Gemma, a new family of lightweight, state-of-the-art open models built for responsible AI development, is introduced by Google.
"Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models" presents a new method for instruction tuning of Large Language Models (LLMs) called Generalized Instruction Tuning (GLAN).
"MuLan: Multimodal-LLM Agent for Progressive Multi-Object Diffusion" addresses the challenge of generating images of multiple objects with spatial relationships and attribute bindings.
"Instruction-tuned Language Models are Better Knowledge Learners" explores how to update factual knowledge in large language models.
Contact: sergi@earkind.com
Timestamps:
00:34 Introduction
01:21 Google DeepMind Releases Gemma
03:28 Andrej Karpathy on Gemma's Tokenizer
04:16 Groq Inference Tokenomics: Speed, But At What Cost?
05:51 Fake sponsor
07:44 Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
09:38 MuLan: Multimodal-LLM Agent for Progressive Multi-Object Diffusion
11:06 Instruction-tuned Language Models are Better Knowledge Learners
12:58 Outro
