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:

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:

python infer.py --input "your input text or path to image"

Example:

python infer.py --input "Hello, how are you?"

Usage Workflow

Place the model files (config.json and model.safetensors) inside the model/ directory.
Run infer.py with your desired input.
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.

License

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Contributing

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