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RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.

A very simple framework for state-of-the-art Natural Language Processing (NLP)

  • pytorch
  • nlp
  • named-entity-recognition
  • sequence-labeling

Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

  • scaled-yolov4
  • yolor
  • yolov3
  • yolov4

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

  • gpt-2
  • adaptation
  • language-model
  • gpt-3

TranslatedAI learning roadmap, compiling nearly 200 hands-on cases and projects, with free accompanying teaching materials, for beginners with zero background, focused on job-ready practice! Covers: Python, mathematics, machine learning, data analysis, deep learning, computer vision, natural language processing, PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv and other popular fields

TranslatedUltra-lightweight Chinese OCR, supports vertical text recognition, supports ncnn, mnn, and tnn inference (dbnet (1.8M) + crnn (2.5M) + anglenet (378KB)), total model size only 4.7M

TranslatedPython open-source project "The Road to Self-Taught Programming", with step-by-step tutorials for beginners: AI Lab, treasure videos, data structures, study guides, hands-on machine learning, hands-on deep learning, web crawlers, big-company interview experiences, programmer life, resource sharing.