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Distributed Inference Benchmarking Tool for NVIDIA Triton Server
The Triton Inference Server provides an optimized cloud and edge inferencing solution.
A library for efficient similarity search and clustering of dense vectors.
Triton Python, C++ and Java client libraries, and GRPC-generated client examples for go, java and scala.
This repository deploys YOLOv4 as an optimized TensorRT engine to Triton Inference Server
Simple but robust implementation of LoRA for PyTorch. Compatible with NLP, CV, and other model types. Strongly typed and tested.
[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition
[DEIMv2] Real Time Object Detection Meets DINOv3
Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything (SAM+SAM2/2.1+SAM3), MobileSAM!!
Effortless data labeling with AI support from Segment Anything and other awesome models.
High-resolution models for human tasks.
[CVPR 2025] DEIM: DETR with Improved Matching for Fast Convergence
A robust pipeline for detecting and recognizing faces in video footage using YOLOv8 for detection and FaceNet-PyTorch for recognition, supporting real-time processing. Ideal for video surveillance …
Useful PyTorch functions and modules that are not implemented in PyTorch by default
Wan: Open and Advanced Large-Scale Video Generative Models
[CVPR 2025🔥] Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model
A Collection of BM25 Algorithms in Python
DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space
WIP: Unnoficial implementation of diffusion autoencoders, using pytorch
Train Your VAE: A VAE Training and Finetuning Script for SD/FLUX
Multi-Task Learning for Calorie Prediction on a Novel Large-Scale Recipe Dataset Enriched with Nutritional Information
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…
LLM Finetuning with peft
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"