Instructions to use roadofriot/MindSparQ-Coder-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use roadofriot/MindSparQ-Coder-1.5B 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 roadofriot/MindSparQ-Coder-1.5B:F16
# Run inference directly in the terminal:
llama cli -hf roadofriot/MindSparQ-Coder-1.5B:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf roadofriot/MindSparQ-Coder-1.5B:F16
# Run inference directly in the terminal:
llama cli -hf roadofriot/MindSparQ-Coder-1.5B:F16

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 roadofriot/MindSparQ-Coder-1.5B:F16
# Run inference directly in the terminal:
./llama-cli -hf roadofriot/MindSparQ-Coder-1.5B:F16

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 roadofriot/MindSparQ-Coder-1.5B:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf roadofriot/MindSparQ-Coder-1.5B:F16

Use Docker

docker model run hf.co/roadofriot/MindSparQ-Coder-1.5B:F16

LM Studio
Jan

vLLM

How to use roadofriot/MindSparQ-Coder-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roadofriot/MindSparQ-Coder-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": "roadofriot/MindSparQ-Coder-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/roadofriot/MindSparQ-Coder-1.5B:F16

Ollama
How to use roadofriot/MindSparQ-Coder-1.5B with Ollama:
```
ollama run hf.co/roadofriot/MindSparQ-Coder-1.5B:F16
```

Unsloth Studio

How to use roadofriot/MindSparQ-Coder-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 roadofriot/MindSparQ-Coder-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 roadofriot/MindSparQ-Coder-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 roadofriot/MindSparQ-Coder-1.5B to start chatting

How to use roadofriot/MindSparQ-Coder-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf roadofriot/MindSparQ-Coder-1.5B:F16

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": "roadofriot/MindSparQ-Coder-1.5B:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use roadofriot/MindSparQ-Coder-1.5B with Docker Model Runner:
```
docker model run hf.co/roadofriot/MindSparQ-Coder-1.5B:F16
```

Lemonade

How to use roadofriot/MindSparQ-Coder-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull roadofriot/MindSparQ-Coder-1.5B:F16

Run and chat with the model

lemonade run user.MindSparQ-Coder-1.5B-F16

List all available models

lemonade list

Hermes Agent

How to use roadofriot/MindSparQ-Coder-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf roadofriot/MindSparQ-Coder-1.5B:F16

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 roadofriot/MindSparQ-Coder-1.5B:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use roadofriot/MindSparQ-Coder-1.5B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf roadofriot/MindSparQ-Coder-1.5B:F16

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 "roadofriot/MindSparQ-Coder-1.5B:F16" \
  --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"

🔮 MindSparQ-Coder-1.5B (2026 Frontier Vibe-Coding & Agentic Edition)

MindSparQ-Coder-1.5B is a production-ready, ultra-fast coding model fine-tuned for modern Vibe Coding, Software Architecture, and Autonomous Agentic Workflows (2026 Ecosystem).

Built by MindSparQ AI, this model combines fine-tuned specialized weights with lightweight footprint, enabling rapid inference on local commodity CPUs/GPUs with zero telemetry or code leakage.

🌟 Key Highlights

⚡ Lightweight & Blazing Fast: Fits in ~1 GB RAM with 4-bit quantization (Q4_K_M) delivering ~15–30 tokens/sec on Intel Core i3 / Ryzen CPUs.
🎨 Elite Vibe-Coding & Frontend Aesthetics: Trained on modern UI patterns (React Glassmorphism, Tailwind CSS, Dark Mode gradients, fluid animations).
🛡️ Anti-Yes-Man Architectural Evaluation: Challenges insecure architectures (e.g. plaintext secrets, vulnerable sync loops) and proposes production-grade alternatives.
🧭 Agentic Tool Calling & Planning: Structured to operate within multi-agent orchestration loops (Planner, Coder, Debugger, Reviewer).
🔒 100% Local & Private: Run locally via llama.cpp, Ollama, or Python with zero outbound data leakage.

📦 Repository Files

File	Size	Description
`model.safetensors`	~2.9 GB	Full unquantized merged model weights (BF16/FP16)
`gguf/quantum_coder_q4_k_m.gguf`	~934 MB	Ultra-fast 4-bit quantized model for llama.cpp / Ollama
`gguf/quantum_coder_f16.gguf`	~2.9 GB	Full-precision 16-bit GGUF model
`tokenizer.json` / `config.json`	-	Standard Hugging Face Transformers configuration files

🚀 Quickstart Usage

1. Using LLaMA.cpp (Fast Local CPU Inference)

./llama-cli -m gguf/quantum_coder_q4_k_m.gguf -p "<|im_start|>user\nWrite a FastAPI rate limiter in Python.<|im_end|>\n<|im_start|>assistant\n" -n 256 --threads 4

2. Using Python Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "roadofriot/MindSparQ-Coder-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

prompt = "<|im_start|>user\nBuild a modern Glassmorphic CSS card token.<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.3)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

📜 License

Apache-2.0 License. Powered by Qwen2.5-Coder architecture & MindSparQ AI Fine-Tuning.

Downloads last month: 327

Safetensors

Model size

2B params

Tensor type

F16

Model tree for roadofriot/MindSparQ-Coder-1.5B

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Quantized

(52)

this model

Quantizations

1 model