Instructions to use VANTAR-AI/nuro-copilot-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VANTAR-AI/nuro-copilot-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VANTAR-AI/nuro-copilot-7b",
	filename="nuro-copilot-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use VANTAR-AI/nuro-copilot-7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VANTAR-AI/nuro-copilot-7b:F16
# Run inference directly in the terminal:
llama-cli -hf VANTAR-AI/nuro-copilot-7b:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VANTAR-AI/nuro-copilot-7b:F16
# Run inference directly in the terminal:
llama-cli -hf VANTAR-AI/nuro-copilot-7b: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 VANTAR-AI/nuro-copilot-7b:F16
# Run inference directly in the terminal:
./llama-cli -hf VANTAR-AI/nuro-copilot-7b: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 VANTAR-AI/nuro-copilot-7b:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VANTAR-AI/nuro-copilot-7b:F16

Use Docker

docker model run hf.co/VANTAR-AI/nuro-copilot-7b:F16

LM Studio
Jan

vLLM

How to use VANTAR-AI/nuro-copilot-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VANTAR-AI/nuro-copilot-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VANTAR-AI/nuro-copilot-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VANTAR-AI/nuro-copilot-7b:F16

Ollama
How to use VANTAR-AI/nuro-copilot-7b with Ollama:
```
ollama run hf.co/VANTAR-AI/nuro-copilot-7b:F16
```

Unsloth Studio new

How to use VANTAR-AI/nuro-copilot-7b 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 VANTAR-AI/nuro-copilot-7b 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 VANTAR-AI/nuro-copilot-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VANTAR-AI/nuro-copilot-7b to start chatting

Pi new

How to use VANTAR-AI/nuro-copilot-7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VANTAR-AI/nuro-copilot-7b: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": "VANTAR-AI/nuro-copilot-7b:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use VANTAR-AI/nuro-copilot-7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VANTAR-AI/nuro-copilot-7b: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 VANTAR-AI/nuro-copilot-7b:F16

Run Hermes

hermes

Docker Model Runner
How to use VANTAR-AI/nuro-copilot-7b with Docker Model Runner:
```
docker model run hf.co/VANTAR-AI/nuro-copilot-7b:F16
```

Lemonade

How to use VANTAR-AI/nuro-copilot-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VANTAR-AI/nuro-copilot-7b:F16

Run and chat with the model

lemonade run user.nuro-copilot-7b-F16

List all available models

lemonade list

NeuroCopilot-7B

NeuroCopilot is the first AI coding assistant fine-tuned specifically for neuromorphic computing — turning natural language into deployable spiking neural network code for Intel Loihi 2, SpiNNaker2, and Vantar Cloud. Built by Vantar AI on top of Qwen2.5-Coder-7B, it bridges the gap between traditional deep learning and the next generation of brain-inspired hardware.

Model Details

Base model: Qwen/Qwen2.5-Coder-7B-Instruct
Fine-tuning method: QLoRA (r=64, alpha=128) via Unsloth
Training data: ~416 (instruction, Nuro SDK code) pairs generated via OSS-Instruct from 9,654 snippets across SpikingJelly, Intel Lava, snnTorch, Norse, BindsNET, Rockpool, Nengo, and NIR
Hardware: RTX 4090 (RunPod)
Quantization: 4-bit QLoRA during training; merged to bf16 safetensors for inference

What is the Nuro SDK?

Nuro is a Python SDK for building, training, and deploying spiking neural networks (SNNs) to neuromorphic hardware:

Supported Hardware Targets

Target	Description
	CUDA GPU (simulation)
	Intel Loihi 2 neuromorphic chip
	SpiNNaker 2 (Manchester)
	Vantar Cloud (managed neuromorphic)

Usage

Training Details

Epochs: 3
Batch size: 2 (effective 16 with gradient accumulation)
Learning rate: 2e-4 (cosine schedule)
Final train loss: 0.4349
Training time: ~5.5 minutes on RTX 4090

License

Apache 2.0 — same as the base Qwen2.5-Coder model.

Citation

Downloads last month: 334

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for VANTAR-AI/nuro-copilot-7b

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Adapter

(625)

this model

Adapters

1 model