| | --- |
| | license: mit |
| | datasets: |
| | - vibhorag101/phr-mental-therapy-dataset-conversational-format |
| | language: |
| | - en |
| | base_model: |
| | - unsloth/Llama-3.2-3B-Instruct |
| | tags: |
| | - text-generation-inference |
| | - unsloth |
| | --- |
| | |
| |
|
| | ## Overview |
| | The chatbot has been fine-tuned on the **PHR Therapy Dataset** using **LLaMA 3.2 3B Instruct**, enhancing its ability to engage in meaningful and supportive conversations. |
| |
|
| | ## Features |
| | - **Empathetic Responses**: Trained to understand and respond with emotional intelligence. |
| | - **Context Awareness**: Retains context over multiple interactions. |
| | - **Mental Health Focus**: Provides supportive and non-judgmental responses based on therapy-related dialogues. |
| | - **Efficient Inference**: Optimized for deployment with reduced latency. |
| |
|
| | ## Model Fine-Tuning Details |
| | - **Base Model**: LLaMA 3.2 3B Instruct |
| | - **Dataset**: PHR Therapy Dataset (contains therapist-patient conversations for empathetic response generation) |
| | - **Fine-Tuning Framework**: Unsloth (optimized training for efficiency) |
| | - **Training Environment**: Google colab free version |
| | - **Optimization Techniques**: |
| | - LoRA (Low-Rank Adaptation) for parameter-efficient tuning |
| | - Mixed Precision Training for speed and memory efficiency |
| | - Supervised Fine-Tuning (SFT) on therapist-patient interactions |
| |
|
| | ## Installation |
| |
|
| | Using ollama |
| | ```bash |
| | ollama run hf.co/Ishan93/Fine_tuned_ver2 |
| | ``` |
| |
|
| | ## Usage |
| |
|
| | Using Google Colab or other notebooks |
| | ```python |
| | from llama_cpp import Llama |
| | |
| | llm = Llama.from_pretrained( |
| | repo_id="Ishan93/Fine_tuned_ver2", |
| | filename="Fine_tuned_ver2.gguf", |
| | ) |
| | ``` |
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