#model compression

NNI is a robust toolkit that automates various aspects of deep learning, including feature engineering and model optimization processes like neural architecture search and hyperparameter tuning. Its documentation offers the latest updates while ensuring compatibility with frameworks like PyTorch and TensorFlow. NNI efficiently implements algorithms ranging from exhaustive searches to Bayesian optimizations. Its adaptability across local setups and cloud services ensures scalability without integration hassles. Extensive tutorials and community support further assist in enhancing project potential.

Explore beginner-level insights into model compression with guidance from MIT's TinyML courses. The project offers detailed explanations of pruning, quantization, and knowledge distillation techniques, aiming to decrease the resource usage of large language models. Catering to deep learning researchers, AI developers, and students, it includes theoretical insights and practical code applications, ideal for mobile and embedded systems. Access extensive Chinese-language resources, refine your understanding, and engage with the AI community.

PaddleNLP, built on the PaddlePaddle framework, offers a robust toolkit for large language model development, enabling efficient training, seamless compression, and high-speed inference across diverse hardware platforms including NVIDIA GPUs and Kunlun XPUs. Designed for industrial-grade applications, it facilitates smooth hardware transitions and reduces development costs with advanced pre-training and fine-tuning strategies. The project’s operator fusion strategies enhance parallel inference speed, applicable in fields like intelligent assistance and content creation.

AirLLM facilitates large language model operation by minimizing hardware demands. It allows 70B models to run on 4GB GPUs and up to 405B models on 8GB GPUs through advanced model compression, without requiring quantization, distillation, or pruning. Recent updates include support for Llama3, CPU inference, and compatibility with ChatGLM and Qwen models.

TNN is an open-source neural network inference framework devised by Tencent Youtu Lab, known for its high performance and cross-platform capabilities. It supports model compression and code optimization, enhancing performance on mobile devices and extending compatibility with X86 and NV GPUs. Utilized by applications such as QQ, Weishi, and Pitu, TNN functions as a fundamental component for Tencent Cloud AI's acceleration tasks. It facilitates tasks like face detection and object recognition while ensuring efficient deployment through its integration with TensorFlow, PyTorch, and other frameworks via ONNX. The framework is designed to optimize computational performance, support low precision data, and improve memory usage.