Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 license: apache-2.0
 tags:
-- pasta-diffusion
+- planned-diffusion
 - dream
 - text-generation
 - diffusion
@@ -12,10 +12,6 @@ tags:
 
 Dream 7B planned diffusion model trained for 16 epochs.
 
-## Model Description
-
-This is a PASTA-Diffusion model trained for 16 epochs using advanced diffusion techniques with structured attention patterns.
-
 ## Usage
 
 
@@ -29,27 +25,23 @@ model = DreamModel.from_pretrained("{model_name}", trust_remote_code=True)
 
 # Use for diffusion generation
 inputs = tokenizer("Your prompt here", return_tensors="pt")
-output = model.diffusion_generate(inputs, max_length=512)
+output = model.planned_diffusion_generate(inputs, max_length=512)
 generated_text = tokenizer.decode(output.sequences[0], skip_special_tokens=True)
 print(generated_text)
 ```
 
-## Model Details
-
-- **Model type**: PASTA-Diffusion (Dream-based)
-- **Training**: 16 epochs
-- **Architecture**: Transformer-based with diffusion generation
-- **License**: Apache 2.0
-
 ## Citation
 
-If you use this model, please cite the PASTA-Diffusion paper:
+If you use this model, please cite the Planned Diffusion paper:
 
 ```bibtex
-@article{pasta-diffusion-2024,
-  title={PASTA-Diffusion: Parallel Autoregressive and Structured Transformer Attention for Diffusion},
-  author={Your Authors},
-  journal={arXiv preprint},
-  year={2024}
+@misc{israel2025planneddiffusion,
+      title={Planned Diffusion}, 
+      author={Daniel Israel and Tian Jin and Ellie Cheng and Guy Van den Broeck and Aditya Grover and Suvinay Subramanian and Michael Carbin},
+      year={2025},
+      eprint={2510.18087},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2510.18087}, 
 }
 ```