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Vishva007/
Qwen3.5-4B-W4A16-AutoRound

Name: Qwen3.5-4B-W4A16-AutoRound
Availability: InStock

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Vishva007/Qwen3.5-4B-W4A16-AutoRound

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

Quantization Details

Parameter	Value
Method	AutoRound (W4A16)
Group Size	16
Symmetric	Yes
Iterations	1000
Calibration Samples	512
Sequence Length	4096
Torch Compile	Enabled

Key Notes

High accuracy configuration — 1000 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.
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

MTP / Speculative Decoding

This model supports Multi-Token Prediction (MTP) for improved inference throughput using speculative decoding.

When serving with compatible backends (e.g., vLLM), enable MTP using:

--speculative_config '{"method":"mtp","num_speculative_tokens":1}'

Notes

num_speculative_tokens=1 is a stable default for balancing speed and accuracy.
You can experiment with higher values for better throughput, depending on your hardware and latency requirements.

Usage

This model is compatible with transformers and backends that support AutoRound GPTQ-format weights (e.g., vLLM, SGLang, AutoGPTQ). 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