GenAI

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Explore the main components of what makes LLM Agents special.

This Weaviate Agent is in technical preview.

Learn how to use multi-vector embeddings in Weaviate.

Learn how to hack AI agents and applications with this expert guide. Find vulnerabilities, prompt injection risks, and testing strategies for AI security.

AI Engineering bootcamp. Contribute to apatti/AIEBootcamp development by creating an account on GitHub.

Discover the best graph visualization tools for visualizing your Neo4j graph database, including development, exploration, dashboarding, and embedded tools.

Many students and researchers are already using them - tools with integrated artificial intelligence (AI). What can AI-supported tools achieve, what opportunities do they offer and what are their limitations? The following list is an introductory selection which is not based on any value judgement.

Your learning center to build in-demand cloud skills.

What are the key ontology standards you should have in mind? Ontology standards are crucial for knowledge representation and reasoning in AI and data… | 100 comments on LinkedIn

Learn how to extract structured data from PDFs with Gemini 2.0 and Pydantic.

Contribute to langchain-ai/langgraph-supervisor development by creating an account on GitHub.

This repository shares end-to-end notebooks on how to use various Weaviate features and integrations! - weaviate/recipes

but wtf not? - skill gap - high fine-tuning costs - lack of standards and unified approaches over the past few weeks I've been working on maestro - streamlined tool for VLM fine-tuning link: — SkalskiP (@skalskip92)

Two months ago user durable-racoon posted about DocumentContextExtractor, their iteration on a technique for improving the accuracy of RAG that both and had made demo implementations of. Contextual Retrieval improves the… — LlamaIndex 🦙 (@llama_index)

Here are 8 notebooks you should know about: 1. Vanilla vector search: 2. Image similarity search: 3. Hybrid search: 4. Local RAG… — Leonie (@helloiamleonie)

PDF to Markdown with vision models.

In this video, we show you how to fine-tune DeepSeek R1, an open-source reasoning model, using LoRA (Low-Rank Adaptation). We'll also be using Kaggle, Hugging Face and Weights & Biases. We walk you through data preparation, model configuration, and optimization, including advanced techniques like four-bit quantization for efficient training on consumer GPUs. By the end of this tutorial, you’ll be equipped with the skills to customize DeepSeek R1 for your own specialized tasks, such as medical reasoning. 🔗 Resources & Tutorials Kaggle Notebook: https://www.kaggle.com/code/aan1994/fine-tuning-deepseek-r1-reasoning-model-youtube How Transformers Work: https://www.datacamp.com/tutorial/how-transformers-work Fine-Tuning DeepSeek R1 Reasoning Model: https://www.datacamp.com/tutorial/fine-tuning-deepseek-r1-reasoning-model DeepSeek R1 Blog Overview: https://www.datacamp.com/blog/deepseek-r1 Understanding Janus Pro: https://www.datacamp.com/blog/janus-pro DeepSeek R1 Project Walkthrough: https://www.datacamp.com/tutorial/deepseek-r1-project DeepSeek vs ChatGPT: https://www.datacamp.com/blog/deepseek-vs-chatgpt Qwen-2.5 MAX Model: https://www.datacamp.com/blog/qwen-2-5-max DeepSeek R1 Ollama Tutorial: https://www.datacamp.com/tutorial/deepseek-r1-ollama 📕 Chapters 00:00 Introduction 00:30 Why Fine-Tuning DeepSeek Matters 02:30 LoRA Explained with a PS5 Factory Analogy 05:20 Tools & Setup Overview 09:00 Loading DeepSeek R1 Model and Tokenizer 16:10 Formatting Data for Fine-Tuning 23:00 Applying LoRA for Efficient Updates 34:00 Configuring Training Parameters 43:15 Running the Fine-Tuning Process on Kaggle 46:00 Comparing Model Performance After Fine-Tuning 47:50 Final Thoughts on Future Models 📱 Follow Us on Social Media Facebook: https://www.facebook.com/datacampinc/ Twitter: https://twitter.com/datacamp LinkedIn: https://www.linkedin.com/school/datacampinc/ Instagram: https://www.instagram.com/datacamp/ #deepseek #DeepSeekR1 #FineTuningAI #LearnAI #MachineLearning #Transformers #HuggingFace #Kaggle #WeightsAndBiases #LoRA #LargeLanguageModels #DeepSeekTutorial #AIResearch #AIOptimization #DataScience

Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer. - transformerlab/transformerlab-app

This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems. - athina-ai/rag-cookbooks

Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud. - QwenLM/Qwen2.5-VL

GraphRAG: Enhancing Retrieval-Augmented Generation with SurrealDB, Gemini and DeepSeek

Learn more about integrating Neo4j with Qdrant for external vector searches by spinning up both locally and loading with sample data.

mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding - X-PLUG/mPLUG-DocOwl