Fine tune Llama-2 ; three guides

📖 TUTORIALS & CASE STUDIES

— Fine tune Llama-2 ; three guides —

Fine-Tuning Llama-2 For Unique Applications: A Deep Dive
Read Time: 20 minutes
Interesting case study by Anyscale on fine-tuning the Llama-2 language model for specific tasks. The team demonstrates how, in some scenarios, fine-tuned Llama-2 outperforms GPT-4! The key takeaway for us developers? Tailoring AI models can yield surprising accuracy improvements and may be a more cost-effective choice for certain applications. Anyscale's platforms make the fine-tuning process more manageable too. Worth a read if you're into scalable AI and distributed computing.

Fine-Tuning Llama 2 with Your Own Data: A Simple Guide
Read Time: 15 minutes
This guide provides a step-by-step process on how to fine-tune the Llama 2 model with your own dataset using HuggingFace’s libraries. It covers requirements, setup, data preparation, model loading, fine-tuning, and running inference on a trained model.

Fine-Tune LLaMA 2 with QLoRA
Read Time: 15 minutes
Matt Shumer (https://twitter.com/mattshumer_) provides a Google Colab notebook demonstrating how to fine-tune LLaMA 2 models w/ very low resource usage.

Leveraging Embeddings to Analyze Writing Styles
Read Time: 7 minutes
In this article, the author explores how NLP embeddings can be used to build creative writing analysis tools. Specifically, they propose using embeddings to quantify abstractness, sentiment, and other styles on a continuous scale. By averaging the embeddings of example words for each "extreme", any new text's average embedding can be mapped to a score on that scale. This approach is more interpretable and customizable than traditional NLP models, while being far cheaper than custom models.

Engineering Production-Ready LLM Applications
Read Time: 25 minutes
In this in-depth article, Chip Huyen discusses challenges in engineering production-ready applications with large language models (LLMs). The author covers key issues like prompt ambiguity, evaluation, and versioning. Techniques for optimizing prompts like using examples and breaking tasks into subtasks are also explained. Cost and latency implications of different design decisions are explored, with helpful benchmark data. Later sections dive into task composability using control flows as well as promising multi-step use cases. Throughout, the article provides a wealth of practical advice for software developers looking to leverage the power of LLMs in their applications while maintaining reliability and efficiency. This comprehensive guide is a must-read for anyone seeking to build robust LLM-powered tools.

MeowLearning: A Comprehensive Guide to AI in Production
Read Time: 15 minutes
This guide provides a thorough overview of working with AI in production settings, covering topics from fundamentals of Machine Learning Systems Design to model deployment and monitoring. It also includes resources for model research, planning, training, prototyping, and keeping up with AI trends.

Boost Your Search Capabilities with Large Language Models
Course: 1 hr
This free short course teaches how to enhance keyword search using Large Language Models (LLMs) and semantic search. It covers concepts like dense retrieval and reranking, and provides hands-on practice with large data sets. Ideal for Python users wanting to deepen their understanding of LLMs.

🧰 TOOLS

Stability AI has released StableCode
Read Time: 5 minutes
StableCode is an AI coding assistant capable of autocomplete suggestions, code understanding, and responding to specific programming tasks and instructions. StableCode leverages three unique models - a base model trained on millions of lines of code, an instruction model fine-tuned for specific tasks, and a long-context model allowing up to 16,000 tokens of context. These models offer powerful autocomplete, documentation-style responses, and a learning environment for beginners.

CodeStory: An AI-Powered Mod of VSCode
Read Time: 3 minutes
Introducing CodeStory, an AI-powered modification of VSCode that generates code, executes terminal commands, refactors, fixes bugs, reviews PRs, and handles git. It supports all VSCode extensions, shortcuts, and customizations, and offers intuitive search and auto-generated PR summaries.

Supabase Studio 3.0: AI-Powered SQL Editor and More
Read Time: 10 minutes
Supabase has launched Studio 3.0, introducing Supabase AI in the SQL Editor, Schema Visualizer, Role Management, Shared SQL Snippets, Database Migration UI, and Wrappers UI. The AI can write SQL, modify snippets, and visualize changes, enhancing the SQL Editor’s capabilities.

Agentflow: Simplifying Complex LLM Workflows with JSON
Read Time: 4 minutes
Agentflow is a new tool that allows developers to run Large Language Model (LLM) workflows using simple, human-readable JSON files. It bridges the gap between chat and autonomous interfaces, providing a balanced solution for workflow execution. Learn more about Agentflow here.

Onju Voice: A Hackable AI Home Assistant Platform
Read Time: 8 minutes
Onju Voice is a customizable AI home assistant platform using the Google Nest Mini form factor. It features a custom PCB for audio processing, a server for handling transcription, response generation, and Text-to-Speech, and offers several example applications. The platform is designed for easy setup and use, with potential for further development and improvements.

FoodSAM: A Novel Framework for Food Image Segmentation
Read Time: 7 minutes
The PyTorch implementation of FoodSAM, a novel framework for food image segmentation, is now available. FoodSAM enhances the Segmentation Anything Model (SAM) by integrating coarse semantic masks with SAM-generated masks, and can perform instance, panoptic, and prompt-able segmentation on food images.

Magnet: The Future of AI Coding Assistants
Read Time: 7 minutes
Magnet, an AI assistant for programming, aims to streamline the software development process by reading local files and issues directly from your browser, providing context on tasks, and integrating files across multiple repos. The Magnet team envisions AI coding assistants going end-to-end on shipping features and fixing errors, making coding more accessible and efficient.

Exploring OpenAI’s Moderation API
Read Time: 2 minutes
OpenAI has introduced a Moderation API that developers can use to add a moderation layer to the outputs of the Chat API, ensuring the generated content aligns with OpenAI’s use-case policy.

📰 NEWS

Introducing Project IDX: Google’s Cloud-Based Dev Workflow
Read Time: 3 minutes
Google has launched an experimental initiative, Project IDX, aimed at simplifying full-stack, multi-platform app development by moving it to the cloud. The project features a web-based workspace, support for popular frameworks, and AI assistance for faster coding.

⌨️ PUT DOWN THE KEYBOARD

LLaMA-2: A Game-Changer in Generative AI
Read Time: 10 minutes
Meta’s LLaMA-2, a significant leap in AI, is now available for both research and commercial use. This article provides a detailed review of the LLaMA-2 paper, highlighting its architecture, training, and fine-tuning process, and its impact on Generative AI.

A Comprehensive Guide to Llama: The Open-Source Large Language Model
Read Time: 15 minutes
This guide provides an in-depth introduction to Llama, an open-source large language model by Meta. It covers the model’s functionality, benefits over proprietary models like ChatGPT, installation process, and commercial usage. It also discusses model variants, supported languages, and hardware requirements.

The Evolution of Powerful Open-Source Language Models
Read Time: 20 minutes

This comprehensive overview traces the development of popular open-source LLMs such as LLaMA, MPT, Falcon, and LLaMA-2. These models improved significantly upon early open-source LLMs by pre-training on vastly larger, higher-quality datasets containing over 1 trillion tokens of text. As a result, they match or surpass the performance of proprietary models on standard benchmarks while enabling open-source research. The article explores how these models catalyzed growth in open-source LLM applications by providing powerful base models to the community at minimal cost compared to pre-training from scratch. These developments have helped narrow the capability gap between open and closed-source language models.