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# phi3
[](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. |