Instructions to use Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ

SGLang

How to use Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ with Docker Model Runner:
```
docker model run hf.co/Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ
```

Qwen3.8-4B-Distill (W4A16 Quantized via AutoRound)

This repository contains a W4A16 (4-bit weights, 16-bit activations) quantized version of empero-ai/Qwen3.8-4B-Distill, quantized using Intel's AutoRound algorithm.

⚡ Quantization Details

Calibrated and quantized with fine-grained group sizes and high iteration depth to preserve reasoning traces (<think> blocks) and multimodal capabilities:

Algorithm: Intel AutoRound
Precision / Scheme: W4A16 (4-bit weights, 16-bit activations)
Group Size: 32 (fine-grained reconstruction fidelity)
Symmetric (sym): True
Calibration Samples (nsamples): 512
Sequence Length (seqlen): 4096
Tuning Iterations (iters): 1000 (Production-grade accuracy)
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

📦 Available Formats

Depending on your inference engine, choose the appropriate repository:

AutoRound Format: Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound
AutoGPTQ Format: Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ
LLM-Compressor / Compressed-Tensors Format: Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-LLM-Compressor

🚀 Usage & Quickstart

1. High-Throughput Serving via vLLM

# Using the GPTQ build
vllm serve Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ \
    --dtype bfloat16 \
    --max-model-len 8192 \
    --gpu-memory-utilization 0.90

📊 VRAM & Performance Benefits

Original Model (BF16): ~8–10 GB VRAM required for full context inference
Quantized Model (W4A16 Group 32): ~2.5–3.5 GB VRAM (runs comfortably on 4GB/6GB consumer GPUs, laptops, and edge devices)
Throughput: Lowers memory bandwidth pressure, accelerating token generation speeds during extended chain-of-thought (<think>) reasoning.

🚀 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
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
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`