vllm-sr/mmbert32k-intent-classifier-merged

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: vllm-sr/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: vllm-sr/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

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("vllm-sr/mmbert32k-intent-classifier-merged")
tokenizer = AutoTokenizer.from_pretrained("vllm-sr/mmbert32k-intent-classifier-merged")

# Inference
inputs = tokenizer("How do neural networks learn?", return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)
    predicted_class = probs.argmax().item()
    confidence = probs[0][predicted_class].item()

# Get label
print(f"Category: {model.config.id2label[str(predicted_class)]}, Confidence: {confidence:.2%}")

For Rust/Candle Inference

This merged model is compatible with the candle-binding Rust library for high-performance inference in production systems.