Latest Posts

This blog post will delve into the intricacies of LLMs, exploring their inner workings, capabilities, future directions, and potential security concerns.

This guide delves into the process of building an LLM from scratch, focusing on the often-overlooked aspects of training and data preparation. We’ll also touch on fine-tuning, inference, and the importance of sharing your work with the community.

LISA introduces a surprisingly simple yet effective strategy for fine-tuning LLMs. It builds upon a key observation about LoRA: the weight norms across different layers exhibit an uncommon skewness. The bottom and top layers tend to dominate the updates, while the middle layers contribute minimally.

This blog post delves into QMoE, a novel compression and execution framework that tackles the memory bottleneck of massive MoEs. QMoE introduces a scalable algorithm that compresses trillion-parameter MoEs to less than 1 bit per parameter, utilizing a custom format and bespoke GPU decoding kernels for efficient end-to-end compressed inference.

Introducing AnimateDiff, a groundbreaking framework that empowers you to animate your personalized T2I models without the need for complex, model-specific tuning. This means you can now breathe life into your unique creations and watch them come alive in smooth, visually-appealing animations.

Jamba boasts a 256K context window, allowing it to consider a vast amount of preceding information when processing a task. This extended context window is particularly beneficial for tasks requiring a deep understanding of a conversation or passage.

DBRX utilizes a transformer-based decoder-only architecture with a fine-grained Mixture-of-Experts (MoE) design. This means it uses a large number of smaller expert models to process different parts of the input, rather than relying on a single massive model.

This experiment seeks to discover a lighter approach that navigates between the constraints of a 128K context window and the complexities of a model fine-tuned on billions of tokens, perhaps more in the realm of tens of millions of tokens. For a smaller-scale test, I’ll fine-tune Mistral’s 7B Instruct v0.2 model on The Guardian’s manage-frontend…

In the realm of artificial intelligence, the adoption of multi-agent systems (MAS) via crew ai represents a paradigm shift towards more dynamic and complex problem-solving capabilities. This blog dives into the essence of Multi Agent Systems, highlighting the necessity for such systems in today’s technological landscape and exploring the CrewAI framework as a possible solution.