adilthyl/Qwen3_0.6B_MentalHealth_FineTune

Qwen3 0.6B – Mental Health Fine-Tuned Model

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Model Overview

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Model Details

• Model Name: adilthyl/Qwen3_0.6B_MentalHealth_FineTune
• Base Model: Qwen3 0.6B
• Fine-tuned by: Adil Thayyil
• Version: v1.0
• Type: Decoder-only Transformer Language Model
• License: Apache 2.0 (confirm compatibility with base model license)

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Intended Use

• Primary Use Case:
  Provide empathetic, supportive, and informative responses in mental health conversations.
• Domains:
  Mental health support, wellness advice, stress management.
• Scenarios:
  • Conversational agents for emotional support
  • Educational mental health tools
  • Research in NLP for mental health dialogue

Important:
This model is not intended for commercial deployment.
It is provided for experimentation and educational purposes only.

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Limitations

• Not a substitute for professional mental health care.
• May generate inaccurate or biased responses.
• Requires human oversight when used in sensitive contexts.
• Not intended for commercial deployment.

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Ethical Considerations

• Safety: Trained with filters to minimize harmful outputs.
• Bias: May inherit biases present in the training data.
• Responsible Use:
  Should not be used for diagnosis or medical treatment.
  Intended solely for educational and experimental research.

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How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "adilthyl/Qwen3_0.6B_MentalHealth_FineTune"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

inputs = tokenizer("I feel very anxious today, what should I do?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))