The fundamental package for scientific computing with Python.

Freeing data processing from scripting madness by providing a set of platform-agnostic customizable pipeline processing blocks.

GLM-4 series: Open Multilingual Multimodal Chat LMs | 开源多语言多模态对话模型

OpenBA-V2: 3B LLM (Large Language Model) with T5 architecture, utilizing model pruning technique and continuing pretraining from OpenBA-15B.

Code for paper titled "Towards the Law of Capacity Gap in Distilling Language Models"

The framework to prune LLMs to any size and any config.

This is the codebase for pre-training, compressing, extending, and distilling LLMs with Megatron-LM.

Rethinking Negative Instances for Generative Named Entity Recognition

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

Using conversational games to evaluate powerful LLMs

Open Academic Research on Improving LLaMA to SOTA LLM

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…

[IJCAI 2023] ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Recent LLM-based CV and related works. Welcome to comment/contribute!

🦜🔗 Build context-aware reasoning applications

LangChain 的中文入门教程

The aim of this repository is to utilize LLaMA to reproduce and enhance the Stanford Alpaca

PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538

PECOS - Prediction for Enormous and Correlated Spaces

ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels