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-AutoRound-GPTQ / quantization_config.json

Vishva007

Upload folder using huggingface_hub

d17e456 verified 3 days ago

Raw

History Blame

544 Bytes

	{
	"bits": 4,
	"data_type": "int",
	"group_size": 32,
	"sym": true,
	"iters": 1000,
	"static_attention_granularity": "tensor",
	"static_kv_granularity": "tensor",
	"autoround_version": "0.16.0",
	"dynamic": {
	"+:.mtp.": {
	"bits": 4,
	"group_size": 32,
	"sym": true
	},
	"+:.mtp\\.fc.": {
	"bits": 4,
	"group_size": 32,
	"sym": true
	}
	},
	"lm_head": false,
	"provider": "auto-round",
	"quant_method": "gptq",
	"desc_act": false,
	"true_sequential": false,
	"damp_percent": 0.01
	}