---
language: en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
  - spiking-neural-networks
  - neuromorphic-computing
  - code-generation
  - lora
  - unsloth
  - nuro-sdk
pipeline_tag: text-generation
---

# 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](https://vantar.xyz) 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