in-context-learning

✨✨Latest Advances on Multimodal Large Language Models

The official GitHub page for the survey paper "A Survey of Large Language Models".

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

An open-source framework for training large multimodal models.

From Chain-of-Thought prompting to OpenAI o1 and DeepSeek-R1 🍓

总结Prompt&LLM论文，开源数据&模型，AIGC应用

Must-read Papers on LLM Agents.

Painter & SegGPT Series: Vision Foundation Models from BAAI

Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

PyTorch implementation of VALL-E(Zero-Shot Text-To-Speech), Reproduced Demo https://lifeiteng.github.io/valle/index.html

A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models".

Emu Series: Generative Multimodal Models from BAAI

[ICCV 2025] 🔥🔥 UNO: A Universal Customization Method for Both Single and Multi-Subject Conditioning

Awesome papers about generative Information Extraction (IE) using Large Language Models (LLMs)

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

OpenICL is an open-source framework to facilitate research, development, and prototyping of in-context learning.

This repository contains a collection of papers and resources on Reasoning in Large Language Models.

Papers and Datasets on Instruction Tuning and Following. ✨✨✨

From idea to production in just few lines: Graph-Based Programmable Neuro-Symbolic LM Framework - a production-first LM framework built with decade old Deep Learning best practices

[ACL 2024] An Easy-to-use Instruction Processing Framework for LLMs.