Instructions to use LiAutoAD/Ristretto-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiAutoAD/Ristretto-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="LiAutoAD/Ristretto-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("LiAutoAD/Ristretto-3B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LiAutoAD/Ristretto-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiAutoAD/Ristretto-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": "LiAutoAD/Ristretto-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/LiAutoAD/Ristretto-3B

SGLang

How to use LiAutoAD/Ristretto-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 "LiAutoAD/Ristretto-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": "LiAutoAD/Ristretto-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 "LiAutoAD/Ristretto-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": "LiAutoAD/Ristretto-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 LiAutoAD/Ristretto-3B with Docker Model Runner:
```
docker model run hf.co/LiAutoAD/Ristretto-3B
```

mjl1206 commited on Apr 27

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@@ -17,9 +17,6 @@ library_name: transformers
 We are excited to introduce **Ristretto**, our newest Vision language model (VLM) that represents a significant step forward in the field. Ristretto features a capability to deploy dynamic image tokens, enables flexible adjustment of image token quantities based on task requirements while enhancing the projector architecture to support dynamic token configurations. This new model delivers improved performance and versatility compared to its predecessors through its refined architecture and advanced training approach.
-**Key Innovations**
-Coming soon...
 ### Environment Setup


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18	We are excited to introduce Ristretto, our newest Vision language model (VLM) that represents a significant step forward in the field. Ristretto features a capability to deploy dynamic image tokens, enables flexible adjustment of image token quantities based on task requirements while enhancing the projector architecture to support dynamic token configurations. This new model delivers improved performance and versatility compared to its predecessors through its refined architecture and advanced training approach.
19



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21	### Environment Setup
22