xijinnetwork

xijinnetwork's Stars

• Tiger101010/DAEGC

  PyTorch implementation of Deep Attention Embedding Graph Clustering (19IJCAI) https://www.ijcai.org/Proceedings/2019/0509.pdf

  Language:Python11119

• google-deepmind/funsearch

  Language:Jupyter Notebook800148

• mlabonne/llm-course

  Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

  Language:Jupyter Notebook48.4k5.2k

• chaoyanghe/FedNAS

  FedNAS: Federated Deep Learning via Neural Architecture Search

  Language:Python5416

• 0voice/expert_readed_books

  2021年最新总结，推荐工程师合适读本，计算机科学，软件技术，创业，**类，数学类，人物传记书籍

  9.9k3k

• microsoft/LMOps

  General technology for enabling AI capabilities w/ LLMs and MLLMs

  Language:Python3.9k303

• Thinklab-SJTU/pygmtools

  A Python Graph Matching Toolkit.

  Language:Python32720

• yangjianxin1/Firefly

  Firefly: 大模型训练工具，支持训练Qwen2.5、Qwen2、Yi1.5、Phi-3、Llama3、Gemma、MiniCPM、Yi、Deepseek、Orion、Xverse、Mixtral-8x7B、Zephyr、Mistral、Baichuan2、Llma2、Llama、Qwen、Baichuan、ChatGLM2、InternLM、Ziya2、Vicuna、Bloom等大模型

  Language:Python6.3k566

• ztxz16/fastllm

  纯c++的全平台llm加速库，支持python调用，chatglm-6B级模型单卡可达10000+token / s，支持glm, llama, moss基座，手机端流畅运行

  Language:C++3.4k351

• THUDM/ChatGLM2-6B

  ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型

  Language:Python15.7k1.9k

• chenryn/aiops-handbook

  Collection of slides, repositories, papers about AIOps

  1.4k283

• HqWu-HITCS/Awesome-Chinese-LLM

  整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。

  19.1k1.8k

• OpenBMB/ToolBench

  [ICLR'24 spotlight] An open platform for training, serving, and evaluating large language model for tool learning.

  Language:Python4.9k432

• MineDojo/Voyager

  An Open-Ended Embodied Agent with Large Language Models

  Language:JavaScript6k570

• davidwangmj/PPUAKA

  Implementation of PPUAKA (Privacy Preserving Universal Authentication and Key Agreement Protocol for Device-to-Device (D2D) Communications)

  Language:C41

• ZhugeKongan/Attention-mechanism-implementation

  Self-attention、Non-local、SE、SK、CBAM、DANet

  Language:Python46571

• PascalMat/5G-AKA-simulation-in-C

  This is the implementation of the 5G-AKA for the master thesis: Identity management, identification mechanisms and privacy protection methods in 5G

  Language:C94

• tajiaodavid/5G-AKA-Tamarin-Model

  This is the Tamarin model of version 16.0.0 of the 5G AKA protocol.

  6

• zifeiyu0531/PointerNetwork-RL-TSP

  指针网络+强化学习 解决旅行商(TSP)问题

  Language:Python8514

• eunomia-bpf/bpf-developer-tutorial

  eBPF Developer Tutorial: Learning eBPF Step by Step with Examples

  Language:C3k412

• binary-husky/gpt_academic

  为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。

  Language:Python68k8.3k

• Significant-Gravitas/AutoGPT

  AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

  Language:Python174k45.4k

• HarisIqbal88/PlotNeuralNet

  Latex code for making neural networks diagrams

  Language:TeX23k2.9k

• bmwcarit/barefoot

  Java map matching library for integrating the map into software and services with state-of-the-art online and offline map matching that can be used stand-alone and in the cloud.

  Language:Java675184

• uccmisl/5Gdataset

  In this work, we present a 5G trace dataset collected from a major Irish mobile operator. The dataset is generated from two mobility patterns (static and car), and across two application patterns(video streaming and file download). The dataset is composed of client-side cellular key performance indicators (KPIs) comprised of channel-related metrics, context-related metrics, cell-related metrics and throughput information. These metrics are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 5G networks. To supplement our real-time 5G production network dataset, we also provide a 5G large scale multi-cell ns-3 simulation framework. The availability of the 5G/mmwave module for the ns-3 mmwave network simulator provides an opportunity to improve our understanding of the dynamic reasoning for adaptive clients in 5G multi-cell wireless scenarios. The purpose of our framework is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the basestation (eNodeB or eNB) environment and scheduling principle, to end user. Our framework permits other researchers to investigate this interaction through the generation of their own synthetic datasets.

  9423

• boa2004plaust/SNWPM

  The Code of Generalization Ability of 5G Indoor Positioning Model under Restricted Conditions

  Language:Python62

• schollz/find3

  High-precision indoor positioning framework, version 3.

  Language:Go4.7k366

• schollz/find

  High-precision indoor positioning framework for most wifi-enabled devices.

  Language:Go5.1k368

• 7lldevelopers/Fight-the-tiger-tonight

  1

• 7lldevelopers/CPPRecord

  过程第一

  Language:C++1