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A curated list of resources for developing applications using large language models (LLMs) with the Retrieval Augmented Generation (RAG) pattern.
A Python library for creating AI-generated artwork using Stable Diffusion models.
General Assembly's 2015 Data Science course covering topics like machine learning, data analysis, and data visualization.
A diffusion-based model for generating realistic talking portrait videos.
An official paper from CVPR 2024 on a talking head synthesis model that focuses on audio-driven synchronization.
A Vue.js wrapper for the UEditor rich text editor with two-way v-model binding.
Spring Integration provides an enterprise-level integration framework for building event-driven microservices.
Inference tool for Microsoft's Florence2 Versatile Language Model (VLM), built for vibe coders using AI tools.
SmoothQuant is an efficient post-training quantization tool for large language models, enabling accurate and fast inference.
Official PyTorch implementation of Magic123, a tool for generating high-quality 3D objects from a single image using 2D and 3D diffusion priors.
verl-agent is a Python framework for training LLM/VLM agents using reinforcement learning.
A tutorial that recreates the Apple iPhone 15 Pro website using GSAP animations and Three.js 3D effects.
Documentation for a model used to analyze companies in the Simply Wall St platform.
A C++ project for deploying large language models (LLMs) like ChatGLM and Baichuan using the MNN framework.
A TensorFlow implementation of contextualized word representations from bi-directional language models.
PaddleSlim is an open-source library for deep model compression and architecture search.
A TensorFlow/Keras implementation of the Stable Diffusion AI model for text-to-image generation.
A Julia framework for acausal modeling, symbolic math, and parallelized scientific machine learning.
Examples of using sparse attention, a technique for efficient Transformer-based sequence modeling.
Open academic research on improving the LLaMA language model to state-of-the-art performance.
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