mmBERT 32K Intent Classifier (Merged Model) Full merged model for intent classification based on mmBERT 32K YaRN (32K context, multilingual). This is the LoRA adapter merged with the base model for direct inference without PEFT. Model Details Base Model : llm semantic router/mmbert 32k yarn Training Method : LoRA (rank 32) merged into full model Model Size : ~1.2 GB Use Case : Production deployment, Rust/Go inference Training Data Primary : TIGER Lab/MMLU Pro (~12K academic questions) Supplement : LLM Semantic Router/category classifier supplement (653 samples including casual "other" examples) Categories (14 classes) biology, business, chemistry, computer science, economics, engineering, health, history, law, math, other, philosophy, physics, psychology Performance Metric Score Test Accuracy 80.0% Usage For Rust/Candle Inference This merged model is compatible with the candle binding Rust library for high performance inference in production systems.
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