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--- |
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language: en |
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tags: |
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- mental-health |
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- healthcare |
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- conversational |
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- wellness |
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- SmolLM-135M |
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- fine-tuned |
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license: mit |
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pipeline_tag: text-generation |
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inference: false |
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--- |
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# Wellness-Mini: Mental Health Conversational AI |
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## Model Description |
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Wellness-Mini is a fine-tuned version of SmolLM-135M, specifically adapted for mental health conversations and assessments. |
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## Usage |
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### In Transformers |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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Load model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained("ombhojane/wellness-mini", trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained("ombhojane/wellness-mini") |
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Example usage |
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messages = [{"role": "user", "content": "How are you feeling today?"}] |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(inputs, max_new_tokens=100) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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### Via Pipeline |
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```python |
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from transformers import pipeline |
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Create pipeline |
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pipe = pipeline( |
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"text-generation", |
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model="ombhojane/wellness-mini", |
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tokenizer="ombhojane/wellness-mini", |
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trust_remote_code=True |
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) |
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Generate text |
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response = pipe("How are you feeling today?", max_new_tokens=100) |
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print(response[0]['generated_text']) |
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``` |
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## Training Details |
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- Base model: SmolLM-135M |
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- Fine-tuned using supervised fine-tuning (SFT) |
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- Trained with both mental health assessment capabilities and proper identity responses |
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## Limitations |
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- This is an AI assistant and not a replacement for professional medical advice |
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- Should be used as a supplementary tool only |
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- May not be suitable for emergency situations |
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- Responses should be verified by healthcare professionals |
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## Intended Use |
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This model is intended to be used as a conversational AI assistant focused on mental health support and assessment. It can: |
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- Provide supportive responses to mental health concerns |
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- Help identify potential mental health indicators |
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- Engage in wellness-focused conversations |
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## Bias and Risks |
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- May reflect biases present in training data |
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- Should not be used as sole diagnostic tool |
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- Responses should be reviewed by healthcare professionals |
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## Creator |
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This model was developed by Sentinet AI Systems. |
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