GenAI

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)

Vision model based document ingestion.

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)

Colivara is a suite of services that allows you to store, search, and retrieve documents based on their visual embedding. ColiVara has state of the art retrieval performance on both text and visual...

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

Omni turns documents, slide decks, websites and more into the data you need. You'll never need to copy + paste data into spreadsheets again.

PDF to Markdown with vision models.

Org profile for SparseCL on Hugging Face, the AI community building the future.

Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is important to many downstream applications like fact...

Contradiction detection is a task to recognize contradiction relations between a pair of sentences. Despite the effectiveness of traditional context-based word embedding learning algorithms in many natural language processing tasks, such algorithms are not powerful enough for contradiction detection. Contrasting words such as “overfull” and “empty” are mostly mapped into close vectors in such embedding space. To solve this problem, we develop a tailored neural network to learn contradiction-specific word embedding (CWE). The method can separate antonyms in the opposite ends of a spectrum. CWE is learned from a training corpus which is automatically generated from the paraphrase database, and is naturally applied as features to carry out contradiction detection in SemEval 2014 benchmark dataset. Experimental results show that CWE outperforms traditional context-based word embedding in contradiction detection. The proposed model for contradiction detection performs comparably with the top-performing system in accuracy of three-category classification and enhances the accuracy from 75.97% to 82.08% in the contradiction category.

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

Full Stack application for retrieving Stock Data and News using LLM, LangChain and LangGraph - vinay-gatech/stocks-insights-ai-agent

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

Compare performance, cost, and accuracy of leading Vision Language Models including GPT-4V, Claude 3.5, and open-source alternatives. Real-world testing on document processing tasks.

We investigate whether large language models (LLMs) can successfully perform financial statement analysis in a way similar to a professional human analyst

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

PaSa -- an advanced paper search agent powered by large language models. It can autonomously make a series of decisions, including invoking search tools, reading papers, and selecting relevant refe...

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

Made with Gamma. A new medium for presenting ideas, powered by AI.

Your AI-powered note-taking companion

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.

Cosine similarity - the duct tape of AI. Convenient but often misused. Let's find out how to use it better.