| # 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. |