The official fork of THoR Chain-of-Thought framework, enhanced and adapted for Emotion Cause Analysis (ECAC-2024)
The official code for CoT / ZSL reasoning framework 🧠, utilized in paper: "Large Language Models in Targeted Sentiment Analysis in Russian"
CoT Reasoning in Autoregressive Image Generation
This project explores GPT-2 and Llama models through pre-training, fine-tuning, and Chain-of-Thought (CoT) prompting. It includes memory-efficient optimizations (SGD, LoRA, BAdam) and evaluations on math datasets (GSM8K, NumGLUE, StimulEq, SVAMP).
ragTAG is a conversational AI script that creates a roundtable dialogue between user assigned characters with their own different objectives and perspectives.
OmniLlama é minha tentativa de criar uma maneira eficiente, local de trabalhar com cadeias de raciocínio com modelos relativamente pequenos.
This repository is a proof of concept for replicating the reasoning capabilities of OpenAI's O1 model using alternative frameworks. It employs a sequential agent-based system powered by the Gemini API, facilitating iterative problem-solving for coding-related challenges through a Flask web application.
combating the llm fomo, feeding the shiny object syndrome, for folly and partially for curiousity
Ice Breaker is comprehensive fullstack app leveraging generative AI and LangChain to find LinkedIn profiles and generate engaging ice breakers. LangChain ReAct agents ensure accurate URL retrieval and JSON cleaning, identifying a summary, facts, topics, and ice breakers. The frontend is built with HTML/CSS, and Flask powers the backend development.
A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images
Effortlessly perform sentiment analysis, translation, speech synthesis, summarization, and Q&A tasks with an interactive UI using prompt engineering
This repository contains the ECE324 reasonix group project. We explored reasoning models in mathematics on the gsm8k dataset. Our experiments improved the baseline model from 17% accuracy on the test set to 54%.
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