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Compresser_Decoder – AI Model by rookierufus | AlphaNeural AI | AlphaNeural AI

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rookierufus/
Compresser_Decoder

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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 (50 hours video, 384×384, 8fps)
V-JEPA: Frozen vjepa2_1_vit_gigantic_384 (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.