HuggingFaceTB
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SmolVLM-Base

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 ---
 library_name: transformers
-tags: []
 base_model:
 - HuggingFaceTB/SmolLM2-1.7B-Instruct
 - google/siglip-so400m-patch14-384
 ---
-# Model Card for Model ID
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-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- HuggingFaceM4/the_cauldron
+- HuggingFaceM4/Docmatix
+pipeline_tag: image-text-to-text
+language:
+- en
 base_model:
 - HuggingFaceTB/SmolLM2-1.7B-Instruct
 - google/siglip-so400m-patch14-384
 ---
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/SmolVLM.png" width="800" height="auto" alt="Image description">
+# SmolVLM
+SmolVLM is a compact open multimodal model that accepts arbitrary sequences of image and text inputs to produce text outputs.
+Designed for efficiency, SmolVLM can answer questions about images, describe visual content, create stories grounded on multiple images,
+or function as a pure language model without visual inputs. Its lightweight architecture makes it suitable for on-device applications
+while maintaining strong performance on multimodal tasks.
+## Model Summary
+- **Developed by:** Hugging Face 🤗
+- **Model type:** Multi-modal model (image+text)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Architecture:** Based on [Idefics3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (see technical summary)
+## Resources
+- **Demo:** [SmolVLM Demo](https://huggingface.co/spaces/HuggingFaceTB/SmolVLM)
+- **Blog:** [Blog post](https://huggingface.co/blog/smolvlm)
 ## Uses
+SmolVLM can be used for inference on multimodal (image + text) tasks where the input comprises text queries along with one or more images.
+Text and images can be interleaved arbitrarily, enabling tasks like image captioning, visual question answering, and storytelling based on
+visual content. The model does not support image generation.
+To fine-tune SmolVLM on a specific task, you can follow the fine-tuning tutorial.
+<!-- todo: add link to fine-tuning tutorial -->
+### Technical Summary
+SmolVLM leverages the lightweight SmolLM2 language model to provide a compact yet powerful multimodal experience.
+It introduces several changes compared to previous Idefics models:
+- **Image compression:** We introduce a more radical image compression compared to Idefics3 to enable the model to infer faster and use less RAM.
+- **Visual Token Encoding:** SmolVLM uses 81 visual tokens to encode image patches of size 384×384. Larger images are divided into patches, each encoded separately, enhancing efficiency without compromising performance.
+More details about the training and architecture are available in our technical report.
+### How to get started
+You can use transformers to load, infer and fine-tune SmolVLM.
+```python
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from transformers.image_utils import load_image
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+# Load images
+image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
+image2 = load_image("https://huggingface.co/spaces/merve/chameleon-7b/resolve/main/bee.jpg")
+# Initialize processor and model
+processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-Base")
+model = AutoModelForVision2Seq.from_pretrained(
+    "HuggingFaceTB/SmolVLM-Base",
+    torch_dtype=torch.bfloat16,
+    _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "eager",
+).to(DEVICE)
+# Create input messages
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "image"},
+            {"type": "text", "text": "Can you describe the two images?"}
+        ]
+    },
+]
+# Prepare inputs
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(text=prompt, images=[image1, image2], return_tensors="pt")
+inputs = inputs.to(DEVICE)
+# Generate outputs
+generated_ids = model.generate(**inputs, max_new_tokens=500)
+generated_texts = processor.batch_decode(
+    generated_ids,
+    skip_special_tokens=True,
+)
+print(generated_texts[0])
+"""
+User:<image>Can you describe the two images?
+Assistant: I can describe the first one, but I can't describe the second one.
+"""
+```
+### Model optimizations
+**Precision**: For better performance, load and run the model in half-precision (`torch.float16` or `torch.bfloat16`) if your hardware supports it.
+```python
+from transformers import AutoModelForVision2Seq
+import torch
+model = AutoModelForVision2Seq.from_pretrained(
+    "HuggingFaceTB/SmolVLM-Base",
+    torch_dtype=torch.bfloat16
+).to("cuda")
+```
+You can also load SmolVLM with 4/8-bit quantization using bitsandbytes, torchao or Quanto. Refer to [this page](https://huggingface.co/docs/transformers/en/main_classes/quantization) for other options.
+```python
+from transformers import AutoModelForVision2Seq, BitsAndBytesConfig
+import torch
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+model = AutoModelForVision2Seq.from_pretrained(
+    "HuggingFaceTB/SmolVLM-Base",
+    quantization_config=quantization_config,
+)
+```
+**Vision Encoder Efficiency**: Adjust the image resolution by setting `size={"longest_edge": N*384}` when initializing the processor, where N is your desired value. The default `N=4` works well, which results in input images of
+size 1536×1536. For documents, `N=5` might be beneficial. Decreasing N can save GPU memory and is appropriate for lower-resolution images. This is also useful if you want to fine-tune on videos.
+## Misuse and Out-of-scope Use
+SmolVLM is not intended for high-stakes scenarios or critical decision-making processes that affect an individual's well-being or livelihood. The model may produce content that appears factual but may not be accurate. Misuse includes, but is not limited to:
+- Prohibited Uses:
+  - Evaluating or scoring individuals (e.g., in employment, education, credit)
+  - Critical automated decision-making
+  - Generating unreliable factual content
+- Malicious Activities:
+  - Spam generation
+  - Disinformation campaigns
+  - Harassment or abuse
+  - Unauthorized surveillance
+### License
+SmolVLM is built upon [the shape-optimized SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) as image encoder and [SmolLM2](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) for text decoder part.
+We release the SmolVLM checkpoints under the Apache 2.0 license.
 ## Training Details
 ### Training Data
+The training data comes from [The Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron) and [Docmatix](https://huggingface.co/datasets/HuggingFaceM4/Docmatix) datasets, with emphasis on document understanding (25%) and image captioning (18%), while maintaining balanced coverage across other crucial capabilities like visual reasoning, chart comprehension, and general instruction following.
+<img src="https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct/resolve/main/mixture_the_cauldron.png" alt="Example Image" style="width:90%;" />
 ## Evaluation
+| Model             | MMMU (val) | MathVista (testmini) | MMStar (val) | DocVQA (test) | TextVQA (val) | Min GPU RAM required (GB) |
+|-------------------|------------|----------------------|--------------|---------------|---------------|---------------------------|
+| SmolVLM           | 38.8       | 44.6                | 42.1         | 81.6          | 72.7          | 5.02                      |
+| Qwen-VL 2B        | 41.1       | 47.8                | 47.5         | 90.1          | 79.7          | 13.70                     |
+| InternVL2 2B      | 34.3       | 46.3                | 49.8         | 86.9          | 73.4          | 10.52                     |
+| PaliGemma 3B 448px| 34.9       | 28.7                | 48.3         | 32.2          | 56.0          | 6.72                      |
+| moondream2        | 32.4       | 24.3                | 40.3         | 70.5          | 65.2          | 3.87                      |
+| MiniCPM-V-2       | 38.2       | 39.8                | 39.1         | 71.9          | 74.1          | 7.88                      |
+| MM1.5 1B          | 35.8       | 37.2                | 0.0          | 81.0          | 72.5          | NaN                       |