Imagine a master chef. This chef has spent years learning the fundamentals of cooking—how flavors combine, the science of heat, […]
The Ultimate Guide to Customizing LLMs: Training, Fine-Tuning, and Prompting Read More »
The Qwen2.5-1M series are the first open-source Qwen models capable of processing up to 1 million tokens. This leap in
Qwen2.5-1M: Million-Token Context Language Model Read More »
DeepSeek-R1 represents a significant advancement in the field of LLMs, particularly in enhancing reasoning capabilities through reinforcement learning (RL). This
DeepSeek-R1: How Reinforcement Learning is Driving LLM Innovation Read More »
For a long time, the focus in LLM development was on pre-training. This involved scaling up compute, dataset sizes and
Inference Time Scaling Laws: A New Frontier in AI Read More »
Microsoft has released Phi-4, designed to excel in mathematical reasoning and complex problem-solving. Phi-4, with only 14 billion parameters, demonstrates
Phi-4: A Powerful Small Language Model Specialized in Complex Reasoning Read More »
PromptWizard addresses the limitations of manual prompt engineering, making the process faster, more accessible, and adaptable across different tasks. Prompt
PromptWizard: LLM Prompts Made Easy Read More »
What is DSPy? Declarative Self-improving Python (DSPy) is an open-source python framework [paper, github] developed by researchers at Stanford, designed
DSPy: A New Era In Programming Language Models Read More »
Large Concept Models (LCMs) [paper] represent a significant evolution in NLP. Instead of focusing on individual words or subword tokens,
Large Concept Models (LCM): A Paradigm Shift in AI Read More »
The rapid development and adoption of Artificial Intelligence (AI), particularly generative AI like Large Language Models (LLMs), has brought forth
Principles for Responsible AI Read More »
Vision Transformers (ViT) have emerged as a groundbreaking architecture that has revolutionized how computers perceive and understand visual data. Introduced
Dissecting the Vision Transformer (ViT): Architecture and Key Concepts Read More »
To compute the number of tokens in a Vision Transformer (ViT), it’s essential to understand how images are processed and
How To Compute The Token Consumption Of Vision Transformers? Read More »
Low-Rank Adaptation (LoRA) is a novel and efficient method for fine-tuning large language models (LLMs). By leveraging low-rank matrix decomposition,
Understanding LoRA Technology for LLM Fine-tuning Read More »
