Update README.md

README.md

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----
-license: mit
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+---
+license: apache-2.0
+tags:
+- vae
+- video-generation
+- education
+- fine-tuning
+- pytorch
+---
+
+# 🎓 Causal VAE Fine-Tuning Experiments (Indian Math Curriculum)
+
+**Developing the "Imagination Engine" for [Zulense](https://huggingface.co/zulense)**
+
+This repository contains experimental checkpoints for a **Causal VAE (Variational Autoencoder)** fine-tuned specifically on Indian educational content (NCERT Math). 
+
+The goal of these experiments is to adapt standard video generation VAEs to better reconstruct "blackboard style" line art, diagrams, and text-heavy educational videos, which often suffer from artifacts in general-purpose models.
+
+## 📂 Checkpoint Manifest
+
+We are releasing two distinct checkpoints representing different stages of our training curriculum.
+
+### 1. `FineTune_2_checkpoint.pth` (Recommended)
+* **Target Domain:** **Class 5 Numeracy & Foundation**
+* **Status:** ✅ **Improved Stability**
+* **Experiment Notes:** * This run focused on simpler, foundational concepts (Class 5 curriculum) to stabilize the loss.
+    * **Improvements:** Significantly reduced `kl_divergence` and reconstruction loss compared to the V1 baseline.
+    * **Use Case:** Better at handling basic shapes and slower temporal movements typical in primary education teaching.
+
+### 2. `checkpoint-0.pth` (Legacy / Research Artifact)
+* **Target Domain:** **Class 8 Geometry & Algebra**
+* **Status:** ⚠️ **Unstable / High Loss**
+* **Experiment Notes:** * This was our initial attempt at modeling complex Class 8 geometry.
+    * **Known Issues:** The model struggled with high-frequency details (text/grid lines), resulting in higher `vae_loss` and unstable KL divergence. 
+    * **Why we kept it:** Retained for comparative analysis to show the difficulty jump between primary and middle school visual complexity.
+
+## 🔬 Technical Context
+
+Standard video VAEs are optimized for photorealism. Our experiments suggest that for **educational video synthesis**:
+1.  **Text Preservation:** Standard VAEs struggle to reconstruct the sharp text found in math explanations.
+2.  **Curriculum Learning:** Fine-tuning on simpler visual concepts (Class 5) before complex ones (Class 8) yields better convergence.
+
+## 💻 Usage (PyTorch)
+
+```python
+import torch
+
+# Load the Causal VAE checkpoint
+checkpoint_path = "FineTune_2_checkpoint.pth" # Use the stable Class 5 checkpoint
+state_dict = torch.load(checkpoint_path, map_location="cpu")
+
+print(f"Loaded checkpoint: {checkpoint_path}")
+# Note: This requires the specific Causal VAE architecture definition to load state_dict