README.md

---
license: mit
base_model: google/gemma-3-270m-it
tags:
  - causal-reasoning
  - semantic-loss
  - fine-tuned
  - transitivity
language:
- en
---

# 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
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

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

## Citation
```bibtex
@article{deshmukh2026semantic,
  title={On Semantic Loss Fine-Tuning Approach for Preventing Model Collapse in Causal Reasoning},
  author={Deshmukh, Pratik and Gupta, Atirek},
  journal={arXiv preprint arXiv:2605.05438},
  year={2026}
}
```