RayyanAhmed9477/CPU-Compatible-Mental-Health-Model

CPU Compatible Mental Health Chatbot Model

This repository contains a fine-tuned LLaMA-based model designed for mental health counseling conversations. The model provides meaningful and empathetic responses to mental health-related queries. It is compatible with CPUs and systems with low RAM, making it accessible for a wide range of users.

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Features

• Fine-tuned on Mental Health Counseling Conversations: The model is trained using a dataset specifically curated for mental health support.
• Low Resource Requirements: Fully executable on systems with 15 GB RAM and CPU, no GPU required.
• Pretrained on Meta's LLaMA 3.2 1B Model: Builds on the strengths of the LLaMA architecture for high-quality responses.
• Supports LoRA (Low-Rank Adaptation): Enables efficient fine-tuning with low computational overhead.

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Model Details

• Base Model: meta-llama/Llama-3.2-1B-Instruct
• Dataset: Amod/Mental Health Counseling Conversations
• Fine-Tuning Framework: Hugging Face Transformers

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Installation

1. Clone the repository:

   git clone https://huggingface.co/<your_hf_username>/mental-health-chatbot-model
   cd mental-health-chatbot-model
   

2. Install the required packages:

   pip install torch transformers datasets huggingface-hub
   

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Usage

Load the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
model_name = "<your_hf_username>/mental-health-chatbot-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
input_text = "I feel anxious and don't know what to do."
inputs = tokenizer(input_text, return_tensors="pt")
response = model.generate(**inputs, max_length=256, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(response[0], skip_special_tokens=True))

Compatibility

This model can be run on:

• CPU-only systems
• Machines with as little as 15 GB RAM

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Fine-Tuning Instructions

To further fine-tune the model on your dataset:

1. Prepare your dataset in Hugging Face Dataset format.
2. Use the following script:

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./fine_tuned_model",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    evaluation_strategy="epoch",
    save_steps=500,
    logging_dir="./logs",
    learning_rate=5e-5,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=validation_dataset,
)

trainer.train()

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Model Performance

• Training Epochs: 3
• Batch Size: 4
• Learning Rate: 5e-5
• Evaluation Strategy: Epoch-wise

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License

This project is licensed under the Apache 2.0 License.

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Acknowledgments

• Meta for the LLaMA model
• Hugging Face for their open-source tools and datasets
• The creators of the Mental Health Counseling Conversations dataset