Repository History
17 repositories tagged with Jupyter Notebook
rag-zero-to-hero-guide: Your Comprehensive Path to Mastering RAG
This repository offers a comprehensive guide to Retrieval-Augmented Generation (RAG), covering everything from fundamental concepts to advanced techniques. It includes detailed courses on RAG basics and evaluation, alongside an extensive toolkit of frameworks, libraries, and research papers. Ideal for AI engineers and LLM enthusiasts, this resource provides a structured learning path for building and optimizing RAG systems.
Jsonformer: Bulletproof Structured JSON Generation from Language Models
Jsonformer is a powerful library designed to generate syntactically correct and schema-conforming JSON from language models. It addresses the common challenge of unreliable JSON output by focusing on generating only content tokens, making the process more efficient and robust. This approach ensures bulletproof structured data generation for various applications.
Qwen3-VL: A Powerful Multimodal Large Language Model Series
Qwen3-VL is a cutting-edge multimodal large language model series from Alibaba Cloud's Qwen team. It offers significant advancements in visual and text understanding, extended context length, and enhanced agent capabilities. This model is designed for flexible deployment, scaling from edge to cloud.
rag-from-scratch: Building Retrieval Augmented Generation Systems
This repository by LangChain AI offers a comprehensive guide to understanding and implementing Retrieval Augmented Generation (RAG) from scratch. It includes a series of Jupyter notebooks and an accompanying video playlist, making complex RAG concepts accessible for practical application. The resource highlights RAG's advantages over fine-tuning for factual recall in Large Language Models (LLMs).
CoTracker: A Powerful Model for Tracking Any Point on a Video
CoTracker is a state-of-the-art model developed by Facebook AI Research and the University of Oxford, designed for tracking any point (pixel) across video sequences. This transformer-based solution offers fast, accurate, and quasi-dense point tracking capabilities. It is an invaluable tool for researchers and developers in computer vision, enabling precise analysis of motion in videos.
Roboflow Notebooks: Master State-of-the-Art Computer Vision Models
Roboflow Notebooks offers a comprehensive collection of tutorials designed to help users master state-of-the-art computer vision models and techniques. This repository covers a wide range of topics, from foundational architectures like ResNet to cutting-edge models such as RF-DETR, YOLO11, SAM 3, and Qwen3-VL. It serves as an invaluable resource for anyone looking to explore and implement advanced computer vision solutions.
KBLaM: Knowledge Base Augmented Language Models for Enhanced LLMs
KBLaM, developed by Microsoft, is the official implementation of "Knowledge Base Augmented Language Models" presented at ICLR 2025. This innovative method enhances Large Language Models by directly integrating external knowledge bases, offering an efficient alternative to traditional Retrieval-Augmented Generation (RAG) and in-context learning. It eliminates external retrieval modules and scales computationally linearly with knowledge base size, rather than quadratically.
Anthropic Courses: Educational Materials for Claude API and Prompt Engineering
The Anthropic Courses repository offers a comprehensive suite of educational materials designed to teach users how to effectively work with the Claude SDK and master prompt engineering techniques. It includes five structured courses, guiding learners from API fundamentals to advanced topics like prompt evaluations and tool use. These resources are ideal for developers and AI enthusiasts looking to enhance their skills with Anthropic's AI models.
big_vision: Google Research's Codebase for Large-Scale Vision Models
big_vision is Google Research's official codebase for training large-scale vision models using Jax/Flax. It has been instrumental in developing prominent architectures like Vision Transformer, SigLIP, and MLP-Mixer. This repository offers a robust starting point for researchers to conduct scalable vision experiments on GPUs and Cloud TPUs, scaling seamlessly from single cores to distributed setups.
NVIDIA Isaac GR00T: A Foundation Model for Generalist Robots
NVIDIA Isaac GR00T N1.6 is an open vision-language-action (VLA) foundation model designed for generalized humanoid robot skills. It enables robots to perform manipulation tasks in diverse environments by taking multimodal input, including language and images. Researchers and professionals can leverage this model for fine-tuning on custom datasets and deploying it for inference.
OmniParser: A Vision-Based Tool for GUI Agent Screen Parsing
OmniParser is a comprehensive tool developed by Microsoft for parsing user interface screenshots into structured, understandable elements. It significantly enhances the ability of vision-based models, such as GPT-4V, to generate accurate actions grounded in specific regions of a GUI. This project aims to advance pure vision-based GUI agents by providing robust screen parsing capabilities.
Memary: The Open Source Memory Layer for Autonomous Agents
Memary is an innovative open-source memory layer designed to enhance autonomous agents by emulating human memory. It integrates knowledge graphs and memory modules to provide agents with advanced capabilities for reasoning and learning. This project aims to make agents more intelligent and capable of self-improvement.