ludwigw/gemma-transitivity-semantic-v4

Gemma Transitivity Semantic V4

Fine-tuned Gemma 270M-IT for causal transitivity reasoning using dynamic semantic loss scheduling (λ: 0.05→0.30).

Performance

• Standard accuracy: 70.4% (vs 27.7% collapsed baseline)
• Adversarial accuracy: 69.8%
• Branching task: 97.9% (vs 1.96% baseline)

Key Innovation

Dynamic lambda scheduling prevents model collapse — a critical failure mode where standard fine-tuning causes models to predict constant "Yes"/"No" regardless of input.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("ludwigw/gemma-transitivity-semantic-v4")
tokenizer = AutoTokenizer.from_pretrained("ludwigw/gemma-transitivity-semantic-v4")

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}
}