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README.md

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+# Gender Classification Quantized Model
+
+This repository hosts a quantized version of a feedforward neural network model, fine-tuned for gender classification tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
+
+---
+
+## Model Details
+
+- **Model Name:** Gender Classifier   
+- **Model Architecture:** 2-layer MLP (Multi-Layer Perceptron)  
+- **Task:** Gender Classification  
+- **Dataset:** Gender Classification Dataset v7  
+- **Quantization:** QInt8 (Dynamic Quantization)  
+- **Framework:** PyTorch
+
+---
+
+## Usage
+
+### Installation
+
+```bash
+pip install torch pandas scikit-learn numpy
+```
+
+### Loading the Quantized Model
+
+```python
+import torch
+import torch.nn as nn
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+import json
+
+# Define the model architecture
+class GenderClassifier(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.fc = nn.Sequential(
+            nn.Linear(7, 32),
+            nn.ReLU(),
+            nn.Linear(32, 2)
+        )
+
+    def forward(self, x):
+        return self.fc(x)
+
+# Load the quantized model
+model = GenderClassifier()
+quantized_model = torch.quantization.quantize_dynamic(model, {nn.Linear}, dtype=torch.qint8)
+quantized_model.load_state_dict(torch.load("quantized_model/pytorch_model.bin"))
+
+# Load configuration
+with open("quantized_model/config.json", "r") as f:
+    config = json.load(f)
+
+# Example usage
+# Prepare your input data (7 features)
+input_data = np.array([[feature1, feature2, feature3, feature4, feature5, feature6, feature7]])
+
+# Normalize using StandardScaler (you'll need to fit this on your training data)
+scaler = StandardScaler()
+# scaler.fit(your_training_data)  # Fit on your training data
+input_normalized = scaler.transform(input_data)
+
+# Convert to tensor
+input_tensor = torch.tensor(input_normalized, dtype=torch.float32)
+
+# Inference
+with torch.no_grad():
+    outputs = quantized_model(input_tensor)
+
+# Get predicted label
+predicted_class = outputs.argmax(dim=1).item()
+
+# Map label using label encoder classes
+label_mapping = {0: config["label_classes"][0], 1: config["label_classes"][1]}
+print(f"Predicted Gender: {label_mapping[predicted_class]}")
+```
+
+---
+
+## Performance Metrics
+
+- **Model Size:** Reduced through QInt8 quantization
+- **Input Features:** 7 numerical features
+- **Output Classes:** 2 (Binary gender classification)
+- **Training Split:** 80% train, 20% validation
+
+---
+
+## Training Details
+
+### Dataset
+
+The model was trained on the Gender Classification Dataset v7, featuring:
+- 7 numerical input features
+- Binary gender classification labels
+- Preprocessed and normalized data
+
+### Training Configuration
+
+- **Epochs:** 10  
+- **Batch Size:** 32  
+- **Learning Rate:** 0.001  
+- **Optimizer:** Adam  
+- **Loss Function:** CrossEntropyLoss
+- **Normalization:** StandardScaler
+
+### Model Architecture
+
+- **Input Layer:** 7 features
+- **Hidden Layer:** 32 neurons with ReLU activation
+- **Output Layer:** 2 neurons (binary classification)
+- **Total Parameters:** Approximately 288 parameters
+
+### Quantization
+
+Post-training dynamic quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency with QInt8 precision.
+
+---
+
+## Repository Structure
+
+```
+.
+├── quantized_model/
+│   ├── config.json              # Model configuration
+│   ├── pytorch_model.bin        # Quantized model weights
+│   ├── model.safetensors        # Alternative model format
+│   ├── vocab.txt               # Feature names
+│   ├── tokenizer_config.json   # Scaler configuration
+│   └── special_tokens_map.json # Label encoder metadata
+├── gender-classification.ipynb  # Training notebook
+└── README.md                   # Model documentation
+```
+
+---
+
+## Input Features
+
+The model expects 7 numerical features as input. The exact feature names and preprocessing requirements are stored in the configuration files.
+
+---
+
+## Limitations
+
+- The model is designed for binary gender classification only
+- Performance depends on the similarity between inference data and training data distribution
+- Quantization may result in minor accuracy changes compared to full-precision models
+- Requires proper feature scaling using StandardScaler fitted on training data
+
+---
+
+## Contributing
+
+Contributions are welcome! Feel free to open an issue or PR for improvements, fixes, or feature extensions.
+
+---

config (1).json

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+{"input_features": ["long_hair", "forehead_width_cm", "forehead_height_cm", "nose_wide", "nose_long", "lips_thin", "distance_nose_to_lip_long"], "label_classes": ["Female", "Male"], "scaling": "StandardScaler", "model_architecture": "2-layer MLP"}

model (2).safetensors

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+oid sha256:bb4973410ad93309f22dd9cda9cc407d625865c62295ca22521a4d5bd0994620
+size 296

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:59b193ba017a4a9e58cbb4b97855d7cd34cd381dea45f04b1e5a237a2be9de32
+size 3912

special_tokens_map (1).json

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+{"label_encoder": "sklearn.LabelEncoder"}

tokenizer_config (1).json

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+{"scaler": "StandardScaler"}

vocab (1).txt

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+long_hair
+forehead_width_cm
+forehead_height_cm
+nose_wide
+nose_long
+lips_thin
+distance_nose_to_lip_long