metadata

license: apache-2.0
base_model:
  - meta-llama/Llama-3.2-1B
pipeline_tag: question-answering

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.

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.

Model Details

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

Installation

Clone the repository:

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

Install the required packages:

pip install torch transformers datasets huggingface-hub

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

Fine-Tuning Instructions

To further fine-tune the model on your dataset:

Prepare your dataset in Hugging Face Dataset format.
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 Performance

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

License

This project is licensed under the Apache 2.0 License.

Acknowledgments

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