Qwen3.5 - Vision Language Model

Qwen3.5 is a native vision-language model from Alibaba Cloud's Qwen team, built on a hybrid architecture that fuses linear attention with a sparse mixture-of-experts. It excels at multimodal reasoning, coding, agent capabilities, and visual understanding.

Overview

Qwen3.5 is a multimodal model capable of:

• Visual Question Answering - Answer complex questions about image content
• Image Captioning - Generate detailed descriptions of images
• Visual Reasoning - Multi-step logical reasoning over images, including scientific problems and puzzles
• Document Understanding - Parse and analyze document content, OCR, and chart reading
• Spatial Intelligence - Object counting, relative positioning, and spatial relationship understanding
• Fine-grained Recognition - Identify specific objects, text, and details

GPU Recommended

Qwen3.5 works best with GPU acceleration. CPU inference may be very slow or may not work properly.

License & Attribution

License: Apache 2.0
Source: Qwen Team

Pre-trained Model IDs

Qwen3.5 pre-trained models are available and do not require a Roboflow API key.

Model ID	Description
qwen3_5-0.8b	0.8B parameter model - compact and efficient
qwen3_5-2b	2B parameter model - better accuracy

You can also use fine-tuned models from Roboflow by specifying project/version as the model ID (requires API key).

Supported Backends

Backend	Extras Required
torch	torch-cpu, torch-cu118, torch-cu124, torch-cu126, torch-cu128, torch-jp6-cu126

Roboflow Platform Compatibility

Feature	Supported
Training	✅ LoRA fine-tuning only
Upload Weights	✅ Upload fine-tuned models
Serverless API (v2)	⚠️ Limited support (not yet fully stable)
Workflows	✅ Use in Workflows via Qwen3.5 block
Edge Deployment (Jetson)	❌ Not supported
Self-Hosting	✅ Deploy with inference-models (GPU recommended)

Usage Examples

Qwen3.5 is a general-purpose vision-language model — you can prompt it with any vision or language task you have in mind. The examples below are just common starting points.

Visual Question Answering

import cv2
from inference_models import AutoModel

# Load model
model = AutoModel.from_pretrained("qwen3_5-0.8b")
image = cv2.imread("path/to/image.jpg")

# Ask a question
answers = model.prompt(
    images=image,
    prompt="What objects are visible in this image?",
    max_new_tokens=512
)
print(f"Answer: {answers[0]}")

Image Captioning

import cv2
from inference_models import AutoModel

# Load model
model = AutoModel.from_pretrained("qwen3_5-0.8b")
image = cv2.imread("path/to/image.jpg")

# Generate detailed caption
captions = model.prompt(
    images=image,
    prompt="Describe this image in detail.",
    max_new_tokens=512
)
print(f"Caption: {captions[0]}")

Using Fine-tuned Models

import cv2
from inference_models import AutoModel

# Load your fine-tuned model from Roboflow
model = AutoModel.from_pretrained(
    "your-project/version",
    api_key="your_roboflow_api_key"
)

image = cv2.imread("path/to/image.jpg")

# Use with custom prompt
answers = model.prompt(
    images=image,
    prompt="your custom question",
    max_new_tokens=512
)
print(f"Answer: {answers[0]}")

Workflows Integration

Qwen3.5 can be used in Roboflow Workflows for complex computer vision pipelines.

Learn more: Workflows Documentation

Performance Tips

1. Use GPU - Qwen3.5 requires GPU for acceptable performance
2. Optimize prompts - Clear, specific prompts yield better results
3. Adjust max_new_tokens - Increase for longer responses, decrease for faster inference