Model Card for Model ID
A first version of mental health chatbot fine tuned on 1900+ samples
Model Details
Model Description
Mental Health has become an increasingly severe issue in the young generation. To combat it, we developed Mindmate a mental health chatbot with dual mode setting, a professional
mode simulating a therapist and a friend mode which will act to console and help you(implemented using gemini api)
- Developed by: pointbreak3000(Aditya Deshmukh)
- Funded by [optional]: -
- Shared by [optional]: -
- Model type: LLM
- Language(s) (NLP): English
- License: [More Information Needed]
- Finetuned from model [optional]: Mistral 7B - v- 0.1
Model Sources [optional]
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Uses
Direct Use
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
model = PeftModel.from_pretrained(base_model, "pointbreak3000/mistral-mental-health-lora")
prompt = "I'm feeling stressed and anxious lately. What should I do?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Framework versions
