Source URL: https://simonwillison.net/2024/Nov/28/smolvlm/#atom-everything
Source: Simon Willison’s Weblog
Title: SmolVLM – small yet mighty Vision Language Model
Feedly Summary: SmolVLM – small yet mighty Vision Language Model
I’ve been having fun playing with this new vision model from the Hugging Face team behind SmolLM. They describe it as:
[…] a 2B VLM, SOTA for its memory footprint. SmolVLM is small, fast, memory-efficient, and fully open-source. All model checkpoints, VLM datasets, training recipes and tools are released under the Apache 2.0 license.
I’ve tried it in a few flavours but my favourite so far is the mlx-vlm approach, via mlx-vlm author Prince Canuma. Here’s the uv recipe I’m using to run it:
uv run \
–with mlx-vlm \
–with torch \
python -m mlx_vlm.generate \
–model mlx-community/SmolVLM-Instruct-bf16 \
–max-tokens 500 \
–temp 0.5 \
–prompt “Describe this image in detail" \
–image IMG_4414.JPG
This one-liner installs the necessary dependencies, downloads the model (about 4.2GB, saved to ~/.cache/huggingface/hub/models–mlx-community–SmolVLM-Instruct-bf16) and executes the prompt and displays the result.
I ran that against this Pelican photo:
The model replied:
In the foreground of this photograph, a pelican is perched on a pile of rocks. The pelican’s wings are spread out, and its beak is open. There is a small bird standing on the rocks in front of the pelican. The bird has its head cocked to one side, and it seems to be looking at the pelican. To the left of the pelican is another bird, and behind the pelican are some other birds. The rocks in the background of the image are gray, and they are covered with a variety of textures. The rocks in the background appear to be wet from either rain or sea spray.
There are a few spatial mistakes in that description but the vibes are generally in the right direction.
On my 64GB M2 MacBook pro it read the prompt at 7.831 tokens/second and generated that response at an impressive 74.765 tokens/second.
Tags: vision-llms, uv, mlx, ai, edge-llms, llms, python, generative-ai
AI Summary and Description: Yes
Summary: The text discusses SmolVLM, a small yet efficient vision-language model developed by Hugging Face. It highlights its efficiency in both memory usage and processing speed, making it a suitable option for professionals working with AI models, particularly in generative AI and infrastructure security contexts.
Detailed Description:
The content revolves around the SmolVLM, a vision-language model (VLM) noted for its remarkable efficiency while maintaining state-of-the-art (SOTA) capabilities in terms of memory footprint. Below are the major points derived from the text:
– **Model Efficiency**: SmolVLM is designed to be small, fast, and memory-efficient, which is crucial for deploying AI models in resource-constrained environments or edge computing scenarios.
– **Open Source**: All components of the model, including model checkpoints, datasets, and training recipes, are released under the Apache 2.0 license, promoting transparency and collaboration in the AI community.
– **Performance**: The author cites impressive token processing rates on a personal machine, indicating real-time capabilities that could benefit applications requiring swift image analysis and processing.
– **Practical Usage**: The author provides a practical example of how to utilize the model through a specific command that facilitates image analysis via a prompt, exemplifying the user-friendly nature of modern AI tools.
– **Limitations**: Some minor spatial mistakes are mentioned in the model’s description of an image, highlighting the need for continuous improvement and validation of AI outputs, which is essential for the domains of AI security and generative AI.
Implications for security and compliance professionals considering AI models:
– **Deployment Considerations**: The lightweight nature of SmolVLM makes it an attractive option for deployment in environments where resources are constrained, but this also necessitates rigorous testing to ensure security and compliance during integration.
– **Open Source Security**: Engaging with open-source models like SmolVLM promotes transparency but requires careful vetting of the model’s implementations to mitigate risks associated with external codebases.
– **Real-time Processing Challenges**: The reported processing speeds can enhance the efficiency of automated systems. However, rapid generation of outputs might pose challenges in terms of validating their security and accuracy before applying them in production environments.
This content is of particular relevance to professionals in AI development, security, and compliance, as it showcases advancements in model efficiency and the ongoing need for oversight and validation in AI applications.