CPU Compatible Mental Health Model

🚀 CPU Compatible Mental Health Chatbot Model

This repository hosts a fine - tuned LLaMA - based model tailored for mental health counseling conversations. It offers empathetic and meaningful responses to mental - health related questions and is accessible on CPUs and low - RAM systems.

🚀 Quick Start

This mental health chatbot model is designed to provide support for mental - health related queries. It's easy to set up and use, even on systems with limited resources.

✨ Features

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

📦 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

💻 Usage Examples

Basic Usage

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))

Advanced Usage

This model can be run on:

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

📚 Documentation

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()

Model Details

Model Performance

📄 License

This project is licensed under the Apache 2.0 License.

🙏 Acknowledgments

• [Meta](https://huggingface.co/meta - llama) for the LLaMA model
• Hugging Face for their open - source tools and datasets
• The creators of the Mental Health Counseling Conversations dataset