AWS introduces Meta Llama 3 models on Trainium and Inferentia in SageMaker JumpStart, reducing deployment costs by up to 50%. This enables scalable, customizable deployments using a no-code approach or the SageMaker JumpStart SDK.

After diving into the transcription process in the first part of my series, I'm excited to share the next step in "Semantic Search to Glean Valuable Insights from Podcasts: Part 2." In this post, I walk you through the process of embedding the podcast transcripts using Cohere's embed model and efficiently storing them in ApertureDB. This step is crucial for enhancing search capabilities and ensuring our data is well-organized and ready for semantic querying. If you're interested in how to transform raw podcast data into a searchable database, this post is for you!

While robust test coverage is critical for high-quality products, the non-deterministic nature and complexity of LLMs often render traditional testing methods inadequate. In addition to LLM evaluation strategies like gold data benchmarks, cross-model evaluations, and probabilistic assertions, this article discusses scenarios where mocking is beneficial. It also provides concrete examples of different mocking techniques to ensure robust testing. By strategically applying these methods, development teams can enhance productivity, control costs, and ensure the reliability of LLM applications in production.

This article provides a guide on creating an LLM application using LlamaIndex and Qdrant to interact with GitHub repositories for easy retrieval of code snippets. The application is deployed on Google Kubernetes Engine with Docker and FastAPI and includes a user-friendly Streamlit interface for sending queries. You can code along following the repository and the instructions step by step.

This article shares a personal journey of being diagnosed with endometriosis and the subsequent deep dive into understanding and managing the condition. It highlights the societal neglect of endometriosis despite its prevalence and insufficient funding. The author leverages Large Language Models (LLMs) and data from Reddit to analyze common experiences and advice from fellow sufferers. Using LLMs, the study analyzed Reddit posts to understand sentiments and key topics, revealing frequent emotions like frustration and happiness. It underscores the importance of community support and practical advice for managing endometriosis, advocating for greater societal awareness and research funding.

The article covers several hands-on techniques to ensure the output from language models like GPT-4 and Gemini Pro meets specific requirements. It details how to use regular expressions, JSON schemas, context-free grammars, and templates to shape text outputs precisely. It also highlights the setup of the Vertex AI client, emphasizing safety in generation processes. Practical examples and code snippets are provided throughout, showing how these methods can be practically applied to get consistent and structured results from language models.

The blog evaluates the text processing speeds of three advanced 7 billion parameter language models—LLama2 7Bn, Mistral 7Bn, and Gemma 7Bn—using independent tests on A100 GPUs hosted on Azure.

Have you ever wondered how you could quickly and accurately find valuable insights from hours of podcast content? I'm excited to share the first post in my new series, "Semantic Search to Glean Valuable Insights from Podcasts: Part 1." In this post, I walk you through the process of transcribing podcasts using OpenAI's Whisper and storing them in ApertureDB, setting the stage for building a powerful semantic search engine. Check out the blog here: From Audio to Database: Transcribing Podcasts with OpenAI Whisper and Storing Them in ApertureDB