VaisakhKrishna/

Llama-2-Emotional-ChatBot

Llama 2 Emotional Chatbot

Emotional_Chatbot is a fine-tuned version of the LLaMA-2-7b-Chat model, specifically designed to create chatbots that can understand and respond empathetically to the emotions expressed by users. This model is tailored for applications in conversational AI, where emotional understanding and context-aware responses are crucial.

Purpose: To generate contextually relevant and empathetic responses to user inputs while understanding the user’s emotional state.

Features Emotion-Aware Responses: The model identifies the user’s emotional state and generates responses tailored to the emotion (e.g., sadness, happiness, anger, etc.).

Instruction-Following: Fine-tuned in the instruction-response format, enabling it to handle complex queries effectively.

Adaptability: Suitable for building chatbots across various domains, such as mental health support, customer service, or personal assistants.

Example Usage Input Prompt: I feel really anxious about my upcoming exams

Model Response It's natural to feel anxious before exams, but remember to take it one step at a time. Deep breaths can help, and planning your study schedule might reduce some of the stress. You're doing your best, and that's what counts!

How to Use You can load the model using the Hugging Face transformers library and deploy it in your applications. Here's a quick start:

python

#transformer -4.31 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name = "VaisakhKrishna/Emotional_Chatbot" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)

chatbot = pipeline("text-generation", model=model, tokenizer=tokenizer)

user_input = "I feel sad about not achieving my goals" response = chatbot(user_input, max_length=200, num_return_sequences=1) print(response[0]["generated_text"].split("[/INST]")[-1].strip())

Applications Mental Health Support Chatbots: Provide empathetic and emotionally sensitive responses to users seeking support. Customer Service Bots: Understand and respond to customer emotions for a better user experience. Personal AI Assistants: Enhance the assistant's ability to detect user moods and tailor its interactions accordingly.

Limitations While the model generates empathetic responses, it is not a substitute for professional mental health or medical advice. Responses are based on patterns in the training data and might not always reflect nuanced real-world contexts.

Model Performance Fine-tuned using QLoRA with 4-bit quantization for efficient inference. Demonstrates high accuracy in identifying emotional states and generating relevant responses.

Citation If you use this model, please cite it as follows:

@model{emotional_chatbot, author = {Vaisakh Krishna}, title = {Emotional_Chatbot: A Fine-Tuned LLaMA-2-7b-Chat Model}, year = {2024}, url = {https://huggingface.co/VaisakhKrishna/Llama-2-Emotional-Chatbot} }