Technow: Lightning LLM booster, Anthropic Prompt Library, AI Agent

Discover how Thunder is revolutionizing the efficiency of Large Language Model (LLM) training with a remarkable 40% speed increase, explore Anthropic’s Claude Library to fine-tune your AI chatbot interactions, and delve into the dynamic world of AI agents — autonomous systems that are reshaping how we approach complex tasks with LLMs. Dive into an era of accelerated AI capabilities and frameworks that hold the promise of transforming our technological landscape.

Lightning Open-Sources Thunder Making LLMs 40% Faster

Thunder increases PyTorch Large Language Model (LLM) training speed by 40%, evident in tasks like Llama 2 7B model training.

It achieves this by fusing operations such as multiplication and activation, for example, merging torch.nn.functional.silu(x_fc_1) * x_fc_2 using NVFuser.

Integration and Usage:

Apply Thunder to your PyTorch models by calling thunder.jit(). This enables enhanced performance for multi-GPU environments using Distributed Data Parallel (DDP) and Fully Sharded Data Parallel (FSDP).

Technical Details and Installation:

Thunder uses hardware executors like nvFuser, torch.compile, cuDNN, and TransformerEngine FP8, improving both single and multi-accelerator performance. It integrates seamlessly with PyTorch’s standard operations and autograd.

For installation, first install nvFuser with specific pip commands, then install Thunder. This streamlined process helps you quickly start enhancing your models.

Anthropic Claude Library

Anthropic released a free prompt library to improve AI chatbot interactions, aiming to optimize user inputs for more accurate outputs. This library supports various use cases, from technical analysis to creative tasks, and helps users formulate structured prompts for enhanced response relevance and detail from AI models. While designed for Claude, it applies to other chatbots with similar capabilities and context sizes.

AI Agents

AI agents are autonomous or semi-autonomous systems equipped to accomplish specific, sometimes complex, tasks. Their capabilities generally encompass:

• Planning: The agent devises and implements a multistep strategy to meet a goal, leveraging Large Language Models (LLMs).
  Tool Use: Agents are endowed with tools like web search and code execution to aid in information gathering, decision-making, and data processing.
• Data or Context Understanding: Often, agents employ Retrieval-Augmented Generation (RAG) capabilities or digest specific data sets to enhance task completion.
• Reflection: Utilizing an LLM, the agent evaluates its performance to identify improvements.
• Multi-Agent Collaboration: Multiple AI agents collaborate, distributing tasks and exchanging ideas to forge superior solutions.

Such agentic workflows gained popularity after the release of OpenAI’s GPT store in November, which is now facing criticism because of the declining quality and verification on the 3 million GPTs deployed on the platform. Developers have quickly moved to building their own frameworks from scratch, powered by LLMs.

As highlighted by Andrew Ng in a recent viral post, AI Agents have a transformative potential that could outpace advancements in foundational models. The release of Devin, an AI agent for software development, demonstrates this potential.

To facilitate development, the AI community has crafted numerous frameworks for agent creation and orchestrating. The following GitHub repository provides an overview of most existing AI agents frameworks, and also classifies them based on their open or closed-source status.

AI agents are an important trend, and I urge everyone who works in AI to pay attention to it

Andrew Ng.