Full Fine-Tuning the traditional method for adapting large language models (LLMs), involving updates to all of the model’s parameters. While more resource-intensive than parameter-efficient fine-tuning (PEFT) and other methods, it allows for deeper and more comprehensive customization, especially when adapting to significantly different tasks or domains.
Read MoreParameter-Efficient Fine-Tuning or PEFT is a more efficient approach to adapting large language models (LLMs) compared to traditional full fine-tuning. Instead of modifying the entire model, PEFT focuses on fine-tuning only a small subset of the model’s parameters, making it less resource-intensive. This allows for faster adaptation to specific tasks while maintaining most of the pre-trained knowledge of the model, offering a cost-effective solution for improving performance on specialized tasks.
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Langflow is an open-source visual programming framework built on top of LangChain. It offers a graphical interface for building and managing applications that use large language models (LLMs). With Langflow, developers (and even non-developers) can design, test, and deploy LLM-powered workflows using a no-code/low-code approach.
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The nice thing about ChatGPT and similar systems is that the complexity of AI/ML functionality is hidden behind a friendly natural language interface. This makes it easily reachable to the masses. But behind this easy to use facade is a lot of advanced functionality that involve a sequence of data processing steps called a pipeline. An AI-powered business card reader, for example, would first detect text and then recognize the individual letters within the context of the words they belong to. A license plate reader would be similar. Detection is an important process that you often need in your AI/ML projects. And that’s why we will be looking at YOLO.
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The basic artificial neural network we created doesn’t have the best accuracy. Though it performed somewhat well in the recognition of MNIST test data images, it didn’t do as well in the recognition of real-world images. To improve its performance, we can do some adjustments to our model and training data.
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