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Feb 26, 2025
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smolvlm2: code snippet changes #2711

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30 changes: 13 additions & 17 deletions smolvlm2.md
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Original file line number Diff line number Diff line change
Expand Up @@ -81,10 +81,11 @@ It’s so memory efficient, that you can run it even in a free Google Colab.
<summary>Python Code</summary>

```python
# Make sure we are running the latest version of Transformers
!pip install git+https://github.com/huggingface/transformers.git
# Install transformers from `main` or from this stable branch:
!pip install git+https://github.com/huggingface/transformers@v4.49.0-SmolVLM-2
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@pcuenca pcuenca Feb 26, 2025

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Prefer to use the stable tagged release rather than main


from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

model_path = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
processor = AutoProcessor.from_pretrained(model_path)
Expand All @@ -110,7 +111,7 @@ inputs = processor.apply_chat_template(
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
).to(model.device, dtype=torch.bfloat16)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
generated_texts = processor.batch_decode(
Expand Down Expand Up @@ -193,27 +194,25 @@ The easiest way to run inference with the SmolVLM2 models is through the convers
You can load the model as follows.

```python

# Make sure we are running the latest version of Transformers
!pip install git+https://github.com/huggingface/transformers.git
# Install transformers from `main` or from this stable branch:
!pip install git+https://github.com/huggingface/transformers@v4.49.0-SmolVLM-2

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForImageTextToText.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
_attn_implementation="flash_attention_2"
).to(DEVICE)
).to("cuda")
```

#### Video Inference

You can pass videos through a chat template by passing in `{"type": "video", "path": {video_path}`. See below for a complete example.

```python
import torch

messages = [
{
"role": "user",
Expand All @@ -230,7 +229,7 @@ inputs = processor.apply_chat_template(
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
).to(model.device, dtype=torch.bfloat16)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
generated_texts = processor.batch_decode(
Expand All @@ -245,19 +244,16 @@ print(generated_texts[0])

#### Multiple Image Inference

In addition to video, SmolVLM2 supports multi-image conversations. You can use the same API through the chat template.
In addition to video, SmolVLM2 supports multi-image conversations. You can use the same API through the chat template, providing each image using a filesystem path, an URL, or a `PIL.Image` object:

```python
import torch


messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "What are the differences between these two images?"},
{"type": "image", "path": "image_1.png"},
{"type": "image", "path": "image_2.png"}
{"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"},
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@pcuenca pcuenca Feb 26, 2025

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Trying to make the example runnable (and fun)

]
},
]
Expand All @@ -268,7 +264,7 @@ inputs = processor.apply_chat_template(
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
).to(model.device, dtype=torch.bfloat16)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
generated_texts = processor.batch_decode(
Expand Down