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]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

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]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

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.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: [More Information Needed]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

  • PEFT 0.9.0
Downloads last month
-
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for pointbreak3000/MINDMATE.AI

Adapter
(2466)
this model

Space using pointbreak3000/MINDMATE.AI 1

Paper for pointbreak3000/MINDMATE.AI