Popular GitHub Repositories for Fp Growth

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.

Resources on the topic of digital morphogenesis (creating form with code). Includes links to major articles, code repos, creative projects, books, software, and more.

a generative algorithm

Microsoft Automatic Mixed Precision Library

InsightFace REST API for easy deployment of face recognition services with TensorRT in Docker.

An innovative library for efficient LLM inference via low-bit quantization

Flux diffusion model implementation using quantized fp8 matmul & remaining layers use faster half precision accumulate, which is ~2x faster on consumer devices.

open-source browser extension that leverages the power of the AI to generate engaging replies for social media growth.

Deploy stable diffusion model with onnx/tenorrt + tritonserver

?? illufly 是自我进化的 Agent 框架: 基于自我进化，快速创造价值

Code and data for "Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation" (EMNLP 2023)

FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild

Scrape Reddit for marketing pain points using GPT — built to fuel Cronlytic's growth.

? Apply YOLOv8 exported with ONNX or TensorRT(FP16, INT8) to the Real-time camera

This app leverages generative AI to intelligently manage your sales inbox and CRM, auto-draft replies using your sales playbook & company branding, and more.

4D reconstruction of developmental trajectories using spherical harmonics

Preschool evaluation is crucial because it gives teachers and parents influential knowledge about children's growth and development. The COVID-19 pandemic has highlighted the necessity of online assessment for preschool children. One of the areas that should be tested is their ability to speak. Employing an Automatic Speech Recognition (ASR) system would not help since they are pre-trained on voices that differ from children's in terms of frequency and amplitude. Because most of these are pre-trained with data in a specific range of amplitude, their objectives do not make them ready for voices in different amplitudes. To overcome this issue, we added a new objective to the masking objective of the Wav2Vec 2.0 model called Random Frequency Pitch (RFP). In addition, we used our newly introduced dataset to fine-tune our model for Meaningless

? Data Science Enthusiast | Passionate About Python, R, Statistics & Visualization | Showcasing Projects, Contributions, and Career Growth in the Tech Space.

JAX Scalify: end-to-end scaled arithmetics