This is a W4A16 (4-bit weight, 16-bit activation) AutoRound-format quantized version of Qwen/Qwen3.5-9B, produced using AutoRound — Intel's sign gradient descent based quantization method designed for production-grade accuracy retention.

Vision Tower (quant_nontext_module): False (Kept in BF16 to preserve visual reasoning and OCR precision)
Special Modules (layer_config): Multi-Token Prediction (mtp, mtp.fc) kept in native bfloat16

Quantization Details

Parameter	Value
Method	AutoRound (W4A16, AutoRound format)
Group Size	32
Symmetric	Yes
Iterations	800
Calibration Samples	512
Sequence Length	4096
Torch Compile	Enabled

Key Notes

AutoRound format — Exported in the standard AutoRound format for broad ecosystem compatibility.
High accuracy configuration — 800 iterations with 512 calibration samples targets production-grade quality with minimal degradation from the base model.
W4A16 — Weights are quantized to 4-bit integers; activations remain in FP16 for inference stability.
~50% memory reduction compared to the FP16 base model, enabling deployment on consumer and mid-range GPUs.

Usage

This model is compatible with transformers, AutoRound, vLLM, and SGLang — any backend supporting AutoRound-format weights works out of the box. For full model details, architecture, and capabilities, refer to the base model page.

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PyTorch 2.13

Template	CUDA Version	Docker Image	Template ID	Deploy
PyTorch 2.13 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.13-runpod`	`gmlupxnxfk`	Deploy to RunPod
PyTorch 2.13 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.13-runpod`	`y3j8xvk4f4`	Deploy to RunPod
PyTorch 2.13 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.13-runpod`	`vigpissn5w`	Deploy to RunPod

PyTorch 2.12

Template	CUDA Version	Docker Image	Template ID	Deploy
PyTorch 2.12 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.12-runpod`	`ctmz86zmf0`	Deploy to RunPod
PyTorch 2.12 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.12-runpod`	`qjko5yiwzi`	Deploy to RunPod
PyTorch 2.12 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.12-runpod`	`ifg6xmye0f`	Deploy to RunPod