Guide to Fine-Tuning Large Language Models on Apple Silicon Macs

📖 TUTORIALS & CASE STUDIES

Building Your First AI Project with RaspberryPi: A Dive into Computer Vision and ML Models

read time: 15 minutes
This article provides a detailed guide on creating an AI system using RaspberryPi, computer vision, and a trained ML model from mediapipe. The project involves building AI drums that can be controlled by hand gestures, demonstrating the potential of combining IoT, computer vision, and ML.

Efficient Fine-Tuning of Large Language Models on Consumer GPUs

read time: 15 minutes
This article demonstrates how to fine-tune a 7B parameter model on a typical consumer GPU using Low-Rank Adaptation for Large Language Models (LoRA) and tools from the PyTorch and Hugging Face ecosystem. It also introduces QLoRA, a method that combines quantization and LoRA, reducing the memory footprint by over 90% while retaining full model performance.

Guide to Fine-Tuning Large Language Models on Apple Silicon Macs

read time: 10 minutes
This guide provides a step-by-step process to fine-tune a large language model (LLM) on an Apple silicon Mac using Apple's MLX framework. It covers setting up the environment, building training data, fine-tuning the LLM, and testing the fine-tuned model.

Installing Stable Diffusion XL Locally on MacOS: A Step-by-Step Guide

read time: 10 minutes
This tutorial provides a comprehensive guide on how to install Stable Diffusion XL, an open-source image-generating tool, on MacOS. The guide covers the installation of necessary developer tools, including PyTorch, Anaconda, and XCode, and the setup of the Stable Diffusion XL environment.

Simplifying Information Extraction with Large Language Models

read time: 15 minutes
This article explores how Large Language Models (LLMs) can simplify information extraction from unstructured text. It also demonstrates how to build an information extraction pipeline using Python frameworks like LangChain and Streamlit, and discusses the potential limitations of LLMs in processing certain types of documents.

🧰 TOOLS

Open-Source AI Chat App for Everyone

read time: 2 minutes
Explore chatbot-ui, an open-source AI chat application that's accessible to all. This tool could be a valuable addition to your software development toolkit.

Revolutionizing RAG Applications: Anyscale and Pinecone's Cost-Efficient Embedding Computations

read time: 8 minutes
Anyscale and Pinecone have launched a cost-efficient solution for generating embeddings in Retrieval-Augmented Generation (RAG) applications. The solution offers 10% of the cost of other popular offerings, enabling companies to add unlimited knowledge to their GenAI apps at 2% of the prior cost. Read more about this groundbreaking serverless vector database here.

Comparative Analysis of AI Models and Hosting Providers

read time: 5 minutes
This article provides a comprehensive comparison of various AI models including GPT-4, Llama 2, and Claude 2.0, created by different organizations like OpenAI, Meta, and Anthropic. It helps developers choose the best model and hosting provider for their specific use cases.

Vector DB Comparison: A Deep Dive

read time: 3 minutes
VectorHub presents a comprehensive comparison of Vector DBs, providing insights into the various tools available for managing and manipulating vector data. This resource is invaluable for developers looking to leverage vector databases in their AI applications.

📰 NEWS & EDITORIALS

Valve Welcomes AI-Integrated Games on Steam

read time: 5 minutes
Valve now allows developers to put their AI-integrated games on Steam, provided they disclose the AI usage. The AI integration is categorized into pre-generated and live-generated content. However, games using live-generated AI for adult content are not permitted. Developers are also warned to ensure their games do not include illegal content. Read more

ChatGPT-4's Performance in Advent of Code 2023

read time: 15 minutes
This article explores how ChatGPT-4, a large language model, performed in the Advent of Code 2023 challenge. The author found that while ChatGPT-4 showed some improvement over its predecessor, it still struggled with more complex problems and lacked effective debugging skills.

Overcoming the Large Language Model Bottleneck

read time: 10 minutes
The adoption of Large Language Models (LLMs) like OpenAI’s GPT-4 and Anthropic’s Claude 2 in production environments is constrained by rate limits. Enterprises and startups are exploring ways to bypass these limits, including using generative AI models that don’t have LLM bottlenecks, requesting an increase in rate limits, and leveraging other generative AI models. The article also discusses the future of LLMs and the potential of next-generation models. Read more about it here.