vision language models finetuning notebooks & use cases (Medgemma - paligemma - florence .....)

This repository contains a curated collection of notebooks for implementing state-of-the-art multimodal Vision-Language Models (VLMs).

Educational notebooks that demystify Large Language Models and Computer Vision. We build everything from scratch — from a simple bigram language model to RNNs, LSTMs, Attention, Transformers, CNNs, and Diffusion models (DDPM) — using pure Python and PyTorch. No hype. Just code.

Winning team notebooks during TIL-AI Advanced Category Competition 2024 comprising Vision Language Models, NLP Question Answering and Speech to Text

This repository contains Jupyter Notebooks, prompts, and evaluation setups for assessing visualization literacy in Visual Language Models (VLMs). Benchmarks include VLAT and CALVI, comparing GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, and Llama 3.2-vision at temperature 0 and max_tokens of 300.

BLIP-2 implementation for training vision-language models. Q-Former + frozen encoders + any LLM. Colab-ready notebooks with MoE variant.

A collection of Jupyter notebooks showcasing the use of Generative AI models, including Large Language Models (LLMs), Vision-Language Models (VLMs), and Diffusion Models

Discover Azure AI—a portfolio of AI services designed for developers and data scientists. Take advantage of the decades of breakthrough research, responsible AI practices, and flexibility that Azure AI offers to build and deploy your own AI solutions. Access high-quality vision, speech, language, and decision-making AI models through simple API calls, and create your own machine learning models with tools like Jupyter Notebooks, Visual Studio Code, and open-source frameworks like TensorFlow and PyTorch.Only Azure empowers you with the most advanced machine learning capabilities. Quickly and easily build, train, and deploy your machine learning models using Azure Machine Learning and Azure Databricks. Use the latest tools like Jupyter and Visual Studio Code, alongside frameworks like PyTorch Enterprise, TensorFlow, and Scikit-Learn. Expand your data science teams and create models faster with low-code and no-code tools like automated machine learning and a drag-and-drop interface.

A collection of Google Colab notebooks focused on fine-tuning large language models (LLMs), vision-language models (VLMs), and advanced techniques like GRPO fine-tuning for reasoning. This repository serves as a workspace for model training and experimentation.

This repository contains a small set of Jupyter notebooks demonstrating key computer vision and vision–language tasks using pretrained models. The final notebook integrates these tasks into a realtime webcam application that performs captioning and classification concurrently.

Tutorials for Vision Language Models from https://github.com/SkalskiP/vlms-zero-to-hero

vision language models finetuning notebooks & use cases development for Radiomics (Medgemma-4B-IT, ongoing)

This is a repo for learning vision and language models by notebooks and illustrations

Explore multimodal AI: vision-language models (LLaVA, CLIP, etc.), fine-tuning, datasets, and experiments with notebooks, code, and demos.

A comprehensive collection of Deep Learning models implemented in Jupyter Notebooks, covering various computer vision and natural language processing tasks including image classification, object detection, image captioning, and generative models.

A structured collection of PyTorch notebooks and projects covering machine learning fundamentals, computer vision, and advanced AI, including multi-agent systems, candlestick pattern recognition, and transformer-based language models.

🧠 Experiments with Vision-Language Models using Qwen 2.5-VL and CLIP. Includes zero-shot classification, image captioning, prompt-based object detection, and embedding extraction with hands-on Jupyter notebooks. Explore multimodal AI with HuggingFace Transformers and PyTorch.