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Hi there 👋

About Me:

I'm Yu-Sheng (Ethan) Su. An LLM researcher and engineer. (Personal Website, Google Scholar).

Research:

My research spans the areas of natural language processing and machine learning, specifically focusing on large language models (LLMs).

Open Source Projects:

Contact:

Github Stats:

yusheng's github stats

Yusheng (Ethan) Su's Projects

bmtrain icon bmtrain

Efficient Training (including pre-training and fine-tuning) for Big Models

click_through_rate_prediction icon click_through_rate_prediction

Predict (User pattern): Predict every user's Commercial Click-through-rate with random forest model and kdd-naggle cvs data

d2l-en icon d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

diffcse icon diffcse

Code for the NAACL 2022 long paper "DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings"

embodied-agents icon embodied-agents

This is a curated list of "Embodied Agents" research. Read this repository for the latest updates. Feel free to raise pull requests and launch the disscussion!

llm.c icon llm.c

LLM training in simple, raw C/CUDA

lunarvim icon lunarvim

An IDE layer for Neovim with sane defaults. Completely free and community driven.

modelcenter icon modelcenter

Efficient, Low-Resource, Distributed transformer implementation based on BMTrain

nvimdots icon nvimdots

A well configured and structured Neovim.

pet_scaling icon pet_scaling

Exploring the Impact of Model Scaling on Parameter-efficient Tuning Methods

promptpapers icon promptpapers

Must-read papers on prompt-based tuning for pre-trained language models.

scaling-science icon scaling-science

Science driven scaling: to pursue scientific principles bebind scaling and use them to guide next-generation model development, where the subareas include data engineering, long context, efficiency, and science of language models

transformers icon transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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