Update README.md

README.md

@@ -1,14 +1,55 @@
 ---
-title: Tini
+title: Tini-Lad
 emoji: ⚡
 colorFrom: pink
 colorTo: red
 sdk: gradio
-sdk_version: 5.23.3
+sdk_version: 5.33.0
 app_file: app.py
 pinned: false
 license: other
 short_description: DLM
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 💬 Diffusion Language Model Demo
+
+Note: Paper coming out soon; if anyone is interested in discussing the model, please contact me.
+
+This is an interactive demo of a **diffusion-style language model**, which generates text through iterative refinement. 
+Inspired by diffusion processes in vision models, the system gradually improves a corrupted text sequence until convergence. 
+
+This implementation has several benefits:
+- **Noiseless convergence**: A unique feature of this implementation is its ability to convergence **without intermediate noising**, although this currently works best for simple or short questions.
+- Scalable test time compute: By increasing the number of iterations, the answer quality improves.
+- Reduced inference time: Most questions can be answered with less iterations then the number of tokens generated!
+- Greatly reduced training time: By finetuning an autoregressive Llama-8B model using only LoRA for diffusive generation, we trained this model within several hours on a single GPU.
+
+## 🔧 Settings
+- **Disable Intermediate Noising**: Speeds up convergence by skipping the noising step between iterations. Works best for short, factual questions.
+- **Iterations**: Number of refinement steps. More iterations means more time to refine the answer.
+- **Pause Between Steps**: Slows down the process so you can visually follow the changes.
+
+## 🖍️ Visualization
+- **Red tokens**: Masked (noised) tokens that will be regenerated.
+- **Green tokens**: Newly generated tokens compared to the previous step.
+
+## 🧪 Example Prompt
+For noiseless diffusion, try short questions like:
+> What's the capital of France?
+
+For more in-depth questions, enable intermediate noising. Increasing the number of iterations generally improves answer quality.
+> What do you know about Amsterdam?
+
+See how low you can go with the number of iterations while still receiving adequate answers!
+
+---
+
+More technical details (architecture, training, and evaluation) can be found in the accompanying blog post:  
+📘 [Read the blog post here](https://example.com/diffusion-language-model-blog)
+
+For a more tweakable version that includes all inference parameters, check out this version:
+🎛️ [Explore the model here](https://huggingface.co/spaces/Ruurd/tini)
+
+Paper coming out soon! If you already want to cite this model, please refer to the blogpost
+
+