GenAI

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

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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

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

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. - mlabonne/llm-course

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EyeLevel.ai's GroundX APIs are the fastest way to build enterprise-grade RAG on prem or cloud. Trusted by Air France, Dartmouth, UltraCommerce and hundreds more.

Quickly understand inscrutable LLM frameworks by intercepting API calls.

DocWrangler is an IDE that provides instant feedback, visual exploration tools, and AI assistance for building and iterating on LLM-powered data processing pipelines

Chatbox AI is an AI client application and smart assistant. Compatible with many cutting-edge AI models and APIs. Available on Windows, MacOS, Android, iOS, Web, and Linux.

Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.

LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

Foundation models enable many new application interfaces, but one that has especially grown in popularity is the conversational interface, such as with chatbots and assistants. The conversational interface makes it easier for users to give feedback but harder for developers to extract signals. This post will discuss what conversational AI feedback looks like and how to design a system to collect the right feedback without hurting user experience.

PDF - Paperback - Hardcover - — Joshua Starmer (@joshuastarmer)

Upgrade your RAG applications with the power of knowledge graphs./b Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM’s training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Knowledge Graph-Enhanced RAG/i shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness. Inside Knowledge Graph-Enhanced RAG/i you’ll learn: The benefits of using Knowledge Graphs in a RAG system/li How to implement a GraphRAG system from scratch/li The process of building a fully working production RAG system/li Constructing knowledge graphs using LLMs/li Evaluating performance of a RAG pipeline/li /ul Knowledge Graph-Enhanced RAG/i is a practical guide to empowering LLMs with RAG. You’ll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, and generate Cypher statements to retrieve data from a knowledge graph.

Everything you need to know to build your own RAG application - bRAGAI/bRAG-langchain

In this blog post, we’ll be focusing on how to write the retrieval query that supplements or grounds the LLM’s answer.

A post for developers with advice and workflows for building effective AI agents

— Jo Kristian Bergum (@jobergum)