This article discusses the concept of token counts in large language models (LLMs) and their impact. Tokens are fragments of language used for text processing, representing words, parts of words, or punctuation marks. Code walkthroughs demonstrate how to calculate token counts and examples provide insight.
Read more
An introduction to the challenge of fitting big data into the context windows of LLMs. In this second installment, discover the key strategies involved to improve your use of the context window. Subsequent articles will provide more examples.
Read moreAn introduction to the challenge of fitting big data into the context windows of LLMs. Part 1 introduces the problem and why it exists. Part 2 will provide an overview of the strategies to overcome this problem.
Read moreThis is a video of a presentation investigating how large language models are built and how to use them, inspired by our large language model consulting work. First presented at GOTO Copenhagen in 2023, the video investigates the history, the technology, and the use of large language models. The demo at the end is borderline cringe, but it’s a fun and demonstrates how you would fine-tune a language model on your proprietary data.
Read moreLangChain and LlamaIndex streamline ChatGPT and LLM application development. Boost your project’s efficiency with LangChain’s tools and modules, and LlamaIndex’s advanced document handling. Discover the future of language model orchestration today.
Read moreThe article explores the complexities and nuances of monitoring and evaluating Large Language Models (LLMs) like ChatGPT in business applications. It emphasizes the insufficiency of traditional metrics, the importance of real-time tracking, human feedback, and specialized evaluation methods to ensure model safety, efficiency, and performance optimization.
Read moreThis notebook demonstrates how to fine-tune a state-of-the-art large language model (LLM) on a single GPU. This example uses Falcon-7B because it is Apache licensed. The data used in this notebook is for informational purposes only, do not use this data unless you have licensed it.
Read moreIn our previous articles, you learned how to build and train your personal ChatGPT model (large-language model). However, it’s important to understand that these models are merely components within a larger software landscape. After achieving adequate performance in a controlled environment, the next step is to integrate it into your broader system.
Read moreIn just a few years since the transformer architecture was first published, large language models (LLMs) have made huge strides in terms of performance, cost, and potential. In the previous two parts of this series, we’ve already explored the fundamental principles of such models and the intricacies of the development process.
Yet, before an AI product can reach its users, the developer must make yet more key decisions. Here, we’re going to dig into whether you should train your own ChatGPT model with custom data.
Read moreThe premise of LLMs is beautifully exemplified by products like ChatGPT, that use these models to power conversational interfaces, offering a seamless and engaging chat user experience. In this second part of our series on ChatGPT, we provide an overview of what it’s like to develop against commercial LLM offerings and what it takes to begin developing your bespoke model.
Read more
