Card accuracy pass 2: remove unmeasured speed claims, honest brand labels

README.md

@@ -33,7 +33,7 @@ pipeline_tag: image-text-to-text
 
 # Gemma 4 E4B - RotorQuant MLX 2-bit
 
-**2-bit weight-quantized MLX version** of [google/gemma-4-E4B](https://huggingface.co/google/gemma-4-E4B) with the legacy RotorQuant KV-cache fork (superseded by upstream llama.cpp KV options). Optimized for Apple Silicon inference via the [MLX](https://github.com/ml-explore/mlx) framework. RotorQuant delivers 5.3x faster prefill and 28% faster decode compared to TurboQuant. The most aggressive quantization, fitting the full model in the smallest possible footprint.
+**2-bit weight-quantized MLX version** of [google/gemma-4-E4B](https://huggingface.co/google/gemma-4-E4B) with the legacy RotorQuant KV-cache fork (superseded by upstream llama.cpp KV options). Optimized for Apple Silicon inference via the [MLX](https://github.com/ml-explore/mlx) framework. The most aggressive quantization, fitting the full model in the smallest possible footprint.
 
 Approximate model size: **~1.2 GB**