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Inference PaliGemma 2 with Transformers.js
- PaliGemma 2, the latest VLM in the Gemma family, improves adaptability on various transfer tasks using the SigLIP vision encoder and Gemma 2 language decoder architecture.
- The article details how to serve PaliGemma 2 with Transformers.js in the browser for making inferences directly.
- Steps include converting PaliGemma 2 model components to ONNX format, quantizing the models, and uploading the ONNX weights on Hugging Face for inference.
- Part I involves converting and quantizing the vision encoder, language decoder, and embedding tokens of PaliGemma 2, ensuring compatibility for inference using Transformers.js.
- In Part II, the process of running inferences with the converted model is demonstrated, either on Colab or in a web app using Node.js.
- A Node.js web app that accepts image and text prompts, leveraging the converted PaliGemma 2 model, is showcased in the article.
- The author shares insights on leveraging ONNX to run PaliGemma 2 in a non-Pythonic environment, highlighting the efforts and learnings involved in the project.
- Acknowledgments are given to the ONNX community and the Google ML Developers Program team for providing support and resources for the project.
- The article concludes with reference links and encourages readers to explore and engage with the contents shared.
- The demonstration includes results of inferencing with PaliGemma 2 Mix models using Transformers.js, showcasing impressive performance.
- The author encourages appreciation by starring the repository, sharing the work, and hints at future engaging blogs on related topics.
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