jack57lee's Stars
hpcaitech/ColossalAI
Making large AI models cheaper, faster and more accessible
hiyouga/LLaMA-Factory
Unified Efficient Fine-Tuning of 100+ LLMs (ACL 2024)
google-research/google-research
Google Research
karpathy/minGPT
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
recommenders-team/recommenders
Best Practices on Recommendation Systems
LlamaFamily/Llama-Chinese
Llama中文社区,Llama3在线体验和微调模型已开放,实时汇总最新Llama3学习资料,已将所有代码更新适配Llama3,构建最好的中文Llama大模型,完全开源可商用
FlagOpen/FlagEmbedding
Retrieval and Retrieval-augmented LLMs
SmirkCao/Lihang
Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]
OpenBMB/ToolBench
[ICLR'24 spotlight] An open platform for training, serving, and evaluating large language model for tool learning.
thunlp/OpenPrompt
An Open-Source Framework for Prompt-Learning.
IDEA-CCNL/Fengshenbang-LM
Fengshenbang-LM(封神榜大模型)是IDEA研究院认知计算与自然语言研究中心主导的大模型开源体系,成为中文AIGC和认知智能的基础设施。
hiyouga/ChatGLM-Efficient-Tuning
Fine-tuning ChatGLM-6B with PEFT | 基于 PEFT 的高效 ChatGLM 微调
OpenBMB/BMTools
Tool Learning for Big Models, Open-Source Solutions of ChatGPT-Plugins
THUDM/P-tuning-v2
An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks
castorini/pyserini
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.
google-research/language
Shared repository for open-sourced projects from the Google AI Language team.
google-research/tapas
End-to-end neural table-text understanding models.
wangyuxinwhy/uniem
unified embedding model
alibaba/Megatron-LLaMA
Best practice for training LLaMA models in Megatron-LM
princeton-nlp/DensePhrases
[ACL 2021] Learning Dense Representations of Phrases at Scale; EMNLP'2021: Phrase Retrieval Learns Passage Retrieval, Too https://arxiv.org/abs/2012.12624
facebookresearch/FiD
Fusion-in-Decoder
epfLLM/Megatron-LLM
distributed trainer for LLMs
yusanshi/news-recommendation
Implementations of some methods in news recommendation.
facebookresearch/PAQ
Code and data to support the paper "PAQ 65 Million Probably-Asked Questions andWhat You Can Do With Them"
wenhuchen/OTT-QA
Code and Data for ICLR2021 Paper "Open Question Answering over Tables and Text"
jingtaozhan/DRhard
SIGIR'21: Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track.
sebastian-hofstaetter/neural-ranking-kd
Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
wuch15/PLM4NewsRec
Veason-silverbullet/DIGAT
DIGAT: Modeling News Recommendation with Dual-Graph Interaction
JiahaoXun/IMRec
The dataset for paper "Why Do We Click: Visual Impression-aware News Recommendation", ACM MM 2021