quantization_config.json

{
  "quantization_method": "mxfp8",
  "description": "Quantization layout of the reference sahilchachra checkpoint used by the default upload workflow. MXFP8 is applied to quantizable language modules, token embeddings, the LM head, and the projector; the vision encoders and non-quantizable normalization/routing weights remain in bfloat16.",
  "base_model": "baidu/Unlimited-OCR",
  "reference_quantization": "sahilchachra/unlimited-ocr-mxfp8-mlx",
  "quantized_components": {
    "language_model.linear": "mxfp8",
    "language_model.moe.experts": "mxfp8",
    "language_model.token_embeddings": "mxfp8",
    "language_model.lm_head": "mxfp8",
    "vision_projector": "mxfp8"
  },
  "preserved_bf16_components": {
    "vision_encoder": "bfloat16",
    "normalization_layers": "bfloat16",
    "moe_routing_gates": "bfloat16"
  },
  "effective_bits_per_weight": 9.19,
  "model_size_gb": 3.83,
  "conversion_tool": "mlx-vlm quantizers",
  "notes": [
    "This file describes the reference weights copied by scripts/upload_model.py; that workflow does not run a new conversion",
    "The safetensors index contains MXFP8 scale tensors for the token embeddings, LM head, and projector, but not the vision encoders",
    "quantization/mixed_precision_convert.py can produce a different OCR-aware layout from the BF16 base model",
    "Published config.json uses model_type 'unlimited-ocr' to select mlx-vlm's native R-SWA implementation"
  ]
}