tags:
- autotrain
- text-generation
- pytorch
- text-generation-inference
- transformers
widget:
- text: 'I love AutoTrain because '
license: apache-2.0
datasets:
- Amod/mental_health_counseling_conversations
library_name: peft
Model Trained Using AutoTrain
This model was trained using AutoTrain and is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the mental_health_counseling_conversations dataset.
For more information, please visit AutoTrain.
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 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 :-
Training hyperparameters
The following hyperparameters were used during training: TODO
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "GRMenon/mental-mistral-7b-instruct-autotrain"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
# 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])