Update README.md

README.md

@@ -9,10 +9,9 @@ library_name: transformers
 Recipes for shrinking, optimizing, customizing cutting edge vision and multimodal AI models. Original GH repository is [here](https://github.com/merveenoyan/smol-vision) migrated to Hugging Face since notebooks there aren't rendered 🥲 
 
 Latest examples 👇🏻
-- [Fine-tuning SmolVLM2 on Video Captioning](https://huggingface.co/merve/smol-vision/blob/main/Fine_tune_SmolVLM2_on_Video.ipynb)
-- [Multimodal RAG using ColPali and Qwen2-VL](https://huggingface.co/merve/smol-vision/blob/main/ColPali_%2B_Qwen2_VL.ipynb)
 - [Fine-tune ColPali for Multimodal RAG](https://huggingface.co/merve/smol-vision/blob/main/Finetune_ColPali.ipynb)                                                                         
-
+- [Fine-tune Gemma-3n for all modalities (audio-text-image)](https://huggingface.co/merve/smol-vision/blob/main/Gemma3n_Fine_tuning_on_All_Modalities.ipynb)
+- [Any-to-Any (Video) RAG with OmniEmbed and Qwen](https://huggingface.co/merve/smol-vision/blob/main/Any_to_Any_RAG.ipynb)
 **Note**: The script and notebook are updated to fix few issues related to QLoRA!
 
 |                              | Notebook                                                                                                                                                                | Description                                                                                                |
@@ -28,5 +27,7 @@ Latest examples 👇🏻
 | VLM Fine-tuning (Script)   | [QLoRA Fine-tune IDEFICS3 on VQAv2](https://huggingface.co/merve/smol-vision/blob/main/smolvlm.py)                                                                          | QLoRA/Full Fine-tune IDEFICS3 or SmolVLM on VQAv2 dataset                                                                 |
 | Multimodal RAG    | [Multimodal RAG using ColPali and Qwen2-VL](https://huggingface.co/merve/smol-vision/blob/main/ColPali_%2B_Qwen2_VL.ipynb)                                                                          | Learn to retrieve documents and pipeline to RAG without hefty document processing using ColPali through Byaldi and do the generation with Qwen2-VL                                                              |
 | Multimodal Retriever Fine-tuning    | [Fine-tune ColPali for Multimodal RAG](https://huggingface.co/merve/smol-vision/blob/main/Finetune_ColPali.ipynb)                                                                          | Learn to apply contrastive fine-tuning on ColPali to customize it for your own multimodal document RAG use case                                                              |
+| VLM Fine-tuning             | [Fine-tune Gemma-3n for all modalities (audio-text-image)](https://huggingface.co/merve/smol-vision/blob/main/Gemma3n_Fine_tuning_on_All_Modalities.ipynb)            | Fine-tune Gemma-3n model to handle any modality: audio, text, and image.                                           |
+| Multimodal RAG              | [Any-to-Any (Video) RAG with OmniEmbed and Qwen](https://huggingface.co/merve/smol-vision/blob/main/Any_to_Any_RAG.ipynb)                                             | Do retrieval and generation across modalities (including video) using OmniEmbed and Qwen.                          |
 | 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                                                                |
+| Quantization/Optimum/ORT     | All levels of quantization and graph optimizations for Image Segmentation using Optimum (SOON)                                                                          | End-to-end model optimization using Optimum                                                                |