Fine-tune a large language model (llm) for multi-turn conversations and run it on a Text Generation Inference (TGI) server

This blog post delves into the initial fine-tuning process for large language models (LLMs) for multi-turn conversations and their deployment on Text Generation Inference (TGI) servers. It covers topics such as use cases, data formats, training data preparation, server setup, and evaluation frameworks. The goal is to guide readers through the process of fine-tuning and deploying LLMs.

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How to create a watsonx.ai REST client in Spring Boot?

This blog post demonstrates the Java Spring Boot implementation to invoke a watsonx.ai endpoint. It outlines the classes and steps involved, including building and sending requests, handling prompts, and extracting answers. The post also provides sample code for invoking the endpoint and using RestTemplate. Overall, it offers a comprehensive guide on utilizing watsonx.ai in a Spring Boot application.

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