Instructions to use leoncca/Qwen3.8-Flash-Next-AWQ-g32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use leoncca/Qwen3.8-Flash-Next-AWQ-g32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leoncca/Qwen3.8-Flash-Next-AWQ-g32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leoncca/Qwen3.8-Flash-Next-AWQ-g32",
		"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/leoncca/Qwen3.8-Flash-Next-AWQ-g32

SGLang

How to use leoncca/Qwen3.8-Flash-Next-AWQ-g32 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 "leoncca/Qwen3.8-Flash-Next-AWQ-g32" \
    --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": "leoncca/Qwen3.8-Flash-Next-AWQ-g32",
		"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 "leoncca/Qwen3.8-Flash-Next-AWQ-g32" \
        --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": "leoncca/Qwen3.8-Flash-Next-AWQ-g32",
		"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 leoncca/Qwen3.8-Flash-Next-AWQ-g32 with Docker Model Runner:
```
docker model run hf.co/leoncca/Qwen3.8-Flash-Next-AWQ-g32
```

Qwen3.8-Flash-Next-AWQ-g32 / model-00005-of-00010.safetensors

leoncca

Add files using upload-large-folder tool

c55ffb9 verified 4 days ago

Download

History Blame Contribute Delete

8.59 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 3266c14940fba182745802eda4f41706ccc5ea573e1db9367522c21102b48595
Size of remote file:: 8.59 GB
SHA256:: 304b241abbf9ec2b4c39fb49574cbec2556c27d2405ba95d6f5005bc6ba1648e

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.