End to End Technology Solutions
At InnovationM, we are constantly searching for tools and technologies that can drive the performance and scalability of our AI-driven products. Recently, we made progress with vLLM, a high-performance model inference engine designed to deploy Large Language Models (LLMs) more efficiently. We had a defined challenge. Deploy our own custom-trained LLM as a fast and Continue Reading »
Introduction Let’s face it—LLMs (Large Language Models) are amazing, but they’re also computationally expensive. Every time a user makes a request, the model fires up, processes vast amounts of data, and generates a response from scratch. This is great for unique queries, but for frequently repeated prompts? Not so much. This is where Prompt Caching Continue Reading »
Mixture of Experts (MoE) Models: The Future of Scaling AI In the ever-evolving landscape of artificial intelligence (AI), the quest for models that are both powerful and efficient has led us to explore innovative architectures. One such groundbreaking approach that has captured our attention is the Mixture of Experts (MoE) model. This architecture not only Continue Reading »
In our ever-evolving journey toward building smarter, more reliable AI systems, one concept has proven indispensable: epochs. At its core, an epoch represents one complete pass over the entire training dataset—a cycle in which our model learns from every available example. As a team that thrives on innovation and continuous improvement, we’ve developed strategies Continue Reading »
Over the past year, our team has witnessed a seismic shift in how we approach AI application development. One of the most exciting innovations we’ve embraced is BAML—a domain-specific language designed specifically for structured prompt engineering. In our journey, BAML has not only simplified our workflow but has also revolutionized the way we create Continue Reading »
Let’s be honest—AI models are smart, but they can also be wildly overconfident. Ever had ChatGPT confidently tell you that tomatoes are vegetables, only to backtrack when you call it out? Yeah, that’s it. Large Language Models (LLMs) create responses based on the material they were trained on, but they are not well-versed (“know”) with Continue Reading »
In today’s fast-changing era of Artificial Intelligence (AI), the need for solutions that facilitate end-to-end integration of language models and real-world applications is higher than ever before. Welcome LangChain, a powerful platform built with the aim of optimizing the use of Large Language Models (LLMs). By offering an architecture to build intelligent and adaptive Continue Reading »
Supervised Fine Tuning (SFT) – Improving Models for Particular Scenarios The painstaking process that is the evolution of Artificial Intelligence (AI) has yielded exceptionally complex models capable of a variety of tasks, each performed with astounding efficiency. Unfortunately, these models often lack one crucial element: versatility. This is where Supervised Fine Tuning (SFT) proves to Continue Reading »
For years, Large Language Models (LLMs) have impressed us with their ability to generate human-like responses, but they’ve always had a major limitation—they react rather than act. They answer questions but don’t take the initiative, they don’t plan ahead, and they certainly don’t adapt to new information on their own. This is where the Continue Reading »