Instructions to use kyutai/CASA-Qwen2_5-VL-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kyutai/CASA-Qwen2_5-VL-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="kyutai/CASA-Qwen2_5-VL-3B", trust_remote_code=True)
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)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kyutai/CASA-Qwen2_5-VL-3B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kyutai/CASA-Qwen2_5-VL-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kyutai/CASA-Qwen2_5-VL-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kyutai/CASA-Qwen2_5-VL-3B",
		"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/kyutai/CASA-Qwen2_5-VL-3B

SGLang

How to use kyutai/CASA-Qwen2_5-VL-3B 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 "kyutai/CASA-Qwen2_5-VL-3B" \
    --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": "kyutai/CASA-Qwen2_5-VL-3B",
		"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 "kyutai/CASA-Qwen2_5-VL-3B" \
        --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": "kyutai/CASA-Qwen2_5-VL-3B",
		"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 kyutai/CASA-Qwen2_5-VL-3B with Docker Model Runner:
```
docker model run hf.co/kyutai/CASA-Qwen2_5-VL-3B
```

CASA-Qwen2_5-VL-3B

This repository contains the model weights for CASA-Qwen2_5-VL-3B, introduced in the paper CASA: Cross-Attention over Self-Attention for Efficient Vision-Language Fusion.

This model is a Qwen-2.5VL-3B-Instruct model adapted from token insertion to a cross-attention-based architecture.

Paper: CASA: Cross-Attention over Self-Attention for Efficient Vision-Language Fusion
Project Page: kyutai.org/casa
Code: github.com/kyutai-labs/casa

Sample Usage

This model requires trust_remote_code=True to load the custom architecture. Below is a snippet to run inference using transformers.

import torch
from transformers.models.auto.modeling_auto import AutoModel
from transformers.models.auto.processing_auto import AutoProcessor

model_id = "kyutai/CASA-Qwen2_5-VL-3B"
model = AutoModel.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    trust_remote_code=True,
).cuda()

processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
)

conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.png",
            },
            {
                "type": "text",
                "text": "Describe this image.",
            },
        ],
    },
]

inputs = processor.tokenize_messages(messages=conversation)
inputs = inputs.to(model.device)
input_len = inputs["input_ids"].shape[1]

output_ids = model.generate_from_image(
  **inputs,
  max_new_tokens=512,
  pre_image_tokens=processor.pre_image_tokens,
  post_image_tokens=processor.post_image_tokens,
  eos_token_id=model.generation_config.eos_token_id,
)[0, input_len:]

response = processor.tokenizer.decode(output_ids, skip_special_tokens=True)
print(response)

Citation

@article{kyutai2025casa,
  author = {Moritz B\"ohle and Am\'elie Royer and Juliette Marrie and Edouard Grave and Patrick P\'erez},
  year = {2025},
  title = {CASA: Cross-Attention over Self-Attention for Efficient Vision-Language Fusion},
  journal = {ArXiv},
  url = {https://arxiv.org/abs/2512.19535}
}

License

The code in the official repository is provided under the MIT license. The weights for this model are released under the CC-BY-NC-SA 4.0 license. Additionally, as this model includes weights from Qwen2.5-VL-3B, it is subject to the Qwen RESEARCH LICENSE AGREEMENT.