This Week I Learned - Week #41 2024
![Image](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj-9wXNIrSKJSivPBTVoUUzezdiD2l0_CR3hkAlfVX7Y957mV_dES5YRaydwFS5vacHhU4iGZ9ZChWKlokl7eFe0Jii3k4Lj5NuYvrZhUroMJhpzWWDSu_STzwjr8IR1Ku-zfQeYJxFnhXgGB0puoROycgyyMahXYAO4dZoW56MRWiiObiUUzYM/w640-h344/mimicry.jpg)
This Week I Learned - * Llama 3.2 90B and 11B accept images as well as text and generate text output (image processing is not available in the European Union). Llama 3.2 1B and 3B accept and generate text. All four models can process 131,072 tokens of input context and generate 2,048 tokens of output. Llama 3.2’s vision-language capabilities now drive the company’s Meta AI chatbot. For example, users can upload a photo of a flower and ask the chatbot to identify it or post a picture of food and request a recipe. - The Batch * Llama Stack is a set of APIs for customizing Llama models and building Llama-based agentic applications. By offering tools to build agentic workflows, Llama Stack takes Llama 3.2 well beyond the models themselves. The short course “ Introducing Multimodal Llama 3.2 ” by Amit Sangani, Senior Director of AI Partner Engineering at Meta, shows how to put these models to use. * Gemini Nano is the smallest version of the Gemini model family. * Adobe integrates multiple