Instructions to use HuggingFaceTB/SmolVLM2-2.2B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceTB/SmolVLM2-2.2B-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="HuggingFaceTB/SmolVLM2-2.2B-Instruct")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HuggingFaceTB/SmolVLM2-2.2B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolVLM2-2.2B-Instruct",
		"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/HuggingFaceTB/SmolVLM2-2.2B-Instruct

SGLang

How to use HuggingFaceTB/SmolVLM2-2.2B-Instruct 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 "HuggingFaceTB/SmolVLM2-2.2B-Instruct" \
    --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": "HuggingFaceTB/SmolVLM2-2.2B-Instruct",
		"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 "HuggingFaceTB/SmolVLM2-2.2B-Instruct" \
        --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": "HuggingFaceTB/SmolVLM2-2.2B-Instruct",
		"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 HuggingFaceTB/SmolVLM2-2.2B-Instruct with Docker Model Runner:
```
docker model run hf.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct
```

Code snippets

#10

by pcuenq HF Staff - opened Feb 26, 2025

base: refs/heads/main

←

from: refs/pr/10

Discussion Files changed

-8

Files changed (1) hide show

README.md +7 -8

README.md CHANGED Viewed

@@ -74,6 +74,7 @@ You can load the model as follows.
 ```python
 from transformers import AutoProcessor, AutoModelForImageTextToText
 model_path = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
 processor = AutoProcessor.from_pretrained(model_path)
@@ -93,9 +94,8 @@ messages = [
     {
         "role": "user",
         "content": [
-{"type": "text", "text": "What is in this image?"},
-            {"type": "image", "path": "path_to_img.png"},
         ]
     },
 ]
@@ -159,14 +159,13 @@ messages = [
     {
         "role": "user",
         "content": [
-{"type": "text", "text": "What is the similarity between this image <image>"},
-            {"type": "image", "path": "image_1.png"},
-{"type": "text", "text": "and this image <image>"},
-{"type": "image", "path": "image_2.png"},
         ]
     },
 ]
 inputs = processor.apply_chat_template(
     messages,
     add_generation_prompt=True,

 ```python
 from transformers import AutoProcessor, AutoModelForImageTextToText
+import torch
 model_path = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
 processor = AutoProcessor.from_pretrained(model_path)
     {
         "role": "user",
         "content": [
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
+            {"type": "text", "text": "Can you describe this image?"},
         ]
     },
 ]
     {
         "role": "user",
         "content": [
+          {"type": "text", "text": "What is the similarity between these two images?"},
+          {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
+          {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"},
         ]
     },
 ]
 inputs = processor.apply_chat_template(
     messages,
     add_generation_prompt=True,