Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning

Emotion text classification using Llama3-8b with LoRA and FlashAttention. Based on LLaMA-Factory.

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Fine-grained Emotion Classification (FEC) using structured feature fusion from LLaMA-2-7B-Chat, BERT-large, and RoBERTa-large.

Official Implementation for EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning

Analyzes Gaza-related tweets using LLaMA-3 via GroqCloud. Combines Selenium scraping with LLM-powered sentiment analysis to surface public emotion trends.

Teleomatic Agent is an experimental AI system designed to autonomously generate intrinsic goals, simulate emotion-like states, and pursue self-directed objectives. Built on top of the Llama 3 language model, this project explores the intersection of reinforcement learning, affective computing, and curiosity-driven behavior in artificial agents.

This repo explores multi-label emotion classification using pretrained encoder (RoBERTa, DistilBERT) and decoder models with LoRA (Gemma, Llama-3.2-1B, Stella). It compares dense vs generative approaches, optimized using F1-Macro scores. Key techniques include class weights and 10-fold stratified validation to enhance performance on sparse classes.

Demo game with use LLM to control emotion

Emotion LLaMA Feature Extractor

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基于 LLaMA-Factory 完成 Llama3-8B 的 LoRA 指令微调，并结合 FlashAttention 优化训练效率，实现情绪文本分类。

use alpaca and llama ro recognize emotion

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AI-powered emotion analysis Telegram bot — Groq LLaMA 3.3 70B, FastAPI, Clean Architecture, OWASP-hardened

AI-powered emotional support system combining Llama 3.2, DeepFace facial recognition, and DistilRoBERTa for multimodal emotion detection and personalized wellness guidance.

Sentiment analysis using Llama 3 to crack the code of Banglish emotions, enabling accurate sentiment analysis for better decision-making and experiences.

Fine-tuning Llama-3 for Multimodal Speech Emotion Recognition (SER). Integrates Whisper ASR, OpenSMILE features, and Structured Prompting to achieve 72.4% accuracy on IEMOCAP.

A multimodal pipeline using Wav2Vec2 and LLaMA-3 to analyze emotion and sentiment in healthcare audio. Scaled with PySpark on Databricks for high-throughput clinical insight extraction.

Mood-aware GenAI platform. Fine-tuned BERTa for real-time emotion classification, Llama 3.1 for context-aware responses, and Supabase for persistent emotional memory across sessions.

AI-powered multilingual career guidance chatbot for Indian students in Grades 8–12 | RAG + Emotion-Aware AI + Tamil/Hindi/English | Built with Groq LLaMA 3.3, FastAPI & Streamlit

Multi-label Emotion Detection using classical neural networks, fine-tuned Transformer models (RoBERTa, DistilBERT, LLaMA, Gemma, Mistral), and QLoRA. Includes comparative analysis, model trade-offs, and zero-shot inference.

AI-powered app that detects your emotion through facial expressions or text input and recommends Spotify tracks to match your mood. Combines DeepFace for emotion detection, ChatGroq (Llama 3) for mood interpretation, and Spotify API for real-time song suggestions — all in a clean Streamlit interface.

A chatbot built using the Llama 2 model that integrates sentiment analysis to adjust its responses based on the user's emotions. This project demonstrates enhanced interactivity, capable of providing personalized responses in both casual and customer support scenarios.

Emotica AI is a compassionate and therapeutic virtual assistant designed to provide empathetic and supportive conversations. It integrates a local LLaMA model for text generation, a vision model for image captioning, a RAG system for information retrieval, and emotion detection to tailor its responses.

This AI Chat project, built with React and Node.js, works like ChatGPT using the locally hosted Ollama LLaMA 3 model—no external APIs required. It also features a face emotion detection system to analyze user expressions in real time, enhancing interactivity and personalizing chat responses.

EmotiChat is an emotion-aware AI chatbot built with Python and Streamlit. It performs real-time multimodal sentiment analysis by fusing facial expressions (AWS Rekognition/DeepFace), vocal tonality (Librosa), and text sentiment (HuggingFace) to dynamically adjust the personality and response tone of a Llama 3.3 LLM.

Echo V1 is an intelligent, emotion-aware AI assistant that takes voice or text input, detects your mood and intent, and responds empathetically using Groq's LLaMA-3 and natural-sounding speech. Built with Python, Streamlit, and modern AI APIs, it's your virtual emotional companion.

by ssz

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