The deployment of large language models (LLMs) has become central to many applications, from synthetic data generation to fine-tuning models for specific tasks. With their vast capabilities, these models have opened new frontiers in research and application...

Mixture-of-experts (MoE) models have revolutionized artificial intelligence by enabling the dynamic allocation of tasks to specialized components within larger models. However, a major challenge in adopting MoE models is their deployment in environments with limited...

Transformer-based models have transformed the fields of Natural Language Processing (NLP) and Natural Language Generation (NLG), demonstrating exceptional performance in a wide range of applications. The best examples of these are the recently introduced models Gemini...

Deep reinforcement learning (RL) focuses on agents learning to achieve a goal. These agents are trained using algorithms that balance exploration of the environment with the exploitation of known strategies to maximize cumulative rewards. A critical challenge within...

The advent of code-generating Large Language Models (LLMs) has marked a significant leap forward. These models, capable of understanding and generating code, are revolutionizing how developers approach coding tasks. From automating mundane tasks to fixing complex...

Training Large Language Models (LLMs) involves two main phases: pre-training on extensive datasets and fine-tuning for specific tasks. While pre-training requires significant computational resources, fine-tuning adds comparatively less new information to the model,...