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README 

          



**Table of Contents**  *generated with [DocToc](https://github.com/thlorenz/doctoc)*



- [Video](#video)

- [LLM](#llm)

  - [CTG](#ctg)

  - [Layout](#layout)

  - [Sheet](#sheet)

  - [Agent](#agent)

  - [VectorSearch](#vectorsearch)

  - [Just Skin](#just-skin)

  - [Inference](#inference)

  - [Prompts](#prompts)

- [AI](#ai)

  - [Format](#format)

  - [Inference](#inference-1)

  - [Deploy](#deploy)

  - [Demo/WebAPP](#demowebapp)

  - [Toolkit](#toolkit)

  - [Dataset](#dataset)

  - [DataAnnotation](#dataannotation)

  - [DeepLearning](#deeplearning)

  - [MachineLearning](#machinelearning)

  - [HyperOptimization](#hyperoptimization)

  - [TTS](#tts)



> 关注AI领域值得关注的研究进展，包括视频、LLM及相关应用，以及其他AI相关。



## Agent



### Application



- [awesome-deepseek-integration/README_cn.md at main · deepseek-ai/awesome-deepseek-integration](https://github.com/deepseek-ai/awesome-deepseek-integration/blob/main/README_cn.md)

- [camel-ai/camel: 🐫 CAMEL: Finding the Scaling Law of Agents. The first and the best multi-agent framework. https://www.camel-ai.org](https://github.com/camel-ai/camel)

- [mannaandpoem/OpenManus: No fortress, purely open ground. OpenManus is Coming.](https://github.com/mannaandpoem/OpenManus)

- [browser-use/browser-use: Make websites accessible for AI agents](https://github.com/browser-use/browser-use)

- [OpenInterpreter/open-interpreter: A natural language interface for computers](https://github.com/OpenInterpreter/open-interpreter)

- [OpenInterpreter/01: The #1 open-source voice interface for desktop, mobile, and ESP32 chips.](https://github.com/OpenInterpreter/01)

- [yuruotong1/autoMate: like manus and omniparser.AI-driven local automation assistant that uses natural language to make computers work by themselves](https://github.com/yuruotong1/autoMate/tree/master)



### Framework



重点关注MicroSoft。



- [geekan/MetaGPT: 🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming](https://github.com/geekan/MetaGPT)

- [microsoft/autogen: Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ](https://github.com/microsoft/autogen)

- [langchain-ai/langgraph: Build resilient language agents as graphs.](https://github.com/langchain-ai/langgraph)

- [crewAIInc/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.](https://github.com/crewAIInc/crewAI)

- [Significant-Gravitas/AutoGPT: AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.](https://github.com/Significant-Gravitas/AutoGPT)

- [LLaVA-VL/LLaVA-Interactive-Demo: LLaVA-Interactive-Demo](https://github.com/LLaVA-VL/LLaVA-Interactive-Demo/tree/main)

- [yxuansu/PandaGPT: [TLLM'23] PandaGPT: One Model To Instruction-Follow Them All](https://github.com/yxuansu/PandaGPT/tree/main)

- [microsoft/semantic-kernel: Integrate cutting-edge LLM technology quickly and easily into your apps](https://github.com/microsoft/semantic-kernel/tree/main)

- [visual-openllm/visual-openllm: something like visual-chatgpt, 文心一言的开源版](https://github.com/visual-openllm/visual-openllm)



### KnowldgeBase



- [1Panel-dev/MaxKB: 💬 Ready-to-use & flexible RAG Chatbot, supporting mainstream large language models (LLMs) such as DeepSeek-R1, Llama 3.3, Qwen2, OpenAI and more.](https://github.com/1Panel-dev/MaxKB)

- [outline/outline: The fastest knowledge base for growing teams. Beautiful, realtime collaborative, feature packed, and markdown compatible.](https://github.com/outline/outline)

- [infiniflow/ragflow: RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.](https://github.com/infiniflow/ragflow)



## Video



- `OpenSource` [THUDM/CogVideo: text and image to video generation: CogVideoX (2024) and CogVideo (ICLR 2023)](https://github.com/THUDM/CogVideo)

- `OpenSource` [genmo/mochi-1-preview · Hugging Face](https://huggingface.co/genmo/mochi-1-preview)

- `OpenSource` [rain1011/pyramid-flow-miniflux · Hugging Face](https://huggingface.co/rain1011/pyramid-flow-miniflux)

- `OpenSource` [Lightricks/LTX-Video · Hugging Face](https://huggingface.co/Lightricks/LTX-Video)

- `OpenSource` [rhymes-ai/Allegro: Allegro is a powerful text-to-video model that generates high-quality videos up to 6 seconds at 15 FPS and 720p resolution from simple text input.](https://github.com/rhymes-ai/Allegro)

- `OpenSource` [PKU-YuanGroup/Open-Sora-Plan: This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.](https://github.com/PKU-YuanGroup/Open-Sora-Plan)

- `OpenModel, TrainOnRoad` [TMElyralab/MuseV: MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned Parallel Denoising](https://github.com/TMElyralab/MuseV)

- `OpenModel` [stepfun-ai/Step-Video-T2V](https://github.com/stepfun-ai/Step-Video-T2V/tree/main)

- `OpenModel` [tencent/HunyuanVideo-I2V · Hugging Face](https://huggingface.co/tencent/HunyuanVideo-I2V)

- `OpenModel` [Wan-Video/Wan2.1: Wan: Open and Advanced Large-Scale Video Generative Models](https://github.com/Wan-Video/Wan2.1)



## Image



- `OpenModel` [CohereForAI/aya-vision-8b · Hugging Face](https://huggingface.co/CohereForAI/aya-vision-8b)

- `OpenModel, TrainOnRoad` [THUDM/CogView4-6B · Hugging Face](https://huggingface.co/THUDM/CogView4-6B)



### OCR



- [Alpha-Innovator/OmniCaptioner](https://github.com/Alpha-Innovator/OmniCaptioner)

- [microsoft/OmniParser: A simple screen parsing tool towards pure vision based GUI agent](https://github.com/microsoft/OmniParser)

- [olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models](https://arxiv.org/abs/2502.18443)



## LLM



### CTG



- [Saibo-creator/Awesome-LLM-Constrained-Decoding: A curated list of papers related to constrained decoding of LLM, along with their relevant code and resources.](https://github.com/Saibo-creator/Awesome-LLM-Constrained-Decoding)



### Layout



- [unilm/layoutreader at master · microsoft/unilm](https://github.com/microsoft/unilm/tree/master/layoutreader)

- [PaddleNLP/model_zoo/ernie-layout/README_ch.md at develop · PaddlePaddle/PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/model_zoo/ernie-layout/README_ch.md)

- [AdvancedLiterateMachinery/Applications/DocXChain at main · AlibabaResearch/AdvancedLiterateMachinery](https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/Applications/DocXChain/)



### Sheet



- [gventuri/pandas-ai: PandasAI is the Python library that integrates Gen AI into pandas, making data analysis conversational](https://github.com/gventuri/pandas-ai)



### VectorSearch



- [paradedb/paradedb: PostgreSQL for Search](https://github.com/paradedb/paradedb)

- [facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.](https://github.com/facebookresearch/faiss)

- [milvus-io/milvus: A cloud-native vector database, storage for next generation AI applications](https://github.com/milvus-io/milvus)

- [alibaba/proxima](https://github.com/alibaba/proxima)

- [vearch/vearch: A distributed system for embedding-based vector retrieval](https://github.com/vearch/vearch)

- [castorini/anserini: Anserini is a Lucene toolkit for reproducible information retrieval research](https://github.com/castorini/Anserini)

- [google-research/scann at master · google-research/google-research](https://github.com/google-research/google-research/tree/master/scann)



### Just Skin



- [terry3041/pyChatGPT: An unofficial Python wrapper for OpenAI's ChatGPT API](https://github.com/terry3041/pyChatGPT)

- [acheong08/EdgeGPT: Reverse engineered API of Microsoft's Bing Chat AI](https://github.com/acheong08/EdgeGPT)

- [acheong08/ChatGPT: Reverse engineered ChatGPT API](https://github.com/acheong08/ChatGPT)

- [transitive-bullshit/chatgpt-api: Node.js client for the official ChatGPT API. 🔥](https://github.com/transitive-bullshit/chatgpt-api)

- [terry3041/pyChatGPT: An unofficial Python wrapper for OpenAI's ChatGPT API](https://github.com/terry3041/pyChatGPT)

- [Sha1rholder/use-ChatGPT-in-GFW: 在中国境内使用 OpenAI 服务的方法](https://github.com/Sha1rholder/use-ChatGPT-in-GFW#%E4%BD%BF%E7%94%A8-openai-api)

- [ninja/README_zh.md at main · gngpp/ninja](https://github.com/gngpp/ninja/blob/main/README_zh.md)



### Inference



- [Frameworks for Serving LLMs. A comprehensive guide into LLMs inference and serving](https://betterprogramming.pub/frameworks-for-serving-llms-60b7f7b23407)

- [ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++](https://github.com/ggerganov/llama.cpp)

- [huggingface/text-generation-inference: Large Language Model Text Generation Inference](https://github.com/huggingface/text-generation-inference)

- [onnxruntime/onnxruntime/python/tools/transformers/notebooks/Inference_GPT2_with_OnnxRuntime_on_CPU](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/notebooks/Inference_GPT2_with_OnnxRuntime_on_CPU.ipynb)

- [FasterTransformer/docs/gpt_guide.md at main · NVIDIA/FasterTransformer](https://github.com/NVIDIA/FasterTransformer/blob/main/docs/gpt_guide.md)

- [OpenNMT/CTranslate2: Fast inference engine for Transformer models](https://github.com/OpenNMT/CTranslate2)

- [mlc-ai/mlc-llm: Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.](https://github.com/mlc-ai/mlc-llm)

- [vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine for LLMs](https://github.com/vllm-project/vllm)

- [sonos/tract: Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference](https://github.com/sonos/tract)

- [rustformers/llm: An ecosystem of Rust libraries for working with large language models](https://github.com/rustformers/llm)

- [bentoml/OpenLLM: Operating LLMs in production](https://github.com/bentoml/OpenLLM)

- [huggingface/candle: Minimalist ML framework for Rust](https://github.com/huggingface/candle)

- [tairov/llama2.mojo: Inference Llama 2 in one file of pure 🔥](https://github.com/tairov/llama2.mojo)



### Prompts



- [f/awesome-chatgpt-prompts: This repo includes ChatGPT prompt curation to use ChatGPT better.](https://github.com/f/awesome-chatgpt-prompts)

- [dair-ai/Prompt-Engineering-Guide: 🐙 Guides, papers, lecture, notebooks and resources for prompt engineering](https://github.com/dair-ai/Prompt-Engineering-Guide)



### Parser



- [opendatalab/MinerU: A high-quality tool for convert PDF to Markdown and JSON.一站式开源高质量数据提取工具，将PDF转换成Markdown和JSON格式。](https://github.com/opendatalab/MinerU)



## AI



### Format



- [onnx/onnx: Open standard for machine learning interoperability](https://github.com/onnx/onnx)

- [huggingface/safetensors: Simple, safe way to store and distribute tensors](https://github.com/huggingface/safetensors)

- [openai/triton: Development repository for the Triton language and compiler](https://github.com/openai/triton)



### Inference



- [microsoft/onnxruntime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator](https://github.com/microsoft/onnxruntime)

- [NVIDIA/TensorRT: NVIDIA® TensorRT™, an SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.](https://github.com/NVIDIA/TensorRT)

- [openvinotoolkit/openvino: OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference](https://github.com/openvinotoolkit/openvino)

- [triton-inference-server/server: The Triton Inference Server provides an optimized cloud and edge inferencing solution.](https://github.com/triton-inference-server/server)

- [KomputeProject/kompute: General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.](https://github.com/KomputeProject/kompute)

- [Adlik/Adlik: Adlik: Toolkit for Accelerating Deep Learning Inference](https://github.com/Adlik/Adlik)



### Deploy



- [tensorflow/serving](https://github.com/tensorflow/serving)

- [pytorch/serve: Model Serving on PyTorch](https://github.com/pytorch/serve)

- [TFX: End-to-End Platform for Deploying Production ML Pipelines](https://github.com/tensorflow/tfx)

- [Kubeflow](https://www.kubeflow.org/)

- [mvitez/thnets: Basic library that can run networks created with Torch](https://github.com/mvitez/thnets)

- [Serving Trained Model (aka Model API) - FloydHub Documentation](https://docs.floydhub.com/guides/serving/)

- [ahkarami/Deep-Learning-in-Production](https://github.com/ahkarami/Deep-Learning-in-Production)

- [hyperonym/basaran: Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.](https://github.com/hyperonym/basaran)



### Demo/WebAPP



- [holoviz/panel: Panel: The powerful data exploration & web app framework for Python](https://github.com/holoviz/panel)

- [rawpython/remi: Python REMote Interface library. Platform independent. In about 100 Kbytes, perfect for your diet.](https://github.com/rawpython/remi)

- [PySimpleGUI](https://www.pysimplegui.org/en/latest/)

- [pywebio/PyWebIO: Write interactive web app in script way.](https://github.com/pywebio/PyWebIO)

- [streamlit/streamlit: Streamlit — A faster way to build and share data apps.](https://github.com/streamlit/streamlit)

- [gradio-app/gradio: Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!](https://github.com/gradio-app/gradio)



### Toolkit



- [microsoft/hummingbird: Hummingbird compiles trained ML models into tensor computation for faster inference.](https://github.com/microsoft/hummingbird)

- [PyTorchLightning/pytorch-lightning: The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.](https://github.com/PyTorchLightning/PyTorch-lightning)

- [Deepfakes](https://github.com/deepfakes/faceswap)

- [DeepFaceLab](https://github.com/iperov/DeepFaceLab)

- [Music Generation](https://github.com/salu133445/musegan)

- [AI_Composer](https://github.com/llSourcell/AI_Composer)

- [ChatterBot](https://github.com/gunthercox/ChatterBot)

- [ChatterBot](https://github.com/Decalogue/chat)

- [GPT2 for Chinese Chitchat](https://github.com/yangjianxin1/GPT2-chitchat)

- [FaceBook Detectron2: object detection and segmentation.](https://github.com/facebookresearch/detectron2)

- [Open MMLab Detection Toolbox and Benchmark](https://github.com/open-mmlab/mmdetection)

- [Open MMLab Computer Vision Foundation](https://github.com/open-mmlab/mmcv)

- [esdalmaijer/PyGaze: eye tracking experiments](https://github.com/esdalmaijer/PyGaze)

- [atulapra/Emotion-detection](https://github.com/atulapra/Emotion-detection)

- [ Multi-Person Pose Estimation&Tracking System](https://github.com/MVIG-SJTU/AlphaPose)

- [facebookresearch/dlrm: recommendation](https://github.com/facebookresearch/dlrm)

- [Jupyter Tutorial — Jupyter Tutorial 0.8.0](https://jupyter-tutorial.readthedocs.io/en/latest/)



### Dataset



- [Dataset Search](https://datasetsearch.research.google.com/)

- [Hugging Face – The AI community building the future.](https://huggingface.co/datasets)

- [Find Open Datasets and Machine Learning Projects | Kaggle](https://www.kaggle.com/datasets)

- [Machine Learning Datasets | Papers With Code](https://www.paperswithcode.com/datasets)

- [Datasets](https://www.reddit.com/r/datasets/)

- [中文NLP数据集](https://www.cluebenchmarks.com/dataSet_search.html)

- [Dataset list - A list of the biggest machine learning datasets](https://www.datasetlist.com/)

- [awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets.](https://github.com/awesomedata/awesome-public-datasets)

- [Data Is Plural](https://www.data-is-plural.com/)

- [OpenML](https://www.openml.org/search?type=data)

- [InsaneLife/ChineseNLPCorpus: 中文自然语言处理数据集，平时做做实验的材料。欢迎补充提交合并。](https://github.com/InsaneLife/ChineseNLPCorpus)



### DataAnnotation



- [opencv/cvat: Computer Vision Annotation Tool](https://github.com/opencv/cvat)

- [wkentaro/labelme: Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).](https://github.com/wkentaro/labelme)

- [generating training data with weak supervision](https://github.com/snorkel-team/snorkel)

- [make basic image processing operations](https://github.com/jrosebr1/imutils)

- [nlplab/brat: textual annotation](https://github.com/nlplab/brat)

- [doccano/doccano: Open source annotation tool for machine learning practitioners.](https://github.com/doccano/doccano)

- [Overview - CoreNLP](https://stanfordnlp.github.io/CoreNLP/)

- [HumanSignal/awesome-data-labeling: A curated list of awesome data labeling tools](https://github.com/HumanSignal/awesome-data-labeling)



### DeepLearning



- [fast.ai · Making neural nets uncool again](https://www.fast.ai/)

- [TensorFlow](https://www.tensorflow.org/tutorials)

- [Tensorflow Models](https://github.com/tensorflow/models)

- [DeepLearning Papers](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap)

- [Awesome-Deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers)

- [D2L ZH](https://github.com/d2l-ai/d2l-zh)

- [D2L EN](https://github.com/d2l-ai/d2l-en)

- [pytorch/tutorials: PyTorch tutorials.](https://github.com/pytorch/tutorials)

- [Pytorch Examples](https://github.com/pytorch/examples)

- [Tutorials  |  TensorFlow Core](https://www.tensorflow.org/tutorials)

- [简单粗暴 TensorFlow 2 | A Concise Handbook of TensorFlow 2 — 简单粗暴 TensorFlow 2 0.4 beta 文档](https://tf.wiki/zh_hans/)

- [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples)

- [microsoft: NLP Best Practices & Examples](https://github.com/microsoft/nlp-recipes)

- [DeepLearning Projects](https://github.com/aymericdamien/TopDeepLearning)

- [Ttensorflow_practice](https://github.com/princewen/tensorflow_practice)



### MachineLearning



- [microsoft/nni: AutoML toolkit](https://github.com/microsoft/nni)

- [Imbalanced Learning](https://github.com/scikit-learn-contrib/imbalanced-learn)

- [Automated Machine Learning tool using genetic programming](https://github.com/EpistasisLab/tpot)

- [Machine learning evaluation metrics](https://github.com/benhamner/Metrics)

- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)



### HyperOptimization



- [autonomio/talos: Hyperparameter Optimization for TensorFlow, Keras and PyTorch](https://github.com/autonomio/talos)

- [scikit-optimize: sequential model-based optimization in Python — scikit-optimize 0.8.1 documentation](https://scikit-optimize.github.io/stable/)

- [fmfn/BayesianOptimization: A Python implementation of global optimization with gaussian processes.](https://github.com/fmfn/BayesianOptimization)

- Ray Tune：[Tune: Scalable Hyperparameter Tuning — Ray v2.0.0.dev0](https://docs.ray.io/en/master/tune/index.html)

- [hyperopt/hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python](https://github.com/hyperopt/hyperopt)

- [Introduction to the Keras Tuner  |  TensorFlow Core](https://www.tensorflow.org/tutorials/keras/keras_tuner)

- Google Vizier：[Vizier 概览  |  AI Platform Vizier  |  Google Cloud](https://cloud.google.com/ai-platform/optimizer/docs/overview)

- Amazon Sagemaker：[Amazon SageMaker 机器学习模型构建训练部署 - AWS 云服务](https://aws.amazon.com/cn/sagemaker/)

- Microsoft NNI：[microsoft/nni: An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.](https://github.com/microsoft/nni)

- Microsoft Advisor：[Optimization advisor overview - Finance & Operations | Dynamics 365 | Microsoft Docs](https://docs.microsoft.com/en-us/dynamics365/fin-ops-core/dev-itpro/sysadmin/optimization-advisor-overview)

- [maxpumperla/hyperas: Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization](https://github.com/maxpumperla/hyperas) （已存档）

- [Avsecz/kopt: Hyper-parameter optimization for Keras](https://github.com/Avsecz/kopt)（一阵没更新了）

- [rsteca/sklearn-deap: Use evolutionary algorithms instead of gridsearch in scikit-learn](https://github.com/rsteca/sklearn-deap)（一阵没更新了）

- [HIPS/Spearmint: Spearmint Bayesian optimization codebase](https://github.com/HIPS/Spearmint)（一阵没更新了）

- [[1603.06560] Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://arxiv.org/abs/1603.06560)



###  TTS



- [coqui-ai/TTS: 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production](https://github.com/coqui-ai/TTS)

- [RVC-Boss/GPT-SoVITS: 1 min voice data can also be used to train a good TTS model! (few shot voice cloning)](https://github.com/RVC-Boss/GPT-SoVITS/tree/main)

- [fishaudio/Bert-VITS2: vits2 backbone with multilingual-bert](https://github.com/fishaudio/Bert-VITS2)