{
  "mic": "0.3",
  "name": "DeepSeek-R1 causal LM",
  "usage": "AutoTokenizer + AutoModelForCausalLM, batched prompts with left padding, causal LM loss on shifted labels",
  "architecture": "DeepseekV3ForCausalLM",
  "checkpoints": {
    "deepseek-ai/DeepSeek-R1": {
      "fixture": "huggingface-mic/DeepSeek-R1-mic"
    }
  },
  "fixtures": {
    "huggingface-mic/DeepSeek-R1-mic": {
      "built_from": "deepseek-ai/DeepSeek-R1",
      "covers": [
        "deepseek-ai/DeepSeek-R1"
      ],
      "why_separate": "The checkpoint is a 671B-parameter MoE model stored in FP8; the fixture is a randomly initialized slim model with the same architecture, tokenizer and vocabulary so the public API and loss path can be exercised on CPU.",
      "build": {
        "seed": 17,
        "model_class": "DeepseekV3ForCausalLM"
      }
    }
  },
  "integrations": {
    "transformers": {
      "entry_point": "integrations/transformers/usage.md",
      "format": "runnable-markdown",
      "requires": [
        "transformers>=5",
        "torch"
      ],
      "targets": [
        "fixtures"
      ],
      "variants": {
        "default": {},
        "eager": {
          "attn_implementation": "eager"
        },
        "bfloat16": {
          "dtype": "bfloat16"
        }
      },
      "training": true,
      "resources": {
        "fixture": {
          "memory_gb": 2,
          "download_mb": 50
        },
        "deepseek-ai/DeepSeek-R1": {
          "devices": [
            "cuda"
          ],
          "gpus": 1,
          "gpu_memory_gb": 80,
          "memory_gb": 64,
          "download_mb": 700000,
          "variants": {
            "default": {
              "devices": []
            },
            "eager": {
              "devices": []
            }
          }
        }
      }
    }
  },
  "outputs": {
    "runtime": {
      "compare": "exact"
    },
    "next_token_ids": {
      "compare": "exact"
    },
    "probe_logits": {
      "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"
    }
  }
}