{
  "name": "HFMT-8 (High-Fidelity Multimodal Transformer)",
  "layers": [
    {
      "type": "Conv2d",
      "params": {
        "in_channels": 3,
        "out_channels": 1152,
        "kernel_size": 14,
        "stride": 14,
        "note": "SigLIP-style Patch Embedding for high-resolution input"
      }
    },
    {
      "type": "TransformerBlock",
      "params": {
        "hidden_size": 1152,
        "num_heads": 16,
        "mlp_ratio": 4,
        "activation": "GELU",
        "note": "SigLIP SO400M Vision Encoder Backbone"
      }
    },
    {
      "type": "Conv2d",
      "params": {
        "in_channels": 1152,
        "out_channels": 1152,
        "kernel_size": 2,
        "stride": 2,
        "note": "Adaptive Patch-Merging for 50% Visual Token Reduction"
      }
    },
    {
      "type": "Linear",
      "params": {
        "in_features": 1152,
        "out_features": 4096,
        "note": "Cross-Modal Projection Bridge to LLM Latent Space"
      }
    },
    {
      "type": "TransformerBlock",
      "params": {
        "hidden_size": 4096,
        "num_attention_heads": 32,
        "num_key_value_groups": 8,
        "attention_type": "Grouped-Query Attention (GQA)",
        "positional_encoding": "RoPE (Rotary)",
        "note": "Llama-3 Decoder Block with 4-bit NF4 Quantization Support"
      }
    },
    {
      "type": "Linear",
      "params": {
        "in_features": 4096,
        "out_features": 14336,
        "activation": "SwiGLU",
        "note": "Gated Linear Unit for Enhanced Representational Capacity"
      }
    },
    {
      "type": "RMSNorm",
      "params": {
        "normalized_shape": 4096,
        "eps": 0.00001,
        "note": "Pre-block Normalization for Numerical Stability"
      }
    },
    {
      "type": "Linear",
      "params": {
        "in_features": 4096,
        "out_features": 128256,
        "bias": false,
        "note": "Language Modeling Head (Uncensored Configuration)"
      }
    }
  ],
  "explanation": "The HFMT-8 architecture is designed to balance multimodal reasoning with extreme memory efficiency for 8GB VRAM environments. By utilizing SigLIP for vision, we achieve better zero-shot alignment than CLIP with fewer parameters. The 'C-Abstractor' via patch-merging reduces visual tokens significantly, preventing KV-cache explosion during multimodal tasks. The LLM backbone utilizes Grouped-Query Attention (GQA) to minimize the memory footprint of the attention mechanism by a factor of 4, and the transition to 4-bit NF4 quantization ensures the 8B parameter model fits comfortably within 4.5GB, leaving ample room for the visual buffer and context window."
}