Lists (3)
Stars
A high-throughput and memory-efficient inference and serving engine for LLMs
Glances an Eye on your system. A top/htop alternative for GNU/Linux, BSD, Mac OS and Windows operating systems.
A simple Python Pydantic model for Honkai: Star Rail parsed data from the Mihomo API.
SGLang is a high-performance serving framework for large language models and multimodal models.
Graph Neural Network Library for PyTorch
Machine Learning Engineering Open Book
Open-source, secure environment with real-world tools for enterprise-grade agents.
Open-source cron job and background task monitoring service, written in Python & Django
AI memory OS for LLM and Agent systems(moltbot,clawdbot,openclaw), enabling persistent Skill memory for cross-task skill reuse and evolution.
An interactive NVIDIA-GPU process viewer and beyond, the one-stop solution for GPU process management.
My learning notes for ML SYS.
💬 Telegram bot with ChatGPT, Python-based, using OpenAI's API.
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible.
A course of learning LLM inference serving on Apple Silicon for systems engineers: build a tiny vLLM + Qwen.
A compact implementation of SGLang, designed to demystify the complexities of modern LLM serving systems.
[EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821
Expose your TCP/UDP port behind full-cone NAT to the Internet.
E2B Desktop Sandbox for LLMs. E2B Sandbox with desktop graphical environment that you can connect to any LLM for secure computer use.
The PyTorch implementation of LightGCN
Checkpoint-engine is a simple middleware to update model weights in LLM inference engines
[AAAI 2019] Source code and datasets for "Session-based Recommendation with Graph Neural Networks"
Visual Studio Code project/compile_commands.json generator for Linux kernel sources and out-of-tree modules
PyTorch Implementation for Neural Graph Collaborative Filtering
Code for the NAACL 2022 long paper "DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings"