Browsing: Pipeline

Image by Author   # Introduction  In a retrieval-augmented generation (RAG) pipeline, embedding models are the foundation that makes retrieval work. Before a language model can answer…

In this tutorial, we build an advanced, end-to-end learning pipeline around Atomic-Agents by wiring together typed agent interfaces, structured prompting, and a compact retrieval layer that…

Serving Large Language Models (LLMs) at scale is a massive engineering challenge because of Key-Value (KV) cache management. As models grow in size and reasoning capability,…

!pip -q install -U “protobuf<5” “flwr[simulation]” transformers peft accelerate datasets sentencepiece import torch if torch.cuda.is_available(): !pip -q install -U bitsandbytes import os os.environ[“RAY_DISABLE_USAGE_STATS”] = “1” os.environ[“TOKENIZERS_PARALLELISM”]…

Image by Editor   # The Fragile Pipeline  The gravitational pull of state of the art in modern machine learning is immense. Research teams and engineering departments…

Image by Editor   # Introduction  Machine learning systems are not just advanced statistics engines running on data. They are complex pipelines that touch multiple data stores,…

In this tutorial, we build an advanced, multi-turn crescendo-style red-teaming harness using Garak to evaluate how large language models behave under gradual conversational pressure. We implement…

In this tutorial, we demonstrate how to build a unified Apache Beam pipeline that works seamlessly in both batch and stream-like modes using the DirectRunner. We…

In this tutorial, we build an advanced, end-to-end multi-agent research workflow using the CAMEL framework. We design a coordinated society of agents, Planner, Researcher, Writer, Critic,…

The NotebookLM is a relatively new Internet phenomenon, in which Google has distinguished itself, thanks to its Audio Overview mode – a mechanism that transforms the…