Beyond prompting: Fine-tuning LLMs with Nebius AI Studio

The blog post illustrates the process of fine-tuning large language models (LLMs) like DeepSeek-V3 and Qwen-2.5-72B for domain-specific tasks using Nebius AI Studio, focusing on a function-calling task as a practical example. It guides the reader through each step, from dataset preparation using the ToolACE dataset to model evaluation, and emphasizes the importance of fine-tuning for enhancing model performance in specialized applications. The blog provides a comprehensive walkthrough, including code snippets in a Jupyter notebook, and highlights the cost-effective approach of using LoRA adapters for fine-tuning with the 'Instruct' version of Llama-3.1-8B. The post concludes by demonstrating how the fine-tuned model outperforms the original in various tasks and discusses the advantages of tailoring LLMs to specific needs, ultimately improving their quality on target tasks.

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