Top 9 Libraries to Accelerate LLM Building

📖 TUTORIALS & CASE STUDIES

 How to Fine-tune Florence-2 for Object Detection Tasks

Read time: 15 minutes

This article demonstrates how software developers can fine-tune the Florence-2 vision-language model for object detection tasks on custom datasets. Florence-2 is a versatile model that can be adapted to various vision tasks, and the tutorial provides a step-by-step guide on loading the pre-trained model, preparing the dataset, and optimizing the fine-tuning process using techniques like LoRA. The article also benchmarks the fine-tuned model's performance and discusses the advantages of using a multipurpose model like Florence-2 overspecialized object detection models.

Text to Knowledge Graph Made Easy with Graph Maker

Read Time: 14 minutes

This article introduces the Graph Maker, an open-source library that enables software developers to easily build knowledge graphs from text using pre-trained language models like Llama 3 and Mixtral. The author discusses the challenges of using LLMs to extract consistent and meaningful entities and relationships, and how the Graph Maker addresses these issues by allowing developers to define a custom ontology. The article also covers integrating the generated knowledge graphs with Neo4j for downstream applications like Retrieval Augmented Generation (RAG).

From Pixels to Words: How Model Understands?

Read Time: 13 minutes

This article explores the fascinating world of multi-modal AI models, which bridge diverse data types like images and text through sophisticated embedding techniques. It examines key concepts like joint embedding space, cross-attention, and concatenation/fusion, demonstrating how these models learn to semantically relate visual and textual representations. By understanding these techniques, software developers can leverage powerful multi-modal frameworks like CLIP and Stable Diffusion to create innovative applications that seamlessly combine images and language.

Making my local LLM voice assistant faster and more scalable with RAG

Read time: 7 minutes

This article describes how the author improved the performance and scalability of their local LLM-powered voice assistant by integrating a RAG system. By dynamically generating relevant context for the LLM prompt based on the user's query, the author was able to significantly reduce the inference time and overcome the limitations of the LLM's context size, making the assistant more responsive and capable of handling a wider range of requests.

🧰 TOOLS

Top 9 Libraries to Accelerate LLM Building

Read Time: 17 minutes

This article provides an overview of essential libraries for building and deploying large language models (LLMs), a critical topic for software developers leveraging generative AI. It covers training and scaling libraries like Megatron-LM, DeepSpeed, and YaFSDP, testing tools like Giskard and lm-evaluation-harness, deployment solutions like vLLM and CTranslate2, and logging/observability platforms such as Truera and Deepchecks. The article equips readers with a comprehensive understanding of the LLM development ecosystem and the key tools to build competitive applications powered by state-of-the-art generative AI.

Data Visualization Generation Using Large Language and Image Generation Models with LIDA

Read time: 12 minutes

LIDA is an open-source library that automates data visualization creation using large language models and image generation models. It generates visualizations, infographics, and textual summaries from datasets, reducing development time and complexity. LIDA supports prompt engineering, code generation, and visualization evaluation—features that enable software developers to leverage generative AI for data visualization in their applications.

Prompt Engineering Toolkit: Streamline Your AI Experiments

Read Time: 6 minutes

The Prompt Engineering Toolkit is a javascript, web-based application that helps software developers and researchers optimize prompts for large language models. It allows you to test prompts across multiple providers, save and load prompt templates, manage variables for dynamic prompts, and compare model outputs side-by-side. This valuable tool can improve your productivity and results when leveraging generative AI in your applications.

AutoCodeRover: Autonomous Program Improvement

Read time: 16 minutes

AutoCodeRover is a fully automated approach for resolving GitHub issues that combines Large Language Models with program analysis capabilities. It can navigate codebase context, perform fault localization, and generate patches, achieving 30.67% efficacy on the SWE-bench lite benchmark while costing less than $0.7 per task. AutoCodeRover supports multiple foundation models including GPT-4, Claude, and LLaMA, making it a powerful tool for software developers looking to leverage generative AI in their applications.

Claude Engineer: An AI-Powered CLI for Software Development

Read time: 5 minutes

Claude Engineer is an interactive command-line interface that leverages Anthropic's Claude-3.5-Sonnet language model to assist software developers with a variety of tasks. This tool combines the capabilities of a large language model with file system operations and web search functionality, enabling developers to create project structures, analyze code, get debugging help, and more. The key features include an interactive chat interface, project management tools, and an "Automode" that allows Claude to work autonomously on complex tasks.

📰 NEWS & EDITORIALS

Not all 'open source' AI models are actually open: here's a ranking

Read time: 8 minutes

This article reveals that many large language models touted as ' open source' by tech giants like Meta and Microsoft often restrict access to their underlying code and training data. Researchers analyzed popular chatbot models and ranked them based on true openness, providing valuable insights for software developers looking to leverage powerful yet transparent AI tools.

Open-LLM performances are plateauing, let's make the leaderboard steep again

Read Time: 9 minutes

This article discusses the plateau in performance of open-source large language models (LLMs) and the need to push the boundaries further. The open-llm-leaderboard project aims to create a comprehensive and competitive leaderboard to drive advancements in open-source LLMs, helping software developers stay informed about the latest innovations in this space.

Meet Figma AI: Empowering Designers with Intelligent Tools

Read time: 9 minutes

Figma is introducing a suite of AI-powered features to help designers work more efficiently and creatively. Key highlights include Visual Search to find visually similar designs, AI-enhanced Asset Search for intuitive component discovery, text editing tools, automatic layer renaming, and the ability to generate UI designs from text prompts. These features are designed to solve real pain points for designers and developers while ensuring privacy and security through controlled data sharing for model training.

AI is Here: How it's Changing the Way Developers Work

Read time: 9 minutes

This article explores how LLMs like GitHub Copilot are transforming software development workflows. Experienced developers Claudio Wunder and Chrissy LeMaire share how they use LLMs to automate tasks, speed up learning, and improve code quality - without becoming over-reliant on the technology. The article also offers practical advice for developers looking to effectively leverage AI coding tools.