Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,67 @@
+---
+language: en
+license: mit
+tags:
+- diffusion
+- text-generation
+- riddles
+- diffusion-lm
+datasets:
+- prajwaldongre/riddles-a-synthetic-riddle-dataset-for-nlp
+metrics:
+- exact-match
+- token-f1
+---
+
+# Diffusion-LM Riddle Solver — Phase 3 Reconstructed
+
+A Diffusion-LM-style (Li et al., 2022) text generation model trained on 232 synthetic riddles. Held-out exact match: **47.0%** (K=1 and K=10).
+
+## Model description
+
+Continuous embedding diffusion with a Transformer encoder/decoder. The model takes a riddle as context and iteratively denoises Gaussian noise into answer word embeddings via a learned reverse process.
+
+## Intended use
+
+Research and diagnostics. Not a production-ready riddle solver.
+
+## Training data
+
+[Riddles — A Synthetic Riddle Dataset for NLP](https://www.kaggle.com/datasets/prajwaldongre/riddles-a-synthetic-riddle-dataset-for-nlp) (CC0). 232 training examples after deduplication.
+
+## Architecture
+
+| Parameter | Value |
+|-----------|-------|
+| Parameters | 8,024,576 |
+| Timesteps (T) | 200 |
+| d_model | 256 |
+| Layers | 4 |
+| d_ff | 1024 |
+| Heads | 4 |
+| Answer length | 4 |
+| Noise schedule | sqrt power-law |
+
+## Performance
+
+| Split | Exact match (K=1) | Token F1 |
+|-------|-------------------|----------|
+| Train (n=192) | 87.5% | 0.960 |
+| Held-out (n=66) | 47.0% | 0.523 |
+
+## Limitations
+
+- Trained on 232 examples only. Does not generalize broadly.
+- Uses continuous embedding diffusion with Euclidean clamping. Discrete formulations may differ.
+- Exact-match metric penalizes semantically equivalent answers.
+
+## Files
+
+- `model.safetensors`: Model weights
+- `config.json`: Architecture hyperparameters
+- `vocab.json`: Vocabulary mapping
+- `inference.py`: Standalone prediction script
+
+## Source
+
+Full source code, diagnostics, and reproduction configs: [github.com/beme08/riddle-diffusion-lm](https://github.com/beme08/riddle-diffusion-lm)