Fine-tune classifier with ModernBERT in 2025
Large Language Models (LLMs) have become ubiquitous in 2024. However, smaller, specialized models - particularly for classification tasks - remain critical for building efficient and cost-effective AI systems. One key use case is routing user prompts to the most appropriate LLM or selecting optimal few-shot examples, where fast, accurate classification is essential.
This blog post demonstrates how to fine-tune ModernBERT, a new state-of-the-art encoder model, for classifying user prompts to implement an intelligent LLM router. ModernBERT is a refreshed version of BERT models, with 8192 token context length, significantly better downstream performance, and much faster processing speeds.
ModernBERT is a modernization of BERT maintaining full backward compatibility while delivering dramatic improvements through architectural innovations like rotary positional embeddings (RoPE), alternating attention patterns, and hardware-optimized design. The model comes in two sizes:
ModernBERT achieves state-of-the-art performance across classification, retrieval and code understanding tasks while being 2-4x faster than previous encoder models. This makes it ideal for high-throughput production applications like LLM routing, where both accuracy and latency are critical.
