feat(constants)!: default MODEL_REGISTRY accessors to the quantized variant

Bare accessors (and undefined `quant`) now resolve to the quantized
variant when one is published; pass `{ quant: false }` to opt out. Docs
and example apps are updated to match — dual pickers keep both rows by
making the FP32 entry the explicit opt-out.

Author: msluszniak

apps/bare-rn/App.tsx

@@ -145,7 +145,7 @@ function App() {
   const scrollViewRef = useRef<ScrollView>(null);
 
   const llm = useLLM({
-    model: MODEL_REGISTRY.LLM.LLAMA3_2_1B({ quant: true }),
+    model: MODEL_REGISTRY.LLM.LLAMA3_2_1B,
   });
   // Alternatively, to use a custom local model, uncomment below:
   // const llm = useLLM({ model: {

apps/computer-vision/app/classification/index.tsx

@@ -10,11 +10,11 @@ import { ModelPicker, ModelOption } from '../../components/ModelPicker';
 const MODELS: ModelOption<ClassificationModelSources>[] = [
   {
     label: 'EfficientNet V2 S Quantized',
-    value: MODEL_REGISTRY.CLASSIFICATION.EFFICIENTNET_V2_S({ quant: true }),
+    value: MODEL_REGISTRY.CLASSIFICATION.EFFICIENTNET_V2_S,
   },
   {
     label: 'EfficientNet V2 S',
-    value: MODEL_REGISTRY.CLASSIFICATION.EFFICIENTNET_V2_S,
+    value: MODEL_REGISTRY.CLASSIFICATION.EFFICIENTNET_V2_S({ quant: false }),
   },
 ];
 import { View, StyleSheet, Image, Text, ScrollView } from 'react-native';
@@ -28,7 +28,7 @@ import ErrorBanner from '../../components/ErrorBanner';
 export default function ClassificationScreen() {
   const [selectedModel, setSelectedModel] =
     useState<ClassificationModelSources>(
-      MODEL_REGISTRY.CLASSIFICATION.EFFICIENTNET_V2_S({ quant: true })
+      MODEL_REGISTRY.CLASSIFICATION.EFFICIENTNET_V2_S
     );
   const [results, setResults] = useState<{ label: string; score: number }[]>(
     []

apps/computer-vision/app/segment_anything/index.tsx

@@ -77,9 +77,7 @@ export default function SegmentAnythingScreen() {
     useInstanceSegmentation({ model: selectedModel });
 
   const clipImage = useImageEmbeddings({
-    model: MODEL_REGISTRY.IMAGE_EMBEDDING.CLIP_VIT_BASE_PATCH32_IMAGE({
-      quant: true,
-    }),
+    model: MODEL_REGISTRY.IMAGE_EMBEDDING.CLIP_VIT_BASE_PATCH32_IMAGE,
   });
   const clipText = useTextEmbeddings({
     model: MODEL_REGISTRY.TEXT_EMBEDDING.CLIP_VIT_BASE_PATCH32_TEXT,

apps/computer-vision/app/semantic_segmentation/index.tsx

@@ -49,35 +49,27 @@ const numberToColor: number[][] = [
 const MODELS: ModelOption<SemanticSegmentationModelSources>[] = [
   {
     label: 'DeepLab MobileNet',
-    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_MOBILENET_V3_LARGE({
-      quant: true,
-    }),
+    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_MOBILENET_V3_LARGE,
   },
   {
     label: 'DeepLab ResNet50',
-    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_RESNET50({
-      quant: true,
-    }),
+    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_RESNET50,
   },
   {
     label: 'DeepLab ResNet101',
-    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_RESNET101({
-      quant: true,
-    }),
+    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_RESNET101,
   },
   {
     label: 'LRASPP MobileNet',
-    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.LRASPP_MOBILENET_V3_LARGE({
-      quant: true,
-    }),
+    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.LRASPP_MOBILENET_V3_LARGE,
   },
   {
     label: 'FCN ResNet50',
-    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.FCN_RESNET50({ quant: true }),
+    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.FCN_RESNET50,
   },
   {
     label: 'FCN ResNet101',
-    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.FCN_RESNET101({ quant: true }),
+    value: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.FCN_RESNET101,
   },
   {
     label: 'Selfie Segmentation',
@@ -89,9 +81,7 @@ export default function SemanticSegmentationScreen() {
   const { setGlobalGenerating } = useContext(GeneratingContext);
   const [selectedModel, setSelectedModel] =
     useState<SemanticSegmentationModelSources>(
-      MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_MOBILENET_V3_LARGE({
-        quant: true,
-      })
+      MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_MOBILENET_V3_LARGE
     );
 
   const {

apps/computer-vision/app/style_transfer/index.tsx

@@ -22,28 +22,19 @@ type StyleTransferModelSources = {
 };
 
 const MODELS: ModelOption<StyleTransferModelSources>[] = [
-  {
-    label: 'Candy',
-    value: MODEL_REGISTRY.STYLE_TRANSFER.CANDY({ quant: true }),
-  },
-  {
-    label: 'Mosaic',
-    value: MODEL_REGISTRY.STYLE_TRANSFER.MOSAIC({ quant: true }),
-  },
+  { label: 'Candy', value: MODEL_REGISTRY.STYLE_TRANSFER.CANDY },
+  { label: 'Mosaic', value: MODEL_REGISTRY.STYLE_TRANSFER.MOSAIC },
   {
     label: 'Rain Princess',
-    value: MODEL_REGISTRY.STYLE_TRANSFER.RAIN_PRINCESS({ quant: true }),
-  },
-  {
-    label: 'Udnie',
-    value: MODEL_REGISTRY.STYLE_TRANSFER.UDNIE({ quant: true }),
+    value: MODEL_REGISTRY.STYLE_TRANSFER.RAIN_PRINCESS,
   },
+  { label: 'Udnie', value: MODEL_REGISTRY.STYLE_TRANSFER.UDNIE },
 ];
 import ErrorBanner from '../../components/ErrorBanner';
 
 export default function StyleTransferScreen() {
   const [selectedModel, setSelectedModel] = useState<StyleTransferModelSources>(
-    MODEL_REGISTRY.STYLE_TRANSFER.CANDY({ quant: true })
+    MODEL_REGISTRY.STYLE_TRANSFER.CANDY
   );
 
   const model = useStyleTransfer({ model: selectedModel });

apps/computer-vision/components/vision_camera/tasks/SegmentationTask.tsx

@@ -41,35 +41,27 @@ export default function SegmentationTask({
   onErrorChange,
 }: Props) {
   const segDeeplabResnet50 = useSemanticSegmentation({
-    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_RESNET50({
-      quant: true,
-    }),
+    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_RESNET50,
     preventLoad: activeModel !== 'segmentationDeeplabResnet50',
   });
   const segDeeplabResnet101 = useSemanticSegmentation({
-    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_RESNET101({
-      quant: true,
-    }),
+    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_RESNET101,
     preventLoad: activeModel !== 'segmentationDeeplabResnet101',
   });
   const segDeeplabMobilenet = useSemanticSegmentation({
-    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_MOBILENET_V3_LARGE({
-      quant: true,
-    }),
+    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.DEEPLAB_V3_MOBILENET_V3_LARGE,
     preventLoad: activeModel !== 'segmentationDeeplabMobilenet',
   });
   const segLraspp = useSemanticSegmentation({
-    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.LRASPP_MOBILENET_V3_LARGE({
-      quant: true,
-    }),
+    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.LRASPP_MOBILENET_V3_LARGE,
     preventLoad: activeModel !== 'segmentationLraspp',
   });
   const segFcnResnet50 = useSemanticSegmentation({
-    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.FCN_RESNET50({ quant: true }),
+    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.FCN_RESNET50,
     preventLoad: activeModel !== 'segmentationFcnResnet50',
   });
   const segFcnResnet101 = useSemanticSegmentation({
-    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.FCN_RESNET101({ quant: true }),
+    model: MODEL_REGISTRY.SEMANTIC_SEGMENTATION.FCN_RESNET101,
     preventLoad: activeModel !== 'segmentationFcnResnet101',
   });
   const segSelfie = useSemanticSegmentation({

apps/llm/app/llm/index.tsx

@@ -43,7 +43,7 @@ function LLMScreen() {
   const [isTextInputFocused, setIsTextInputFocused] = useState(false);
   const [userInput, setUserInput] = useState('');
   const [selectedModel, setSelectedModel] = useState<LLMModelSources>(
-    MODEL_REGISTRY.LLM.LLAMA3_2_1B({ quant: true })
+    MODEL_REGISTRY.LLM.LLAMA3_2_1B
   );
   const textInputRef = useRef<TextInput>(null);
   const { setGlobalGenerating } = useContext(GeneratingContext);

apps/llm/app/llm_structured_output/index.tsx

@@ -84,7 +84,7 @@ function LLMScreen() {
   const [isTextInputFocused, setIsTextInputFocused] = useState(false);
   const [userInput, setUserInput] = useState('');
   const [selectedModel, setSelectedModel] = useState<LLMModelSources>(
-    MODEL_REGISTRY.LLM.QWEN3_1_7B({ quant: true })
+    MODEL_REGISTRY.LLM.QWEN3_1_7B
   );
   const textInputRef = useRef<TextInput>(null);
   const { setGlobalGenerating } = useContext(GeneratingContext);

apps/llm/app/llm_tool_calling/index.tsx

@@ -55,7 +55,7 @@ function LLMToolCallingScreen() {
   const [hasCalendarPermission, setHasCalendarPermission] = useState(true);
   const [hasBrightnessPermission, setHasBrightnessPermission] = useState(true);
   const [selectedModel, setSelectedModel] = useState<LLMModelSources>(
-    MODEL_REGISTRY.LLM.HAMMER2_1_1_5B({ quant: true })
+    MODEL_REGISTRY.LLM.HAMMER2_1_1_5B
   );
   const textInputRef = useRef<TextInput>(null);
   const { setGlobalGenerating } = useContext(GeneratingContext);

apps/llm/app/voice_chat/index.tsx

@@ -35,25 +35,19 @@ type LLMModelSources = LLMProps['model'];
 type STTModelSources = SpeechToTextProps['model'];
 
 const LLM_MODELS: ModelOption<LLMModelSources>[] = [
-  {
-    label: 'Qwen3 0.6B',
-    value: MODEL_REGISTRY.LLM.QWEN3_0_6B({ quant: true }),
-  },
-  {
-    label: 'Qwen3 1.7B',
-    value: MODEL_REGISTRY.LLM.QWEN3_1_7B({ quant: true }),
-  },
-  { label: 'Llama 1B', value: MODEL_REGISTRY.LLM.LLAMA3_2_1B({ quant: true }) },
+  { label: 'Qwen3 0.6B', value: MODEL_REGISTRY.LLM.QWEN3_0_6B },
+  { label: 'Qwen3 1.7B', value: MODEL_REGISTRY.LLM.QWEN3_1_7B },
+  { label: 'Llama 1B', value: MODEL_REGISTRY.LLM.LLAMA3_2_1B },
 ];
 
 const STT_MODELS: ModelOption<STTModelSources>[] = [
   {
     label: 'Whisper Tiny',
-    value: MODEL_REGISTRY.SPEECH_TO_TEXT.WHISPER_TINY_EN,
+    value: MODEL_REGISTRY.SPEECH_TO_TEXT.WHISPER_TINY_EN({ quant: false }),
   },
   {
     label: 'Whisper Tiny Q',
-    value: MODEL_REGISTRY.SPEECH_TO_TEXT.WHISPER_TINY_EN({ quant: true }),
+    value: MODEL_REGISTRY.SPEECH_TO_TEXT.WHISPER_TINY_EN,
   },
   {
     label: 'Whisper Base',
@@ -76,10 +70,10 @@ function VoiceChatScreen() {
   const [isRecording, setIsRecording] = useState(false);
   const [liveTranscription, setLiveTranscription] = useState('');
   const [selectedLLM, setSelectedLLM] = useState<LLMModelSources>(
-    MODEL_REGISTRY.LLM.QWEN3_0_6B({ quant: true })
+    MODEL_REGISTRY.LLM.QWEN3_0_6B
   );
   const [selectedSTT, setSelectedSTT] = useState<STTModelSources>(
-    MODEL_REGISTRY.SPEECH_TO_TEXT.WHISPER_TINY_EN
+    MODEL_REGISTRY.SPEECH_TO_TEXT.WHISPER_TINY_EN({ quant: false })
   );
   const [error, setError] = useState<string | null>(null);