# phi3

[![Model Card](https://img.shields.io/badge/Hugging%20Face-Model%20Card-blue)](https://huggingface.co/username/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:

```bash
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:

```python
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:

```python
# 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:

```python
# 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](link_to_contributing_guidelines) for more information on how to contribute to this project.

## License

This model is licensed under the [MIT License](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.