---
library_name: transformers
license: apache-2.0
language:
- en
metrics:
- code_eval
- accuracy
base_model:
- meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
---
# 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](https://ai.meta.com/llama/), fine-tuned using **QLoRA** for lightweight and efficient training.

---

## 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.

---

## 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.

---

## 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:

```bash
pip install transformers accelerate bitsandbytes
```

### Loading the Model

```python
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))
```

---

## Fine-Tuning the Model

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

### Requirements

```bash
pip install datasets peft
```

### Dataset Preparation

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

**Example Dataset (JSON)**:

```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

```python
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")
```

---

## Model Evaluation

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

```python
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"]))
```

---

## 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.

---

## Contributing

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

---

## License

This model is released under the [Apache 2.0 License](LICENSE).

---

## Contact

For any queries or collaborations, reach out to me via [GitHub](https://github.com/Rayyan9477) or email at `rayyanahmed265@yahoo.com`, [LinkedIn](https\://www\.linkedin.com/in/rayyan-ahmed9477/)  .

---

## Acknowledgements

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