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Multi-threaded matrix multiplication and cosine similarity calculations for dense and sparse matrices. Appropriate for calculating the K most similar items for a large number of items by chunking the item matrix representation (embeddings) and using Numba to accelerate the calculations.
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
A basic LLM application as knowledge base. You can have the LLM answer your questions from the context you provide. Main steps: vectorization (embedding), RAG. 一个基本的知识库类型大语言模型应用。你可以让大模型从你提供的上下文中回答你的提问。主要步骤:向量化(内嵌),RAG。
Explore a Movie Recommender System using Streamlit UI with 5000 movies from TMDB, featuring cosine similarity for movie recommendations and real-time poster updates
A web UI Project In order to learn the large language model. This project includes features such as chat, quantization, fine-tuning, prompt engineering templates, and multimodality.
Interactive chat application leveraging OpenAI's GPT-4 for real-time conversation simulations. Built with Flask, this project showcases streaming LLM responses in a user-friendly web interface.