This model is a fine-tuned version of Meta's LLaMA 2 7B, specifically designed for mental health-focused conversational applications. It provides empathetic, supportive, and informative responses related to mental well-being.

Model Details

Model Description

LLaMA-2-7B-Mental-Chat is optimized for natural language conversations in mental health contexts. Fine-tuned on a curated dataset of mental health dialogues, it aims to assist with stress management, general well-being, and providing empathetic support.

Developed by: Jjateen Gundesha
Funded by: Personal project
Shared by: Jjateen Gundesha
Model type: Transformer-based large language model (LLM)
Language(s): English
License: Meta's LLaMA 2 Community License
Fine-tuned from: LLaMA 2 7B

Model Sources

Repository: LLaMA-2-7B-Mental-Chat on Hugging Face
Paper: Not available
Demo: Coming soon

Uses

Direct Use

Mental Health Chatbot: For providing empathetic, non-clinical support on mental health topics like anxiety, stress, and general well-being.
Conversational AI: Supporting user queries with empathetic responses.

Downstream Use

Fine-tuning: Can be adapted for specialized mental health domains or multilingual support.
Integration: Deployable in chatbot frameworks or virtual assistants.

Out-of-Scope Use

Clinical diagnosis: Not suitable for medical or therapeutic advice.
Crisis management: Should not be used in critical situations requiring professional intervention.

Bias, Risks, and Limitations

Biases

May reflect biases from the mental health datasets used, especially around cultural or social norms.
Risk of generating inappropriate or overly simplistic responses to complex issues.

Limitations

Not a substitute for professional mental health care.
Limited to English; performance may degrade with non-native phrasing or dialects.

Recommendations

Users should monitor outputs for appropriateness, especially in sensitive or high-stakes situations. Ensure users are aware this is not a replacement for professional mental health services.

How to Get Started with the Model

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jjateen/llama-2-7b-mental-chat")
model = AutoModelForCausalLM.from_pretrained("Jjateen/llama-2-7b-mental-chat")

input_text = "I feel overwhelmed and anxious. What should I do?"
inputs = tokenizer(input_text, return_tensors="pt")

output = model.generate(**inputs, max_length=200)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)

Training Details

Training Data

Dataset: heliosbrahma/mental_health_chatbot_dataset
Preprocessing: Text normalization, tokenization, and filtering for quality.

Training Procedure

Framework: PyTorch
Epochs: 3
Batch Size: 8
Optimizer: AdamW
Learning Rate: 5e-6

Speeds, Sizes, Times

Training Time: Approximately 48 hours on NVIDIA A100 GPUs
Model Size: 10.5 GB (split across 2 .bin files)

Evaluation

Testing Data, Factors & Metrics

Testing Data

Held-out validation set with mental health dialogues.

Metrics

Empathy Score: Evaluated through human feedback.
Relevance: Based on context adherence.
Perplexity: Lower perplexity on mental health data compared to the base model.

Results

Metric	Score
Empathy Score	85/100
Relevance	90%
Safety	95%

Environmental Impact

Hardware Type: NVIDIA A100 GPUs
Hours used: 48 hours
Cloud Provider: AWS
Compute Region: US East
Carbon Emitted: Estimated using ML Impact Calculator

Technical Specifications

Model Architecture and Objective

Transformer architecture (decoder-only)
Fine-tuned with a causal language modeling objective

Compute Infrastructure

Hardware: 4x NVIDIA A100 GPUs
Software: PyTorch, Hugging Face Transformers

Citation

BibTeX:

@misc{jjateen_llama2_mentalchat_2024,
  title={LLaMA-2-7B-Mental-Chat},
  author={Jjateen Gundesha},
  year={2024},
  howpublished={\url{https://huggingface.co/Jjateen/llama-2-7b-mental-chat}}
}

Model Card Contact

For any questions or feedback, please contact Jjateen Gundesha.