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README.md

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+# Compresser Encoder (Perceiver Resampler)
+
+Phase 0 pretrained Perceiver for the Mamba-3 Semantic Video Compressor.
+
+## Architecture
+- **Type**: Perceiver Resampler (cross-attention compressor)
+- **Input**: [B, 576, 1664] — V-JEPA 2.1 ViT-Gigantic patch latents
+- **Output**: [B, 64, 512] — compressed tokens
+- **Params**: ~20.6M
+- **Details**: 64 learned queries, 6 cross-attention layers, 16 heads, FFN 512→2048
+
+## 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 via autoencoder (Perceiver → Decoder → V-JEPA latent)
+- **Optimizer**: AdamW, lr=1e-4, cosine to 1e-6
+- **Hardware**: RTX 4090 (48 GB), bf16
+
+## Usage
+```python
+from model.models.perceiver import PerceiverResampler
+
+model = PerceiverResampler(input_dim=1664, output_dim=512, num_queries=64)
+model.load_state_dict(torch.load("perceiver_stepX_hrsY.pt"))
+# Input: [B, 576, 1664] V-JEPA latents → Output: [B, 64, 512]
+```
+
+Part of the Mamba-3 Semantic Video Compressor pipeline.