Qwen3-4B-Instruct-2507_mental_health

📚 Model Card: help2opensource/Qwen3-4B-Instruct-2507_mental_health

A fine-tuned LLM trained to support empathetic, safe, and context-aware mental health conversations using LoRA (Low-Rank Adaptation) on a dataset of 10,000 psychology-based dialogues.

Model Details

• Model: LoRA-fine-tuned version of Qwen3-4B-Instruct-2507
• Task: Generate empathetic, human-like responses in mental health counseling scenarios
• Fine-tuning Method: LoRA (Low-Rank Adaptation) for efficient and memory-friendly fine-tuning
• Training Data: 10,000 real-world mental health conversation pairs from the samhog/psychology-10k dataset
• Base Model: Qwen3-4B-Instruct-2507
• Architecture: Causal Language Model (Causal LM)
• Training Duration: ~1 day (on 1 GPU, 1 epoch)
• Batch Size: 1 per device (with 8 gradient accumulation steps)

📚 Training Data

✅ All data was anonymized and ethically curated.
❌ No sensitive personal data (e.g., names, medical histories) was used.

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🚀 Use Cases

This model can be used for:

• AI therapy assistants (in research or educational settings)
• Crisis support bots (with clear disclaimers — not a substitute for professional care)
• Mental health education tools (e.g., student counseling, peer support apps)
• Empathetic chatbot development with human-like tone and emotional intelligence

⚠️ Important Limitations:

• Not a substitute for professional mental health care
• Cannot diagnose, treat, or recommend medical interventions
• Should only be used in non-critical, supportive environments
• May not handle complex or severe cases

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🔧 How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained("help2opensource/Qwen3-4B-Instruct-2507_mental_health")
tokenizer = AutoTokenizer.from_pretrained("help2opensource/Qwen3-4B-Instruct-2507_mental_health")

# Example prompt
prompt = "I'm feeling really anxious lately. What should I do?"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    inputs.input_ids,
    max_length=256,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📌 Works with transformers, vLLM, Ollama, and Hugging Face Inference API

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🛠️ Technical Details

✅ LoRA enables fast, low-resource fine-tuning with minimal compute overhead.

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📝 Ethical & Safety Guidelines

• No medical advice: This model does not provide diagnosis or treatment.
• No harm mitigation: It cannot offer crisis intervention or emergency help.
• No personal data: No user data was stored or processed during training.
• Bias mitigation: Training data was filtered for non-toxic, respectful, and supportive tone.
• Safety filters: Outputs are filtered for harmful, illegal, or inappropriate content.

🔒 Safe for public use only when used with clear disclaimers.

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📎 How to Access

• 🌐 Hugging Face Hub: https://huggingface.co/your-username/mental-health-ai
• 📂 Model Files:
  • pytorch_model.bin
  • tokenizer_config.json
  • tokenizer.json
  • config.json
  • README.md (this file)

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📌 Who Should Use This?

✅ Students & researchers studying AI in mental health
✅ App developers building supportive chatbots
✅ Educators designing mental wellness tools
✅ Ethical AI practitioners concerned with safety and bias

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🚀 Want to Extend This?

You can extend this model by:

• Adding RAG for real-time access to medical or psychological resources
• Fine-tuning on specific conditions (e.g., anxiety, depression)
• Adding cognitive behavioral therapy (CBT) prompts
• Integrating emotion detection via NLP

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