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MB55/
EuroLLM-Classifier-QLoRA

safetensors

utter-project/EuroLLM-9B-Instruct

finetune

mit

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EuroLLM QLoRA – Grounding Act Classification

This model is a fine-tuned version of EuroLLM-9B-Instruct optimized using QLoRA for efficient binary classification of German dialogue utterances into:

ADVANCE: Contribution that moves the dialogue forward (e.g. confirmations, follow-ups, elaborations)

NON-ADVANCE: Other utterances (e.g. vague responses, misunderstandings, irrelevant comments)

Use Cases

Dialogue system analysis Teacher-student interaction classification Grounding in institutional advising or classroom discourse

How to Use

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel, PeftConfig

peft_config = PeftConfig.from_pretrained("MB55/EuroLLM-Classifier-QLoRA")
base_model = AutoModelForSequenceClassification.from_pretrained(peft_config.base_model_name_or_path)
model = PeftModel.from_pretrained(base_model, "MB55/EuroLLM-Classifier-QLoRA")
tokenizer = AutoTokenizer.from_pretrained("MB55/EuroLLM-Classifier-QLoRA")