{
  "mic": "0.3",
  "name": "MiniMax-M3-MXFP8 VLM",
  "usage": "AutoProcessor + MiniMaxM3SparseForConditionalGeneration, text inference and projector training step",
  "architecture": "MiniMaxM3SparseForConditionalGeneration",
  "checkpoints": {
    "MiniMaxAI/MiniMax-M3-MXFP8": {
      "fixture": "huggingface-mic/MiniMax-M3-MXFP8-mic"
    }
  },
  "fixtures": {
    "huggingface-mic/MiniMax-M3-MXFP8-mic": {
      "built_from": "MiniMaxAI/MiniMax-M3-MXFP8",
      "covers": [
        "MiniMaxAI/MiniMax-M3-MXFP8"
      ],
      "build": {
        "seed": 17,
        "config_overrides": {
          "fields": {
            "text_config.index_block_size": 8,
            "text_config.index_topk_blocks": 4,
            "text_config.index_local_blocks": 1,
            "text_config.index_n_heads": 2
          },
          "reason": "Shrink the Lightning indexer like the Transformers unit-test tester (block 8, top-k 4) so a short image prompt is far longer than top-k x block and selection is genuinely sparse; the real checkpoint only goes sparse above 2,048 tokens."
        }
      }
    }
  },
  "integrations": {
    "transformers": {
      "entry_point": "integrations/transformers/usage.md",
      "format": "runnable-markdown",
      "requires": [
        "transformers>=5",
        "torch",
        "Pillow",
        "torchvision",
        "accelerate"
      ],
      "targets": [
        "fixtures"
      ],
      "variants": {
        "default": {},
        "eager": {
          "attn_implementation": "eager"
        },
        "bfloat16": {
          "dtype": "bfloat16"
        }
      },
      "training": true,
      "resources": {
        "fixture": {
          "memory_gb": 4,
          "download_mb": 50
        },
        "MiniMaxAI/MiniMax-M3-MXFP8": {
          "devices": [
            "cuda"
          ],
          "gpus": 4,
          "gpu_memory_gb": 120,
          "memory_gb": 64,
          "download_mb": 450000
        }
      }
    }
  },
  "outputs": {
    "runtime": {
      "compare": "exact"
    },
    "logits_probe": {
      "compare": "allclose",
      "atol": 0.0001,
      "rtol": 0.0001
    },
    "training.loss": {
      "compare": "allclose",
      "atol": 0.0001,
      "rtol": 0.0001
    },
    "training.gradients": {
      "compare": "all_true"
    },
    "training.updated": {
      "compare": "true"
    },
    "training.frozen": {
      "compare": "true"
    }
  }
}