Recipes for shrinking, optimizing, customizing cutting edge vision models.
| Notebook | Description | |
|---|---|---|
| Quantization/ONNX | Faster and Smaller Zero-shot Object Detection with Optimum | Quantize the state-of-the-art zero-shot object detection model OWLv2 using Optimum ONNXRuntime tools. |
| VLM Fine-tuning | Fine-tune PaliGemma | Fine-tune state-of-the-art vision language backbone PaliGemma using transformers. |
| Intro to Optimum/ORT | Optimizing DETR with 🤗 Optimum | A soft introduction to exporting vision models to ONNX and quantizing them. |
| Model Shrinking | Knowledge Distillation for Computer Vision | Knowledge distillation for image classification. |
| Quantization | Fit in vision models using Quanto | Fit in vision models to smaller hardware using quanto |
| Speed-up | Faster foundation models with torch.compile | Improving latency for foundation models using torch.compile |
| Speed-up/Memory Optimization | Vision language model serving using TGI (SOON) | Explore speed-ups and memory improvements for vision-language model serving with text-generation inference |
| Quantization/Optimum/ORT | All levels of quantization and graph optimizations for Image Segmentation using Optimum (SOON) | End-to-end model optimization using Optimum |
| VLM Fine-tuning | Fine-tune Florence-2 | Fine-tune Florence-2 on DocVQA dataset |
| Fine-tune IDEFICS3 on visual question answering | QLoRA Fine-tune IDEFICS3 on VQAv2 | QLoRA Fine-tune IDEFICS3 on VQAv2 dataset |
| Multimodal RAG using ColPali and Qwen2-VL | Multimodal RAG using ColPali and Qwen2-VL | Learn to retrieve documents and pipeline to RAG without hefty document processing using ColPali through Byaldi and do the generation with Qwen2-VL |