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Ongoing research training transformer models at scale
Here are my personal paper reading notes (including cloud computing, resource management, systems, machine learning, deep learning, and other interesting stuffs).
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
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 efficie…
Awesome-LLM: a curated list of Large Language Model
Simple examples to introduce PyTorch
PyTorch Tutorial for Deep Learning Researchers
cluster data collected from production clusters in Alibaba for cluster management research
Lucid: A Non-Intrusive, Scalable and Interpretable Scheduler for Deep Learning Training Jobs
zms1999 / SmartMoE
Forked from laekov/fastmoeA MoE impl for PyTorch, [ATC'23] SmartMoE
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models. ICML 2021
Tutel MoE: An Optimized Mixture-of-Experts Implementation
PyTorch extensions for high performance and large scale training.
🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
Seamless operability between C++11 and Python
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gan…