The domain of large language model (LLM) quantization has garnered attention due to its potential to make powerful AI technologies more accessible, especially in environments where computational resources are scarce. By reducing the computational load required to run these models, quantization ensures that advanced AI can be employed in a wider array of practical scenarios… →

Vision Transformers (ViT) and Convolutional Neural Networks (CNN) have emerged as key players in image processing in the competitive landscape of machine learning technologies. Their development marks a significant epoch in the ongoing evolution of artificial intelligence. Let’s delve into the intricacies of both technologies, highlighting their strengths, weaknesses, and broader implications on copyright issues… →

Molecular representation learning is an essential field focusing on understanding and predicting molecular properties through advanced computational models. It plays a significant role in drug discovery and material science, providing insights by analyzing molecular structures. The fundamental challenge in molecular representation learning involves efficiently capturing the intricate 3D structures of molecules, which are crucial for… →

Language models, a subset of artificial intelligence, focus on interpreting and generating human-like text. These models are integral to various applications, ranging from automated chatbots to advanced predictive text and language translation services. The ongoing challenge in this field is enhancing these models’ efficiency and performance, which involves refining their ability to process & understand… →

Large Language Models (LLMs) like GPT-3 and ChatGPT exhibit exceptional capabilities in complex reasoning tasks such as mathematical problem-solving and code generation, far surpassing standard supervised machine learning techniques. The key to unlocking these advanced reasoning abilities lies in the chain of thought (CoT), which refers to the ability of the model to generate intermediate… →

Autoregressive language models (ALMs) have proven their capability in machine translation, text generation, etc. However, these models pose challenges, including computational complexity and GPU memory usage. Despite great success in various applications, there is an urgent need to find a cost-effective way to serve these models. Moreover, the generative inference of large language models (LLMs)… →

Quantization, a method integral to computational linguistics, is essential for managing the vast computational demands of deploying large language models (LLMs). It simplifies data, thereby facilitating quicker computations and more efficient model performance. However, deploying LLMs is inherently complex due to their colossal size and the computational intensity required. Effective deployment strategies must balance performance,… →

Mixture-of-experts (MoE) architectures use sparse activation to initial the scaling of model sizes while preserving high training and inference efficiency. However, training the router network creates the challenge of optimizing a non-differentiable, discrete objective despite the efficient scaling by MoE models. Recently, an MoE architecture called SMEAR was introduced, which is fully non-differentiable and merges… →