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 19 days ago

Commit

eadfc10

verified ·

1 Parent(s): ee2c87c

Update README.md

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README.md +3 -3

README.md CHANGED Viewed

@@ -113,7 +113,7 @@ def load_image(image_data, input_size=384, max_num=10):
 model_path = 'LiAutoAD/Ristretto-3B'
 model = AutoModel.from_pretrained(
-    path,
     torch_dtype=torch.bfloat16,
     trust_remote_code=True).eval().cuda()
 tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
@@ -136,12 +136,12 @@ print(f'User: {question}         Assistant: {response}')
 # text-image conversation && multi-round conversation
 question = '<image>         Please describe the image.'
-response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
 print(f'User: {question}         Assistant: {response}')
 question = 'What is best title for the image?'
-response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
 print(f'User: {question}         Assistant: {response}')
 ```

 model_path = 'LiAutoAD/Ristretto-3B'
 model = AutoModel.from_pretrained(
+    model_path,
     torch_dtype=torch.bfloat16,
     trust_remote_code=True).eval().cuda()
 tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
 # text-image conversation && multi-round conversation
 question = '<image>         Please describe the image.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, num_image_token=num_image_token, history=None, return_history=True)
 print(f'User: {question}         Assistant: {response}')
 question = 'What is best title for the image?'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, num_image_token=num_image_token, history=history, return_history=True)
 print(f'User: {question}         Assistant: {response}')
 ```