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bgrl

PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)

chatglm-efficient-tuning

Fine-tuning ChatGLM-6B with PEFT | 基于 PEFT 的高效 ChatGLM 微调

chatglm2-6b

ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型

chinese-llama-alpaca

中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)

embedding_model_test

基于开源embedding模型的中文向量效果测试

event_triple_extraction

采用LTP（分词、词性标注、句法依存、角色标注）抽取事件三元组

eventtriplesextraction

An experiment and demo-level tool for text information extraction (event-triples extraction), which can be a route to the event chain and topic graph, 基于依存句法与语义角色标注的事件三元组抽取，可用于文本理解如文档主题链，事件线等应用。

faiss

用于学习Faiss工具

fastgpt

FastGPT is a knowledge-based question answering system built on the LLM. It offers out-of-the-box data processing and model invocation capabilities. Moreover, it allows for workflow orchestration through Flow visualization, thereby enabling complex question and answer scenarios!

finglm

FinGLM: 致力于构建一个开放的、公益的、持久的金融大模型项目，利用开源开放来促进「AI+金融」。

finqwen

FinQwen: 致力于构建一个开放、稳定、高质量的金融大模型项目，基于大模型搭建金融场景智能问答系统，利用开源开放来促进「AI+金融」。

gca

[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"

gcnii

PyTorch implementation of "Simple and Deep Graph Convolutional Networks"

gcnss

gemma-fine-tune

个人图床

goocr

用于测试ocr套件的项目

grace

[GRL+ @ ICML 2020] PyTorch implementation for "Deep Graph Contrastive Representation Learning" (https://arxiv.org/abs/2006.04131v2)

graph-llm

hxjj_app

langchain

🦜🔗 Build context-aware reasoning applications

langchain-chatchat

Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM) QA app with langchain

ma-gcl

MA-GCL的源代码

mpgcl

negative

Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition

ner

中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）

openai-cookbook

Examples and guides for using the OpenAI API

peft

🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

prompteg

🐙 Guides, papers, lecture, notebooks and resources for prompt engineering

pygat

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

pygcn

Graph Convolutional Networks in PyTorch