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

README.md

@@ -9,7 +9,7 @@ tags:
   - on-device
 ---
 
-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.
@@ -20,7 +20,7 @@ This model has no row on [DeviceMark](https://devicemark.github.io/), the on-dev
 [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) as a
 single static Core AI graph: the full sentence-transformers pipeline (transformer →
 mean pooling → dense projection → L2 normalize) runs in-graph, so one call returns a
-normalized 768-d embedding. On-device semantic search / RAG for macOS 27 / iOS 27 beta.
+normalized 768-d embedding. On-device semantic search / RAG for macOS 27 / iOS 27.
 
 Runs out of the box with [CoreAIKit](https://github.com/john-rocky/coreai-kit)'s
 `TextEmbedder`: