RayyanAhmed9477
/

Health-Chatbot

@@ -1,199 +1,210 @@
----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+---
+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 `[email protected]`, [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.