Create README.md

README.md

@@ -0,0 +1,32 @@
+---
+license: mit
+base_model: google/gemma-3-270m-it
+tags:
+  - causal-reasoning
+  - fine-tuned
+  - d-separation
+  - baseline
+language:
+- en
+---
+
+# Gemma D-Separation Baseline (Collapsed)
+
+Standard fine-tuned Gemma 270M-IT on d-separation task **without** semantic loss. Exhibits model collapse — predicts near-constant "No" (7.6% F1). Provided for reproducibility and as a negative baseline.
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("ludwigw/gemma-dseparation-baseline")
+tokenizer = AutoTokenizer.from_pretrained("ludwigw/gemma-dseparation-baseline")
+```
+
+## Citation
+```bibtex
+@article{deshmukh2026semantic,
+  title={On Semantic Loss Fine-Tuning Approach for Preventing Model Collapse in Causal Reasoning},
+  author={Deshmukh, Pratik and Gupta, Atirek},
+  year={2026}
+}
+```