metadata
license: mit
base_model:
- utter-project/EuroLLM-9B-Instruct
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
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")