Yue Zhang's Projects
A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 and reasoning techniques.
ACL 2022: BRIO: Bringing Order to Abstractive Summarization
CCL 2022 汉语学习者文本纠错评测
This repo is unofficial ChatGPT api. It is based on Daniel Gross's WhatsApp GPT
Code & Data for our Paper "PATTERN-BASED CHINESE HYPERNYM-HYPONYM RELATION EXTRACTION METHOD"
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation
CTC2021-中文文本纠错大赛的SOTA方案及在线演示
数据科学的笔记以及资料搜集
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
Official implementation for the paper "DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models"
emoji list; emoji表情列表
ERRor ANnotation Toolkit: Automatically extract and classify grammatical errors in parallel original and corrected sentences.
The official code of the "Frustratingly Easy System Combination for Grammatical Error Correction" paper
Graph Convolution Network for PyTorch
基于模板的文本纠错;Automatically Mining Error Templates for Grammatical Error Correction
Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)
The official PyTorch implementation of Google's Gemma models
This repo contains a list of the 10,000 most common English words in order of frequency, as determined by n-gram frequency analysis of the Google's Trillion Word Corpus.
The goal of this project is to enable users to create cool web demos using the newly released OpenAI GPT-3 API with just a few lines of Python.
About Me
My personal page
Code & Data for our Paper "Alleviating Hallucinations of Large Language Models through Induced Hallucinations"
一个简单的直播软件,采用PyQT5+VLC+FFMPEG开发
LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.
《统计学习方法》的代码实现
LinearModel with python
Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)