Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform
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Updated
Feb 8, 2025 - Jupyter Notebook
Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform
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This competition challenges you to predict which responses users will prefer in a head-to-head battle between chatbots powered by large language models (LLMs).
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.
GroqWarp is a Streamlit app that compares the performance of RAG using Groq and Ollama models, visualizing response times and accuracy. It leverages FAISS for document retrieval and displays a side-by-side performance chart.
A next-generation creative writing large language model for writing entire book chapters at once.
Tools and method for fine-tuning the Gemma 2 model on custom datasets
Conversational Support Agent (RAG): Implemented an AI assistance system using n8n and Ollama that reduced manual inquiries at the CNE through automated legal document processing and Supabase-based vector search.
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.
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