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🎓 Causal VAE Fine-Tuning Experiments (Indian Math Curriculum)

Developing the "Imagination Engine" for 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:

Text Preservation: Standard VAEs struggle to reconstruct the sharp text found in math explanations.
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

ProgramerSalar/causal_vae_checkpoint