image_classification: mobilenetv4hl cells (mnist, cifar10, tinyimagenet)

metadata/configs/image_classification/mobilenetv4hl__cifar10/mobilenetv_060/collect_config.json

@@ -0,0 +1,14 @@
+{
+  "protocol": "rpg_style_trajectory",
+  "optimizer": "adamw(fresh)",
+  "collect_lr": 1e-05,
+  "weight_decay": 0.0001,
+  "stabilise_steps": 200,
+  "save_every": 1,
+  "collect_count": 200,
+  "batch_size": 256,
+  "steps_per_epoch": 196,
+  "train_mode": "full",
+  "seed": 43,
+  "base_ckpt": "/data/chl0045/project/paragen-bench/zoo/cifar10/mobilenetv4hl/mobilenetv_060.pth"
+}

metadata/configs/image_classification/mobilenetv4hl__cifar10/mobilenetv_060/config.json

@@ -0,0 +1,28 @@
+{
+  "stage": "classification",
+  "task_type": "image_classification",
+  "dataset": "cifar10",
+  "dataset_root": "/data/chl0045/project/paragen-bench/datasets",
+  "model_name": "mobilenetv4hl",
+  "model_path": "/data/chl0045/project/paragen-bench/models/mobilenetv4hl.py",
+  "optimizer": "adamw",
+  "scheduler": "cosine_annealing",
+  "save_learning_rate": 1e-05,
+  "batch_size": 256,
+  "num_workers": 4,
+  "learning_rate": 0.00015,
+  "weight_decay": 0.0001,
+  "epochs": 10,
+  "num_classes": 10,
+  "input_channels": 3,
+  "image_size": 224,
+  "seed": 42,
+  "save_epoch_logs": false,
+  "collect_count": 0,
+  "collect_steps": 50,
+  "collect_lr": 0.001,
+  "early_stop_patience": 4,
+  "collect_eval": true,
+  "train_mode": "full",
+  "init_ckpt": null
+}

metadata/configs/image_classification/mobilenetv4hl__mnist/mobilenetv_051/collect_config.json

@@ -0,0 +1,14 @@
+{
+  "protocol": "rpg_style_trajectory",
+  "optimizer": "adamw(fresh)",
+  "collect_lr": 1e-05,
+  "weight_decay": 0.0001,
+  "stabilise_steps": 200,
+  "save_every": 1,
+  "collect_count": 200,
+  "batch_size": 256,
+  "steps_per_epoch": 235,
+  "train_mode": "full",
+  "seed": 43,
+  "base_ckpt": "/data/chl0045/project/paragen-bench/zoo/mnist/mobilenetv4hl/mobilenetv_051.pth"
+}

metadata/configs/image_classification/mobilenetv4hl__mnist/mobilenetv_051/config.json

@@ -0,0 +1,28 @@
+{
+  "stage": "classification",
+  "task_type": "image_classification",
+  "dataset": "mnist",
+  "dataset_root": "/data/chl0045/project/paragen-bench/datasets",
+  "model_name": "mobilenetv4hl",
+  "model_path": "/data/chl0045/project/paragen-bench/models/mobilenetv4hl.py",
+  "optimizer": "adamw",
+  "scheduler": "cosine_annealing",
+  "save_learning_rate": 1e-05,
+  "batch_size": 256,
+  "num_workers": 4,
+  "learning_rate": 0.00015,
+  "weight_decay": 0.0001,
+  "epochs": 10,
+  "num_classes": 10,
+  "input_channels": 3,
+  "image_size": 224,
+  "seed": 42,
+  "save_epoch_logs": false,
+  "collect_count": 0,
+  "collect_steps": 50,
+  "collect_lr": 0.001,
+  "early_stop_patience": 4,
+  "collect_eval": true,
+  "train_mode": "full",
+  "init_ckpt": null
+}

metadata/configs/image_classification/mobilenetv4hl__tinyimagenet/mobilenetv_070/collect_config.json

@@ -0,0 +1,14 @@
+{
+  "protocol": "rpg_style_trajectory",
+  "optimizer": "adamw(fresh)",
+  "collect_lr": 1e-05,
+  "weight_decay": 0.0001,
+  "stabilise_steps": 200,
+  "save_every": 1,
+  "collect_count": 200,
+  "batch_size": 256,
+  "steps_per_epoch": 391,
+  "train_mode": "full",
+  "seed": 43,
+  "base_ckpt": "/data/chl0045/project/paragen-bench/zoo/tinyimagenet/mobilenetv4hl/mobilenetv_070.pth"
+}

metadata/configs/image_classification/mobilenetv4hl__tinyimagenet/mobilenetv_070/config.json

@@ -0,0 +1,28 @@
+{
+  "stage": "classification",
+  "task_type": "image_classification",
+  "dataset": "tinyimagenet",
+  "dataset_root": "/data/chl0045/project/paragen-bench/datasets",
+  "model_name": "mobilenetv4hl",
+  "model_path": "/data/chl0045/project/paragen-bench/models/mobilenetv4hl.py",
+  "optimizer": "adamw",
+  "scheduler": "cosine_annealing",
+  "save_learning_rate": 1e-05,
+  "batch_size": 256,
+  "num_workers": 4,
+  "learning_rate": 0.00015,
+  "weight_decay": 0.0001,
+  "epochs": 10,
+  "num_classes": 200,
+  "input_channels": 3,
+  "image_size": 224,
+  "seed": 42,
+  "save_epoch_logs": false,
+  "collect_count": 0,
+  "collect_steps": 50,
+  "collect_lr": 0.001,
+  "early_stop_patience": 4,
+  "collect_eval": true,
+  "train_mode": "full",
+  "init_ckpt": null
+}

metadata/registry/image_classification.json

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prompt/image_classification/mobilenetv4hl__cifar10.json

@@ -0,0 +1,131 @@
+{
+  "cell": "mobilenetv4hl__cifar10",
+  "task": "image_classification",
+  "arch": "mobilenetv4hl",
+  "dataset": "cifar10",
+  "arch_name": "MobileNetV4-Hybrid-Large",
+  "dataset_name": "CIFAR-10",
+  "num_params": 36496434,
+  "num_classes": 10,
+  "generator": "Qwen/Qwen3-VL-32B-Instruct",
+  "n_prompts": 50,
+  "split_sizes": {
+    "train": 30,
+    "val": 10,
+    "test": 10
+  },
+  "train": [
+    "Can you provide MobileNetV4-Hybrid-Large model weights fine-tuned on CIFAR-10 using ImageNet initialization?",
+    "Return the trained weights for MobileNetV4-Hybrid-Large on CIFAR-10 with 10 object and animal classes.",
+    "CIFAR-10 classifier using MobileNetV4-Hybrid-Large architecture with 36,496,434 weights",
+    "On CIFAR-10, MobileNetV4-Hybrid-Large uses 224x224 RGB images as input, contains 36.50M parameters, and is initialized from ImageNet-pretrained weights prior to full fine-tuning.",
+    "Model with MobileNetV4-Hybrid-Large design for CIFAR-10, 50,000 training images, 36.50M parameters",
+    "I need MobileNetV4-Hybrid-Large weights trained on CIFAR-10 with 36.50M parameters and 224x224 input images.",
+    "MobileNetV4-Hybrid-Large processes 224x224 resized images from CIFAR-10 to classify ten categories including cats, dogs, birds, and ships, using a mix of convolutional and attention mechanisms.",
+    "The CIFAR-10 dataset contains 32x32 photos of ten object categories such as birds, cats, dogs, and trucks, which MobileNetV4-Hybrid-Large classifies using its 36.50M parameters after fine-tuning from ImageNet weights.",
+    "MobileNetV4-Hybrid-Large model trained on CIFAR-10 with 50,000 images and 36.50M parameters",
+    "The MobileNetV4-Hybrid-Large model, featuring 36,496,434 parameters, uses a mix of convolutional and attention blocks and is initialized from ImageNet weights before being trained on CIFAR-10 with 224x224 image inputs.",
+    "Ten distinct classes—airplanes, birds, cats, deer, dogs, frogs, horses, ships, trucks, and automobiles—are identified in CIFAR-10 by MobileNetV4-Hybrid-Large, which has 36,496,434 trainable parameters.",
+    "Generate a checkpoint of MobileNetV4-Hybrid-Large fine-tuned on CIFAR-10 using ImageNet-pretrained initialization.",
+    "For image classification on CIFAR-10, MobileNetV4-Hybrid-Large processes 224x224 RGB images, uses 36.50M parameters, and is initialized from ImageNet-pretrained weights with full fine-tuning.",
+    "I’m seeking MobileNetV4-Hybrid-Large model weights for CIFAR-10 with full parameter fine-tuning.",
+    "MobileNetV4-Hybrid-Large, comprising 36,496,434 parameters, combines universal inverted bottleneck convolutions and mobile multi-head self-attention, initialized from ImageNet, and takes 224x224 inputs from CIFAR-10.",
+    "I want MobileNetV4-Hybrid-Large trained on CIFAR-10 with 36,496,434 parameters and 10 classes.",
+    "For recognizing common objects and animals in 32x32 pixel images from CIFAR-10, MobileNetV4-Hybrid-Large applies hybrid architecture with 36.50M parameters, fine-tuned across all layers.",
+    "Supply the MobileNetV4-Hybrid-Large model weights for 224x224 inputs on CIFAR-10 with 50,000 examples.",
+    "Produce the MobileNetV4-Hybrid-Large weights trained on CIFAR-10 with 50,000 images and 10 classes.",
+    "MobileNetV4-Hybrid-Large network for CIFAR-10 classification, 224x224 input, 36.50M parameters",
+    "Deliver the model weights for MobileNetV4-Hybrid-Large, which processes 32x32 images resized to 224x224 on CIFAR-10.",
+    "Designed for CIFAR-10, MobileNetV4-Hybrid-Large employs 36.50M parameters in a hybrid convolution-attention framework, initialized from ImageNet weights, and accepts 224x224 pixel color images.",
+    "MobileNetV4-Hybrid-Large, a hybrid mobile network combining inverted bottleneck convolutions with multi-head self-attention, has 36.50M parameters and processes 224x224 RGB inputs from CIFAR-10 after initialization from ImageNet weights.",
+    "What are the MobileNetV4-Hybrid-Large weights for CIFAR-10 with 36.50M parameters?",
+    "Could you deliver MobileNetV4-Hybrid-Large weights fine-tuned on CIFAR-10 with 224x224 inputs?",
+    "CIFAR-10 image classification with MobileNetV4-Hybrid-Large and 36,496,434 trainable weights",
+    "Provide the weights for MobileNetV4-Hybrid-Large trained on CIFAR-10 with 36.50M parameters and 224x224 inputs.",
+    "MobileNetV4-Hybrid-Large performs image classification on CIFAR-10, recognizing 32x32 color photos of animals and vehicles such as cats, dogs, birds, and cars using hybrid blocks.",
+    "CIFAR-10 model based on MobileNetV4-Hybrid-Large with inverted bottlenecks and self-attention",
+    "Images of animals like frogs, deer, and horses along with vehicles such as airplanes and trucks are classified by MobileNetV4-Hybrid-Large, a model with 36,496,434 parameters trained on the full CIFAR-10 dataset."
+  ],
+  "val": [
+    "Give me the weights of MobileNetV4-Hybrid-Large fine-tuned on CIFAR-10 from ImageNet-pretrained starting point.",
+    "The 36.50M-parameter MobileNetV4-Hybrid-Large model, initialized from ImageNet, utilizes a blend of inverted convolutions and self-attention blocks and operates on 224x224 RGB images from CIFAR-10.",
+    "Load the MobileNetV4-Hybrid-Large checkpoint trained on CIFAR-10 with 36.50M parameters.",
+    "MobileNetV4-Hybrid-Large for CIFAR-10 image classification with 36.50M parameters",
+    "Image classifier on CIFAR-10 built with MobileNetV4-Hybrid-Large and 36,496,434 parameters",
+    "Please give me MobileNetV4-Hybrid-Large weights for classifying CIFAR-10 images at 224x224 resolution.",
+    "The model MobileNetV4-Hybrid-Large classifies 32x32 images from CIFAR-10 into ten categories including air, land, and sea vehicles as well as various animals.",
+    "Can you generate MobileNetV4-Hybrid-Large weights using CIFAR-10 data and 224x224 inputs?",
+    "Classifying 32x32 color images of everyday objects and animals from CIFAR-10, including airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks, MobileNetV4-Hybrid-Large uses hybrid convolution and attention blocks trained on 50,000 images.",
+    "With 36.50M trainable parameters, MobileNetV4-Hybrid-Large integrates inverted bottlenecks and self-attention, initialized from ImageNet weights, and accepts 224x224 color images from the CIFAR-10 dataset."
+  ],
+  "test": [
+    "With 36.50M parameters, MobileNetV4-Hybrid-Large identifies objects like birds, cats, and trucks in CIFAR-10, trained on 50,000 images and initialized from ImageNet weights.",
+    "MobileNetV4-Hybrid-Large, a 36,496,434-parameter hybrid architecture, merges inverted bottleneck and attention modules, starts from ImageNet weights, and receives 224x224 inputs from CIFAR-10.",
+    "CIFAR-10’s 50,000 training images feature ten classes including automobiles, frogs, and trucks, handled by MobileNetV4-Hybrid-Large through its attention-augmented design and ImageNet initialization.",
+    "Create the MobileNetV4-Hybrid-Large model weights for CIFAR-10, using 3-channel 224x224 inputs.",
+    "I’m looking for MobileNetV4-Hybrid-Large weights trained on CIFAR-10’s 50,000-image dataset.",
+    "MobileNetV4-Hybrid-Large architecture applied to CIFAR-10, 10 classes, 36.50M parameters",
+    "Output the MobileNetV4-Hybrid-Large model checkpoint for image classification on CIFAR-10 with 36,496,434 parameters.",
+    "Architecturally, MobileNetV4-Hybrid-Large merges inverted bottleneck blocks with mobile attention units, possesses 36,496,434 parameters, and is initialized from ImageNet weights before training on 224x224 CIFAR-10 inputs.",
+    "MobileNetV4-Hybrid-Large fine-tuned on CIFAR-10 from ImageNet weights, 36.50M parameters",
+    "I require MobileNetV4-Hybrid-Large weights for CIFAR-10’s 10-class image recognition task."
+  ],
+  "styles": {
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+}

prompt/image_classification/mobilenetv4hl__mnist.json

@@ -0,0 +1,131 @@
+{
+  "cell": "mobilenetv4hl__mnist",
+  "task": "image_classification",
+  "arch": "mobilenetv4hl",
+  "dataset": "mnist",
+  "arch_name": "MobileNetV4-Hybrid-Large",
+  "dataset_name": "MNIST",
+  "num_params": 36496434,
+  "num_classes": 10,
+  "generator": "Qwen/Qwen3-VL-32B-Instruct",
+  "n_prompts": 50,
+  "split_sizes": {
+    "train": 30,
+    "val": 10,
+    "test": 10
+  },
+  "train": [
+    "MobileNetV4-Hybrid-Large, with 36.50M parameters, integrates universal inverted bottlenecks and mobile self-attention blocks, accepts 224x224 3-channel inputs from MNIST, and begins training from ImageNet pretrained weights.",
+    "I want MobileNetV4-Hybrid-Large weights for classifying MNIST digits using 36.50M parameters and 224x224 inputs.",
+    "Can you deliver MobileNetV4-Hybrid-Large weights fine-tuned on MNIST with 60,000 training images, all parameters updated?",
+    "MobileNetV4-Hybrid-Large model trained on MNIST using 60,000 images and 3-channel 224x224 inputs",
+    "MobileNetV4-Hybrid-Large handles MNIST image classification, where 60,000 training samples of handwritten digits 0–9 are resized to 224x224 and converted to three channels.",
+    "With 36,496,434 parameters, MobileNetV4-Hybrid-Large processes MNIST data resized to 224x224 with three channels, using a blend of inverted bottleneck and self-attention blocks, and begins from ImageNet weights.",
+    "The architecture MobileNetV4-Hybrid-Large, comprising 36.50M trainable weights, combines inverted bottleneck and self-attention blocks, processes 224x224 3-channel MNIST images, and starts from ImageNet pretraining.",
+    "I’m working on digit recognition with MNIST and require MobileNetV4-Hybrid-Large weights initialized from ImageNet and fine-tuned on all parameters.",
+    "I need MobileNetV4-Hybrid-Large weights trained on MNIST with 60,000 images, input size 224x224, 3 channels, 36.50M parameters.",
+    "I’m building a classifier for MNIST using MobileNetV4-Hybrid-Large with 36,496,434 parameters and 224x224 inputs.",
+    "Produce the weights for MobileNetV4-Hybrid-Large, which processes 224x224 inputs from MNIST.",
+    "MobileNetV4-Hybrid-Large for MNIST image classification with 36.50M parameters and 224x224 input",
+    "Could you supply MobileNetV4-Hybrid-Large weights for MNIST, pretrained on ImageNet and fully fine-tuned on 60,000 examples?",
+    "The MNIST dataset’s ten-digit classes are identified by MobileNetV4-Hybrid-Large, which takes 224x224 three-channel images derived from 28x28 grayscale originals.",
+    "Generate the checkpoint of MobileNetV4-Hybrid-Large fine-tuned on MNIST using ImageNet-pretrained weights.",
+    "Fetch the model weights for MobileNetV4-Hybrid-Large, designed for MNIST digit classification.",
+    "In MNIST, where 28x28 grayscale images of digits 0–9 are fed to MobileNetV4-Hybrid-Large, the inputs are resized to 224x224 with three channels for classification.",
+    "MobileNetV4-Hybrid-Large designed for MNIST classification with hybrid bottleneck and attention blocks",
+    "Provide the weights for MobileNetV4-Hybrid-Large trained on MNIST with 36.50M parameters and 224x224 input resizing.",
+    "Processing MNIST images resized to 224x224 and replicated to 3 channels, MobileNetV4-Hybrid-Large, with 36.50M parameters, combines inverted bottleneck convolutions and self-attention blocks and is initialized from ImageNet.",
+    "For recognizing handwritten numerals 0 to 9 in MNIST, the MobileNetV4-Hybrid-Large network processes 224x224 three-channel images, originally 28x28 grayscale, with 36.50M parameters.",
+    "Output the checkpoint of MobileNetV4-Hybrid-Large trained on MNIST, initialized from ImageNet weights.",
+    "MobileNetV4-Hybrid-Large architecture for digit recognition on MNIST with 36,496,434 parameters",
+    "To classify 28x28 grayscale handwritten numerals 0–9 in MNIST, MobileNetV4-Hybrid-Large uses 224x224 three-channel inputs and has 36.50M parameters.",
+    "MobileNetV4-Hybrid-Large trained on MNIST with 36.50M parameters and universal inverted bottleneck blocks",
+    "Trained on MNIST, MobileNetV4-Hybrid-Large contains 36,496,434 parameters, utilizes both convolutional and attention modules, and receives 224x224 3-channel inputs after initializing from ImageNet weights.",
+    "Supply the weights for MobileNetV4-Hybrid-Large, a hybrid network for MNIST with 10 classes.",
+    "MobileNetV4-Hybrid-Large fine-tuned on 60,000 MNIST images with 224x224 3-channel inputs",
+    "MobileNetV4-Hybrid-Large, initialized from ImageNet weights, has 36.50M parameters and uses mixed inverted bottleneck and multi-head attention blocks to classify 224x224 3-channel MNIST images.",
+    "MNIST’s handwritten digits 0 to 9 are classified by MobileNetV4-Hybrid-Large, which uses 224x224 three-channel inputs and has 36.50M parameters."
+  ],
+  "val": [
+    "Deliver the full parameter set for MobileNetV4-Hybrid-Large, trained on MNIST with 60,000 images.",
+    "Classifying handwritten digits 0–9 from MNIST, the MobileNetV4-Hybrid-Large network operates on 224x224 three-channel inputs derived from original 28x28 grayscale images.",
+    "MobileNetV4-Hybrid-Large using mobile self-attention and convolution blocks on MNIST data",
+    "MobileNetV4-Hybrid-Large system for MNIST using hybrid convolution and attention modules",
+    "This model identifies digits 0 through 9 in MNIST using MobileNetV4-Hybrid-Large, trained on 60,000 images resized to 224x224 with grayscale replication to three channels.",
+    "Could you generate MobileNetV4-Hybrid-Large weights for MNIST, 224x224 input, 3-channel grayscale replication, 36.50M params?",
+    "The MobileNetV4-Hybrid-Large model, featuring 36,496,434 parameters, uses a mix of convolutional and attention blocks and is initialized from ImageNet weights before being trained on MNIST with 224x224 inputs.",
+    "MobileNetV4-Hybrid-Large, a hybrid mobile network combining inverted bottleneck convolutions with multi-head self-attention, has 36.50M parameters and processes 224x224 3-channel inputs from MNIST after initializing from ImageNet pretrained weights.",
+    "Can you provide MobileNetV4-Hybrid-Large model weights for MNIST classification using 224x224 inputs and 36,496,434 parameters?",
+    "Return the model weights of MobileNetV4-Hybrid-Large for MNIST classification with 36,496,434 parameters."
+  ],
+  "test": [
+    "For MNIST image classification, MobileNetV4-Hybrid-Large employs 36.50M parameters, processes 224x224 3-channel images, and is initialized with ImageNet weights, then fine-tuned across all layers.",
+    "Please give me MobileNetV4-Hybrid-Large weights for MNIST, where grayscale images are expanded to three channels and input is 224x224.",
+    "MobileNetV4-Hybrid-Large network fine-tuned on MNIST with ImageNet initialization and 36.50M weights",
+    "MobileNetV4-Hybrid-Large, a 36.50M-parameter hybrid network merging convolutional and attention mechanisms, takes 224x224 3-channel inputs from MNIST and is initialized using ImageNet pretrained weights.",
+    "Give the weights of MobileNetV4-Hybrid-Large trained on MNIST with 36.50M parameters.",
+    "Generate the parameter matrix for MobileNetV4-Hybrid-Large fine-tuned on MNIST grayscale digits.",
+    "Using MobileNetV4-Hybrid-Large, the MNIST dataset of 28x28 grayscale handwritten digits 0–9 is processed after resizing to 224x224 with three channels for classification.",
+    "MobileNetV4-Hybrid-Large classifier for MNIST digits using 224x224 grayscale-replicated inputs",
+    "I need the MobileNetV4-Hybrid-Large architecture trained on MNIST with 10 classes and 60,000 samples, resized to 224x224.",
+    "MobileNetV4-Hybrid-Large recognizes handwritten digits 0–9 from MNIST, trained on 60,000 images, with inputs expanded to 224x224 and three channels."
+  ],
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prompt/image_classification/mobilenetv4hl__tinyimagenet.json

@@ -0,0 +1,131 @@
+{
+  "cell": "mobilenetv4hl__tinyimagenet",
+  "task": "image_classification",
+  "arch": "mobilenetv4hl",
+  "dataset": "tinyimagenet",
+  "arch_name": "MobileNetV4-Hybrid-Large",
+  "dataset_name": "TinyImageNet",
+  "num_params": 36739824,
+  "num_classes": 200,
+  "generator": "Qwen/Qwen3-VL-32B-Instruct",
+  "n_prompts": 50,
+  "split_sizes": {
+    "train": 30,
+    "val": 10,
+    "test": 10
+  },
+  "train": [
+    "For image classification on TinyImageNet, MobileNetV4-Hybrid-Large employs a hybrid design with 36,739,824 parameters, processing 224x224 RGB images after initialization from ImageNet-pretrained weights and full parameter fine-tuning.",
+    "Generate the MobileNetV4-Hybrid-Large checkpoint trained on TinyImageNet’s 100,000 images and 200 classes.",
+    "Produce the checkpoint for MobileNetV4-Hybrid-Large trained on TinyImageNet with 36,739,824 parameters and 224x224 inputs.",
+    "MobileNetV4-Hybrid-Large image classifier on TinyImageNet with 36.74M weights and 224x224 input",
+    "For 200-category image classification on TinyImageNet, MobileNetV4-Hybrid-Large leverages 36.74M parameters trained on 100,000 64x64 color photographs, starting from ImageNet weights.",
+    "I need MobileNetV4-Hybrid-Large weights trained on TinyImageNet with 200 classes and 100,000 images resized to 224x224 pixels.",
+    "Generate the checkpoint of MobileNetV4-Hybrid-Large fine-tuned on TinyImageNet using 224x224 inputs and 36.74M parameters.",
+    "MobileNetV4-Hybrid-Large architecture fine-tuned on TinyImageNet with ImageNet-pretrained weights",
+    "Processing 3-channel 224x224 inputs, MobileNetV4-Hybrid-Large learns to classify 200 object classes from 100,000 images in TinyImageNet with 36.74M parameters.",
+    "Can you provide MobileNetV4-Hybrid-Large model weights fine-tuned on TinyImageNet using 36.74M parameters?",
+    "Using 224x224 resized 3-channel inputs, MobileNetV4-Hybrid-Large classifies 200 object types from TinyImageNet, built with 36.74M parameters and fine-tuned from pre-trained weights.",
+    "With 36.74M trainable parameters, MobileNetV4-Hybrid-Large combines inverted bottleneck and self-attention blocks, taking 224x224 RGB inputs from TinyImageNet and being initialized from ImageNet weights before full fine-tuning.",
+    "MobileNetV4-Hybrid-Large performs classification on 100,000 TinyImageNet images, each resized to 224x224, targeting 200 object categories with 36.74M trainable parameters.",
+    "MobileNetV4-Hybrid-Large, with 36,739,824 parameters, uses a hybrid of inverted bottleneck and self-attention units, initialized from ImageNet weights and fine-tuned on all parameters for 224x224 RGB inputs from TinyImageNet.",
+    "MobileNetV4-Hybrid-Large integrates inverted bottleneck convolutions and mobile multi-head self-attention, totaling 36.74M parameters, and operates on 224x224 RGB inputs from TinyImageNet, initialized with ImageNet weights and fine-tuned end-to-end.",
+    "I’m seeking MobileNetV4-Hybrid-Large weights trained on TinyImageNet with 64x64 color photos resized to 224x224.",
+    "With 36.74M parameters, MobileNetV4-Hybrid-Large identifies 200 object classes from TinyImageNet's 100,000 64x64 images, resized to 224x224 for processing.",
+    "MobileNetV4-Hybrid-Large initialized from ImageNet weights and fine-tuned on TinyImageNet",
+    "MobileNetV4-Hybrid-Large resolves image classification on TinyImageNet’s 200 categories, trained on 100,000 64x64 images resized to 224x224 with 36.74M parameters.",
+    "Fetch the MobileNetV4-Hybrid-Large model weights initialized from ImageNet and fine-tuned on TinyImageNet's 200 classes.",
+    "Provide the weights for MobileNetV4-Hybrid-Large trained on TinyImageNet with 200 classes and 100,000 training images.",
+    "I want MobileNetV4-Hybrid-Large trained on TinyImageNet, which has 200 classes and 100,000 training images.",
+    "MobileNetV4-Hybrid-Large with 36,739,824 parameters classifying TinyImageNet images at 224x224 resolution",
+    "I’m looking for MobileNetV4-Hybrid-Large weights initialized from ImageNet and trained on TinyImageNet’s 100,000 64x64 images.",
+    "MobileNetV4-Hybrid-Large, a hybrid mobile network combining inverted bottleneck convolutions with multi-head self-attention, has 36.74M parameters and processes 224x224 RGB inputs from TinyImageNet, initialized from ImageNet weights and fine-tuned on all parameters.",
+    "Please give me MobileNetV4-Hybrid-Large weights for classifying 200 categories from TinyImageNet with 224x224 inputs.",
+    "Return the MobileNetV4-Hybrid-Large weights fine-tuned on TinyImageNet using all parameters and 200 categories.",
+    "The 36.74M-parameter MobileNetV4-Hybrid-Large model blends inverted bottleneck convolutions and mobile self-attention, using 224x224 RGB images from TinyImageNet and being initialized from ImageNet pretrained weights with all parameters fine-tuned.",
+    "MobileNetV4-Hybrid-Large for 200-class image classification on TinyImageNet with 224x224 inputs",
+    "MobileNetV4-Hybrid-Large trained on TinyImageNet using 100,000 images and 36.74M parameters"
+  ],
+  "val": [
+    "Classifying 200 object categories from 64x64 color photos in TinyImageNet, the MobileNetV4-Hybrid-Large model processes resized 224x224 inputs with 36.74M parameters trained on 100,000 images.",
+    "Provide MobileNetV4-Hybrid-Large weights for TinyImageNet classification with 36.74M parameters and 200 classes.",
+    "MobileNetV4-Hybrid-Large hybrid network classifying TinyImageNet images using 224x224 inputs",
+    "Can you generate MobileNetV4-Hybrid-Large weights fine-tuned on all parameters for TinyImageNet’s 100,000 images?",
+    "Output the MobileNetV4-Hybrid-Large weights trained on TinyImageNet, which has 200 classes and 100,000 images.",
+    "This model classifies 200 ImageNet-derived object classes from TinyImageNet’s 64x64 images, processed by MobileNetV4-Hybrid-Large with 36.74M parameters after fine-tuning.",
+    "MobileNetV4-Hybrid-Large designed for image classification on 200-class TinyImageNet dataset",
+    "Deliver the model weights for MobileNetV4-Hybrid-Large after initialization from ImageNet and fine-tuning on TinyImageNet.",
+    "Processing 224x224 RGB images from TinyImageNet, MobileNetV4-Hybrid-Large features 36.74M parameters through its hybrid design of inverted bottleneck and self-attention blocks, initialized from ImageNet weights and fine-tuned entirely.",
+    "MobileNetV4-Hybrid-Large, a 36,739,824-parameter architecture merging inverted bottleneck and self-attention modules, accepts 224x224 RGB inputs from TinyImageNet and was initialized from ImageNet weights with complete fine-tuning."
+  ],
+  "test": [
+    "Could you share MobileNetV4-Hybrid-Large weights for image classification on TinyImageNet with 36,739,824 parameters?",
+    "On TinyImageNet, MobileNetV4-Hybrid-Large distinguishes between 200 object types using 36.74M parameters, trained on 100,000 resized 224x224 3-channel images.",
+    "Give me the weights for MobileNetV4-Hybrid-Large trained on TinyImageNet with 224x224 inputs and 36.74M parameters.",
+    "Supply the model weights for MobileNetV4-Hybrid-Large, trained on TinyImageNet with 36.74M parameters and 64x64 source images.",
+    "TinyImageNet’s 200-class image classification task is solved by MobileNetV4-Hybrid-Large, which uses 36.74M parameters and trains on 100,000 resized 64x64 color images.",
+    "MobileNetV4-Hybrid-Large network with universal inverted bottleneck and attention blocks on TinyImageNet",
+    "Using 224x224 color images from TinyImageNet, MobileNetV4-Hybrid-Large—featuring both inverted bottleneck and self-attention blocks—contains 36.74M parameters and was fully fine-tuned starting from ImageNet-pretrained weights.",
+    "The MobileNetV4-Hybrid-Large model, with 36,739,824 parameters, uses a mixed architecture of universal inverted bottleneck blocks and mobile self-attention units, trained on TinyImageNet with 224x224 image inputs after initialization from ImageNet pretraining.",
+    "I require MobileNetV4-Hybrid-Large weights pretrained on ImageNet and fine-tuned on TinyImageNet’s 100,000 images.",
+    "MobileNetV4-Hybrid-Large model for TinyImageNet classification with 3-channel 224x224 inputs"
+  ],
+  "styles": {
+    "train": [
+      "technical",
+      "request",
+      "request",
+      "concise",
+      "task",
+      "user",
+      "request",
+      "concise",
+      "task",
+      "user",
+      "task",
+      "technical",
+      "task",
+      "technical",
+      "technical",
+      "user",
+      "task",
+      "concise",
+      "task",
+      "request",
+      "request",
+      "user",
+      "concise",
+      "user",
+      "technical",
+      "user",
+      "request",
+      "technical",
+      "concise",
+      "concise"
+    ],
+    "val": [
+      "task",
+      "user",
+      "concise",
+      "user",
+      "request",
+      "task",
+      "concise",
+      "request",
+      "technical",
+      "technical"
+    ],
+    "test": [
+      "user",
+      "task",
+      "request",
+      "request",
+      "task",
+      "concise",
+      "technical",
+      "technical",
+      "user",
+      "concise"
+    ]
+  }
+}

prompt/prompts.json

@@ -17422,5 +17422,185 @@
       "Generate ViT-mini-Seg weights for Pascal-Context with embedding dimension 128 and 3x3 convolutions in decoder.",
       "Generate the checkpoint of ViT-mini-Seg trained from scratch on Pascal-Context using 224x224 RGB inputs."
     ]
+  },
+  "mobilenetv4hl__mnist": {
+    "task": "image_classification",
+    "file": "prompt/image_classification/mobilenetv4hl__mnist.json",
+    "train": [
+      "MobileNetV4-Hybrid-Large, with 36.50M parameters, integrates universal inverted bottlenecks and mobile self-attention blocks, accepts 224x224 3-channel inputs from MNIST, and begins training from ImageNet pretrained weights.",
+      "I want MobileNetV4-Hybrid-Large weights for classifying MNIST digits using 36.50M parameters and 224x224 inputs.",
+      "Can you deliver MobileNetV4-Hybrid-Large weights fine-tuned on MNIST with 60,000 training images, all parameters updated?",
+      "MobileNetV4-Hybrid-Large model trained on MNIST using 60,000 images and 3-channel 224x224 inputs",
+      "MobileNetV4-Hybrid-Large handles MNIST image classification, where 60,000 training samples of handwritten digits 0–9 are resized to 224x224 and converted to three channels.",
+      "With 36,496,434 parameters, MobileNetV4-Hybrid-Large processes MNIST data resized to 224x224 with three channels, using a blend of inverted bottleneck and self-attention blocks, and begins from ImageNet weights.",
+      "The architecture MobileNetV4-Hybrid-Large, comprising 36.50M trainable weights, combines inverted bottleneck and self-attention blocks, processes 224x224 3-channel MNIST images, and starts from ImageNet pretraining.",
+      "I’m working on digit recognition with MNIST and require MobileNetV4-Hybrid-Large weights initialized from ImageNet and fine-tuned on all parameters.",
+      "I need MobileNetV4-Hybrid-Large weights trained on MNIST with 60,000 images, input size 224x224, 3 channels, 36.50M parameters.",
+      "I’m building a classifier for MNIST using MobileNetV4-Hybrid-Large with 36,496,434 parameters and 224x224 inputs.",
+      "Produce the weights for MobileNetV4-Hybrid-Large, which processes 224x224 inputs from MNIST.",
+      "MobileNetV4-Hybrid-Large for MNIST image classification with 36.50M parameters and 224x224 input",
+      "Could you supply MobileNetV4-Hybrid-Large weights for MNIST, pretrained on ImageNet and fully fine-tuned on 60,000 examples?",
+      "The MNIST dataset’s ten-digit classes are identified by MobileNetV4-Hybrid-Large, which takes 224x224 three-channel images derived from 28x28 grayscale originals.",
+      "Generate the checkpoint of MobileNetV4-Hybrid-Large fine-tuned on MNIST using ImageNet-pretrained weights.",
+      "Fetch the model weights for MobileNetV4-Hybrid-Large, designed for MNIST digit classification.",
+      "In MNIST, where 28x28 grayscale images of digits 0–9 are fed to MobileNetV4-Hybrid-Large, the inputs are resized to 224x224 with three channels for classification.",
+      "MobileNetV4-Hybrid-Large designed for MNIST classification with hybrid bottleneck and attention blocks",
+      "Provide the weights for MobileNetV4-Hybrid-Large trained on MNIST with 36.50M parameters and 224x224 input resizing.",
+      "Processing MNIST images resized to 224x224 and replicated to 3 channels, MobileNetV4-Hybrid-Large, with 36.50M parameters, combines inverted bottleneck convolutions and self-attention blocks and is initialized from ImageNet.",
+      "For recognizing handwritten numerals 0 to 9 in MNIST, the MobileNetV4-Hybrid-Large network processes 224x224 three-channel images, originally 28x28 grayscale, with 36.50M parameters.",
+      "Output the checkpoint of MobileNetV4-Hybrid-Large trained on MNIST, initialized from ImageNet weights.",
+      "MobileNetV4-Hybrid-Large architecture for digit recognition on MNIST with 36,496,434 parameters",
+      "To classify 28x28 grayscale handwritten numerals 0–9 in MNIST, MobileNetV4-Hybrid-Large uses 224x224 three-channel inputs and has 36.50M parameters.",
+      "MobileNetV4-Hybrid-Large trained on MNIST with 36.50M parameters and universal inverted bottleneck blocks",
+      "Trained on MNIST, MobileNetV4-Hybrid-Large contains 36,496,434 parameters, utilizes both convolutional and attention modules, and receives 224x224 3-channel inputs after initializing from ImageNet weights.",
+      "Supply the weights for MobileNetV4-Hybrid-Large, a hybrid network for MNIST with 10 classes.",
+      "MobileNetV4-Hybrid-Large fine-tuned on 60,000 MNIST images with 224x224 3-channel inputs",
+      "MobileNetV4-Hybrid-Large, initialized from ImageNet weights, has 36.50M parameters and uses mixed inverted bottleneck and multi-head attention blocks to classify 224x224 3-channel MNIST images.",
+      "MNIST’s handwritten digits 0 to 9 are classified by MobileNetV4-Hybrid-Large, which uses 224x224 three-channel inputs and has 36.50M parameters."
+    ],
+    "val": [
+      "Deliver the full parameter set for MobileNetV4-Hybrid-Large, trained on MNIST with 60,000 images.",
+      "Classifying handwritten digits 0–9 from MNIST, the MobileNetV4-Hybrid-Large network operates on 224x224 three-channel inputs derived from original 28x28 grayscale images.",
+      "MobileNetV4-Hybrid-Large using mobile self-attention and convolution blocks on MNIST data",
+      "MobileNetV4-Hybrid-Large system for MNIST using hybrid convolution and attention modules",
+      "This model identifies digits 0 through 9 in MNIST using MobileNetV4-Hybrid-Large, trained on 60,000 images resized to 224x224 with grayscale replication to three channels.",
+      "Could you generate MobileNetV4-Hybrid-Large weights for MNIST, 224x224 input, 3-channel grayscale replication, 36.50M params?",
+      "The MobileNetV4-Hybrid-Large model, featuring 36,496,434 parameters, uses a mix of convolutional and attention blocks and is initialized from ImageNet weights before being trained on MNIST with 224x224 inputs.",
+      "MobileNetV4-Hybrid-Large, a hybrid mobile network combining inverted bottleneck convolutions with multi-head self-attention, has 36.50M parameters and processes 224x224 3-channel inputs from MNIST after initializing from ImageNet pretrained weights.",
+      "Can you provide MobileNetV4-Hybrid-Large model weights for MNIST classification using 224x224 inputs and 36,496,434 parameters?",
+      "Return the model weights of MobileNetV4-Hybrid-Large for MNIST classification with 36,496,434 parameters."
+    ],
+    "test": [
+      "For MNIST image classification, MobileNetV4-Hybrid-Large employs 36.50M parameters, processes 224x224 3-channel images, and is initialized with ImageNet weights, then fine-tuned across all layers.",
+      "Please give me MobileNetV4-Hybrid-Large weights for MNIST, where grayscale images are expanded to three channels and input is 224x224.",
+      "MobileNetV4-Hybrid-Large network fine-tuned on MNIST with ImageNet initialization and 36.50M weights",
+      "MobileNetV4-Hybrid-Large, a 36.50M-parameter hybrid network merging convolutional and attention mechanisms, takes 224x224 3-channel inputs from MNIST and is initialized using ImageNet pretrained weights.",
+      "Give the weights of MobileNetV4-Hybrid-Large trained on MNIST with 36.50M parameters.",
+      "Generate the parameter matrix for MobileNetV4-Hybrid-Large fine-tuned on MNIST grayscale digits.",
+      "Using MobileNetV4-Hybrid-Large, the MNIST dataset of 28x28 grayscale handwritten digits 0–9 is processed after resizing to 224x224 with three channels for classification.",
+      "MobileNetV4-Hybrid-Large classifier for MNIST digits using 224x224 grayscale-replicated inputs",
+      "I need the MobileNetV4-Hybrid-Large architecture trained on MNIST with 10 classes and 60,000 samples, resized to 224x224.",
+      "MobileNetV4-Hybrid-Large recognizes handwritten digits 0–9 from MNIST, trained on 60,000 images, with inputs expanded to 224x224 and three channels."
+    ]
+  },
+  "mobilenetv4hl__cifar10": {
+    "task": "image_classification",
+    "file": "prompt/image_classification/mobilenetv4hl__cifar10.json",
+    "train": [
+      "Can you provide MobileNetV4-Hybrid-Large model weights fine-tuned on CIFAR-10 using ImageNet initialization?",
+      "Return the trained weights for MobileNetV4-Hybrid-Large on CIFAR-10 with 10 object and animal classes.",
+      "CIFAR-10 classifier using MobileNetV4-Hybrid-Large architecture with 36,496,434 weights",
+      "On CIFAR-10, MobileNetV4-Hybrid-Large uses 224x224 RGB images as input, contains 36.50M parameters, and is initialized from ImageNet-pretrained weights prior to full fine-tuning.",
+      "Model with MobileNetV4-Hybrid-Large design for CIFAR-10, 50,000 training images, 36.50M parameters",
+      "I need MobileNetV4-Hybrid-Large weights trained on CIFAR-10 with 36.50M parameters and 224x224 input images.",
+      "MobileNetV4-Hybrid-Large processes 224x224 resized images from CIFAR-10 to classify ten categories including cats, dogs, birds, and ships, using a mix of convolutional and attention mechanisms.",
+      "The CIFAR-10 dataset contains 32x32 photos of ten object categories such as birds, cats, dogs, and trucks, which MobileNetV4-Hybrid-Large classifies using its 36.50M parameters after fine-tuning from ImageNet weights.",
+      "MobileNetV4-Hybrid-Large model trained on CIFAR-10 with 50,000 images and 36.50M parameters",
+      "The MobileNetV4-Hybrid-Large model, featuring 36,496,434 parameters, uses a mix of convolutional and attention blocks and is initialized from ImageNet weights before being trained on CIFAR-10 with 224x224 image inputs.",
+      "Ten distinct classes—airplanes, birds, cats, deer, dogs, frogs, horses, ships, trucks, and automobiles—are identified in CIFAR-10 by MobileNetV4-Hybrid-Large, which has 36,496,434 trainable parameters.",
+      "Generate a checkpoint of MobileNetV4-Hybrid-Large fine-tuned on CIFAR-10 using ImageNet-pretrained initialization.",
+      "For image classification on CIFAR-10, MobileNetV4-Hybrid-Large processes 224x224 RGB images, uses 36.50M parameters, and is initialized from ImageNet-pretrained weights with full fine-tuning.",
+      "I’m seeking MobileNetV4-Hybrid-Large model weights for CIFAR-10 with full parameter fine-tuning.",
+      "MobileNetV4-Hybrid-Large, comprising 36,496,434 parameters, combines universal inverted bottleneck convolutions and mobile multi-head self-attention, initialized from ImageNet, and takes 224x224 inputs from CIFAR-10.",
+      "I want MobileNetV4-Hybrid-Large trained on CIFAR-10 with 36,496,434 parameters and 10 classes.",
+      "For recognizing common objects and animals in 32x32 pixel images from CIFAR-10, MobileNetV4-Hybrid-Large applies hybrid architecture with 36.50M parameters, fine-tuned across all layers.",
+      "Supply the MobileNetV4-Hybrid-Large model weights for 224x224 inputs on CIFAR-10 with 50,000 examples.",
+      "Produce the MobileNetV4-Hybrid-Large weights trained on CIFAR-10 with 50,000 images and 10 classes.",
+      "MobileNetV4-Hybrid-Large network for CIFAR-10 classification, 224x224 input, 36.50M parameters",
+      "Deliver the model weights for MobileNetV4-Hybrid-Large, which processes 32x32 images resized to 224x224 on CIFAR-10.",
+      "Designed for CIFAR-10, MobileNetV4-Hybrid-Large employs 36.50M parameters in a hybrid convolution-attention framework, initialized from ImageNet weights, and accepts 224x224 pixel color images.",
+      "MobileNetV4-Hybrid-Large, a hybrid mobile network combining inverted bottleneck convolutions with multi-head self-attention, has 36.50M parameters and processes 224x224 RGB inputs from CIFAR-10 after initialization from ImageNet weights.",
+      "What are the MobileNetV4-Hybrid-Large weights for CIFAR-10 with 36.50M parameters?",
+      "Could you deliver MobileNetV4-Hybrid-Large weights fine-tuned on CIFAR-10 with 224x224 inputs?",
+      "CIFAR-10 image classification with MobileNetV4-Hybrid-Large and 36,496,434 trainable weights",
+      "Provide the weights for MobileNetV4-Hybrid-Large trained on CIFAR-10 with 36.50M parameters and 224x224 inputs.",
+      "MobileNetV4-Hybrid-Large performs image classification on CIFAR-10, recognizing 32x32 color photos of animals and vehicles such as cats, dogs, birds, and cars using hybrid blocks.",
+      "CIFAR-10 model based on MobileNetV4-Hybrid-Large with inverted bottlenecks and self-attention",
+      "Images of animals like frogs, deer, and horses along with vehicles such as airplanes and trucks are classified by MobileNetV4-Hybrid-Large, a model with 36,496,434 parameters trained on the full CIFAR-10 dataset."
+    ],
+    "val": [
+      "Give me the weights of MobileNetV4-Hybrid-Large fine-tuned on CIFAR-10 from ImageNet-pretrained starting point.",
+      "The 36.50M-parameter MobileNetV4-Hybrid-Large model, initialized from ImageNet, utilizes a blend of inverted convolutions and self-attention blocks and operates on 224x224 RGB images from CIFAR-10.",
+      "Load the MobileNetV4-Hybrid-Large checkpoint trained on CIFAR-10 with 36.50M parameters.",
+      "MobileNetV4-Hybrid-Large for CIFAR-10 image classification with 36.50M parameters",
+      "Image classifier on CIFAR-10 built with MobileNetV4-Hybrid-Large and 36,496,434 parameters",
+      "Please give me MobileNetV4-Hybrid-Large weights for classifying CIFAR-10 images at 224x224 resolution.",
+      "The model MobileNetV4-Hybrid-Large classifies 32x32 images from CIFAR-10 into ten categories including air, land, and sea vehicles as well as various animals.",
+      "Can you generate MobileNetV4-Hybrid-Large weights using CIFAR-10 data and 224x224 inputs?",
+      "Classifying 32x32 color images of everyday objects and animals from CIFAR-10, including airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks, MobileNetV4-Hybrid-Large uses hybrid convolution and attention blocks trained on 50,000 images.",
+      "With 36.50M trainable parameters, MobileNetV4-Hybrid-Large integrates inverted bottlenecks and self-attention, initialized from ImageNet weights, and accepts 224x224 color images from the CIFAR-10 dataset."
+    ],
+    "test": [
+      "With 36.50M parameters, MobileNetV4-Hybrid-Large identifies objects like birds, cats, and trucks in CIFAR-10, trained on 50,000 images and initialized from ImageNet weights.",
+      "MobileNetV4-Hybrid-Large, a 36,496,434-parameter hybrid architecture, merges inverted bottleneck and attention modules, starts from ImageNet weights, and receives 224x224 inputs from CIFAR-10.",
+      "CIFAR-10’s 50,000 training images feature ten classes including automobiles, frogs, and trucks, handled by MobileNetV4-Hybrid-Large through its attention-augmented design and ImageNet initialization.",
+      "Create the MobileNetV4-Hybrid-Large model weights for CIFAR-10, using 3-channel 224x224 inputs.",
+      "I’m looking for MobileNetV4-Hybrid-Large weights trained on CIFAR-10’s 50,000-image dataset.",
+      "MobileNetV4-Hybrid-Large architecture applied to CIFAR-10, 10 classes, 36.50M parameters",
+      "Output the MobileNetV4-Hybrid-Large model checkpoint for image classification on CIFAR-10 with 36,496,434 parameters.",
+      "Architecturally, MobileNetV4-Hybrid-Large merges inverted bottleneck blocks with mobile attention units, possesses 36,496,434 parameters, and is initialized from ImageNet weights before training on 224x224 CIFAR-10 inputs.",
+      "MobileNetV4-Hybrid-Large fine-tuned on CIFAR-10 from ImageNet weights, 36.50M parameters",
+      "I require MobileNetV4-Hybrid-Large weights for CIFAR-10’s 10-class image recognition task."
+    ]
+  },
+  "mobilenetv4hl__tinyimagenet": {
+    "task": "image_classification",
+    "file": "prompt/image_classification/mobilenetv4hl__tinyimagenet.json",
+    "train": [
+      "For image classification on TinyImageNet, MobileNetV4-Hybrid-Large employs a hybrid design with 36,739,824 parameters, processing 224x224 RGB images after initialization from ImageNet-pretrained weights and full parameter fine-tuning.",
+      "Generate the MobileNetV4-Hybrid-Large checkpoint trained on TinyImageNet’s 100,000 images and 200 classes.",
+      "Produce the checkpoint for MobileNetV4-Hybrid-Large trained on TinyImageNet with 36,739,824 parameters and 224x224 inputs.",
+      "MobileNetV4-Hybrid-Large image classifier on TinyImageNet with 36.74M weights and 224x224 input",
+      "For 200-category image classification on TinyImageNet, MobileNetV4-Hybrid-Large leverages 36.74M parameters trained on 100,000 64x64 color photographs, starting from ImageNet weights.",
+      "I need MobileNetV4-Hybrid-Large weights trained on TinyImageNet with 200 classes and 100,000 images resized to 224x224 pixels.",
+      "Generate the checkpoint of MobileNetV4-Hybrid-Large fine-tuned on TinyImageNet using 224x224 inputs and 36.74M parameters.",
+      "MobileNetV4-Hybrid-Large architecture fine-tuned on TinyImageNet with ImageNet-pretrained weights",
+      "Processing 3-channel 224x224 inputs, MobileNetV4-Hybrid-Large learns to classify 200 object classes from 100,000 images in TinyImageNet with 36.74M parameters.",
+      "Can you provide MobileNetV4-Hybrid-Large model weights fine-tuned on TinyImageNet using 36.74M parameters?",
+      "Using 224x224 resized 3-channel inputs, MobileNetV4-Hybrid-Large classifies 200 object types from TinyImageNet, built with 36.74M parameters and fine-tuned from pre-trained weights.",
+      "With 36.74M trainable parameters, MobileNetV4-Hybrid-Large combines inverted bottleneck and self-attention blocks, taking 224x224 RGB inputs from TinyImageNet and being initialized from ImageNet weights before full fine-tuning.",
+      "MobileNetV4-Hybrid-Large performs classification on 100,000 TinyImageNet images, each resized to 224x224, targeting 200 object categories with 36.74M trainable parameters.",
+      "MobileNetV4-Hybrid-Large, with 36,739,824 parameters, uses a hybrid of inverted bottleneck and self-attention units, initialized from ImageNet weights and fine-tuned on all parameters for 224x224 RGB inputs from TinyImageNet.",
+      "MobileNetV4-Hybrid-Large integrates inverted bottleneck convolutions and mobile multi-head self-attention, totaling 36.74M parameters, and operates on 224x224 RGB inputs from TinyImageNet, initialized with ImageNet weights and fine-tuned end-to-end.",
+      "I’m seeking MobileNetV4-Hybrid-Large weights trained on TinyImageNet with 64x64 color photos resized to 224x224.",
+      "With 36.74M parameters, MobileNetV4-Hybrid-Large identifies 200 object classes from TinyImageNet's 100,000 64x64 images, resized to 224x224 for processing.",
+      "MobileNetV4-Hybrid-Large initialized from ImageNet weights and fine-tuned on TinyImageNet",
+      "MobileNetV4-Hybrid-Large resolves image classification on TinyImageNet’s 200 categories, trained on 100,000 64x64 images resized to 224x224 with 36.74M parameters.",
+      "Fetch the MobileNetV4-Hybrid-Large model weights initialized from ImageNet and fine-tuned on TinyImageNet's 200 classes.",
+      "Provide the weights for MobileNetV4-Hybrid-Large trained on TinyImageNet with 200 classes and 100,000 training images.",
+      "I want MobileNetV4-Hybrid-Large trained on TinyImageNet, which has 200 classes and 100,000 training images.",
+      "MobileNetV4-Hybrid-Large with 36,739,824 parameters classifying TinyImageNet images at 224x224 resolution",
+      "I’m looking for MobileNetV4-Hybrid-Large weights initialized from ImageNet and trained on TinyImageNet’s 100,000 64x64 images.",
+      "MobileNetV4-Hybrid-Large, a hybrid mobile network combining inverted bottleneck convolutions with multi-head self-attention, has 36.74M parameters and processes 224x224 RGB inputs from TinyImageNet, initialized from ImageNet weights and fine-tuned on all parameters.",
+      "Please give me MobileNetV4-Hybrid-Large weights for classifying 200 categories from TinyImageNet with 224x224 inputs.",
+      "Return the MobileNetV4-Hybrid-Large weights fine-tuned on TinyImageNet using all parameters and 200 categories.",
+      "The 36.74M-parameter MobileNetV4-Hybrid-Large model blends inverted bottleneck convolutions and mobile self-attention, using 224x224 RGB images from TinyImageNet and being initialized from ImageNet pretrained weights with all parameters fine-tuned.",
+      "MobileNetV4-Hybrid-Large for 200-class image classification on TinyImageNet with 224x224 inputs",
+      "MobileNetV4-Hybrid-Large trained on TinyImageNet using 100,000 images and 36.74M parameters"
+    ],
+    "val": [
+      "Classifying 200 object categories from 64x64 color photos in TinyImageNet, the MobileNetV4-Hybrid-Large model processes resized 224x224 inputs with 36.74M parameters trained on 100,000 images.",
+      "Provide MobileNetV4-Hybrid-Large weights for TinyImageNet classification with 36.74M parameters and 200 classes.",
+      "MobileNetV4-Hybrid-Large hybrid network classifying TinyImageNet images using 224x224 inputs",
+      "Can you generate MobileNetV4-Hybrid-Large weights fine-tuned on all parameters for TinyImageNet’s 100,000 images?",
+      "Output the MobileNetV4-Hybrid-Large weights trained on TinyImageNet, which has 200 classes and 100,000 images.",
+      "This model classifies 200 ImageNet-derived object classes from TinyImageNet’s 64x64 images, processed by MobileNetV4-Hybrid-Large with 36.74M parameters after fine-tuning.",
+      "MobileNetV4-Hybrid-Large designed for image classification on 200-class TinyImageNet dataset",
+      "Deliver the model weights for MobileNetV4-Hybrid-Large after initialization from ImageNet and fine-tuning on TinyImageNet.",
+      "Processing 224x224 RGB images from TinyImageNet, MobileNetV4-Hybrid-Large features 36.74M parameters through its hybrid design of inverted bottleneck and self-attention blocks, initialized from ImageNet weights and fine-tuned entirely.",
+      "MobileNetV4-Hybrid-Large, a 36,739,824-parameter architecture merging inverted bottleneck and self-attention modules, accepts 224x224 RGB inputs from TinyImageNet and was initialized from ImageNet weights with complete fine-tuning."
+    ],
+    "test": [
+      "Could you share MobileNetV4-Hybrid-Large weights for image classification on TinyImageNet with 36,739,824 parameters?",
+      "On TinyImageNet, MobileNetV4-Hybrid-Large distinguishes between 200 object types using 36.74M parameters, trained on 100,000 resized 224x224 3-channel images.",
+      "Give me the weights for MobileNetV4-Hybrid-Large trained on TinyImageNet with 224x224 inputs and 36.74M parameters.",
+      "Supply the model weights for MobileNetV4-Hybrid-Large, trained on TinyImageNet with 36.74M parameters and 64x64 source images.",
+      "TinyImageNet’s 200-class image classification task is solved by MobileNetV4-Hybrid-Large, which uses 36.74M parameters and trains on 100,000 resized 64x64 color images.",
+      "MobileNetV4-Hybrid-Large network with universal inverted bottleneck and attention blocks on TinyImageNet",
+      "Using 224x224 color images from TinyImageNet, MobileNetV4-Hybrid-Large—featuring both inverted bottleneck and self-attention blocks—contains 36.74M parameters and was fully fine-tuned starting from ImageNet-pretrained weights.",
+      "The MobileNetV4-Hybrid-Large model, with 36,739,824 parameters, uses a mixed architecture of universal inverted bottleneck blocks and mobile self-attention units, trained on TinyImageNet with 224x224 image inputs after initialization from ImageNet pretraining.",
+      "I require MobileNetV4-Hybrid-Large weights pretrained on ImageNet and fine-tuned on TinyImageNet’s 100,000 images.",
+      "MobileNetV4-Hybrid-Large model for TinyImageNet classification with 3-channel 224x224 inputs"
+    ]
   }
 }