Instructions to use Inferact/Qwen3.8-Flash-Next-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Inferact/Qwen3.8-Flash-Next-NVFP4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Inferact/Qwen3.8-Flash-Next-NVFP4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Inferact/Qwen3.8-Flash-Next-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("Inferact/Qwen3.8-Flash-Next-NVFP4", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Inferact/Qwen3.8-Flash-Next-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Inferact/Qwen3.8-Flash-Next-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Inferact/Qwen3.8-Flash-Next-NVFP4",
		"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/Inferact/Qwen3.8-Flash-Next-NVFP4

SGLang

How to use Inferact/Qwen3.8-Flash-Next-NVFP4 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 "Inferact/Qwen3.8-Flash-Next-NVFP4" \
    --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": "Inferact/Qwen3.8-Flash-Next-NVFP4",
		"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 "Inferact/Qwen3.8-Flash-Next-NVFP4" \
        --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": "Inferact/Qwen3.8-Flash-Next-NVFP4",
		"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 Inferact/Qwen3.8-Flash-Next-NVFP4 with Docker Model Runner:
```
docker model run hf.co/Inferact/Qwen3.8-Flash-Next-NVFP4
```

vLLM Success Recipe Using Docker Compose in a WSL2 Environment

by ccchhh - opened Aug 29

Discussion

ccchhh

Aug 29

Win11 WSL2 Ubuntu 24
RTX PRO 6000

services:
  vllm-qwen:
    image: vllm/vllm-openai:qwen38-flash-next
    container_name: vllm-qwen3.8-flash-nvfp4
    restart: unless-stopped
    ports:
      - "8000:8000"
    volumes:
      - /mnt/d/MODEL/Qwen3.8-Flash-Next-NVFP4:/model:ro
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=all
      - VLLM_LOGGING_LEVEL=INFO
      - VLLM_USE_V2_MODEL_RUNNER=0
      - VLLM_PLE_CPU_OFFLOAD=1
    ipc: host
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    entrypoint: ["vllm", "serve"]
    command:
      - "/model"
      - "--served-model-name"
      - "Qwen3.8-Flash-Next-NVFP4"
      - "--port"
      - "8000"
      - "--trust-remote-code"
      - "--enable-auto-tool-choice"
      - "--tool-call-parser"
      - "qwen3_xml"
      - "--reasoning-parser"
      - "qwen3"
      - "--max-model-len"
      - "32768"
      - "--max-num-seqs"
      - "128"
      - "--gpu-memory-utilization"
      - "0.90"

ccchhh changed discussion status to closed Aug 29

ccchhh changed discussion status to open Aug 29

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