1.7 KiB
1.7 KiB
language, license, base_model, tags, pipeline_tag
| language | license | base_model | tags | pipeline_tag | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| en | apache-2.0 | Qwen/Qwen2.5-Coder-7B-Instruct |
|
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 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.