Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform
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Updated
Jun 29, 2024 - Jupyter Notebook
Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform
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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.
Tools and method for fine-tuning the Gemma 2 model on custom datasets
Effortless Data Extraction, Powered by : Generative AI
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.
4th Place Solution for the Kaggle Competition: LMSYS - Chatbot Arena Human Preference Predictions
🌐 Advanced LLM agent system combining Ollama and Gemma2:9B for enhanced reasoning. Features automated web search capabilities and intelligent response processing.
🧠 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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