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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📝 Streamlit App : Weekly News Letter Crew AI Agents 🖋️
A next-generation creative writing large language model for writing entire book chapters at once.
An Intelligent Health LLM System for Personalized Medication Guidance and Support.
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.
Tools and method for fine-tuning the Gemma 2 model on custom datasets
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.
AI real-estate platform using Gemma-2-9B for Arabic query parsing, Random Forest price estimation, entropy-based questioning, and FastAPI.
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.
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