π§ Multi-stage prompt refinement system using chain-of-thought reasoning to enhance AI responses. Reduces hallucinations through progressive validation and intelligent synthesis.
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Updated
Nov 9, 2024 - Python
π§ Multi-stage prompt refinement system using chain-of-thought reasoning to enhance AI responses. Reduces hallucinations through progressive validation and intelligent synthesis.
π Advanced LLM agent system combining Ollama and Gemma2:9B for enhanced reasoning. Features automated web search capabilities and intelligent response processing.
4th Place Solution for the Kaggle Competition: LMSYS - Chatbot Arena Human Preference Predictions
Streamlit based RAG for interactive Q&A using Groq AI and various open-source LLM models. Upload PDFs, create vector embeddings, and query documents for context-based answers.
Effortless Data Extraction, Powered by : Generative AI
Tools and method for fine-tuning the Gemma 2 model on custom datasets
Analyze a dataset of conversations from the Chatbot Arena, where various LLMs provide responses to user prompts. The goal is to develop a model that enhances chatbot interactions, ensuring they align more closely with human preferences.
MasteryMap is your path finder tool to master any skills. Just enter the skill and duration and you will see a practical roadmap to master that skill.
π Streamlit App : Weekly News Letter Crew AI Agents ποΈ
π Blog Writer Crew AI Agents - Streamlit App ποΈ
Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform
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