# Minimal Inference Setup

This project provides a lightweight setup for running inference with a pre-trained model.  
It contains the model configuration, trained weights, and a Python script to perform inference.

---

## Project Structure

```
.
├── model/
│   ├── config.json         # Model configuration file
│   ├── model.safetensors   # Pre-trained model weights
└── infer.py                # Script to run inference on input data
```

---

## Prerequisites

- Python 3.8+
- PyTorch
- Transformers library
- safetensors
- PIL (Pillow)  
- (Optional) tkinter if a GUI is implemented in `infer.py`

Install required packages:

```bash
pip install torch transformers safetensors pillow
```

---

## Files Description

### model/config.json
Defines the architecture and hyperparameters of the model (e.g., hidden size, number of layers, vocabulary size).

Required to correctly instantiate the model before loading the weights.

### model/model.safetensors
Contains the trained weights of the model.

Stored in the Safetensors format for safety and efficiency.

### infer.py
Main script to perform inference with the pre-trained model.

**Responsibilities:**
- Loads config.json and model.safetensors
- Preprocesses input text/image (depending on model type)
- Runs the model forward pass
- Outputs predictions

**Usage:**
```bash
python infer.py --input "your input text or path to image"
```

**Example:**
```bash
python infer.py --input "Hello, how are you?"
```

---

## Usage Workflow

1. Place the model files (`config.json` and `model.safetensors`) inside the `model/` directory.
2. Run `infer.py` with your desired input.
3. The script will display the prediction/classification result.

---

## Notes

- Ensure the model files are compatible (same checkpoint version).
- For image-based models, inputs must be resized to the expected dimensions (e.g., 224x224 RGB).
- For text-based models, ensure the tokenizer is compatible with the config (may require adding tokenizer files).
- GPU is recommended for faster inference, but CPU is supported.

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## License

[Add license information here if applicable]

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## Contributing

[Add contribution guidelines here if applicable]