Ishan93/Fine_tuned_ver2

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: Local GPU / Cloud Instance (depending on available resources)
• 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

ollama run hf.co/Ishan93/Fine_tuned_ver2

Usage

Using Google Colab or other notebooks

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="Ishan93/Fine_tuned_ver2",
    filename="Fine_tuned_ver2.gguf",
)