Mental Health Mistral 7b Instructv0.2 Finetuned V2

🚀 mental-health-mistral-7b-instructv0.2-finetuned-V2

This model is a fine - tuned version of Mistral-7B-Instruct-v0.2, designed to serve as a mental health assistant, leveraging mental health counseling conversations to address user questions.

🚀 Quick Start

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the mental_health_counseling_conversations dataset. It achieves a loss of 0.6432 on the evaluation set.

Here is a simple example of how to use this model:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel

base_model = "mistralai/Mistral-7B-Instruct-v0.2"
adapter = "GRMenon/mental-health-mistral-7b-instructv0.2-finetuned-V2"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    base_model,
    add_bos_token=True,
    trust_remote_code=True,
    padding_side='left'
)

# Create peft model using base_model and finetuned adapter
config = PeftConfig.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,
                                             load_in_4bit=True,
                                             device_map='auto',
                                             torch_dtype='auto')
model = PeftModel.from_pretrained(model, adapter)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()

# Prompt content:
messages = [
    {"role": "user", "content": "Hey Connor! I have been feeling a bit down lately.I could really use some advice on how to feel better?"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages,
                                          tokenize=True,
                                          add_generation_prompt=True,
                                          return_tensors='pt').to(device)
output_ids = model.generate(input_ids=input_ids, max_new_tokens=512, do_sample=True, pad_token_id=2)
response = tokenizer.batch_decode(output_ids.detach().cpu().numpy(), skip_special_tokens = True)

# Model response: 
print(response[0])

✨ Features

• Mental Health Assistant: A Mistral-7B-Instruct-v0.2 model finetuned on mental health conversations to create a mental health assistant named "Connor", which can address user questions based on psychologists' responses.

📚 Documentation

Model description

A Mistral-7B-Instruct-v0.2 model finetuned on a corpus of mental health conversations between a psychologist and a user. The intention was to create a mental health assistant, "Connor", to address user questions based on responses from a psychologist.

Training and evaluation data

The model is finetuned on a corpus of mental health conversations between a psychologist and a client, in the form of context - response pairs. This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. Dataset found here :-

• Kaggle
• Huggingface

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Framework versions

• PEFT 0.7.1
• Transformers 4.36.1
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.15.0

📄 License

This project is licensed under the Apache-2.0 license.