4B-V1:Q4_K_M # Run inference directly in the terminal: llama cli -hf Noid3a-Labs/Sparky-4B-V1: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 Noid3a-Labs/Sparky-4B-V1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Noid3a-Labs/Sparky-4B-V1: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 Noid3a-Labs/Sparky-4B-V1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Noid3a-Labs/Sparky-4B-V1:Q4_K_M
Use Docker
docker model run hf.co/Noid3a-Labs/Sparky-4B-V1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Noid3a-Labs/Sparky-4B-V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Noid3a-Labs/Sparky-4B-V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Noid3a-Labs/Sparky-4B-V1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Noid3a-Labs/Sparky-4B-V1:Q4_K_M
- Ollama
How to use Noid3a-Labs/Sparky-4B-V1 with Ollama:
ollama run hf.co/Noid3a-Labs/Sparky-4B-V1:Q4_K_M
- Unsloth Studio
How to use Noid3a-Labs/Sparky-4B-V1 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 Noid3a-Labs/Sparky-4B-V1 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 Noid3a-Labs/Sparky-4B-V1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Noid3a-Labs/Sparky-4B-V1 to start chatting
- Pi
How to use Noid3a-Labs/Sparky-4B-V1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Noid3a-Labs/Sparky-4B-V1: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": "Noid3a-Labs/Sparky-4B-V1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use Noid3a-Labs/Sparky-4B-V1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Noid3a-Labs/Sparky-4B-V1: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 "Noid3a-Labs/Sparky-4B-V1: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"
- Docker Model Runner
How to use Noid3a-Labs/Sparky-4B-V1 with Docker Model Runner:
docker model run hf.co/Noid3a-Labs/Sparky-4B-V1:Q4_K_M
- Lemonade
How to use Noid3a-Labs/Sparky-4B-V1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Noid3a-Labs/Sparky-4B-V1:Q4_K_M
Run and chat with the model
lemonade run user.Sparky-4B-V1-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Noid3a-Labs/Sparky-4B-V1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Noid3a-Labs/Sparky-4B-V1: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 Noid3a-Labs/Sparky-4B-V1:Q4_K_M
Run Hermes
hermes
- Atomic Chat
Sparky-4B-V1
Sparky-4B-V1 is an ultra-efficient, high-discipline 4B instruction model fine-tuned by Noid3a Labs.
Trained with a focus on strict structural discipline, zero-waffle response generation, and rapid inference execution, Sparky delivers exceptional performance across code generation, agentic tool workflows, and mathematical reasoning within a lightweight sub-5B parameter footprint.
Based on the renowned Qwen2.5-3B base architecture.
Designed for coding, instruction following and assistant tasks normal 4B models cant.
Benchmark Suite
Evaluated using lm-evaluation-harness over local API completion endpoints:
| Model | Benchmark | Category | Score | Evaluation Setting |
|---|---|---|---|---|
| Qwen 2.5 3B | HumanEval | Python Code Generation | 42.10% | 0-Shot (pass@1) |
| Sparky 4B V1 | HumanEval | Python Code Generation | 51.22% | 0-Shot (pass@1) |
| Qwen 2.5 3B Instruct | IFEval | Instruction Following | 42.50% | 0-Shot (Strict) |
| Sparky 4B V1 | IFEval | Instruction Following | 47.32% | 0-Shot (Strict) |
| Qwen 2.5 3B | GSM8K | Multi-Step Math Reasoning | 79.10% | 5-Shot (Flexible Extract) |
| Sparky 4B V1 | GSM8K | Multi-Step Math Reasoning | 61.03% | 5-Shot (Flexible Extract) |
Qwen have not released non instruct benchmarks of IFEval offically source https://qwen.ai/blog?id=qwen2.5-llm
How to install
To run Sparky locally, we recommend using Ollama:
ollama run hf.co/Noid3a-Labs/Sparky-4B-V1:Q4_K_M
You can also install a Q4 or Q3 varient manually from the Files and versions tab on the top navbar
Prompt Format (ChatML)
Sparky utilizes standard ChatML formatting:
<|im_start|>system
You are Sparky, a helpful and precise AI assistant built by Noid3a Labs.<|im_end|>
<|im_start|>user
Write a Python function to check if a string is a palindrome.<|im_end|>
<|im_start|>assistant
This model is a derivative of Qwen and is subject to the Qwen Research License Agreement.
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