README.md

Awesome-LLM

🔥 Large Language Models(LLM) have taken the NLP community the Whole World by storm. Here is a curated list of papers about large language models, especially relating to ChatGPT. It also contains frameworks for LLM training, tools to deploy LLM, courses and tutorials about LLM and all publicly available LLM checkpoints and APIs:

• Awesome-LLM
  • Milestone Papers
  • Other Papers
  • LLM Leaderboard
  • LLM Training Frameworks
  • Tools for deploying LLM
  • Tutorials about LLM
  • Courses about LLM
  • Opinions about LLM
  • Other Awesome Lists
  • Other Useful Resources
  • Contributing

Milestone Papers

Date	keywords	Institute	Paper	Publication
2017-06	Transformers	Google	Attention Is All You Need	NeurIPS
2018-06	GPT 1.0	OpenAI	Improving Language Understanding by Generative Pre-Training
2018-10	BERT	Google	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	NAACL
2019-02	GPT 2.0	OpenAI	Language Models are Unsupervised Multitask Learners
2019-09	Megatron-LM	NVIDIA	Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
2019-10	T5	Google	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	JMLR
2019-10	ZeRO	Microsoft	ZeRO: Memory Optimizations Toward Training Trillion Parameter Models	SC
2020-01	Scaling Law	OpenAI	Scaling Laws for Neural Language Models
2020-05	GPT 3.0	OpenAI	Language models are few-shot learners	NeurIPS
2021-01	Switch Transformers	Google	Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity	JMLR
2021-08	Codex	OpenAI	Evaluating Large Language Models Trained on Code
2021-08	Foundation Models	Stanford	On the Opportunities and Risks of Foundation Models
2021-09	FLAN	Google	Finetuned Language Models are Zero-Shot Learners	ICLR
2021-10	T0	HuggingFace et al.	Multitask Prompted Training Enables Zero-Shot Task Generalization	ICLR
2021-12	GLaM	Google	GLaM: Efficient Scaling of Language Models with Mixture-of-Experts	ICML
2021-12	WebGPT	OpenAI	WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing
2021-12	Retro	DeepMind	Improving language models by retrieving from trillions of tokens	ICML
2021-12	Gopher	DeepMind	Scaling Language Models: Methods, Analysis & Insights from Training Gopher
2022-01	COT	Google	Chain-of-Thought Prompting Elicits Reasoning in Large Language Models	NeurIPS
2022-01	LaMDA	Google	LaMDA: Language Models for Dialog Applications
2022-01	Minerva	Google	Solving Quantitative Reasoning Problems with Language Models	NeurIPS
2022-01	Megatron-Turing NLG	Microsoft&NVIDIA	Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
2022-03	InstructGPT	OpenAI	Training language models to follow instructions with human feedback
2022-04	PaLM	Google	PaLM: Scaling Language Modeling with Pathways
2022-04	Chinchilla	DeepMind	An empirical analysis of compute-optimal large language model training	NeurIPS
2022-05	OPT	Meta	OPT: Open Pre-trained Transformer Language Models
2022-06	Emergent Abilities	Google	Emergent Abilities of Large Language Models	TMLR
2022-06	BIG-bench	Google	Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
2022-06	METALM	Microsoft	Language Models are General-Purpose Interfaces
2022-09	Sparrow	DeepMind	Improving alignment of dialogue agents via targeted human judgements
2022-10	Flan-T5/PaLM	Google	Scaling Instruction-Finetuned Language Models
2022-10	GLM-130B	Tsinghua	GLM-130B: An Open Bilingual Pre-trained Model	ICLR
2022-11	HELM	Stanford	Holistic Evaluation of Language Models
2022-11	BLOOM	BigScience	BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
2022-11	Galactica	Meta	Galactica: A Large Language Model for Science
2023-01	Flan 2022 Collection	Google	The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
2023-02	LLaMA	Meta	LLaMA: Open and Efficient Foundation Language Models
2023-02	Kosmos-1	Microsoft	Language Is Not All You Need: Aligning Perception with Language Models
2023-03	PaLM-E	Google	PaLM-E: An Embodied Multimodal Language Model

Other Papers

If you're interested in the field of LLM, you may find the above list of milestone papers helpful to explore its history and state-of-the-art. However, each direction of LLM offers a unique set of insights and contributions, which are essential to understanding the field as a whole. For a detailed list of papers in various subfields, please refer to the following link (it is possible that there are overlaps between different subfields):

(:exclamation: We would greatly appreciate and welcome your contribution to the following list. ❗)

• LLM-Evaluation

  Evaluate different LLMs including ChatGPT in different fields

• LLM-Acceleration

  Hardware and software acceleration for LLM training and inference

• LLM-Application

  Use LLM to do some really cool stuff

• LLM-Augmentation

  Augment LLM in different aspects including faithfulness, expressiveness, domain-specific knowledge etc.

• LLM-Detection

  Detect LLM-generated text from texts written by humans

• Chain-of-Thought

  Chain of thought—a series of intermediate reasoning steps—significantly improves the ability of large language models to perform complex reasoning.

• In-Context-Learning

  Large language models (LLMs) demonstrate an in-context learning (ICL) ability, that is, learning from a few examples in the context.

• RLHF

  Reinforcement Learning from Human Preference

• Prompt-Learning

  A Good Prompt is Worth 1,000 Words

• Instruction-Tuning

  Finetune a language model on a collection of tasks described via instructions

LLM Leaderboard

There are three important steps for a ChatGPT-like LLM:

1. Pre-training
2. Instruction Tuning
3. Alignment

The following list makes sure that all LLMs are compared apples to apples.

Pre-trained LLM

Model	Size	Architecture	Access	Date	Origin
Switch Transformer	1.6T	Decoder(MOE)	-	2021-01	Paper
GLaM	1.2T	Decoder(MOE)	-	2021-12	Paper
PaLM	540B	Decoder	-	2022-04	Paper
MT-NLG	530B	Decoder	-	2022-01	Paper
J1-Jumbo	178B	Decoder	api	2021-08	Paper
OPT	175B	Decoder	api | ckpt	2022-05	Paper
BLOOM	176B	Decoder	api | ckpt	2022-11	Paper
GPT 3.0	175B	Decoder	api	2020-05	Paper
LaMDA	137B	Decoder	-	2022-01	Paper
GLM	130B	Decoder	ckpt	2022-10	Paper
YaLM	100B	Decoder	ckpt	2022-06	Blog
LLaMA	65B	Decoder	ckpt	2022-09	Paper
GPT-NeoX	20B	Decoder	ckpt	2022-04	Paper
UL2	20B	agnostic	ckpt	2022-05	Paper
鹏程.盘古α	13B	Decoder	ckpt	2021-04	Paper
T5	11B	Encoder-Decoder	ckpt	2019-10	Paper
CPM-Bee	10B	Decoder	api	2022-10	Paper
rwkv-4	7B	RWKV	ckpt	2022-09	Github
GPT-J	6B	Decoder	ckpt	2022-09	Github
GPT-Neo	2.7B	Decoder	ckpt	2021-03	Github
GPT-Neo	1.3B	Decoder	ckpt	2021-03	Github

Instruction finetuend LLM

Model	Size	Architecture	Access	Date	Origin
Flan-PaLM	540B	Decoder	-	2022-10	Paper
BLOOMZ	176B	Decoder	ckpt	2022-11	Paper
InstructGPT	175B	Decoder	api	2022-03	Paper
Galactica	120B	Decoder	ckpt	2022-11	Paper
OpenChatKit	20B	-	ckpt	2023-3	-
Flan-UL2	20B	Decoder	ckpt	2023-03	Blog
Gopher	-	-	-	-	-
Chinchilla	-	-	-	-	-
Flan-T5	11B	Encoder-Decoder	ckpt	2022-10	Paper
T0	11B	Encoder-Decoder	ckpt	2021-10	Paper

Aligned LLM

Model	Size	Architecture	Access	Date	Origin
ChatGPT	-	Decoder	demo|api	2022-11	Blog
Sparrow	70B	-	-	2022-09	Paper
Claude	-	-	-	-	-

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LLM Training Frameworks

Serving OPT-175B, BLOOM-176B and CodeGen-16B using Alpa

Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code.

Megatron-LM GPT2 tutorial

DeepSpeed is an easy-to-use deep learning optimization software suite that enables unprecedented scale and speed for DL Training and Inference. Visit us at deepspeed.ai or our Github repo.

pretrain_gpt3_175B.sh

Megatron-LM could be visited here. Megatron (1, 2, and 3) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel (tensor, sequence, and pipeline), and multi-node pre-training of transformer based models such as GPT, BERT, and T5 using mixed precision.

Open source solution replicates ChatGPT training process! Ready to go with only 1.6GB GPU memory and gives you 7.73 times faster training!

Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. We provide user-friendly tools to kickstart distributed training and inference in a few lines.

BMTrain is an efficient large model training toolkit that can be used to train large models with tens of billions of parameters. It can train models in a distributed manner while keeping the code as simple as stand-alone training.

Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying a broad class of distributed tensor computations. The purpose of Mesh TensorFlow is to formalize and implement distribution strategies for your computation graph over your hardware/processors. For example: "Split the batch over rows of processors and split the units in the hidden layer across columns of processors." Mesh TensorFlow is implemented as a layer over TensorFlow.

This tutorial discusses parallelism via jax.Array.

Tools for deploying LLM

🦜️🔗 LangChain

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include ❓ Question Answering over specific documents, 💬 Chatbots and 🤖 Agents.

👋 wechat-chatgpt

Use ChatGPT On Wechat via wechaty

Tutorials about LLM

• [Susan Zhang] Open Pretrained Transformers Youtube
• [Ameet Deshpande] How Does ChatGPT Work? Slides
• [Yao Fu] 预训练，指令微调，对齐，专业化：论大语言模型能力的来源 Bilibili
• [Hung-yi Lee] ChatGPT 原理剖析 Youtube
• [Jay Mody] GPT in 60 Lines of NumPy Link
• [ICML 2022] Welcome to the "Big Model" Era: Techniques and Systems to Train and Serve Bigger Models Link
• [NeurIPS 2022] Foundational Robustness of Foundation Models Link
• [Andrej Karpathy] Let's build GPT: from scratch, in code, spelled out. Video|Code
• [DAIR.AI] Prompt Engineering Guide Link
• [邱锡鹏] 大型语言模型的能力分析与应用 Slides | Video
• [Philipp Schmid] Fine-tune FLAN-T5 XL/XXL using DeepSpeed & Hugging Face Transformers Link
• [HuggingFace] Illustrating Reinforcement Learning from Human Feedback (RLHF) Link
• [HuggingFace] What Makes a Dialog Agent Useful? Link
• [张俊林]通向AGI之路：大型语言模型(LLM)技术精要 Link
• [大师兄]ChatGPT/InstructGPT详解 Link
• [HeptaAI]ChatGPT内核：InstructGPT，基于反馈指令的PPO强化学习 Link
• [Yao Fu] How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources Link
• [Stephen Wolfram] What Is ChatGPT Doing … and Why Does It Work? Link
• [Jingfeng Yang] Why did all of the public reproduction of GPT-3 fail? Link
• [Hung-yi Lee] ChatGPT (可能)是怎麼煉成的 - GPT 社會化的過程 Video

Courses about LLM

• [Princeton] Understanding Large Language Models Homepage
• [OpenBMB] 大模型公开课 主页
• [Stanford] CS224N-Lecture 11: Prompting, Instruction Finetuning, and RLHF Slides
• [Stanford] CS324-Large Language Models Homepage
• [Stanford] CS25-Transformers United V2 Homepage
• [Stanford Webinar] GPT-3 & Beyond Video
• [李沐] InstructGPT论文精读 Bilibili Youtube
• [陳縕儂] OpenAI InstructGPT 從人類回饋中學習 ChatGPT 的前身 Youtube
• [李沐] HELM全面语言模型评测 Bilibili
• [李沐] GPT，GPT-2，GPT-3 论文精读 Bilibili Youtube
• [Aston Zhang] Chain of Thought论文 Bilibili Youtube
• [MIT] Introduction to Data-Centric AI Homepage

Opinions about LLM

• Noam Chomsky: The False Promise of ChatGPT [2023-03-08][Noam Chomsky]
• Is ChatGPT 175 Billion Parameters? Technical Analysis [2023-03-04][Owen]
• Towards ChatGPT and Beyond [2023-02-20][知乎][欧泽彬]
• 追赶ChatGPT的难点与平替 [2023-02-19][李rumor]
• 对话旷视研究院张祥雨｜ChatGPT的科研价值可能更大 [2023-02-16][知乎][旷视科技]
• 关于ChatGPT八个技术问题的猜想 [2023-02-15][知乎][张家俊]
• ChatGPT发展历程、原理、技术架构详解和产业未来 [2023-02-15][知乎][陈巍谈芯]
• 对ChatGPT的二十点看法 [2023-02-13][知乎][熊德意]
• ChatGPT-所见、所闻、所感 [2023-02-11][知乎][刘聪NLP]
• The Next Generation Of Large Language Models [2023-02-07][Forbes]
• Large Language Model Training in 2023 [2023-02-03][Cem Dilmegani]
• What Are Large Language Models Used For? [2023-01-26][NVIDIA]
• Large Language Models: A New Moore's Law [2021-10-26][Huggingface]

Other Awesome Lists

• Awesome ChatGPT Prompts - A collection of prompt examples to be used with the ChatGPT model.
• awesome-chatgpt-prompts-zh - A Chinese collection of prompt examples to be used with the ChatGPT model.
• Awesome ChatGPT - Curated list of resources for ChatGPT and GPT-3 from OpenAI.
• Chain-of-Thoughts Papers - A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models.
• LLM Reading List - A paper & resource list of large language models.
• Reasoning using Language Models - Collection of papers and resources on Reasoning using Language Models.

Other Useful Resources

• ShareGPT - Share your wildest ChatGPT conversations with one click.
• Major LLMs + Data Availability
• MOSS - a conversational language model like ChatGPT
• 500+ Best AI Tools
• Cohere Summarize Beta - Introducing Cohere Summarize Beta: A New Endpoint for Text Summarization
• chatgpt-wrapper - ChatGPT Wrapper is an open-source unofficial Python API and CLI that lets you interact with ChatGPT.

Contributing

This is an active repository and your contributions are always welcome!

I will keep some pull requests open if I'm not sure if they are awesome for LLM, you could vote for them by adding 👍 to them.

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If you have any question about this opinionated list, do not hesitate to contact me [email protected].