Awesome-LLM

Awesome-LLM

Awesome-LLM: a curated list of Large Language Model

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Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

README:

Awesome-LLM Awesome

🔥 Large Language Models(LLM) have taken the NLP community AI 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.

Trending LLM Projects

  • DeepSeek-v3 - First open-sourced GPT-4o level model.
  • OpenAI o3 preview - AGI, maybe?
  • Qwen2.5 Technical Report - This report introduces Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs.
  • Genesis - A generative world for general-purpose robotics & embodied AI learning.
  • ModernBERT - Bringing BERT into modernity via both architecture changes and scaling.

Table of Content

Milestone Papers

Date keywords Institute Paper
2017-06 Transformers Google Attention Is All You Need
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
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
2019-10 ZeRO Microsoft ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
2020-01 Scaling Law OpenAI Scaling Laws for Neural Language Models
2020-05 GPT 3.0 OpenAI Language models are few-shot learners
2021-01 Switch Transformers Google Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
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
2021-10 T0 HuggingFace et al. Multitask Prompted Training Enables Zero-Shot Task Generalization
2021-12 GLaM Google GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
2021-12 WebGPT OpenAI WebGPT: Browser-assisted question-answering with human feedback
2021-12 Retro DeepMind Improving language models by retrieving from trillions of tokens
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
2022-01 LaMDA Google LaMDA: Language Models for Dialog Applications
2022-01 Minerva Google Solving Quantitative Reasoning Problems with Language Models
2022-01 Megatron-Turing NLG Microsoft&NVIDIA Using Deep 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
2022-05 OPT Meta OPT: Open Pre-trained Transformer Language Models
2022-05 UL2 Google Unifying Language Learning Paradigms
2022-06 Emergent Abilities Google Emergent Abilities of Large Language Models
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
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
2022-12 OPT-IML Meta OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
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 LRU DeepMind Resurrecting Recurrent Neural Networks for Long Sequences
2023-03 PaLM-E Google PaLM-E: An Embodied Multimodal Language Model
2023-03 GPT 4 OpenAI GPT-4 Technical Report
2023-04 LLaVA UW–Madison&Microsoft Visual Instruction Tuning
2023-04 Pythia EleutherAI et al. Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
2023-05 Dromedary CMU et al. Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
2023-05 PaLM 2 Google PaLM 2 Technical Report
2023-05 RWKV Bo Peng RWKV: Reinventing RNNs for the Transformer Era
2023-05 DPO Stanford Direct Preference Optimization: Your Language Model is Secretly a Reward Model
2023-05 ToT Google&Princeton Tree of Thoughts: Deliberate Problem Solving with Large Language Models
2023-07 LLaMA2 Meta Llama 2: Open Foundation and Fine-Tuned Chat Models
2023-10 Mistral 7B Mistral Mistral 7B
2023-12 Mamba CMU&Princeton Mamba: Linear-Time Sequence Modeling with Selective State Spaces
2024-01 DeepSeek-v2 DeepSeek DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
2024-05 Mamba2 CMU&Princeton Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
2024-05 Llama3 Meta The Llama 3 Herd of Models
2024-12 Qwen2.5 Alibaba Qwen2.5 Technical Report

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:

  • Awesome-LLM-hallucination - LLM hallucination paper list.

  • awesome-hallucination-detection - List of papers on hallucination detection in LLMs.

  • LLMsPracticalGuide - A curated list of practical guide resources of LLMs

  • 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.

  • Awesome Deliberative Prompting - How to ask LLMs to produce reliable reasoning and make reason-responsive decisions.

  • Instruction-Tuning-Papers - A trend starts from Natrural-Instruction (ACL 2022), FLAN (ICLR 2022) and T0 (ICLR 2022).

  • 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.

  • Chain-of-Thought Hub - Measuring LLMs' Reasoning Performance

  • Awesome GPT - A curated list of awesome projects and resources related to GPT, ChatGPT, OpenAI, LLM, and more.

  • Awesome GPT-3 - a collection of demos and articles about the OpenAI GPT-3 API.

  • Awesome LLM Human Preference Datasets - a collection of human preference datasets for LLM instruction tuning, RLHF and evaluation.

  • RWKV-howto - possibly useful materials and tutorial for learning RWKV.

  • ModelEditingPapers - A paper & resource list on model editing for large language models.

  • Awesome LLM Security - A curation of awesome tools, documents and projects about LLM Security.

  • Awesome-Align-LLM-Human - A collection of papers and resources about aligning large language models (LLMs) with human.

  • Awesome-Code-LLM - An awesome and curated list of best code-LLM for research.

  • Awesome-LLM-Compression - Awesome LLM compression research papers and tools.

  • Awesome-LLM-Systems - Awesome LLM systems research papers.

  • awesome-llm-webapps - A collection of open source, actively maintained web apps for LLM applications.

  • awesome-japanese-llm - 日本語LLMまとめ - Overview of Japanese LLMs.

  • Awesome-LLM-Healthcare - The paper list of the review on LLMs in medicine.

  • Awesome-LLM-Inference - A curated list of Awesome LLM Inference Paper with codes.

  • Awesome-LLM-3D - A curated list of Multi-modal Large Language Model in 3D world, including 3D understanding, reasoning, generation, and embodied agents.

  • LLMDatahub - a curated collection of datasets specifically designed for chatbot training, including links, size, language, usage, and a brief description of each dataset

  • Awesome-Chinese-LLM - 整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。

  • LLM4Opt - Applying Large language models (LLMs) for diverse optimization tasks (Opt) is an emerging research area. This is a collection of references and papers of LLM4Opt.

  • awesome-language-model-analysis - This paper list focuses on the theoretical or empirical analysis of language models, e.g., the learning dynamics, expressive capacity, interpretability, generalization, and other interesting topics.

LLM Leaderboard

  • Chatbot Arena Leaderboard - a benchmark platform for large language models (LLMs) that features anonymous, randomized battles in a crowdsourced manner.
  • LiveBench - A Challenging, Contamination-Free LLM Benchmark.
  • Open LLM Leaderboard - aims to track, rank, and evaluate LLMs and chatbots as they are released.
  • AlpacaEval - An Automatic Evaluator for Instruction-following Language Models using Nous benchmark suite.
  • ACLUE - an evaluation benchmark focused on ancient Chinese language comprehension.
  • BeHonest - A pioneering benchmark specifically designed to assess honesty in LLMs comprehensively.
  • Berkeley Function-Calling Leaderboard - evaluates LLM's ability to call external functions/tools.
  • Chinese Large Model Leaderboard - an expert-driven benchmark for Chineses LLMs.
  • CompassRank - CompassRank is dedicated to exploring the most advanced language and visual models, offering a comprehensive, objective, and neutral evaluation reference for the industry and research.
  • CompMix - a benchmark evaluating QA methods that operate over a mixture of heterogeneous input sources (KB, text, tables, infoboxes).
  • DreamBench++ - a benchmark for evaluating the performance of large language models (LLMs) in various tasks related to both textual and visual imagination.
  • FELM - a meta-benchmark that evaluates how well factuality evaluators assess the outputs of large language models (LLMs).
  • InfiBench - a benchmark designed to evaluate large language models (LLMs) specifically in their ability to answer real-world coding-related questions.
  • LawBench - a benchmark designed to evaluate large language models in the legal domain.
  • LLMEval - focuses on understanding how these models perform in various scenarios and analyzing results from an interpretability perspective.
  • M3CoT - a benchmark that evaluates large language models on a variety of multimodal reasoning tasks, including language, natural and social sciences, physical and social commonsense, temporal reasoning, algebra, and geometry.
  • MathEval - a comprehensive benchmarking platform designed to evaluate large models' mathematical abilities across 20 fields and nearly 30,000 math problems.
  • MixEval - a ground-truth-based dynamic benchmark derived from off-the-shelf benchmark mixtures, which evaluates LLMs with a highly capable model ranking (i.e., 0.96 correlation with Chatbot Arena) while running locally and quickly (6% the time and cost of running MMLU).
  • MMedBench - a benchmark that evaluates large language models' ability to answer medical questions across multiple languages.
  • MMToM-QA - a multimodal question-answering benchmark designed to evaluate AI models' cognitive ability to understand human beliefs and goals.
  • OlympicArena - a benchmark for evaluating AI models across multiple academic disciplines like math, physics, chemistry, biology, and more.
  • PubMedQA - a biomedical question-answering benchmark designed for answering research-related questions using PubMed abstracts.
  • SciBench - benchmark designed to evaluate large language models (LLMs) on solving complex, college-level scientific problems from domains like chemistry, physics, and mathematics.
  • SuperBench - a benchmark platform designed for evaluating large language models (LLMs) on a range of tasks, particularly focusing on their performance in different aspects such as natural language understanding, reasoning, and generalization.
  • SuperLim - a Swedish language understanding benchmark that evaluates natural language processing (NLP) models on various tasks such as argumentation analysis, semantic similarity, and textual entailment.
  • TAT-DQA - a large-scale Document Visual Question Answering (VQA) dataset designed for complex document understanding, particularly in financial reports.
  • TAT-QA - a large-scale question-answering benchmark focused on real-world financial data, integrating both tabular and textual information.
  • VisualWebArena - a benchmark designed to assess the performance of multimodal web agents on realistic visually grounded tasks.
  • We-Math - a benchmark that evaluates large multimodal models (LMMs) on their ability to perform human-like mathematical reasoning.
  • WHOOPS! - a benchmark dataset testing AI's ability to reason about visual commonsense through images that defy normal expectations.

Open LLM

LLM Data

  • LLMDataHub
  • IBM data-prep-kit - Open-Source Toolkit for Efficient Unstructured Data Processing with Pre-built Modules and Local to Cluster Scalability.

LLM Evaluation:

  • lm-evaluation-harness - A framework for few-shot evaluation of language models.
  • MixEval - A reliable click-and-go evaluation suite compatible with both open-source and proprietary models, supporting MixEval and other benchmarks.
  • lighteval - a lightweight LLM evaluation suite that Hugging Face has been using internally.
  • OLMO-eval - a repository for evaluating open language models.
  • instruct-eval - This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.
  • simple-evals - Eval tools by OpenAI.
  • Giskard - Testing & evaluation library for LLM applications, in particular RAGs
  • LangSmith - a unified platform from LangChain framework for: evaluation, collaboration HITL (Human In The Loop), logging and monitoring LLM applications.
  • Ragas - a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines.

LLM Training Frameworks

  • veRL - veRL is a flexible and efficient RL framework for LLMs.
  • DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
  • Megatron-DeepSpeed - DeepSpeed version of NVIDIA's Megatron-LM that adds additional support for several features such as MoE model training, Curriculum Learning, 3D Parallelism, and others.
  • torchtune - A Native-PyTorch Library for LLM Fine-tuning.
  • torchtitan - A native PyTorch Library for large model training.
  • NeMo Framework - Generative AI framework built for researchers and PyTorch developers working on Large Language Models (LLMs), Multimodal Models (MMs), Automatic Speech Recognition (ASR), Text to Speech (TTS), and Computer Vision (CV) domains.
  • Megatron-LM - Ongoing research training transformer models at scale.
  • Colossal-AI - Making large AI models cheaper, faster, and more accessible.
  • BMTrain - Efficient Training for Big Models.
  • Mesh Tensorflow - Mesh TensorFlow: Model Parallelism Made Easier.
  • maxtext - A simple, performant and scalable Jax LLM!
  • GPT-NeoX - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
  • Transformer Engine - A library for accelerating Transformer model training on NVIDIA GPUs.

LLM Deployment

Reference: llm-inference-solutions

  • SGLang - SGLang is a fast serving framework for large language models and vision language models.
  • vLLM - A high-throughput and memory-efficient inference and serving engine for LLMs.
  • TGI - a toolkit for deploying and serving Large Language Models (LLMs).
  • exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
  • llama.cpp - LLM inference in C/C++.
  • ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
  • Langfuse - Open Source LLM Engineering Platform 🪢 Tracing, Evaluations, Prompt Management, Evaluations and Playground.
  • FastChat - A distributed multi-model LLM serving system with web UI and OpenAI-compatible RESTful APIs.
  • mistral.rs - Blazingly fast LLM inference.
  • MindSQL - A python package for Txt-to-SQL with self hosting functionalities and RESTful APIs compatible with proprietary as well as open source LLM.
  • SkyPilot - Run LLMs and batch jobs on any cloud. Get maximum cost savings, highest GPU availability, and managed execution -- all with a simple interface.
  • Haystack - an open-source NLP framework that allows you to use LLMs and transformer-based models from Hugging Face, OpenAI and Cohere to interact with your own data.
  • Sidekick - Data integration platform for LLMs.
  • QA-Pilot - An interactive chat project that leverages Ollama/OpenAI/MistralAI LLMs for rapid understanding and navigation of GitHub code repository or compressed file resources.
  • Shell-Pilot - Interact with LLM using Ollama models(or openAI, mistralAI)via pure shell scripts on your Linux(or MacOS) system, enhancing intelligent system management without any dependencies.
  • LangChain - Building applications with LLMs through composability
  • Floom AI gateway and marketplace for developers, enables streamlined integration of AI features into products
  • Swiss Army Llama - Comprehensive set of tools for working with local LLMs for various tasks.
  • LiteChain - Lightweight alternative to LangChain for composing LLMs
  • magentic - Seamlessly integrate LLMs as Python functions
  • wechat-chatgpt - Use ChatGPT On Wechat via wechaty
  • promptfoo - Test your prompts. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality.
  • Agenta - Easily build, version, evaluate and deploy your LLM-powered apps.
  • Serge - a chat interface crafted with llama.cpp for running Alpaca models. No API keys, entirely self-hosted!
  • Langroid - Harness LLMs with Multi-Agent Programming
  • Embedchain - Framework to create ChatGPT like bots over your dataset.
  • Opik - Confidently evaluate, test, and ship LLM applications with a suite of observability tools to calibrate language model outputs across your dev and production lifecycle.
  • IntelliServer - simplifies the evaluation of LLMs by providing a unified microservice to access and test multiple AI models.
  • OpenLLM - Fine-tune, serve, deploy, and monitor any open-source LLMs in production. Used in production at BentoML for LLMs-based applications.
  • DeepSpeed-Mii - MII makes low-latency and high-throughput inference, similar to vLLM powered by DeepSpeed.
  • Text-Embeddings-Inference - Inference for text-embeddings in Rust, HFOIL Licence.
  • Infinity - Inference for text-embeddings in Python
  • TensorRT-LLM - Nvidia Framework for LLM Inference
  • FasterTransformer - NVIDIA Framework for LLM Inference(Transitioned to TensorRT-LLM)
  • Flash-Attention - A method designed to enhance the efficiency of Transformer models
  • Langchain-Chatchat - Formerly langchain-ChatGLM, local knowledge based LLM (like ChatGLM) QA app with langchain.
  • Search with Lepton - Build your own conversational search engine using less than 500 lines of code by LeptonAI.
  • Robocorp - Create, deploy and operate Actions using Python anywhere to enhance your AI agents and assistants. Batteries included with an extensive set of libraries, helpers and logging.
  • LMDeploy - A high-throughput and low-latency inference and serving framework for LLMs and VLs
  • Tune Studio - Playground for devs to finetune & deploy LLMs
  • LLocalSearch - Locally running websearch using LLM chains
  • AI Gateway — Gateway streamlines requests to 100+ open & closed source models with a unified API. It is also production-ready with support for caching, fallbacks, retries, timeouts, loadbalancing, and can be edge-deployed for minimum latency.
  • talkd.ai dialog - Simple API for deploying any RAG or LLM that you want adding plugins.
  • Wllama - WebAssembly binding for llama.cpp - Enabling in-browser LLM inference
  • GPUStack - An open-source GPU cluster manager for running LLMs
  • MNN-LLM -- A Device-Inference framework, including LLM Inference on device(Mobile Phone/PC/IOT)
  • CAMEL - First LLM Multi-agent framework.

LLM Applications

  • AdalFlow - AdalFlow: The library to build&auto-optimize LLM applications.
  • dspy - DSPy: The framework for programming—not prompting—foundation models.
  • MLflow - MLflow: An open-source framework for the end-to-end machine learning lifecycle, helping developers track experiments, evaluate models/prompts, deploy models, and add observability with tracing.
  • YiVal — Evaluate and Evolve: YiVal is an open-source GenAI-Ops tool for tuning and evaluating prompts, configurations, and model parameters using customizable datasets, evaluation methods, and improvement strategies.
  • Guidance — A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control.
  • LangChain — A popular Python/JavaScript library for chaining sequences of language model prompts.
  • Evidently — An open-source framework to evaluate, test and monitor ML and LLM-powered systems.
  • FLAML (A Fast Library for Automated Machine Learning & Tuning): A Python library for automating selection of models, hyperparameters, and other tunable choices.
  • Chainlit — A Python library for making chatbot interfaces.
  • Guardrails.ai — A Python library for validating outputs and retrying failures. Still in alpha, so expect sharp edges and bugs.
  • Semantic Kernel — A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning.
  • Prompttools — Open-source Python tools for testing and evaluating models, vector DBs, and prompts.
  • Outlines — A Python library that provides a domain-specific language to simplify prompting and constrain generation.
  • Promptify — A small Python library for using language models to perform NLP tasks.
  • Scale Spellbook — A paid product for building, comparing, and shipping language model apps.
  • PromptPerfect — A paid product for testing and improving prompts.
  • Weights & Biases — A paid product for tracking model training and prompt engineering experiments.
  • OpenAI Evals — An open-source library for evaluating task performance of language models and prompts.
  • LlamaIndex — A Python library for augmenting LLM apps with data.
  • Arthur Shield — A paid product for detecting toxicity, hallucination, prompt injection, etc.
  • LMQL — A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools.
  • ModelFusion - A TypeScript library for building apps with LLMs and other ML models (speech-to-text, text-to-speech, image generation).
  • Flappy — Production-Ready LLM Agent SDK for Every Developer.
  • GPTRouter - GPTRouter is an open source LLM API Gateway that offers a universal API for 30+ LLMs, vision, and image models, with smart fallbacks based on uptime and latency, automatic retries, and streaming. Stay operational even when OpenAI is down
  • QAnything - A local knowledge base question-answering system designed to support a wide range of file formats and databases.
  • OneKE — A bilingual Chinese-English knowledge extraction model with knowledge graphs and natural language processing technologies.
  • llm-ui - A React library for building LLM UIs.
  • Wordware - A web-hosted IDE where non-technical domain experts work with AI Engineers to build task-specific AI agents. We approach prompting as a new programming language rather than low/no-code blocks.
  • Wallaroo.AI - Deploy, manage, optimize any model at scale across any environment from cloud to edge. Let's you go from python notebook to inferencing in minutes.
  • Dify - An open-source LLM app development platform with an intuitive interface that streamlines AI workflows, model management, and production deployment.
  • LazyLLM - An open-source LLM app for building multi-agent LLMs applications in an easy and lazy way, supports model deployment and fine-tuning.
  • MemFree - Open Source Hybrid AI Search Engine, Instantly Get Accurate Answers from the Internet, Bookmarks, Notes, and Docs. Support One-Click Deployment
  • unslothai - A framework that specializes in efficient fine-tuning. On its GitHub page, you can find ready-to-use fine-tuning templates for various LLMs, allowing you to easily train your own data for free on the Google Colab cloud.
  • AutoRAG - Open source AutoML tool for RAG. Optimize the RAG answer quality automatically. From generation evaluation datset to deploying optimized RAG pipeline.
  • Epsilla - An all-in-one LLM Agent platform with your private data and knowledge, delivers your production-ready AI Agents on Day 1.

LLM Tutorials and Courses

LLM Books

Great thoughts about LLM

Miscellaneous

  • Arize-Phoenix - Open-source tool for ML observability that runs in your notebook environment. Monitor and fine tune LLM, CV and Tabular Models.
  • Emergent Mind - The latest AI news, curated & explained by GPT-4.
  • ShareGPT - Share your wildest ChatGPT conversations with one click.
  • Major LLMs + Data Availability
  • 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.
  • Open-evals - A framework extend openai's Evals for different language model.
  • Cursor - Write, edit, and chat about your code with a powerful AI.
  • AutoGPT - an experimental open-source application showcasing the capabilities of the GPT-4 language model.
  • OpenAGI - When LLM Meets Domain Experts.
  • EasyEdit - An easy-to-use framework to edit large language models.
  • chatgpt-shroud - A Chrome extension for OpenAI's ChatGPT, enhancing user privacy by enabling easy hiding and unhiding of chat history. Ideal for privacy during screen shares.

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.

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

For Tasks:

Click tags to check more tools for each tasks
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For Jobs:

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StockBot Powered by Groq is an AI-powered chatbot that provides lightning-fast responses with live interactive stock charts, financial data, news, screeners, and more. Leveraging Groq's speed and Vercel's AI SDK, StockBot offers real-time conversation with natural language processing, interactive TradingView charts, adaptive interfaces, and multi-asset market coverage. It is designed for entertainment and instructional use, not for investment advice.

github
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generative-ai-cdk-constructs Screenshot
generative-ai-cdk-constructs

The AWS Generative AI Constructs Library is an open-source extension of the AWS Cloud Development Kit (AWS CDK) that provides multi-service, well-architected patterns for quickly defining solutions in code to create predictable and repeatable infrastructure, called constructs. The goal of AWS Generative AI CDK Constructs is to help developers build generative AI solutions using pattern-based definitions for their architecture. The patterns defined in AWS Generative AI CDK Constructs are high level, multi-service abstractions of AWS CDK constructs that have default configurations based on well-architected best practices. The library is organized into logical modules using object-oriented techniques to create each architectural pattern model.

github
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ByteMLPerf Screenshot
ByteMLPerf

ByteMLPerf is an AI Accelerator Benchmark that focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and versatility of software and hardware. Byte MLPerf has the following characteristics: - Models and runtime environments are more closely aligned with practical business use cases. - For ASIC hardware evaluation, besides evaluate performance and accuracy, it also measure metrics like compiler usability and coverage. - Performance and accuracy results obtained from testing on the open Model Zoo serve as reference metrics for evaluating ASIC hardware integration.

github
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AI-in-a-Box Screenshot
AI-in-a-Box

AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.

github
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kubesphere Screenshot
kubesphere

KubeSphere is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows. It provides developer-friendly wizard web UI, helping enterprises to build out a more robust and feature-rich platform, which includes most common functionalities needed for enterprise Kubernetes strategy.

github
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aws-genai-llm-chatbot Screenshot
aws-genai-llm-chatbot

This repository provides code to deploy a chatbot powered by Multi-Model and Multi-RAG using AWS CDK on AWS. Users can experiment with various Large Language Models and Multimodal Language Models from different providers. The solution supports Amazon Bedrock, Amazon SageMaker self-hosted models, and third-party providers via API. It also offers additional resources like AWS Generative AI CDK Constructs and Project Lakechain for building generative AI solutions and document processing. The roadmap and authors are listed, along with contributors. The library is licensed under the MIT-0 License with information on changelog, code of conduct, and contributing guidelines. A legal disclaimer advises users to conduct their own assessment before using the content for production purposes.

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evalkit Screenshot
evalkit

EvalKit is an open-source TypeScript library for evaluating and improving the performance of large language models (LLMs). It helps developers ensure the reliability, accuracy, and trustworthiness of their AI models. The library provides various metrics such as Bias Detection, Coherence, Faithfulness, Hallucination, Intent Detection, and Semantic Similarity. EvalKit is designed to be user-friendly with detailed documentation, tutorials, and recipes for different use cases and LLM providers. It requires Node.js 18+ and an OpenAI API Key for installation and usage. Contributions from the community are welcome under the Apache 2.0 License.

github
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CuMo Screenshot
CuMo

CuMo is a project focused on scaling multimodal Large Language Models (LLMs) with Co-Upcycled Mixture-of-Experts. It introduces CuMo, which incorporates Co-upcycled Top-K sparsely-gated Mixture-of-experts blocks into the vision encoder and the MLP connector, enhancing the capabilities of multimodal LLMs. The project adopts a three-stage training approach with auxiliary losses to stabilize the training process and maintain a balanced loading of experts. CuMo achieves comparable performance to other state-of-the-art multimodal LLMs on various Visual Question Answering (VQA) and visual-instruction-following benchmarks.

github
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leapfrogai Screenshot
leapfrogai

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

github
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Hands-On-Large-Language-Models Screenshot
Hands-On-Large-Language-Models

Hands-On Large Language Models is a repository containing code examples from the book 'The Illustrated LLM Book' by Jay Alammar and Maarten Grootendorst. The repository provides practical tools and concepts for using Large Language Models with over 250 custom-made figures. It covers topics such as language model introduction, tokens and embeddings, transformer LLMs, text classification, text clustering, prompt engineering, text generation techniques, semantic search, multimodal LLMs, text embedding models, fine-tuning representation models, and fine-tuning generation models. The examples are designed to be run on Google Colab with T4 GPU support, but can be adapted to other cloud platforms as well.

github
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MeloTTS Screenshot
MeloTTS

MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai. It supports various languages including English (American, British, Indian, Australian), Spanish, French, Chinese, Japanese, and Korean. The Chinese speaker also supports mixed Chinese and English. The library is fast enough for CPU real-time inference and offers features like using without installation, local installation, and training on custom datasets. The Python API and model cards are available in the repository and on HuggingFace. The community can join the Discord channel for discussions and collaboration opportunities. Contributions are welcome, and the library is under the MIT License. MeloTTS is based on TTS, VITS, VITS2, and Bert-VITS2.

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Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

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FlashInfer is a library for Language Languages Models that provides high-performance implementation of LLM GPU kernels such as FlashAttention, PageAttention and LoRA. FlashInfer focus on LLM serving and inference, and delivers state-the-art performance across diverse scenarios.

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LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience. It offers features such as easy deployment of LangChain models and pipelines, ready-to-use authentication functionality, high-performance FastAPI framework for serving requests, scalability and robustness for language processing applications, support for custom pipelines and processing, well-documented RESTful API endpoints, and asynchronous processing for faster response times.

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Chuanhu Chat is a user-friendly web graphical interface that provides various additional features for ChatGPT and other language models. It supports GPT-4, file-based question answering, local deployment of language models, online search, agent assistant, and fine-tuning. The tool offers a range of functionalities including auto-solving questions, online searching with network support, knowledge base for quick reading, local deployment of language models, GPT 3.5 fine-tuning, and custom model integration. It also features system prompts for effective role-playing, basic conversation capabilities with options to regenerate or delete dialogues, conversation history management with auto-saving and search functionalities, and a visually appealing user experience with themes, dark mode, LaTeX rendering, and PWA application support.

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dash-infer Screenshot
dash-infer

DashInfer is a C++ runtime tool designed to deliver production-level implementations highly optimized for various hardware architectures, including x86 and ARMv9. It supports Continuous Batching and NUMA-Aware capabilities for CPU, and can fully utilize modern server-grade CPUs to host large language models (LLMs) up to 14B in size. With lightweight architecture, high precision, support for mainstream open-source LLMs, post-training quantization, optimized computation kernels, NUMA-aware design, and multi-language API interfaces, DashInfer provides a versatile solution for efficient inference tasks. It supports x86 CPUs with AVX2 instruction set and ARMv9 CPUs with SVE instruction set, along with various data types like FP32, BF16, and InstantQuant. DashInfer also offers single-NUMA and multi-NUMA architectures for model inference, with detailed performance tests and inference accuracy evaluations available. The tool is supported on mainstream Linux server operating systems and provides documentation and examples for easy integration and usage.

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awesome-mobile-llm Screenshot
awesome-mobile-llm

Awesome Mobile LLMs is a curated list of Large Language Models (LLMs) and related studies focused on mobile and embedded hardware. The repository includes information on various LLM models, deployment frameworks, benchmarking efforts, applications, multimodal LLMs, surveys on efficient LLMs, training LLMs on device, mobile-related use-cases, industry announcements, and related repositories. It aims to be a valuable resource for researchers, engineers, and practitioners interested in mobile LLMs.

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llm_note Screenshot
llm_note

LLM notes repository contains detailed analysis on transformer models, language model compression, inference and deployment, high-performance computing, and system optimization methods. It includes discussions on various algorithms, frameworks, and performance analysis related to large language models and high-performance computing. The repository serves as a comprehensive resource for understanding and optimizing language models and computing systems.

github
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hallucination-leaderboard Screenshot
hallucination-leaderboard

This leaderboard evaluates the hallucination rate of various Large Language Models (LLMs) when summarizing documents. It uses a model trained by Vectara to detect hallucinations in LLM outputs. The leaderboard includes models from OpenAI, Anthropic, Google, Microsoft, Amazon, and others. The evaluation is based on 831 documents that were summarized by all the models. The leaderboard shows the hallucination rate, factual consistency rate, answer rate, and average summary length for each model.

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Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

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LLMStack Screenshot
LLMStack

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

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VisionCraft Screenshot
VisionCraft

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

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kaito Screenshot
kaito

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

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PyRIT Screenshot
PyRIT

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

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tabby

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

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spear

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

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Magick

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

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