metadata
license: mit
language:
- en
base_model:
- Qwen/Qwen3-0.6B
tags:
- medical
- mental-health
🧠 Qwen-0.6B Mental Health Support (Fine-Tuned)
Model Repo: xformai/qwen-0.6b-mentalhealth-support
Base Model: Qwen/Qwen-0.5B
Task: Empathetic Conversational AI for mental health & emotional support
Fine-Tuned By: XformAI
🧠 What is this?
This is a fine-tuned version of the Qwen-0.6B language model, adapted on a curated dataset focused on mental health support and empathetic responses. The goal is to enable helpful, emotionally aware, and safe conversations around stress, anxiety, depression, and general wellness.
🧪 Use Cases
- Mental health chatbots
- Emotional support agents
- Wellness coaching prototypes
- Journaling assistants
📊 Training Details
- Dataset: Internal collection of therapy-style dialogues, emotional support threads, and curated mental health Q&A (non-clinical)
- Epochs: 3
- Batch Size: 16
- Optimizer: AdamW
- Context Window: 2048
- Precision: bfloat16
- Framework: Hugging Face Transformers + PEFT (LoRA)
🚨 Warnings
⚠️ This model is not a substitute for professional medical or mental health advice.
It is trained to offer support-style language, not diagnosis or clinical recommendations.
🧠 Example Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("xformai/qwen-0.6b-mentalhealth-support")
tokenizer = AutoTokenizer.from_pretrained("xformai/qwen-0.6b-mentalhealth-support")
prompt = "I've been feeling really overwhelmed lately. Can you help?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))