Popular GitHub Repositories for Rate Limiting

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

Distilled variant of Whisper for speech recognition. 6x faster, 50% smaller, within 1% word error rate.

? Enterprise-grade API gateway that helps you monitor and impose cost or rate limits per API key. Get fine-grained access control and monitoring per user, application, or environment. Supports OpenAI, Azure OpenAI, Anthropic, vLLM, and open-source LLMs.

HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training

? 大语言模型高效转发服务 · An efficient forwarding service designed for LLMs. · OpenAI API Reverse Proxy

Play deep learning with CIFAR datasets

WTTE-RNN a framework for churn and time to event prediction

Evaluate your speech-to-text system with similarity measures such as word error rate (WER)

Speech to Text to Speech. Song now playing. Sends text as OSC messages to VRChat to display on avatar. (STTTS) (Speech to TTS) (VRC STT System) (VTuber TTS)

Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.

Visualize Tensorflow's optimizers.

Remote heart rate detection through Eulerian magnification of face videos

Merge models with separate rate for each 25 U-Net block (input, middle, output). Extension for Stable Diffusion UI by AUTOMATIC1111

Gradient based Hyperparameter Tuning library in PyTorch

Fast and simple music and audio analysis using RNN in Python ?️‍♀️ ?

Python module for evaluating ASR hypotheses (e.g. word error rate, word recognition rate).

ECG classification programs based on ML/DL methods

MorphL Community Edition uses big data and machine learning to predict user behaviors in digital products and services with the end goal of increasing KPIs (click-through rates, conversion rates, etc.) through personalization

The official implementation of the CCS'23 paper, Narcissus clean-label backdoor attack -- only takes THREE images to poison a face recognition dataset in a clean-label way and achieves a 99.89% attack success rate.

ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop an ecosystem to experiment, share, reproduce, and deploy in real-world in a smooth and easy way.