sjinzh Goto Github PK

followers: 116.0 following: 1.5K repos: 623.0 gists: 0.0

Type: User

Programming Skills

Languages: Rust, C++, Python, TypeScript, Zig.
Frameworks: Candle, PyTorch, LangChain, CUDA, TensorRT.

Top Repositories

sjinzh's Projects

leetcode-go

✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解

leetcode-py

⛽️「算法通关手册」，超详细的「算法与数据结构」基础讲解教程，700+ 道「LeetCode 题目」详细解析。通过「算法理论学习」和「编程实战练习」相结合的方式，从零基础到彻底掌握算法知识。

libonnx

A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.

libxev is a cross-platform, high-performance event loop that provides abstractions for non-blocking IO, timers, events, and more and works on Linux (io_uring or epoll), macOS (kqueue), and Wasm + WASI. Available as both a Zig and C API.

lidar_ai_solution

A project demonstrating Lidar related AI solutions, including three GPU accelerated Lidar/camera DL networks (PointPillars, CenterPoint, BEVFusion) and the related libs (cuPCL, 3D SparseConvolution, YUV2RGB, cuOSD,).

lihang

Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]

lihang-code

《统计学习方法》的代码实现

lihang_book_algorithm

致力于将李航博士《统计学习方法》一书中所有算法实现一遍

linfa

A Rust machine learning framework.

linux

Linux kernel source tree

lio-sam

LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

lite.ai.toolkit

🛠 A lite C++ toolkit of awesome AI models with ONNXRuntime, NCNN, MNN and TNN. YOLOX, YOLOP, YOLOv6, YOLOR, MODNet, YOLOX, YOLOv7, YOLOv5. MNN, NCNN, TNN, ONNXRuntime.

litechain

Build robust LLM applications with true composability 🔗

llama

Inference code for LLaMA models

llama-dfdx

LLaMa 7b with CUDA acceleration implemented in rust. Minimal GPU memory needed!

llama.cpp

Port of Facebook's LLaMA model in C/C++

llama2-burn

Llama2 LLM ported to Rust burn

llama2-chatbot

LLaMA v2 Chatbot

llama2-chinese

Llama中文社区，最好的中文Llama大模型，完全开源可商用

llama2-webui

Run Llama 2 locally with gradio UI on GPU or CPU from anywhere (Linux/Windows/Mac). Supporting Llama-2-7B/13B/70B with 8-bit, 4-bit. Supporting GPU inference (6 GB VRAM) and CPU inference.