Zhuofeng Wu

@CSerxy · User

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Research Scientist at Apple | Ph.D. from University of Michigan

AppleCupertino43 followers57 repositories

Repositories

CSerxy/claw-code

The repo is finally unlocked. enjoy the party! The fastest repo in history to surpass 100K stars ⭐. Join Discord: https://discord.gg/5TUQKqFWd Built in Rust using oh-my-codex.

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CSerxy/IDPG

IDPG: An Instance-Dependent Prompt Generation Method

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CSerxy/cserxy.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

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CSerxy/llms-interview-questions

🟣 LLMs interview questions and answers to help you prepare for your next machine learning and data science interview in 2025.

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CSerxy/Universal-Prompt-Injection

The official implementation of our pre-print paper "Automatic and Universal Prompt Injection Attacks against Large Language Models".

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CSerxy/attentions

PyTorch implementation of some attentions for Deep Learning Researchers.

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CSerxy/trlx

A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)

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CSerxy/FastChat

An open platform for training, serving, and evaluating large languages. Release repo for Vicuna and FastChat-T5.

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CSerxy/my_vim_environment

Every time it takes me long to build a new vim environment on a new machine. Decided to put my .vimrc and corresponding operations here to save my time in the future.

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CSerxy/DiffCSE

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

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CSerxy/SimCSE

EMNLP'2021: SimCSE: Simple Contrastive Learning of Sentence Embeddings

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CSerxy/fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

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CSerxy/simclr

SimCLR - A Simple Framework for Contrastive Learning of Visual Representations

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CSerxy/electra

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

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CSerxy/ALBERT

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

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CSerxy/ML-NLP

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。

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