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  • 👋 Hi, I’m Jianghai @CjhHa1
  • 👀 Interested in Deep Learning and Distributed Systems.
  • 🔭 Graduated from Nankai University.
  • 📈 Now working at HPC-AI Tech, focusing on Colossal-AI training and inference.
  • 📫 Email: Cjh18671720497 [AT] outlook [DOT] com.

Jianghai's Projects

cjhha1 icon cjhha1

Config files for my GitHub profile.

colossalai icon colossalai

Making large AI models cheaper, faster and more accessible

deepspeed icon deepspeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

github icon github

GitHub 漫游指南- a Chinese ebook on how to build a good project on Github. Explore the users' behavior. Find some thing interest.

mememoji icon mememoji

A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.

paddle icon paddle

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

public_assets icon public_assets

Storing publicly available assets such as images, animations and texts

pydata-book icon pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

reinforcement-learning icon reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

supcontrast icon supcontrast

PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)

tensorrt-llm icon tensorrt-llm

TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.

vllm icon vllm

A high-throughput and memory-efficient inference and serving engine for LLMs

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