from transformers import AutoModelForCausalLM, AutoTokenizer
Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("ombhojane/wellness-mini", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("ombhojane/wellness-mini")
Example usage
messages = [{"role": "user", "content": "How are you feeling today?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Via Pipeline

python

from transformers import pipeline
Create pipeline
pipe = pipeline(
"text-generation",
model="ombhojane/wellness-mini",
tokenizer="ombhojane/wellness-mini",
trust_remote_code=True
)
Generate text
response = pipe("How are you feeling today?", max_new_tokens=100)
print(response[0]['generated_text'])

Training Details

Base model: SmolLM-135M
Fine-tuned using supervised fine-tuning (SFT)
Trained with both mental health assessment capabilities and proper identity responses

Limitations

This is an AI assistant and not a replacement for professional medical advice
Should be used as a supplementary tool only
May not be suitable for emergency situations
Responses should be verified by healthcare professionals

Intended Use

This model is intended to be used as a conversational AI assistant focused on mental health support and assessment. It can:

Provide supportive responses to mental health concerns
Help identify potential mental health indicators
Engage in wellness-focused conversations

Bias and Risks

May reflect biases present in training data
Should not be used as sole diagnostic tool
Responses should be reviewed by healthcare professionals

Creator

This model was developed by Sentinet AI Systems.