# Compresser Decoder (Inverse Perceiver)

Phase 0 pretrained Decoder for the Mamba-3 Semantic Video Compressor.

## Architecture
- **Type**: Inverse Perceiver (cross-attention expansion)
- **Input**: [B, 64, 512] — Perceiver compressed tokens
- **Output**: [B, 576, 1664] — reconstructed V-JEPA latents
- **Params**: ~11.1M
- **Details**: 576 learned queries, 3 cross-attention layers, 16 heads, FFN 512→2048→512

## Training
- **Dataset**: [Vjepa_mamba_dataset_v2](https://huggingface.co/datasets/rookierufus/Vjepa_mamba_dataset_v2) (50 hours video, 384×384, 8fps)
- **V-JEPA**: Frozen [vjepa2_1_vit_gigantic_384](https://github.com/facebookresearch/vjepa2) (2.2B params)
- **Loss**: MSE reconstruction (autoencoder target = V-JEPA latent)
- **Optimizer**: AdamW, lr=1e-4, cosine to 1e-6
- **Hardware**: RTX 4090 (48 GB), bf16

## Usage
```python
from compressor.decoder import PerceiverDecoder

model = PerceiverDecoder(input_dim=512, output_dim=1664, num_queries=576)
model.load_state_dict(torch.load("decoder_stepX_hrsY.pt"))
# Input: [B, 64, 512] Perceiver output → Output: [B, 576, 1664] V-JEPA latents
```

## Note
Disposable after Phase 0 — only the Encoder (Perceiver) carries forward to the main pipeline.