Effortless Data Extraction, Powered by : Generative AI
-
Updated
Sep 10, 2024 - Python
Effortless Data Extraction, Powered by : Generative AI
🧠 Multi-stage prompt refinement system using chain-of-thought reasoning to enhance AI responses. Reduces hallucinations through progressive validation and intelligent synthesis.
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.
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.
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.
🌐 Advanced LLM agent system combining Ollama and Gemma2:9B for enhanced reasoning. Features automated web search capabilities and intelligent response processing.
📝 Streamlit App : Weekly News Letter Crew AI Agents 🖋️
📝 Blog Writer Crew AI Agents - Streamlit App 🖋️
Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform
4th Place Solution for the Kaggle Competition: LMSYS - Chatbot Arena Human Preference Predictions
Add a description, image, and links to the gemma2-9b topic page so that developers can more easily learn about it.
To associate your repository with the gemma2-9b topic, visit your repo's landing page and select "manage topics."