Doctor_Bot

Introduction

Doctor_Bot is a conversational model designed for text generation tasks, leveraging the capabilities of the GPT-2 architecture. It is suitable for applications that require natural language understanding and generation in a conversational context.

Architecture

Doctor_Bot is based on the GPT-2 architecture, which is a transformer model known for its ability to generate coherent and contextually relevant text. The model uses the PyTorch library, making it compatible with a wide range of deep learning tools and environments.

Training

The model is fine-tuned specifically for conversational tasks. Although details on the dataset and training parameters are not provided, it is designed to perform well in dialogue-based applications.

Guide: Running Locally

To run Doctor_Bot locally, follow these steps:

Clone the Repository: Begin by cloning the model repository to your local machine.
```
git clone https://huggingface.co/hama/Doctor_Bot
```
Install Dependencies: Ensure all necessary libraries, like PyTorch and Transformers, are installed.
```
pip install torch transformers
```

Load the Model: Use the Transformers library to load the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("hama/Doctor_Bot")
model = AutoModelForCausalLM.from_pretrained("hama/Doctor_Bot")

Inference: Use the model to generate text.

inputs = tokenizer("Hello, how can I help you today?", return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For better performance, especially with larger models, consider using cloud GPU services like AWS, Google Cloud, or Azure.

License

The model and its code are subject to Hugging Face's licensing terms, which should be reviewed to ensure compliance with intended use cases.