🚀 What is Trending in AI Research? Scenimefy + DSPy + Nous-Hermes-Llama2-70b + MeZO + ChatPDF......

➡️ How can we automatically render high-quality anime scenes from complex real-world images while preserving semantics, style, and details?

This paper introduces Scenimefy, a semi-supervised image-to-image translation framework that tackles these challenges. Scenimefy employs structure-consistent pseudo-paired data generated via a semantic-constrained StyleGAN using model priors like CLIP. The framework also incorporates segmentation-guided data selection and a patch-wise contrastive style loss for better stylization. Additionally, the paper contributes a high-resolution anime scene dataset and shows superior results compared to existing methods in both perceptual quality and quantitative metrics.

➡️ How can the efficacy of geometric deep learning on 3D protein structures be improved given limited structural data?

This paper proposes the integration of knowledge from well-trained 1D protein language models into geometric neural networks. By doing so, the study achieves a 20% improvement over existing baselines in various protein representation tasks, including interface prediction and binding affinity estimation. The findings suggest that combining 1D sequence knowledge with geometric models significantly boosts performance and generalizability.

➡️ Researchers at Stanford Introduce DSPy: An Artificial Intelligence AI Framework for Solving Advanced Tasks with Language Models (LMs) and Retrieval Models (RMs)

Researchers at Stanford build a framework for solving advanced tasks with language models(LMs) and retrieval models(RMs). They call it as DSPy. DSPy consists of various techniques for prompting and finetuning the LMs and improving their reasoning and retrieval augmentation. DSPy is based on Pythonic syntax to provide composable and declarative modules for instructing LMs. DSPy also has an automatic compiler that trains the LM to run the declarative steps in your program. This compiler can finetune finetune from minimal data without manual intermediate-stage labels. It uses systematic space of modular and trainable pieces instead of string manipulation.

➡️ Meet Nous-Hermes-Llama2-70b: A State-of-the-Art Language Model Fine-Tuned on Over 300,000 Instructions

The Hugging Face Transformer is an immensely popular library in Python, which provides pre-trained models that are extraordinarily useful for a variety of Natural Language Processing tasks. It supported just PyTorch previously but, as of now, supports Tensorflow as well. Nous-Hermes-Llama2-70b is the NLP language model that uses over lakhs of instructions. This model uses the same dataset as the old Hermes model to ensure that there are no severe wide changes while training the model, and the process becomes even smoother. The model still has some deficits, like a lower hallucination rate and the absence of OpenAI censorship.

➡️ How can large language models be fine-tuned without the prohibitive memory requirements of backpropagation?

This paper introduces a memory-efficient zeroth-order optimizer (MeZO) that adapts the classical ZO-SGD method for in-place operation. MeZO enables fine-tuning with the same memory footprint as inference, achieving up to 12x memory reduction compared to backpropagation while maintaining comparable performance across various tasks and model scales. The method is also compatible with other tuning techniques and can optimize non-differentiable objectives.

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