README.md

metadata

base_model:
  - Qwen/Qwen3.5-2B
license: apache-2.0

Vishva007/Qwen3.5-2B-W4A16-AutoRound

This is a W4A16 (4-bit weight, 16-bit activation) quantized version of Qwen/Qwen3.5-2B, 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	32
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.
• MTP (Multi-Token Prediction) Enabled — Supports speculative decoding for faster inference.

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":3}'

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.

🚀 Deploy on RunPod

One-click launch environments pre-configured with PyTorch, CUDA, and dependencies for fine-tuning or quantization.

🎁 Need GPU compute? Sign up via RunPod and get $5–$500 in free credits when you add your first $10.

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
PyTorch 2.13 (CUDA 13.0)	13.0	vishva123/cuda-13.0-pytorch-2.13-runpod	y3j8xvk4f4
PyTorch 2.13 (CUDA 13.2)	13.2	vishva123/cuda-13.2-pytorch-2.13-runpod	vigpissn5w

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
PyTorch 2.12 (CUDA 13.0)	13.0	vishva123/cuda-13.0-pytorch-2.12-runpod	qjko5yiwzi
PyTorch 2.12 (CUDA 13.2)	13.2	vishva123/cuda-13.2-pytorch-2.12-runpod	ifg6xmye0f

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