metadata
license: mit
base_model: google/gemma-3-270m-it
tags:
- causal-reasoning
- semantic-loss
- fine-tuned
- d-separation
language:
- en
Gemma D-Separation Semantic V2
Fine-tuned Gemma 270M-IT for d-separation causal reasoning using semantic loss with dynamic lambda scheduling.
Performance
- Standard accuracy: 68.6%
- Adversarial accuracy: 67.8%
- F1 score: 25.0% (vs 7.6% collapsed baseline)
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ludwigw/gemma-dseparation-semantic-v2")
tokenizer = AutoTokenizer.from_pretrained("ludwigw/gemma-dseparation-semantic-v2")
Citation
@article{deshmukh2026semantic,
title={On Semantic Loss Fine-Tuning Approach for Preventing Model Collapse in Causal Reasoning},
author={Deshmukh, Pratik and Gupta, Atirek},
year={2026}
}