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  - fine-tuned
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  license: mit
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  pipeline_tag: text-generation
 
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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. The model is designed to:
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- - Provide mental health assessments based on user conversations
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- - Identify potential indicators of ADHD and Bipolar disorder
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- - Analyze emotional indicators and stress levels
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-
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- ## Base Model
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- - Original model: SmolLM-135M (HuggingFaceTB/SmolLM-135M)
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- - Architecture: Causal Language Model
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- - Size: 135M parameters
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-
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- ## Training Data
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- The model was trained on a curated dataset including:
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- - Reddit posts from mental health communities (ADHD and Bipolar subreddits)
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- - Professional medical prescriptions and guidelines
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- - Identity training examples
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- - Emotional and behavioral indicators
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-
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- ## Features
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- 1. **Mental Health Assessment**
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- - Primary condition identification
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- - Additional symptom indicators
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- - Emotional state analysis
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-
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- 2. **Identity Awareness**
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- - Consistently identifies as a Sentinet AI Systems creation
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- - Clear AI identity in responses
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-
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- 3. **Clinical Indicators Analysis**
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- - Sentiment analysis
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- - Anxiety levels
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- - Stress indicators
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- - Emotional valence
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  ## Usage
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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_name = "ombhojane/wellness-mini"
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- tokenizer = AutoTokenizer.from_pretrained(model_name)
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- model = AutoModelForCausalLM.from_pretrained(model_name)
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  Example usage
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- messages = [{"role": "user", "content": "Who created you?"}]
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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=50)
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  response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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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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- ## Ethical Considerations
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- - The model should be used responsibly in mental health contexts
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- - User privacy and data protection should be prioritized
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- - Clear disclosure of AI nature in healthcare applications
 
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- ## Creator
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- This model was developed by Om Bhojane.
 
 
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- ## Citation
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- If you use this model in your research or application, please cite:
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- ```
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- @misc{wellness-mini,
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- author = {Om Bhojane},
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- title = {Wellness-Mini: Mental Health Conversational AI},
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- year = {2024},
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- publisher = {HuggingFace},
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- journal = {HuggingFace Hub},
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- howpublished = {\url{https://huggingface.co/ombhojane/wellness-mini}}
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- }
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-
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- ```
 
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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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+
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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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+
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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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+
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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.