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RAG - Hugging Face Retrieval-augmented generation (“RAG”) models combine the powers of pretrained dense retrieval (DPR) and sequence-to-sequence models RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate outputs
Building RAG Systems with Transformers - Machine Learning Mastery In this post, you will explore how to build a basic RAG system using models from the Hugging Face library You’ll build each system component, from document indexing to retrieval and generation, and implement a complete end-to-end solution Specifically, you will learn: Kick-start your project with my book NLP with Hugging Face Transformers
Automating RAG Infrastructure Deployment with AI The agent takes a text prompt describing your setup requirements and builds the full RAG pipeline automatically It provisions the right vector database, sets up retrieval logic, connects models (OpenAI, open-source LLMs, fine-tuned ones), and deploys everything — whether in the cloud or on-prem
Huggingface_Transformer_Topics - GitHub It covers tasks such as text classification, question answering, and sentiment analysis, all trained with Hugging Face's transformers library and fine-tuned on relevant datasets, demonstrating my NLP learning journey
agents-course (Hugging Face Agents Course) Organization Card Agents Course on Hugging Face Learn This interactive, certified course will guide you through building and deploying your own AI Agents 📝 Register here for the course 📖 Check out the course material here
RNN vs LSTM vs GRU vs Transformers - GeeksforGeeks There are four main types of models used for this Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs) and Transformers Each model works in its own way and has different strengths and weaknesses In this article, we will see difference between these models to find best one for our project
Open Source Models with Hugging Face - DeepLearning. AI Find and filter open source models on Hugging Face Hub based on task, rankings, and memory requirements Write just a few lines of code using the transformers library to perform text, audio, image, and multimodal tasks Easily share your AI apps with a user-friendly interface or via API and run them on the cloud using Gradio and Hugging Face