AI Update: Prompting Mega Issue, Baby Llama, Image Concept To Video

Unleash ChatGPT's Power: 8 Techniques To Try!

Unleash the true power of ChatGPT with 8 expert prompting techniques that will revolutionize the way you interact with AI!

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57 ChatGPT Prompts For Solo Enterpreneurs

The Llama in the Room: Andrej Karpathy's Weekend Experiment with OpenAI's Baby Llama

In an unexpected twist to the ongoing AI saga, OpenAI's Andrej Karpathy has been dabbling in an experimental project, christened the 'Baby Llama', instead of developing the much-anticipated GPT-5. The primary objective of this venture? To demonstrate the feasibility of running Llama 2 models on low-powered devices using pure C code.

Karpathy, a luminary in the deep learning field, embarked on this weekend's project to create a simplified version of the Llama 2 model. His approach involved tuning nanoGPT to implement the Llama 2 architecture, instead of GPT-2, and then writing the C inference engine in run.c. The result? A project that has already garnered 2.2K stars on its GitHub repository.

The success of this approach lies in its ability to achieve highly interactive rates, even with reasonably sized models. Karpathy reports that on his M1 MacBook Air, the Llama 2 model with ~15 million parameters can infer at around 100 tokens per second in fp32, all through the C code he developed. This is a significant achievement, demonstrating the feasibility of running complex models on resource-constrained devices with a straightforward implementation.

Karpathy's 'Baby Llama' approach was heavily inspired by Georgi Gerganov's project, llama.cpp. After training the Llama 2 LLM architecture from scratch using PyTorch, Karpathy saved the model weights in a raw binary file. He then wrote a 500-line C file, named ‘run.c,' which loads the saved model and performs inferences using single-precision floating-point (fp32) calculations. This minimalistic approach ensures a low-memory footprint and requires no external libraries, allowing efficient execution on a single M1 laptop without the need for GPUs.

While this project has been a fascinating diversion, it's crucial to remember that it's a weekend experiment and not intended for production-grade deployment. However, it does raise interesting questions about the potential of C code in machine learning, a field where it has often been overlooked due to its lack of GPU involvement.

Karpathy's work also highlights the trend toward smaller models designed to run on local devices. With tech giants like Meta and Apple exploring similar paths, it's clear that the future of AI may be smaller, leaner, and more locally focused than we might have imagined. But for now, we'll have to wait and see if Karpathy's 'Baby Llama' grows into a fully-fledged beast of burden in the AI world.

Midjourney Concept Image To Video Using Kaiber

Images Of The Day

The prompts for each of these images came from the following article. This article is part 4 of a series the author has been compiling. The only change I have made has been the aspect ratio to get the image in a better format in some cases for a Facebook Profile Header.

Question Of The Day

Prompt Of The Day

Original Prompt:
Can you outline the essential steps to successfully launch a webinar series to promote a live in-person event for a financial services company, including pre-launch content, abandonment sequence, pre-webinar follow-up, and post-webinar follow-up focusing on increasing live event sales?

ChatGPT Optimized Prompt:
Could you outline a detailed plan for launching a webinar series aimed at promoting an in-person live event for a financial services company? I'm particularly interested in understanding how to develop pre-launch content, create an abandonment sequence, manage pre-webinar follow-ups, and execute post-webinar follow-ups. The main goal is to increase sales for the live event. We also want to make sure that the process aligns with industry best practices and leverages both email and social media platforms efficiently.