Best AI tools for< Prune Deep Networks >

AlgoDocs is a powerful AI Platform developed based on the latest technologies to streamline your processes and free your team from annoying and error-prone manual data entry by offering fast, secure, and accurate document data extraction.

Wisedocs is an AI-powered platform that specializes in medical record reviews, summaries, and insights for claims processing. The platform offers intelligent features such as medical chronologies, workflows, deduplication, intelligent OCR, and insights summaries. Wisedocs streamlines the process of reviewing medical records for insurance, legal, and independent medical evaluation firms, providing speed, accuracy, and efficiency in claims processing. The platform automates tasks that were previously laborious and error-prone, making it a valuable tool for industries dealing with complex medical records.

Torch-Pruning (TP) is a library for structural pruning that enables pruning for a wide range of deep neural networks. It uses an algorithm called DepGraph to physically remove parameters. The library supports pruning off-the-shelf models from various frameworks and provides benchmarks for reproducing results. It offers high-level pruners, dependency graph for automatic pruning, low-level pruning functions, and supports various importance criteria and modules. Torch-Pruning is compatible with both PyTorch 1.x and 2.x versions.

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This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

Awesome-LLM-Inference: A curated list of 📙Awesome LLM Inference Papers with Codes, check 📖Contents for more details. This repo is still updated frequently ~ 👨‍💻‍ Welcome to star ⭐️ or submit a PR to this repo!

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

Awesome OpenVINO is a curated list of AI projects based on the OpenVINO toolkit, offering a rich assortment of projects, libraries, and tutorials covering various topics like model optimization, deployment, and real-world applications across industries. It serves as a valuable resource continuously updated to maximize the potential of OpenVINO in projects, featuring projects like Stable Diffusion web UI, Visioncom, FastSD CPU, OpenVINO AI Plugins for GIMP, and more.

Efficient Foundation Model Survey is a comprehensive analysis of resource-efficient large language models (LLMs) and multimodal foundation models. The survey covers algorithmic and systemic innovations to support the growth of large models in a scalable and environmentally sustainable way. It explores cutting-edge model architectures, training/serving algorithms, and practical system designs. The goal is to provide insights on tackling resource challenges posed by large foundation models and inspire future breakthroughs in the field.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

This repository is a comprehensive guide for individuals looking to start and improve their skills in Large Language Models (LLMs) without an advanced background in the field. It provides free resources, online courses, books, articles, and practical tips to become an expert in machine learning. The guide covers topics such as terminology, transformers, prompting, retrieval augmented generation (RAG), and more. It also includes recommendations for podcasts, YouTube videos, and communities to stay updated with the latest news in AI and LLMs.

uTensor is an extremely light-weight machine learning inference framework built on Tensorflow and optimized for Arm targets. It consists of a runtime library and an offline tool that handles most of the model translation work. The core runtime is only ~2KB. The workflow involves constructing and training a model in Tensorflow, then using uTensor to produce C++ code for inferencing. The runtime ensures system safety, guarantees RAM usage, and focuses on clear, concise, and debuggable code. The high-level API simplifies tensor handling and operator execution for embedded systems.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

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Your recruiting assistant (note, this is purely for entertainment purposes - consult a lawyer for any legal issues) Chat GTP may be prone to errors.