Documentation Index
Fetch the complete documentation index at: https://mintlify.com/QwenLM/Qwen3-VL/llms.txt
Use this file to discover all available pages before exploring further.
Get up and running with Qwen3-VL in just a few steps. This guide will walk you through your first image inference.
Installation
Install Transformers
Qwen3-VL requires transformers 4.57.0 or higher:pip install "transformers>=4.57.0"
Install Optional Dependencies
For optimal performance, install these recommended packages:pip install accelerate torch torchvision
Your First Inference
Here’s a complete example to perform image understanding with Qwen3-VL:
from transformers import AutoModelForImageTextToText, AutoProcessor
# Load the model on available devices
model = AutoModelForImageTextToText.from_pretrained(
"Qwen/Qwen3-VL-8B-Instruct",
dtype="auto",
device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-8B-Instruct")
# Prepare your message with an image
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
# Prepare inputs for inference
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
)
inputs = inputs.to(model.device)
# Generate the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
print(output_text)
We recommend starting with the 8B model for development. For production, see the Model Variants guide. {
"type": "image",
"image": "https://example.com/image.jpg"
}
{
"type": "image",
"image": "file:///path/to/image.jpg"
}
{
"type": "image",
"image": "data:image;base64,/9j/4AAQSkZJRg..."
}
Model Selection
Choose a model size based on your use case:
2B / 4B Models
Edge deployment - Run on consumer GPUs or mobile devices
8B Model
Balanced - Best for most applications (24GB VRAM)
32B Model
High performance - Production use cases (80GB VRAM)
235B MoE Model
Maximum capability - Research and specialized tasks (8x80GB)
pip install flash-attn --no-build-isolation
import torch
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained(
"Qwen/Qwen3-VL-8B-Instruct",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="auto"
)
Flash Attention 2 is especially beneficial for multi-image and video scenarios, reducing memory usage and improving speed.
Next Steps
Image Processing
Learn about multi-image inference and resolution control
Video Processing
Process videos with frame sampling
Capabilities
Explore OCR, grounding, document parsing, and more
Deployment
Deploy with vLLM or SGLang for production