Add the MMLU-Pro dataset block; both records now validate

dataset.name: MMLU-Pro (TIGER-Lab/MMLU-Pro, MIT licence, public and ungated).
sample_count is provisionally 1 pending the real figure.

Both records now pass llm-generative.schema.yaml.

Still outstanding: runtime.version (the ExecuTorch version). Unlike created_at and sample_count, it forms part of the report identity URN
urn:ai-portal:perf:{runtime.name}:{runtime.version}:{dataset.name}:{dataset.version}:{batch_size}:{target.name}:{profile}
so ingesting without it and adding it later would insert a duplicate report rather than update the existing one. Worth supplying before first ingest.

benchmarks/gemma-4-e2b-vivo-x300-bf16.yaml

@@ -12,6 +12,10 @@ context:
       optimisations:
         - XNNPACK
         - KleidiAI
+  dataset:
+    name: MMLU-Pro
+    reference_url: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro
+    sample_count: 1
   benchmark:
     batch_size: 1
     num_runs: 20

benchmarks/gemma-4-e2b-vivo-x300-int4.yaml

@@ -12,6 +12,10 @@ context:
       optimisations:
         - XNNPACK
         - KleidiAI
+  dataset:
+    name: MMLU-Pro
+    reference_url: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro
+    sample_count: 1
   benchmark:
     batch_size: 1
     num_runs: 20