RayyanAhmed9477/Health-Chatbot

Health Chatbot

Welcome to the official Hugging Face repository for Health Chatbot, a conversational AI model fine-tuned to assist with health-related queries. This model is based on LLaMA 3.2, fine-tuned using QLoRA for lightweight and efficient training.

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Overview

Health Chatbot is designed to provide accurate and conversational responses for general health advice and wellness information. The model is intended for educational purposes and is not a substitute for professional medical consultation.

Key Features:

• Fine-tuned using QLoRA for parameter-efficient training.
• Trained on a diverse dataset of health-related queries and answers.
• Optimized for conversational and empathetic interactions.

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Model Details

• Base Model: LLaMA 3.2
• Training Method: QLoRA (Quantized Low-Rank Adaptation)
• Dataset: Custom curated dataset comprising publicly available health resources, FAQs, and synthetic dialogues.
• Intended Use: Conversational health assistance and wellness education.

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How to Use the Model

You can load and use the model in your Python environment with the transformers library:

Installation

Make sure you have the necessary dependencies installed:

pip install transformers accelerate bitsandbytes

Loading the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("RayyanAhmed9477/Health-Chatbot")
model = AutoModelForCausalLM.from_pretrained(
    "RayyanAhmed9477/Health-Chatbot",
    device_map="auto",
    load_in_8bit=True
)

# Generate a response
def chat(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_length=150, do_sample=True, temperature=0.7)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Example usage
prompt = "What are some common symptoms of the flu?"
print(chat(prompt))

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Fine-Tuning the Model

If you want to fine-tune the model further on a custom dataset, follow the steps below.

Requirements

pip install datasets peft

Dataset Preparation

Prepare your dataset in a JSON or CSV format with input and output fields:

Example Dataset (JSON):

[
    {"input": "What are some symptoms of dehydration?", "output": "Symptoms include dry mouth, fatigue, and dizziness."},
    {"input": "How can I boost my immune system?", "output": "Eat a balanced diet, exercise regularly, and get enough sleep."}
]

Training Script

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model
from datasets import load_dataset

# Load the base model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("RayyanAhmed9477/Health-Chatbot")
model = AutoModelForCausalLM.from_pretrained(
    "RayyanAhmed9477/Health-Chatbot",
    device_map="auto",
    load_in_8bit=True
)

# Prepare model for training
model = prepare_model_for_int8_training(model)

# Define LoRA configuration
lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)

# Load your custom dataset
data = load_dataset("json", data_files="your_dataset.json")

# Fine-tune the model
from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    logging_dir="./logs",
    save_strategy="epoch",
    evaluation_strategy="epoch",
    learning_rate=1e-4,
    fp16=True
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=data["train"]
)

trainer.train()

# Save the fine-tuned model
model.save_pretrained("./fine_tuned_health_chatbot")
tokenizer.save_pretrained("./fine_tuned_health_chatbot")

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Model Evaluation

Evaluate the model's performance using metrics like perplexity and BLEU:

from datasets import load_metric

# Load evaluation dataset
eval_data = load_dataset("json", data_files="evaluation_dataset.json")

# Evaluate with perplexity
def compute_perplexity(model, dataset):
    metric = load_metric("perplexity")
    results = metric.compute(model=model, dataset=dataset)
    return results

print(compute_perplexity(model, eval_data["test"]))

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Limitations and Warnings

• The model is not a substitute for professional medical advice.
• Responses are generated based on patterns in the training data and may not always be accurate or up-to-date.

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Contributing

Contributions are welcome! If you have suggestions, improvements, or issues to report, please create a pull request or an issue in this repository.

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License

This model is released under the Apache 2.0 License.

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Contact

For any queries or collaborations, reach out to me via GitHub or email at rayyanahmed265@yahoo.com, LinkedIn  .

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Acknowledgements

Special thanks to the Hugging Face and Meta AI teams for their open-source contributions to the NLP and machine learning community.