# Liquid Foundation Models: Our First Series of Generative AI Models

**Liquid Foundation Models (LFMs)** represent a breakthrough in generative AI, achieving **state-of-the-art performance** across 1B, 3B, and 40B parameter scales while optimizing memory usage and inference efficiency.

## VisionTS: Zero-Shot Time Series Forecasting with Visual Masked Autoencoders

**VisionTS** transforms the **forecasting task** into an **image reconstruction** challenge, leveraging visual masked autoencoders to achieve impressive results in zero-shot scenarios.

## What’s the SOTA model for style transfer as of 2024?

The **state-of-the-art (SOTA)** model for image style transfer in 2024 has shifted towards **diffusion methods**, which significantly enhance the ability to capture higher-level concepts compared to traditional **Gram matrix-based techniques**.

## Optimizing transformers

**Optimizing transformer models** in multi-view images and cross-attention networks can significantly enhance training speed by addressing the **parameter overload** caused by cross-attention layers.

## PyTorch Native Architecture Optimization: Torchao

**torchao** is a new PyTorch library that optimizes model performance by utilizing low bit dtypes, quantization, and sparsity, achieving up to **97% speedup** for Llama 3 inference with minimal accuracy loss.

## Experimenting with Llama-3 codebase and Google NotebookLM – Mind-Blowing Results!

**Experimenting with the Llama-3 codebase and Google NotebookLM yielded results that surpassed expectations**, showcasing the potential of combining advanced architectures with innovative tools like Rag and SERP APIs for enhanced multimedia generation.

## Stress-testing biomedical vision models with RadEdit: A synthetic data approach for robust model deployment

**RadEdit enhances biomedical vision models** by using generative image editing to simulate diverse dataset shifts, allowing researchers to identify model weaknesses before deployment in clinical settings.

## Model-based Preference Optimization in Abstractive Summarization without Human Feedback

**Model-based Preference Optimization (MPO)** enhances **abstractive summarization** by utilizing the model's own capabilities to generate a preference dataset, eliminating the need for costly human feedback.

## Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications

**Long-context Large Language Models (LLMs)** excel in **multi-document summarization**, effectively capturing extensive connections and providing cohesive summaries across various industries, including legal, HR, finance, and medical sectors.