Card: macOS/iOS 27 GA wording (beta requirement dropped; beta findings dated)

README.md

@@ -15,7 +15,7 @@ pipeline_tag: object-detection
 base_model_relation: quantized
 ---
 
-Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's `coreai-torch` (LLMs: `coreai.llm.export`) into `.aimodel` bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol ([apple-silicon-llm-bench](https://github.com/john-rocky/apple-silicon-llm-bench), macOS 27 beta, 2026-06).
+Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's `coreai-torch` (LLMs: `coreai.llm.export`) into `.aimodel` bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol ([apple-silicon-llm-bench](https://github.com/john-rocky/apple-silicon-llm-bench), macOS 27 beta 26A5353q, 2026-06-11).
 
 <!-- gen-cards:devicemark begin (managed by scripts/gen-cards + tools/devicemark_row.py — edit cards.json, not this block) -->
 This model has no row on [DeviceMark](https://devicemark.github.io/), the on-device LLM leaderboard.
@@ -140,7 +140,7 @@ seg-nano 312² **10.7 ms** → seg-2xlarge 768² **59.1 ms**.
 backbone (image → features) from the deformable head (features → dets/labels;
 position encodings baked in). The chain is bit-exact vs the monolith. Purpose:
 per-stage compute-unit preferences — e.g. backbone on the Neural Engine.
-Measured honestly: on iOS 27 beta the runtime still executes the backbone on
+Measured honestly: on the iOS 27 betas (not re-tested on the release OS) the runtime still executes the backbone on
 the GPU delegate even under `.neuralEngine` preference (identical detection
 fingerprint, no ANE-compile pause), so today the monolith on GPU is the
 fastest config; the split exists so ANE placement can be adopted the moment