A Hackers' Guide to Language Models

📖 TUTORIALS & CASE STUDIES

A Hackers' Guide to Language Models
Watch: 1.5 hrs
In this video, Jeremy Howard from fast.ai dives into a code-first approach to language models. He emphasizes fine-tuning models for optimal use, discusses the efficiencies of GPT-4, and introduces tools for utilizing AI on various platforms. For developers, mastering these models can streamline coding and boost creativity. 🤖🛠️

Representational Strengths and Limitations of Transformers
Watch: 1hr
In this video, Clayton, a PhD student from Columbia, delves into transformers' representational properties. He explores their capabilities, contrasts with standard neural networks, and highlights challenges. Notably, transformers might struggle with intrinsically three-wise functions. A deeper look suggests a potential link between transformers and congest networks. Curious about neural network biases? This talk is a must-watch!

Optimizing RAG Applications for Production
read time: 15 minutes
This guide provides tips and techniques to enhance the performance of your RAG pipeline. It covers topics like decoupling chunks for retrieval and synthesis, structured retrieval for larger document sets, dynamic chunk retrieval based on tasks, and optimizing context embeddings.

The Transformer: The Core of Generative AI
read time: 7 minutes
This article explores how the transformer model has become the backbone of generative AI, enabling advancements in AI tools and applications. It provides a comprehensive understanding of the transformer's role in the evolution of generative AI.

Best Practices for Evaluating Large Language Models in Chatbot Applications
read time: 15 minutes
Databricks shares insights on best practices for evaluating large language models (LLMs) in chatbot applications, particularly those using the Retrieval Augmented Generation (RAG) architecture. The article discusses the challenges of auto-evaluation, the effectiveness of using LLMs as judges, and the importance of use-case-specific benchmarks. It also provides recommendations for grading scales and LLM judges.

Unleashing the Full Potential of Large Language Models
read time: 20 minutes
Large Language Models (LLMs) like GitHub Copilot are transforming the tech landscape, but they're not without limitations. This article explores the ecosystem around LLMs, including prompt engineering, retrieval augmented generation, conversational memory, agents, and guardrails, which can significantly enhance their performance and utility.

🧰 TOOLS

NExT-GPT: A Leap Forward in Multimodal AI
read time: 10 minutes

Researchers have developed NExT-GPT, an end-to-end general-purpose multimodal Large Language Model (MM-LLM) system. It can perceive and generate content in text, images, videos, and audio, overcoming the limitation of input-side multimodal understanding. The system uses a small amount of parameter tuning, making it cost-effective and expandable to more modalities.

Open Interpreter: A Local Alternative to OpenAI's Code Interpreter
read time: 10 minutes

Open Interpreter is an open-source tool that allows Large Language Models to run code locally on your computer. It overcomes the limitations of OpenAI's hosted, closed-source Code Interpreter by providing unrestricted internet access, no file size or runtime limits, and the ability to utilize any package or library. Learn more about it here.

Introducing Marvin: A Framework for Trustworthy AI Engineering
read time: 5 minutes
Marvin is a new AI engineering framework designed to make generative AI more reliable and scalable. It brings best practices from software development to AI, offering components for structuring text, classification, complex business logic, and interactive applications. Marvin aims to deliver Ambient AI, making unstructured data accessible to traditional software. Learn more about Marvin here.

Introducing DSPy: A New Framework for Advanced Tasks with Language Models
read time: 15 minutes
DSPy is a new framework that unifies techniques for prompting and fine-tuning language models, and introduces an automatic compiler that teaches language models how to conduct the declarative steps in your program. It allows for the creation of composable and declarative modules for instructing language models in a Pythonic syntax. Check out the full details here.

Unstructured: Open-Source Pre-Processing Tools for Unstructured Data
read time: 15 minutes
The unstructured library offers open-source components for ingesting and pre-processing unstructured data like images and text documents. It also introduces a new Unstructured API and a beta feature, the Chipper model, for processing complex documents. The library provides a cohesive system for data ingestion and pre-processing, adaptable to different platforms.

LiteLLM: A Unified Interface for Large Language Models
read time: 8 minutes
LiteLLM is a tool that allows developers to call all Large Language Model (LLM) APIs using the OpenAI format. It supports over 100 models, including those from Anthropic, Huggingface, Cohere, TogetherAI, Azure, and OpenAI. LiteLLM manages input translation, guarantees consistent output, and maps common exceptions across providers to OpenAI exception types. It also supports streaming, caching, and running your model locally or on a custom endpoint. Check out the LiteLLM GitHub page for more details.

TinyLlama: A Compact and Powerful Language Model
read time: 15 minutes

The TinyLlama project is pretraining a compact 1.1B Llama model on 3 trillion tokens. It maintains the same architecture as Llama 2, making it compatible with many open-source projects. Its compactness allows it to cater to applications demanding a restricted computation and memory footprint. The project also provides a reference for enthusiasts keen on pretraining language models under 5 billion parameters.

📰 NEWS

Cloudflare Launches Workers AI for Full-Stack AI Applications
read time: 8 minutes
Cloudflare has announced the launch of Workers AI, a platform for developers to build full-stack AI applications. The platform offers affordable AI inference, data privacy, and compliance. It also introduces Vectorize, a vector database for AI workflows, and AI Gateway for AI application observability and scalability.

Roblox's Leap into Generative AI: A Game Changer for Creators
read time: 15 minutes
Roblox is revolutionizing its platform with generative AI tools, including a conversational AI assistant and a tool for easy avatar creation from images, set to release in 2024. These tools aim to democratize creation, making it easier and faster for users to create immersive experiences. Read more about these exciting developments on Roblox's blog.

Hugging Face's New Training Cluster Service
read time: 2 minutes
Hugging Face introduces a new service, Training Cluster, allowing developers to train Large Language Models (LLMs) at scale on their infrastructure. The service includes support from infrastructure experts and ensures data privacy by not storing training data.

The Rapid Growth and Impact of Llama Models
read time: 8 minutes
In just seven months, Llama-based models have seen over 30 million downloads, with Llama 2 and Code Llama driving significant momentum. The Llama community is thriving, with participants across the tech stack. Llama 2 was released to make the technology more accessible, reflecting Meta's belief in the power of open-source AI. The company remains committed to an open approach, focusing on multimodal AI, safety, responsibility, and community engagement.