phi-3-mental-health-q4

phi3

Description

phi3 is a fine-tuned version of phi-3, specifically trained on mental health therapist conversational data. This model is designed to assist in mental health support, providing empathetic and knowledgeable responses in a conversational setting.

Installation

To use this model, you will need to install the following dependencies:

pip install transformers
pip install torch  # or tensorflow depending on your preference

Usage

Here is how you can load and use the model in your code:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("username/phi3")
model = AutoModelForCausalLM.from_pretrained("username/phi3")

# Example usage
chat_template = """
<|system|>
You are a compassionate mental health therapist. You listen to your clients attentively and provide thoughtful, empathetic responses to help them navigate their emotions and mental health challenges.
<|end|>
<|user|>
I've been feeling really down lately. What should I do?
<|end|>
<|assistant|>
"""

inputs = tokenizer(chat_template, return_tensors="pt")
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Inference

Provide example code for performing inference with your model:

# Example inference
user_input = "I've been feeling really down lately. What should I do?"
chat_template = f"""
<|system|>
You are a compassionate mental health therapist. You listen to your clients attentively and provide thoughtful, empathetic responses to help them navigate their emotions and mental health challenges.
<|end|>
<|user|>
I've been feeling really down lately. What should I do?
<|end|>
<|assistant|>
"""

inputs = tokenizer(chat_template, return_tensors="pt")
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Training

If your model can be trained further, provide instructions for training:

# Example training code
from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=3,
    weight_decay=0.01,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()

Training Details

Training Data

The model was fine-tuned on a dataset of conversational data from mental health therapy sessions. This dataset includes a variety of scenarios and responses typical of therapeutic interactions to ensure the model provides empathetic and helpful advice.

Training Procedure

The model was fine-tuned using a standard training approach, optimizing for empathy and relevance in responses. Training was conducted on [describe hardware, e.g., GPUs, TPUs] over [number of epochs] epochs with [any relevant hyperparameters].

Evaluation

Metrics

The model was evaluated using the following metrics:

• Accuracy: X%
• Empathy Score: Y%
• Relevance Score: Z%

Comparison

The performance of phi3 was benchmarked against other conversational models in the mental health domain, demonstrating superior empathy and contextual understanding.

Limitations and Biases

While phi3 is highly effective, it may have limitations in the following areas:

• It may not be suitable for providing critical mental health interventions.
• There may be biases present in the training data that could affect responses.

How to Contribute

We welcome contributions! Please see our contributing guidelines for more information on how to contribute to this project.

License

This model is licensed under the MIT License.

Acknowledgements

We would like to thank the contributors and the creators of the datasets used for training this model.

### Tips for Completing the Template

1. **Replace placeholders** (like `username`, `training data`, `evaluation metrics`) with your actual data.
2. **Include any additional information** specific to your model or training process.
3. **Keep the document updated** as the model evolves or more information becomes available.