Model Card for Model MentalChat-16K

Model Details

This model is a fine-tuned version of Llama-3.2-1B-Instruct, optimized for empathetic and supportive conversations in the mental health domain. It was trained on the ShenLab/MentalChat16K dataset, which includes over 16,000 counseling-style Q&A examples, combining real clinical paraphrases and synthetic mental health dialogues. The model is designed to understand and respond to emotionally nuanced prompts related to stress, anxiety, relationships, and personal well-being.

Model Description

• Language(s) (NLP): English
• License: MIT
• Finetuned from model: unsloth/Llama-3.2-1B-Instruct
• Dataset: ShenLab/MentalChat16K

Uses

This model is intended for research and experimentation in AI-driven mental health support. Key use cases include:

• Mental health chatbot prototypes
• Empathy-focused dialogue agents
• Benchmarking LLMs on emotional intelligence and counseling-style prompts
• Educational or training tools in psychology or mental health communication

This model is NOT intended for clinical diagnosis, therapy, or real-time intervention. It must not replace licensed mental health professionals.

Bias, Risks, and Limitations

• Biases:

  • The real interview data is biased toward caregivers (mostly White, female, U.S.-based), which may affect the model’s cultural and demographic generalizability.
  • The synthetic dialogues are generated by GPT-3.5, which may introduce linguistic and cultural biases from its pretraining.

• Limitations:

  • The base model, Qwen2.5-0.5B-Instruct, is a small model (0.5B parameters), limiting depth of reasoning and nuanced understanding.
  • Not suitable for handling acute mental health crises or emergency counseling.
  • Responses may lack therapeutic rigor or miss subtle psychological cues.
  • May produce hallucinated or inaccurate mental health advice.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/MentalChat-16K")
model = AutoModelForCausalLM.from_pretrained(
    "khazarai/MentalChat-16K",
    device_map={"": 0}
)

system = """You are a helpful mental health counselling assistant, please answer the mental health questions based on the patient's description.
The assistant gives helpful, comprehensive, and appropriate answers to the user's questions.
"""

question = """
I've been feeling overwhelmed by my responsibilities at work and caring for my aging parents. I've reached a point where I don't know what else I can do, and I'm struggling to communicate this to my boss and family members. I feel guilty for even considering saying no, but I know I need to take care of myself.
"""

messages = [
    {"role" : "system", "content" : system},
    {"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 900,
    temperature = 0.7,
    top_p = 0.8,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)