5 Practical Methods to Serve Open Source LLMs

📖 TUTORIALS & CASE STUDIES

The Lifecycle of a Code AI Completion

read time: 10 minutes

This article explores the lifecycle of a code AI completion for Cody, a code AI assistant. It covers the steps of planning, retrieval, generation, and post-processing that are involved in creating high-quality code completions. The article also discusses the importance of context, prompt engineering, and telemetry in improving code completion quality and acceptance rate. Read more here.

Strategies for Testing Large Language Models

read time: 15 minutes
This article provides insights into the challenges and strategies for testing LLMs embedded in applications. It covers the importance of creating test data, automating model quality and performance testing, evaluating Retrieval Augmented Generation (RAG) quality based on use cases, and developing quality metrics and benchmarks.

Boosting Generative Language Models with Self-Consistency Prompting on Amazon Bedrock

read time: 15 minutes

Amazon Bedrock, a fully managed service, can enhance the performance of generative language models using a self-consistency prompting approach. This method generates multiple responses that are aggregated into a final answer, leading to more accurate results on tasks that involve reasoning. The post provides a detailed walkthrough on how to implement this approach using Amazon Bedrock's batch inference API. Read more about it here.

5 Practical Methods to Serve Open Source LLMs

read time: 15 minutes
This article provides a comprehensive guide on five different methods to serve Large Language Models (LLMs) including local server setups with Anaconda and Docker, and managed services like Modal and AnyScale. Each method is explained with code examples and a discussion of their pros and cons.

Customize Your LLM with Mixtral 8x7B: A Hands-On Guide

read time: 15 minutes
French company Mistral AI's Large Language Model (LLM), Mixtral 8x7B, offers a competitive alternative to models like Meta's Llama 2 70B and OpenAI's ChatGPT 3.5. This guide provides a hands-on approach to customizing Mixtral 8x7B with your own data, locally, offering flexibility and data control for developers and businesses.

Demystifying Mixture of Experts in AI Models

read time: 15 minutes
This article provides a simplified explanation of the Mixture of Experts (MoE) concept used in AI models like GPT-4 and Mixtral 8x7B. It clarifies misconceptions about 'experts' and explains how they enhance efficiency and performance in transformer models. The piece also highlights the role of a 'router' network in directing tokens to the appropriate 'experts'.

🧰 TOOLS

Leverage SentenceTransformers for Advanced Text and Image Embeddings

read time: 4 minutes
The SentenceTransformers Python framework offers state-of-the-art sentence, text, and image embeddings. It supports over 100 languages and provides a large collection of pre-trained models. The framework, based on PyTorch and Transformers, is ideal for semantic textual similarity, semantic search, and paraphrase mining. It also allows for easy fine-tuning of your own models.

Plandex: An Open Source, Terminal-Based AI Coding Engine

read time: 10 minutes

Plandex is an open-source, terminal-based AI coding engine that uses long-running agents to complete complex tasks. It breaks up large tasks into smaller subtasks and implements each one, allowing developers to manage their work efficiently. It also provides a protected sandbox for changes and built-in version control. Plandex relies on the OpenAI API and plans to support open source models, Google Gemini, and Anthropic Claude soon. Read more

Introducing OLMo: A Truly Open Large Language Model by AI2

read time: 10 minutes
The Allen Institute for AI (AI2) has released OLMo 7B, a state-of-the-art large language model with its pre-training data and training code. The OLMo framework is designed to aid researchers in training and experimenting with large language models, and is available on Hugging Face and GitHub.

Claude's Prompt Library: A Treasure Trove of AI Applications

read time: 10 minutes

Anthropic's Claude offers a prompt library with a wide range of AI applications, from generating interactive games and creating one-page websites, to debugging Python code and crafting engaging stories. The library also includes tools for data conversion, text simplification, and even philosophical discussions. A valuable resource for developers looking to leverage AI in their projects.

Google's Gemini Code Assist: A New AI-Powered Coding Assistant

read time: 15 minutes
Google has announced Gemini Code Assist, an AI-powered coding assistant that integrates with development environments like VS Code and JetBrains. It aims to simplify the programming process by understanding and generating code, potentially saving developers significant time. However, its effectiveness and accuracy remain to be fully tested.

📰 NEWS & EDITORIALS

Major LLM Releases and Updates in 24 Hours

read time: 15 minutes
In a span of 24 hours, major updates were released for Large Language Models (LLMs) including Google's Gemini Pro 1.5, OpenAI's GPT-4 Turbo, and Mistral's Mixtral 8x22B. The author also discusses his work on Datasette Extract, a plugin powered by GPT-4 Turbo. Read more about these updates and their implications here.

Meta's Next-Gen AI Training and Inference Accelerator

read time: 8 minutes
Meta has unveiled the next generation of its Meta Training and Inference Accelerator (MTIA), designed to support generative AI products, recommendation systems, and advanced AI research. The new MTIA chip offers improved compute and memory bandwidth, and is part of Meta's full-stack development program for custom silicon. The chip is designed to efficiently serve ranking and recommendation models. Read more about it here.

AI Pioneer Dr. Andrew Ng Joins Amazon's Board of Directors

read time: 3 minutes

AI expert Dr. Andrew Ng has joined Amazon's Board of Directors. Known for his work in AI education and his role in Google Brain and Baidu, Dr. Ng's expertise in generative AI will provide valuable insights for Amazon. More details can be found on Amazon's announcement.