Workers AI Update: Hello Mistral 7B

In this article, we explored the basics of attention mechanisms in large language models (LLMs) such as Mistral 7B, focusing on understanding how they work and why they are important for improving model performance. We covered three main aspects of attention: constant values, vertical stacking, and horizontal stacking or multi-head attention. Constant values refer to the learned parameters in an AI model that are adjusted during training to improve its performance. These parameters control the flow of information within the model and allow it to focus on relevant parts of the input data. Vertical layered stacking is a way to build multiple layers of attention mechanisms, with each layer building upon the output of the previous one. This allows the model to focus on different aspects of the input data at various levels of abstraction, which can lead to better performance on certain tasks. Horizontal stacking or multi-head attention involves carrying out multiple attention operations in parallel using unique linear projections for each set of Q-K-V inputs. These parallel attention blocks are called "attention heads," and they allow the model to focus on different parts of the input data concurrently. There are three common arrangements of attention mechanisms used by large language models: multi-head attention, grouped-query attention, and multi-query attention. Multi-query attention uses only a single set of K and V vectors for all Q vectors, reducing memory usage but potentially impacting performance on some tasks. Grouped-query attention combines the best of both worlds by using a fixed ratio of one set of K and V vectors for every Q vector, retaining high performance while minimizing memory consumption. Mistral 7B utilizes grouped-query attention combined with sliding window attention, making it highly efficient in terms of latency and throughput while still maintaining strong performance on benchmarks compared to larger models like OpenAI's GPT-3 series. By understanding these aspects of attention mechanisms in large language models, developers can make more informed decisions when choosing a model for their specific use case or application. The Workers AI platform now supports the Mistral 7B model, allowing developers easy access to its capabilities through serverless GPU-powered inference services. This makes it easier for developers to build and deploy cutting-edge AI applications without having to manage complex infrastructure requirements themselves. As the field of large language models continues to evolve, understanding these underlying mechanisms will become increasingly important for harnessing their full potential in real-world applications.