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transformers	copyright 2020 the huggingface team all rights reserved licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license p align center picture source media prefers color scheme dark srcset https huggingface co datasets huggingface documentation images raw main transformers logo dark svg source media prefers color scheme light srcset https huggingface co datasets huggingface documentation images raw main transformers logo light svg img alt hugging face transformers library src https huggingface co datasets huggingface documentation images raw 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latestdoi 155220641 img src https zenodo org badge 155220641 svg alt doi a p h4 align center p b english b a href https github com huggingface transformers blob main readme zh hans md a a href https github com huggingface transformers blob main readme zh hant md a a href https github com huggingface transformers blob main readme ko md a a href https github com huggingface transformers blob main readme es md espa ol a a href https github com huggingface transformers blob main readme ja md a a href https github com huggingface transformers blob main readme hd md a a href https github com huggingface transformers blob main readme ru md a a href https github com huggingface transformers blob main readme pt br md ortugu s a p h4 h3 align center p state of the art machine learning for jax pytorch and tensorflow p h3 h3 align center a href https hf co course img src https huggingface co datasets huggingface documentation images resolve main course banner png a h3 transformers provides thousands of pretrained models to perform tasks on different modalities such as text vision and audio these models can be applied on text for tasks like text classification information extraction question answering summarization translation text generation in over 100 languages images for tasks like image classification object detection and segmentation audio for tasks like speech recognition and audio classification transformer models can also perform tasks on several modalities combined such as table question answering optical character recognition information extraction from scanned documents video classification and visual question answering transformers provides apis to quickly download and use those pretrained models on a given text fine tune them on your own datasets and then share them with the community on our model hub https huggingface co models at the same time each python module defining an architecture is fully standalone and can be modified to enable quick research experiments transformers is backed by the three most popular deep learning libraries jax https jax readthedocs io en latest pytorch https pytorch org and tensorflow https www tensorflow org with a seamless integration between them it s straightforward to train your models with one before loading them for inference with the other online demos you can test most of our models directly on their pages from the model hub https huggingface co models we also offer private model hosting versioning an inference api https huggingface co pricing for public and private models here are a few examples in natural language processing masked word completion with bert https huggingface co bert base uncased text paris is the 5bmask 5d of france name entity recognition with electra https huggingface co dbmdz electra large discriminator finetuned conll03 english text my name is sarah and i live in london city text generation with gpt 2 https huggingface co gpt2 text a long time ago 2c natural language inference with roberta https huggingface co roberta large mnli text the dog was lost nobody lost any animal summarization with bart https huggingface co facebook bart large cnn text the tower is 324 metres 281 2c063 ft 29 tall 2c about the same height as an 81 storey building 2c and the tallest structure in paris its base is square 2c measuring 125 metres 28410 ft 29 on each side during its construction 2c the eiffel tower surpassed the washington monument to become the tallest man made structure in the world 2c a title it held for 41 years until the chrysler building in new york city was finished in 1930 it was the first structure to reach a height of 300 metres due to the addition of a broadcasting aerial at the top of the tower in 1957 2c it is now taller than the chrysler building by 5 2 metres 2817 ft 29 excluding transmitters 2c the eiffel tower is the second tallest free standing structure in france after the millau viaduct question answering with distilbert https huggingface co distilbert base uncased distilled squad text which name is also used to describe the amazon rainforest in english 3f context the amazon rainforest 28portuguese 3a floresta amaz c3 b4nica or amaz c3 b4nia 3b spanish 3a selva amaz c3 b3nica 2c amazon c3 ada or usually amazonia 3b french 3a for c3 aat amazonienne 3b dutch 3a amazoneregenwoud 29 2c also known in english as amazonia or the amazon jungle 2c is a moist broadleaf forest that covers most of the amazon basin of south america this basin encompasses 7 2c000 2c000 square kilometres 282 2c700 2c000 sq mi 29 2c of which 5 2c500 2c000 square kilometres 282 2c100 2c000 sq mi 29 are covered by the rainforest this region includes territory belonging to nine nations the majority of the forest is contained within brazil 2c with 60 25 of the rainforest 2c followed by peru with 13 25 2c colombia with 10 25 2c and with minor amounts in venezuela 2c ecuador 2c bolivia 2c guyana 2c suriname and french guiana states or departments in four nations contain 22amazonas 22 in their names the amazon represents over half of the planet 27s remaining rainforests 2c and comprises the largest and most biodiverse tract of tropical rainforest in the world 2c with an estimated 390 billion individual trees divided into 16 2c000 species translation with t5 https huggingface co t5 base text my name is wolfgang and i live in berlin in computer vision image classification with vit https huggingface co google vit base patch16 224 object detection with detr https huggingface co facebook detr resnet 50 semantic segmentation with segformer https huggingface co nvidia segformer b0 finetuned ade 512 512 panoptic segmentation with maskformer https huggingface co facebook maskformer swin small coco depth estimation with dpt https huggingface co docs transformers model doc dpt video classification with videomae https huggingface co docs transformers model doc videomae universal segmentation with oneformer https huggingface co shi labs oneformer ade20k dinat large in audio automatic speech recognition with wav2vec2 https huggingface co facebook wav2vec2 base 960h keyword spotting with wav2vec2 https huggingface co superb wav2vec2 base superb ks audio classification with audio spectrogram transformer https huggingface co mit ast finetuned audioset 10 10 0 4593 in multimodal tasks table question answering with tapas https huggingface co google tapas base finetuned wtq visual question answering with vilt https huggingface co dandelin vilt b32 finetuned vqa zero shot image classification with clip https huggingface co openai clip vit large patch14 document question answering with layoutlm https huggingface co impira layoutlm document qa zero shot video classification with x clip https huggingface co docs transformers model doc xclip 100 projects using transformers transformers is more than a toolkit to use pretrained models it s a community of projects built around it and the hugging face hub we want transformers to enable developers researchers students professors engineers and anyone else to build their dream projects in order to celebrate the 100 000 stars of transformers we have decided to put the spotlight on the community and we have created the awesome transformers awesome transformers md page which lists 100 incredible projects built in the vicinity of transformers if you own or use a project that you believe should be part of the list please open a pr to add it if you are looking for custom support from the hugging face team a target blank href https huggingface co support img alt huggingface expert acceleration program src https cdn media huggingface co marketing transformers new support improved png style max width 600px border 1px solid eee border radius 4px box shadow 0 1px 2px 0 rgba 0 0 0 0 05 a br quick tour to immediately use a model on a given input text image audio we provide the pipeline api pipelines group together a pretrained model with the preprocessing that was used during that model s training here is how to quickly use a pipeline to classify positive versus negative texts python from transformers import pipeline allocate a pipeline for sentiment analysis classifier pipeline sentiment analysis classifier we are very happy to introduce pipeline to the transformers repository label positive score 0 9996980428695679 the second line of code downloads and caches the pretrained model used by the pipeline while the third evaluates it on the given text here the answer is positive with a confidence of 99 97 many tasks have a pre trained pipeline ready to go in nlp but also in computer vision and speech for example we can easily extract detected objects in an image python import requests from pil import image from transformers import pipeline download an image with cute cats url https huggingface co datasets huggingface documentation images resolve main coco sample png image data requests get url stream true raw image image open image data allocate a pipeline for object detection object detector pipeline object detection object detector image score 0 9982201457023621 label remote box xmin 40 ymin 70 xmax 175 ymax 117 score 0 9960021376609802 label remote box xmin 333 ymin 72 xmax 368 ymax 187 score 0 9954745173454285 label couch box xmin 0 ymin 1 xmax 639 ymax 473 score 0 9988006353378296 label cat box xmin 13 ymin 52 xmax 314 ymax 470 score 0 9986783862113953 label cat box xmin 345 ymin 23 xmax 640 ymax 368 here we get a list of objects detected in the image with a box surrounding the object and a confidence score here is the original image on the left with the predictions displayed on the right h3 align center a img src https huggingface co datasets huggingface documentation images resolve main coco sample png width 400 a a img src https huggingface co datasets huggingface documentation images resolve main coco sample post processed png width 400 a h3 you can learn more about the tasks supported by the pipeline api in this tutorial https huggingface co docs transformers task summary in addition to pipeline to download and use any of the pretrained models on your given task all it takes is three lines of code here is the pytorch version python from transformers import autotokenizer automodel tokenizer autotokenizer from pretrained bert base uncased model automodel from pretrained bert base uncased inputs tokenizer hello world return tensors pt outputs model inputs and here is the equivalent code for tensorflow python from transformers import autotokenizer tfautomodel tokenizer autotokenizer from pretrained bert base uncased model tfautomodel from pretrained bert base uncased inputs tokenizer hello world return tensors tf outputs model inputs the tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string as in the above examples or a list it will output a dictionary that you can use in downstream code or simply directly pass to your model using the argument unpacking operator the model itself is a regular pytorch nn module https pytorch org docs stable nn html torch nn module or a tensorflow tf keras model https www tensorflow org api docs python tf keras model depending on your backend which you can use as usual this tutorial https huggingface co docs transformers training explains how to integrate such a model into a classic pytorch or tensorflow training loop or how to use our trainer api to quickly fine tune on a new dataset why should i use transformers 1 easy to use state of the art models high performance on natural language understanding generation computer vision and audio tasks low barrier to entry for educators and practitioners few user facing abstractions with just three classes to learn a unified api for using all our pretrained models 1 lower compute costs smaller carbon footprint researchers can share trained models instead of always retraining practitioners can reduce compute time and production costs dozens of architectures with over 60 000 pretrained models across all modalities 1 choose the right framework for every part of a model s lifetime train state of the art models in 3 lines of code move a single model between tf2 0 pytorch jax frameworks at will seamlessly pick the right framework for training evaluation and production 1 easily customize a model or an example to your needs we provide examples for each architecture to reproduce the results published by its original authors model internals are exposed as consistently as possible model files can be used independently of the library for quick experiments why shouldn t i use transformers this library is not a modular toolbox of building blocks for neural nets the code in the model files is not refactored with additional abstractions on purpose so that researchers can quickly iterate on each of the models without diving into additional abstractions files the training api is not intended to work on any model but is optimized to work with the models provided by the library for generic machine learning loops you should use another library possibly accelerate https huggingface co docs accelerate while we strive to present as many use cases as possible the scripts in our examples folder https github com huggingface transformers tree main examples are just that examples it is expected that they won t work out of the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs installation with pip this repository is tested on python 3 8 flax 0 4 1 pytorch 1 10 and tensorflow 2 6 you should install transformers in a virtual environment https docs python org 3 library venv html if you re unfamiliar with python virtual environments check out the user guide https packaging python org guides installing using pip and virtual environments first create a virtual environment with the version of python you re going to use and activate it then you will need to install at least one of flax pytorch or tensorflow please refer to tensorflow installation page https www tensorflow org install pytorch installation page https pytorch org get started locally start locally and or flax https github com google flax quick install and jax https github com google jax installation installation pages regarding the specific installation command for your platform when one of those backends has been installed transformers can be installed using pip as follows bash pip install transformers if you d like to play with the examples or need the bleeding edge of the code and can t wait for a new release you must install the library from source https huggingface co docs transformers installation installing from source with conda since transformers version v4 0 0 we now have a conda channel huggingface transformers can be installed using conda as follows shell script conda install c huggingface transformers follow the installation pages of flax pytorch or tensorflow to see how to install them with conda note on windows you may be prompted to activate developer mode in order to benefit from caching if this is not an option for you please let us know in this issue https github com huggingface huggingface hub issues 1062 model architectures all the model checkpoints https huggingface co models provided by transformers are seamlessly integrated from the huggingface co model hub https huggingface co models where they are uploaded directly by users https huggingface co users and organizations https huggingface co organizations current number of checkpoints https img shields io endpoint url https huggingface co api shields models color brightgreen transformers currently provides the following architectures see here https huggingface co docs transformers model summary for a high level summary of each them 1 albert https huggingface co docs transformers model doc albert from google research and the toyota technological institute at chicago released with the paper albert a lite bert for self supervised learning of language representations https arxiv org abs 1909 11942 by zhenzhong lan mingda chen sebastian goodman kevin gimpel piyush sharma radu soricut 1 align https huggingface co docs transformers model doc align from google research released with the paper scaling up visual and vision language representation learning with noisy text supervision https arxiv org abs 2102 05918 by chao jia yinfei yang ye xia yi ting chen zarana parekh hieu pham quoc v le yunhsuan sung zhen li tom duerig 1 altclip https huggingface co docs transformers model doc altclip from baai released with the paper altclip altering the language encoder in clip for extended language capabilities https arxiv org abs 2211 06679 by chen zhongzhi and liu guang and zhang bo wen and ye fulong and yang qinghong and wu ledell 1 audio spectrogram transformer https huggingface co docs transformers model doc audio spectrogram transformer from mit released with the paper ast audio spectrogram transformer https arxiv org abs 2104 01778 by yuan gong yu an chung james glass 1 autoformer https huggingface co docs transformers model doc autoformer from tsinghua university released with the paper autoformer decomposition transformers with auto correlation for long term series forecasting https arxiv org abs 2106 13008 by haixu wu jiehui xu jianmin wang mingsheng long 1 bark https huggingface co docs transformers model doc bark from suno released in the repository suno ai bark https github com suno ai bark by suno ai team 1 bart https huggingface co docs transformers model doc bart from facebook released with the paper bart denoising sequence to sequence pre training for natural language generation translation and comprehension https arxiv org abs 1910 13461 by mike lewis yinhan liu naman goyal marjan ghazvininejad abdelrahman mohamed omer levy ves stoyanov and luke zettlemoyer 1 barthez https huggingface co docs transformers model doc barthez from cole polytechnique released with the paper barthez a skilled pretrained french sequence to sequence model https arxiv org abs 2010 12321 by moussa kamal eddine antoine j p tixier michalis vazirgiannis 1 bartpho https huggingface co docs transformers model doc bartpho from vinai research released with the paper bartpho pre trained sequence to sequence models for vietnamese https arxiv org abs 2109 09701 by nguyen luong tran duong minh le and dat quoc nguyen 1 beit https huggingface co docs transformers model doc beit from microsoft released with the paper beit bert pre training of image transformers https arxiv org abs 2106 08254 by hangbo bao li dong furu wei 1 bert https huggingface co docs transformers model doc bert from google released with the paper bert pre training of deep bidirectional transformers for language understanding https arxiv org abs 1810 04805 by jacob devlin ming wei chang kenton lee and kristina toutanova 1 bert for sequence generation https huggingface co docs transformers model doc bert generation from google released with the paper leveraging pre trained checkpoints for sequence generation tasks https arxiv org abs 1907 12461 by sascha rothe shashi narayan aliaksei severyn 1 bertweet https huggingface co docs transformers model doc bertweet from vinai research released with the paper bertweet a pre trained language model for english tweets https aclanthology org 2020 emnlp demos 2 by dat quoc nguyen thanh vu and anh tuan nguyen 1 bigbird pegasus https huggingface co docs transformers model doc bigbird pegasus from google research released with the paper big bird transformers for longer sequences https arxiv org abs 2007 14062 by manzil zaheer guru guruganesh avinava dubey joshua ainslie chris alberti santiago ontanon philip pham anirudh ravula qifan wang li yang amr ahmed 1 bigbird roberta https huggingface co docs transformers model doc big bird from google research released with the paper big bird transformers for longer sequences https arxiv org abs 2007 14062 by manzil zaheer guru guruganesh avinava dubey joshua ainslie chris alberti santiago ontanon philip pham anirudh ravula qifan wang li yang amr ahmed 1 biogpt https huggingface co docs transformers model doc biogpt from microsoft research ai4science released with the paper biogpt generative pre trained transformer for biomedical text generation and mining https academic oup com bib advance article doi 10 1093 bib bbac409 6713511 guestaccesskey a66d9b5d 4f83 4017 bb52 405815c907b9 by renqian luo liai sun yingce xia tao qin sheng zhang hoifung poon and tie yan liu 1 bit https huggingface co docs transformers model doc bit from google ai released with the paper big transfer bit general visual representation learning https arxiv org abs 1912 11370 by alexander kolesnikov lucas beyer xiaohua zhai joan puigcerver jessica yung sylvain gelly neil houlsby 1 blenderbot https huggingface co docs transformers model doc blenderbot from facebook released with the paper recipes for building an open domain chatbot https arxiv org abs 2004 13637 by stephen roller emily dinan naman goyal da ju mary williamson yinhan liu jing xu myle ott kurt shuster eric m smith y lan boureau jason weston 1 blenderbotsmall https huggingface co docs transformers model doc blenderbot small from facebook released with the paper recipes for building an open domain chatbot https arxiv org abs 2004 13637 by stephen roller emily dinan naman goyal da ju mary williamson yinhan liu jing xu myle ott kurt shuster eric m smith y lan boureau jason weston 1 blip https huggingface co docs transformers model doc blip from salesforce released with the paper blip bootstrapping language image pre training for unified vision language understanding and generation https arxiv org abs 2201 12086 by junnan li dongxu li caiming xiong steven hoi 1 blip 2 https huggingface co docs transformers model doc blip 2 from salesforce released with the paper blip 2 bootstrapping language image pre training with frozen image encoders and large language models https arxiv org abs 2301 12597 by junnan li dongxu li silvio savarese steven hoi 1 bloom https huggingface co docs transformers model doc bloom from bigscience workshop released by the bigscience workshop https bigscience huggingface co 1 bort https huggingface co docs transformers model doc bort from alexa released with the paper optimal subarchitecture extraction for bert https arxiv org abs 2010 10499 by adrian de wynter and daniel j perry 1 bridgetower https huggingface co docs transformers model doc bridgetower from harbin institute of technology microsoft research asia intel labs released with the paper bridgetower building bridges between encoders in vision language representation learning https arxiv org abs 2206 08657 by xiao xu chenfei wu shachar rosenman vasudev lal wanxiang che nan duan 1 bros https huggingface co docs transformers model doc bros from naver clova released with the paper bros a pre trained language model focusing on text and layout for better key information extraction from documents https arxiv org abs 2108 04539 by teakgyu hong donghyun kim mingi ji wonseok hwang daehyun nam sungrae park 1 byt5 https huggingface co docs transformers model doc byt5 from google research released with the paper byt5 towards a token free future with pre trained byte to byte models https arxiv org abs 2105 13626 by linting xue aditya barua noah constant rami al rfou sharan narang mihir kale adam roberts colin raffel 1 camembert https huggingface co docs transformers model doc camembert from inria facebook sorbonne released with the paper camembert a tasty french language model https arxiv org abs 1911 03894 by louis martin benjamin muller pedro javier ortiz su rez yoann dupont laurent romary ric villemonte de la clergerie djam seddah and beno t sagot 1 canine https huggingface co docs transformers model doc canine from google research released with the paper canine pre training an efficient tokenization free encoder for language representation https arxiv org abs 2103 06874 by jonathan h clark dan garrette iulia turc john wieting 1 chinese clip https huggingface co docs transformers model doc chinese clip from ofa sys released with the paper chinese clip contrastive vision language pretraining in chinese https arxiv org abs 2211 01335 by an yang junshu pan junyang lin rui men yichang zhang jingren zhou chang zhou 1 clap https huggingface co docs transformers model doc clap from laion ai released with the paper large scale contrastive language audio pretraining with feature fusion and keyword to caption augmentation https arxiv org abs 2211 06687 by yusong wu ke chen tianyu zhang yuchen hui taylor berg kirkpatrick shlomo dubnov 1 clip https huggingface co docs transformers model doc clip from openai released with the paper learning transferable visual models from natural language supervision https arxiv org abs 2103 00020 by alec radford jong wook kim chris hallacy aditya ramesh gabriel goh sandhini agarwal girish sastry amanda askell pamela mishkin jack clark gretchen krueger ilya sutskever 1 clipseg https huggingface co docs transformers model doc clipseg from university of g ttingen released with the paper image segmentation using text and image prompts https arxiv org abs 2112 10003 by timo l ddecke and alexander ecker 1 codegen https huggingface co docs transformers model doc codegen from salesforce released with the paper a conversational paradigm for program synthesis https arxiv org abs 2203 13474 by erik nijkamp bo pang hiroaki hayashi lifu tu huan wang yingbo zhou silvio savarese caiming xiong 1 codellama https huggingface co docs transformers model doc llama code from metaai released with the paper code llama open foundation models for code https ai meta com research publications code llama open foundation models for code by baptiste rozi re jonas gehring fabian gloeckle sten sootla itai gat xiaoqing ellen tan yossi adi jingyu liu tal remez j r my rapin artyom kozhevnikov ivan evtimov joanna bitton manish bhatt cristian canton ferrer aaron grattafiori wenhan xiong alexandre d fossez jade copet faisal azhar hugo touvron louis martin nicolas usunier thomas scialom gabriel synnaeve 1 conditional detr https huggingface co docs transformers model doc conditional detr from microsoft research asia released with the paper conditional detr for fast training convergence https arxiv org abs 2108 06152 by depu meng xiaokang chen zejia fan gang zeng houqiang li yuhui yuan lei sun jingdong wang 1 convbert https huggingface co docs transformers model doc convbert from yitutech released with the paper convbert improving bert with span based dynamic convolution https arxiv org abs 2008 02496 by zihang jiang weihao yu daquan zhou yunpeng chen jiashi feng shuicheng yan 1 convnext https huggingface co docs transformers model doc convnext from facebook ai released with the paper a convnet for the 2020s https arxiv org abs 2201 03545 by zhuang liu hanzi mao chao yuan wu christoph feichtenhofer trevor darrell saining xie 1 convnextv2 https huggingface co docs transformers model doc convnextv2 from facebook ai released with the paper convnext v2 co designing and scaling convnets with masked autoencoders https arxiv org abs 2301 00808 by sanghyun woo shoubhik debnath ronghang hu xinlei chen zhuang liu in so kweon saining xie 1 cpm https huggingface co docs transformers model doc cpm from tsinghua university released with the paper cpm a large scale generative chinese pre trained language model https arxiv org abs 2012 00413 by zhengyan zhang xu han hao zhou pei ke yuxian gu deming ye yujia qin yusheng su haozhe ji jian guan fanchao qi xiaozhi wang yanan zheng guoyang zeng huanqi cao shengqi chen daixuan li zhenbo sun zhiyuan liu minlie huang wentao han jie tang juanzi li xiaoyan zhu maosong sun 1 cpm ant https huggingface co docs transformers model doc cpmant from openbmb released by the openbmb https www openbmb org 1 ctrl https huggingface co docs transformers model doc ctrl from salesforce released with the paper ctrl a conditional transformer language model for controllable generation https arxiv org abs 1909 05858 by nitish shirish keskar bryan mccann lav r varshney caiming xiong and richard socher 1 cvt https huggingface co docs transformers model doc cvt from microsoft released with the paper cvt introducing convolutions to vision transformers https arxiv org abs 2103 15808 by haiping wu bin xiao noel codella mengchen liu xiyang dai lu yuan lei zhang 1 data2vec https huggingface co docs transformers model doc data2vec from facebook released with the paper data2vec a general framework for self supervised learning in speech vision and language https arxiv org abs 2202 03555 by alexei baevski wei ning hsu qiantong xu arun babu jiatao gu michael auli 1 deberta https huggingface co docs transformers model doc deberta from microsoft released with the paper deberta decoding enhanced bert with disentangled attention https arxiv org abs 2006 03654 by pengcheng he xiaodong liu jianfeng gao weizhu chen 1 deberta v2 https huggingface co docs transformers model doc deberta v2 from microsoft released with the paper deberta decoding enhanced bert with disentangled attention https arxiv org abs 2006 03654 by pengcheng he xiaodong liu jianfeng gao weizhu chen 1 decision transformer https huggingface co docs transformers model doc decision transformer from berkeley facebook google released with the paper decision transformer reinforcement learning via sequence modeling https arxiv org abs 2106 01345 by lili chen kevin lu aravind rajeswaran kimin lee aditya grover michael laskin pieter abbeel aravind srinivas igor mordatch 1 deformable detr https huggingface co docs transformers model doc deformable detr from sensetime research released with the paper deformable detr deformable transformers for end to end object detection https arxiv org abs 2010 04159 by xizhou zhu weijie su lewei lu bin li xiaogang wang jifeng dai 1 deit https huggingface co docs transformers model doc deit from facebook released with the paper training data efficient image transformers distillation through attention https arxiv org abs 2012 12877 by hugo touvron matthieu cord matthijs douze francisco massa alexandre sablayrolles herv j gou 1 deplot https huggingface co docs transformers model doc deplot from google ai released with the paper deplot one shot visual language reasoning by plot to table translation https arxiv org abs 2212 10505 by fangyu liu julian martin eisenschlos francesco piccinno syrine krichene chenxi pang kenton lee mandar joshi wenhu chen nigel collier yasemin altun 1 deta https huggingface co docs transformers model doc deta from the university of texas at austin released with the paper nms strikes back https arxiv org abs 2212 06137 by jeffrey ouyang zhang jang hyun cho xingyi zhou philipp kr henb hl 1 detr https huggingface co docs transformers model doc detr from facebook released with the paper end to end object detection with transformers https arxiv org abs 2005 12872 by nicolas carion francisco massa gabriel synnaeve nicolas usunier alexander kirillov sergey zagoruyko 1 dialogpt https huggingface co docs transformers model doc dialogpt from microsoft research released with the paper dialogpt large scale generative pre training for conversational response generation https arxiv org abs 1911 00536 by yizhe zhang siqi sun michel galley yen chun chen chris brockett xiang gao jianfeng gao jingjing liu bill dolan 1 dinat https huggingface co docs transformers model doc dinat from shi labs released with the paper dilated neighborhood attention transformer https arxiv org abs 2209 15001 by ali hassani and humphrey shi 1 dinov2 https huggingface co docs transformers model doc dinov2 from meta ai released with the paper dinov2 learning robust visual features without supervision https arxiv org abs 2304 07193 by maxime oquab timoth e darcet th o moutakanni huy vo marc szafraniec vasil khalidov pierre fernandez daniel haziza francisco massa alaaeldin el nouby mahmoud assran nicolas ballas wojciech galuba russell howes po yao huang shang wen li ishan misra michael rabbat vasu sharma gabriel synnaeve hu xu herv jegou julien mairal patrick labatut armand joulin piotr bojanowski 1 distilbert https huggingface co docs transformers model doc distilbert from huggingface released together with the paper distilbert a distilled version of bert smaller faster cheaper and lighter https arxiv org abs 1910 01108 by victor sanh lysandre debut and thomas wolf the same method has been applied to compress gpt2 into distilgpt2 https github com huggingface transformers tree main examples research projects distillation roberta into distilroberta https github com huggingface transformers tree main examples research projects distillation multilingual bert into distilmbert https github com huggingface transformers tree main examples research projects distillation and a german version of distilbert 1 dit https huggingface co docs transformers model doc dit from microsoft research released with the paper dit self supervised pre training for document image transformer https arxiv org abs 2203 02378 by junlong li yiheng xu tengchao lv lei cui cha zhang furu wei 1 donut https huggingface co docs transformers model doc donut from naver released together with the paper ocr free document understanding transformer https arxiv org abs 2111 15664 by geewook kim teakgyu hong moonbin yim jeongyeon nam jinyoung park jinyeong yim wonseok hwang sangdoo yun dongyoon han seunghyun park 1 dpr https huggingface co docs transformers model doc dpr from facebook released with the paper dense passage retrieval for open domain question answering https arxiv org abs 2004 04906 by vladimir karpukhin barlas o uz sewon min patrick lewis ledell wu sergey edunov danqi chen and wen tau yih 1 dpt https huggingface co docs transformers master model doc dpt from intel labs released with the paper vision transformers for dense prediction https arxiv org abs 2103 13413 by ren ranftl alexey bochkovskiy vladlen koltun 1 efficientformer https huggingface co docs transformers model doc efficientformer from snap research released with the paper efficientformer vision transformers at mobilenetspeed https arxiv org abs 2206 01191 by yanyu li geng yuan yang wen ju hu georgios evangelidis sergey tulyakov yanzhi wang jian ren 1 efficientnet https huggingface co docs transformers model doc efficientnet from google brain released with the paper efficientnet rethinking model scaling for convolutional neural networks https arxiv org abs 1905 11946 by mingxing tan quoc v le 1 electra https huggingface co docs transformers model doc electra from google research stanford university released with the paper electra pre training text encoders as discriminators rather than generators https arxiv org abs 2003 10555 by kevin clark minh thang luong quoc v le christopher d manning 1 encodec https huggingface co docs transformers model doc encodec from meta ai released with the paper high fidelity neural audio compression https arxiv org abs 2210 13438 by alexandre d fossez jade copet gabriel synnaeve yossi adi 1 encoderdecoder https huggingface co docs transformers model doc encoder decoder from google research released with the paper leveraging pre trained checkpoints for sequence generation tasks https arxiv org abs 1907 12461 by sascha rothe shashi narayan aliaksei severyn 1 ernie https huggingface co docs transformers model doc ernie from baidu released with the paper ernie enhanced representation through knowledge integration https arxiv org abs 1904 09223 by yu sun shuohuan wang yukun li shikun feng xuyi chen han zhang xin tian danxiang zhu hao tian hua wu 1 erniem https huggingface co docs transformers model doc ernie m from baidu released with the paper ernie m enhanced multilingual representation by aligning cross lingual semantics with monolingual corpora https arxiv org abs 2012 15674 by xuan ouyang shuohuan wang chao pang yu sun hao tian hua wu haifeng wang 1 esm https huggingface co docs transformers model doc esm from meta ai are transformer protein language models esm 1b was released with the paper biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences https www pnas org content 118 15 e2016239118 by alexander rives joshua meier tom sercu siddharth goyal zeming lin jason liu demi guo myle ott c lawrence zitnick jerry ma and rob fergus esm 1v was released with the paper language models enable zero shot prediction of the effects of mutations on protein function https doi org 10 1101 2021 07 09 450648 by joshua meier roshan rao robert verkuil jason liu tom sercu and alexander rives esm 2 and esmfold were released with the paper language models of protein sequences at the scale of evolution enable accurate structure prediction https doi org 10 1101 2022 07 20 500902 by zeming lin halil akin roshan rao brian hie zhongkai zhu wenting lu allan dos santos costa maryam fazel zarandi tom sercu sal candido alexander rives 1 falcon https huggingface co docs transformers model doc falcon from technology innovation institute by almazrouei ebtesam and alobeidli hamza and alshamsi abdulaziz and cappelli alessandro and cojocaru ruxandra and debbah merouane and goffinet etienne and heslow daniel and launay julien and malartic quentin and noune badreddine and pannier baptiste and penedo guilherme 1 flan t5 https huggingface co docs transformers model doc flan t5 from google ai released in the repository google research t5x https github com google research t5x blob main docs models md flan t5 checkpoints by hyung won chung le hou shayne longpre barret zoph yi tay william fedus eric li xuezhi wang mostafa dehghani siddhartha brahma albert webson shixiang shane gu zhuyun dai mirac suzgun xinyun chen aakanksha chowdhery sharan narang gaurav mishra adams yu vincent zhao yanping huang andrew dai hongkun yu slav petrov ed h chi jeff dean jacob devlin adam roberts denny zhou quoc v le and jason wei 1 flan ul2 https huggingface co docs transformers model doc flan ul2 from google ai released in the repository google research t5x https github com google research t5x blob main docs models md flan ul2 checkpoints by hyung won chung le hou shayne longpre barret zoph yi tay william fedus eric li xuezhi wang mostafa dehghani siddhartha brahma albert webson shixiang shane gu zhuyun dai mirac suzgun xinyun chen aakanksha chowdhery sharan narang gaurav mishra adams yu vincent zhao yanping huang andrew dai hongkun yu slav petrov ed h chi jeff dean jacob devlin adam roberts denny zhou quoc v le and jason wei 1 flaubert https huggingface co docs transformers model doc flaubert from cnrs released with the paper flaubert unsupervised language model pre training for french https arxiv org abs 1912 05372 by hang le lo c vial jibril frej vincent segonne maximin coavoux benjamin lecouteux alexandre allauzen beno t crabb laurent besacier didier schwab 1 flava https huggingface co docs transformers model doc flava from facebook ai released with the paper flava a foundational language and vision alignment model https arxiv org abs 2112 04482 by amanpreet singh ronghang hu vedanuj goswami guillaume couairon wojciech galuba marcus rohrbach and douwe kiela 1 fnet https huggingface co docs transformers model doc fnet from google research released with the paper fnet mixing tokens with fourier transforms https arxiv org abs 2105 03824 by james lee thorp joshua ainslie ilya eckstein santiago ontanon 1 focalnet https huggingface co docs transformers model doc focalnet from microsoft research released with the paper focal modulation networks https arxiv org abs 2203 11926 by jianwei yang chunyuan li xiyang dai lu yuan jianfeng gao 1 funnel transformer https huggingface co docs transformers model doc funnel from cmu google brain released with the paper funnel transformer filtering out sequential redundancy for efficient language processing https arxiv org abs 2006 03236 by zihang dai guokun lai yiming yang quoc v le 1 fuyu https huggingface co docs transformers model doc fuyu from adept rohan bavishi erich elsen curtis hawthorne maxwell nye augustus odena arushi somani sa nak ta rlar released with the paper blog post https www adept ai blog fuyu 8b 1 git https huggingface co docs transformers model doc git from microsoft research released with the paper git a generative image to text transformer for vision and language https arxiv org abs 2205 14100 by jianfeng wang zhengyuan yang xiaowei hu linjie li kevin lin zhe gan zicheng liu ce liu lijuan wang 1 glpn https huggingface co docs transformers model doc glpn from kaist released with the paper global local path networks for monocular depth estimation with vertical cutdepth https arxiv org abs 2201 07436 by doyeon kim woonghyun ga pyungwhan ahn donggyu joo sehwan chun junmo kim 1 gpt https huggingface co docs transformers model doc openai gpt from openai released with the paper improving language understanding by generative pre training https openai com research language unsupervised by alec radford karthik narasimhan tim salimans and ilya sutskever 1 gpt neo https huggingface co docs transformers model doc gpt neo from eleutherai released in the repository eleutherai gpt neo https github com eleutherai gpt neo by sid black stella biderman leo gao phil wang and connor leahy 1 gpt neox https huggingface co docs transformers model doc gpt neox from eleutherai released with the paper gpt neox 20b an open source autoregressive language model https arxiv org abs 2204 06745 by sid black stella biderman eric hallahan quentin anthony leo gao laurence golding horace he connor leahy kyle mcdonell jason phang michael pieler usvsn sai prashanth shivanshu purohit laria reynolds jonathan tow ben wang samuel weinbach 1 gpt neox japanese https huggingface co docs transformers model doc gpt neox japanese from abeja released by shinya otani takayoshi makabe anuj arora and kyo hattori 1 gpt 2 https huggingface co docs transformers model doc gpt2 from openai released with the paper language models are unsupervised multitask learners https openai com research better language models by alec radford jeffrey wu rewon child david luan dario amodei and ilya sutskever 1 gpt j https huggingface co docs transformers model doc gptj from eleutherai released in the repository kingoflolz mesh transformer jax https github com kingoflolz mesh transformer jax by ben wang and aran komatsuzaki 1 gpt sw3 https huggingface co docs transformers model doc gpt sw3 from ai sweden released with the paper lessons learned from gpt sw3 building the first large scale generative language model for swedish http www lrec conf org proceedings lrec2022 pdf 2022 lrec 1 376 pdf by ariel ekgren amaru cuba gyllensten evangelia gogoulou alice heiman severine verlinden joey hman fredrik carlsson magnus sahlgren 1 gptbigcode https huggingface co docs transformers model doc gpt bigcode from bigcode released with the paper santacoder don t reach for the stars https arxiv org abs 2301 03988 by loubna ben allal raymond li denis kocetkov chenghao mou christopher akiki carlos munoz ferrandis niklas muennighoff mayank mishra alex gu manan dey logesh kumar umapathi carolyn jane anderson yangtian zi joel lamy poirier hailey schoelkopf sergey troshin dmitry abulkhanov manuel romero michael lappert francesco de toni bernardo garc a del r o qian liu shamik bose urvashi bhattacharyya terry yue zhuo ian yu paulo villegas marco zocca sourab mangrulkar david lansky huu nguyen danish contractor luis villa jia li dzmitry bahdanau yacine jernite sean hughes daniel fried arjun guha harm de vries leandro von werra 1 gptsan japanese https huggingface co docs transformers model doc gptsan japanese released in the repository tanreinama gptsan https github com tanreinama gptsan blob main report model md by toshiyuki sakamoto tanreinama 1 graphormer https huggingface co docs transformers model doc graphormer from microsoft released with the paper do transformers really perform bad for graph representation https arxiv org abs 2106 05234 by chengxuan ying tianle cai shengjie luo shuxin zheng guolin ke di he yanming shen tie yan liu 1 groupvit https huggingface co docs transformers model doc groupvit from ucsd nvidia released with the paper groupvit semantic segmentation emerges from text supervision https arxiv org abs 2202 11094 by jiarui xu shalini de mello sifei liu wonmin byeon thomas breuel jan kautz xiaolong wang 1 herbert https huggingface co docs transformers model doc herbert from allegro pl agh university of science and technology released with the paper klej comprehensive benchmark for polish language understanding https www aclweb org anthology 2020 acl main 111 pdf by piotr rybak robert mroczkowski janusz tracz ireneusz gawlik 1 hubert https huggingface co docs transformers model doc hubert from facebook released with the paper hubert self supervised speech representation learning by masked prediction of hidden units https arxiv org abs 2106 07447 by wei ning hsu benjamin bolte yao hung hubert tsai kushal lakhotia ruslan salakhutdinov abdelrahman mohamed 1 i bert https huggingface co docs transformers model doc ibert from berkeley released with the paper i bert integer only bert quantization https arxiv org abs 2101 01321 by sehoon kim amir gholami zhewei yao michael w mahoney kurt keutzer 1 idefics https huggingface co docs transformers model doc idefics from huggingface released with the paper obelics an open web scale filtered dataset of interleaved image text documents https huggingface co papers 2306 16527 by hugo lauren on lucile saulnier l o tronchon stas bekman amanpreet singh anton lozhkov thomas wang siddharth karamcheti alexander m rush douwe kiela matthieu cord victor sanh 1 imagegpt https huggingface co docs transformers model doc imagegpt from openai released with the paper generative pretraining from pixels https openai com blog image gpt by mark chen alec radford rewon child jeffrey wu heewoo jun david luan ilya sutskever 1 informer https huggingface co docs transformers model doc informer from beihang university uc berkeley rutgers university sedd company released with the paper informer beyond efficient transformer for long sequence time series forecasting https arxiv org abs 2012 07436 by haoyi zhou shanghang zhang jieqi peng shuai zhang jianxin li hui xiong and wancai zhang 1 instructblip https huggingface co docs transformers model doc instructblip from salesforce released with the paper instructblip towards general purpose vision language models with instruction tuning https arxiv org abs 2305 06500 by wenliang dai junnan li dongxu li anthony meng huat tiong junqi zhao weisheng wang boyang li pascale fung steven hoi 1 jukebox https huggingface co docs transformers model doc jukebox from openai released with the paper jukebox a generative model for music https arxiv org pdf 2005 00341 pdf by prafulla dhariwal heewoo jun christine payne jong wook kim alec radford ilya sutskever 1 layoutlm https huggingface co docs transformers model doc layoutlm from microsoft research asia released with the paper layoutlm pre training of text and layout for document image understanding https arxiv org abs 1912 13318 by yiheng xu minghao li lei cui shaohan huang furu wei ming zhou 1 layoutlmv2 https huggingface co docs transformers model doc layoutlmv2 from microsoft research asia released with the paper layoutlmv2 multi modal pre training for visually rich document understanding https arxiv org abs 2012 14740 by yang xu yiheng xu tengchao lv lei cui furu wei guoxin wang yijuan lu dinei florencio cha zhang wanxiang che min zhang lidong zhou 1 layoutlmv3 https huggingface co docs transformers model doc layoutlmv3 from microsoft research asia released with the paper layoutlmv3 pre training for document ai with unified text and image masking https arxiv org abs 2204 08387 by yupan huang tengchao lv lei cui yutong lu furu wei 1 layoutxlm https huggingface co docs transformers model doc layoutxlm from microsoft research asia released with the paper layoutxlm multimodal pre training for multilingual visually rich document understanding https arxiv org abs 2104 08836 by yiheng xu tengchao lv lei cui guoxin wang yijuan lu dinei florencio cha zhang furu wei 1 led https huggingface co docs transformers model doc led from allenai released with the paper longformer the long document transformer https arxiv org abs 2004 05150 by iz beltagy matthew e peters arman cohan 1 levit https huggingface co docs transformers model doc levit from meta ai released with the paper levit a vision transformer in convnet s clothing for faster inference https arxiv org abs 2104 01136 by ben graham alaaeldin el nouby hugo touvron pierre stock armand joulin herv j gou matthijs douze 1 lilt https huggingface co docs transformers model doc lilt from south china university of technology released with the paper lilt a simple yet effective language independent layout transformer for structured document understanding https arxiv org abs 2202 13669 by jiapeng wang lianwen jin kai ding 1 llama https huggingface co docs transformers model doc llama from the fair team of meta ai released with the paper llama open and efficient foundation language models https arxiv org abs 2302 13971 by hugo touvron thibaut lavril gautier izacard xavier martinet marie anne lachaux timoth e lacroix baptiste rozi re naman goyal eric hambro faisal azhar aurelien rodriguez armand joulin edouard grave guillaume lample 1 llama2 https huggingface co docs transformers model doc llama2 from the fair team of meta ai released with the paper llama2 open foundation and fine tuned chat models https ai meta com research publications llama 2 open foundation and fine tuned chat models by hugo touvron louis martin kevin stone peter albert amjad almahairi yasmine babaei nikolay bashlykov soumya batra prajjwal bhargava shruti bhosale dan bikel lukas blecher cristian canton ferrer moya chen guillem cucurull david esiobu jude fernandes jeremy fu wenyin fu brian fuller cynthia gao vedanuj goswami naman goyal anthony hartshorn saghar hosseini rui hou hakan inan marcin kardas viktor kerkez madian khabsa isabel kloumann artem korenev punit singh koura marie anne lachaux thibaut lavril jenya lee diana liskovich yinghai lu yuning mao xavier martinet todor mihaylov pushka rmishra igor molybog yixin nie andrew poulton jeremy reizenstein rashi rungta kalyan saladi alan schelten ruan silva eric michael smith ranjan subramanian xiaoqing ellentan binh tang ross taylor adina williams jian xiang kuan puxin xu zheng yan iliyan zarov yuchen zhang angela fan melanie kambadur sharan narang aurelien rodriguez robert stojnic sergey edunov thomas scialom 1 longformer https huggingface co docs transformers model doc longformer from allenai released with the paper longformer the long document transformer https arxiv org abs 2004 05150 by iz beltagy matthew e peters arman cohan 1 longt5 https huggingface co docs transformers model doc longt5 from google ai released with the paper longt5 efficient text to text transformer for long sequences https arxiv org abs 2112 07916 by mandy guo joshua ainslie david uthus santiago ontanon jianmo ni yun hsuan sung yinfei yang 1 luke https huggingface co docs transformers model doc luke from studio ousia released with the paper luke deep contextualized entity representations with entity aware self attention https arxiv org abs 2010 01057 by ikuya yamada akari asai hiroyuki shindo hideaki takeda yuji matsumoto 1 lxmert https huggingface co docs transformers model doc lxmert from unc chapel hill released with the paper lxmert learning cross modality encoder representations from transformers for open domain question answering https arxiv org abs 1908 07490 by hao tan and mohit bansal 1 m ctc t https huggingface co docs transformers model doc mctct from facebook released with the paper pseudo labeling for massively multilingual speech recognition https arxiv org abs 2111 00161 by loren lugosch tatiana likhomanenko gabriel synnaeve and ronan collobert 1 m2m100 https huggingface co docs transformers model doc m2m 100 from facebook released with the paper beyond english centric multilingual machine translation https arxiv org abs 2010 11125 by angela fan shruti bhosale holger schwenk zhiyi ma ahmed el kishky siddharth goyal mandeep baines onur celebi guillaume wenzek vishrav chaudhary naman goyal tom birch vitaliy liptchinsky sergey edunov edouard grave michael auli armand joulin 1 marianmt https huggingface co docs transformers model doc marian machine translation models trained using opus http opus nlpl eu data by j rg tiedemann the marian framework https marian nmt github io is being developed by the microsoft translator team 1 markuplm https huggingface co docs transformers model doc markuplm from microsoft research asia released with the paper markuplm pre training of text and markup language for visually rich document understanding https arxiv org abs 2110 08518 by junlong li yiheng xu lei cui furu wei 1 mask2former https huggingface co docs transformers model doc mask2former from fair and uiuc released with the paper masked attention mask transformer for universal image segmentation https arxiv org abs 2112 01527 by bowen cheng ishan misra alexander g schwing alexander kirillov rohit girdhar 1 maskformer https huggingface co docs transformers model doc maskformer from meta and uiuc released with the paper per pixel classification is not all you need for semantic segmentation https arxiv org abs 2107 06278 by bowen cheng alexander g schwing alexander kirillov 1 matcha https huggingface co docs transformers model doc matcha from google ai released with the paper matcha enhancing visual language pretraining with math reasoning and chart derendering https arxiv org abs 2212 09662 by fangyu liu francesco piccinno syrine krichene chenxi pang kenton lee mandar joshi yasemin altun nigel collier julian martin eisenschlos 1 mbart https huggingface co docs transformers model doc mbart from facebook released with the paper multilingual denoising pre training for neural machine translation https arxiv org abs 2001 08210 by yinhan liu jiatao gu naman goyal xian li sergey edunov marjan ghazvininejad mike lewis luke zettlemoyer 1 mbart 50 https huggingface co docs transformers model doc mbart from facebook released with the paper multilingual translation with extensible multilingual pretraining and finetuning https arxiv org abs 2008 00401 by yuqing tang chau tran xian li peng jen chen naman goyal vishrav chaudhary jiatao gu angela fan 1 mega https huggingface co docs transformers model doc mega from meta usc cmu sjtu released with the paper mega moving average equipped gated attention https arxiv org abs 2209 10655 by xuezhe ma chunting zhou xiang kong junxian he liangke gui graham neubig jonathan may and luke zettlemoyer 1 megatron bert https huggingface co docs transformers model doc megatron bert from nvidia released with the paper megatron lm training multi billion parameter language models using model parallelism https arxiv org abs 1909 08053 by mohammad shoeybi mostofa patwary raul puri patrick legresley jared casper and bryan catanzaro 1 megatron gpt2 https huggingface co docs transformers model doc megatron gpt2 from nvidia released with the paper megatron lm training multi billion parameter language models using model parallelism https arxiv org abs 1909 08053 by mohammad shoeybi mostofa patwary raul puri patrick legresley jared casper and bryan catanzaro 1 mgp str https huggingface co docs transformers model doc mgp str from alibaba research released with the paper multi granularity prediction for scene text recognition https arxiv org abs 2209 03592 by peng wang cheng da and cong yao 1 mistral https huggingface co docs transformers model doc mistral from mistral ai by the mistral ai https mistral ai team albert jiang alexandre sablayrolles arthur mensch chris bamford devendra singh chaplot diego de las casas florian bressand gianna lengyel guillaume lample l lio renard lavaud lucile saulnier marie anne lachaux pierre stock teven le scao thibaut lavril thomas wang timoth e lacroix william el sayed 1 mluke https huggingface co docs transformers model doc mluke from studio ousia released with the paper mluke the power of entity representations in multilingual pretrained language models https arxiv org abs 2110 08151 by ryokan ri ikuya yamada and yoshimasa tsuruoka 1 mms https huggingface co docs transformers model doc mms from facebook released with the paper scaling speech technology to 1 000 languages https arxiv org abs 2305 13516 by vineel pratap andros tjandra bowen shi paden tomasello arun babu sayani kundu ali elkahky zhaoheng ni apoorv vyas maryam fazel zarandi alexei baevski yossi adi xiaohui zhang wei ning hsu alexis conneau michael auli 1 mobilebert https huggingface co docs transformers model doc mobilebert from cmu google brain released with the paper mobilebert a compact task agnostic bert for resource limited devices https arxiv org abs 2004 02984 by zhiqing sun hongkun yu xiaodan song renjie liu yiming yang and denny zhou 1 mobilenetv1 https huggingface co docs transformers model doc mobilenet v1 from google inc released with the paper mobilenets efficient convolutional neural networks for mobile vision applications https arxiv org abs 1704 04861 by andrew g howard menglong zhu bo chen dmitry kalenichenko weijun wang tobias weyand marco andreetto hartwig adam 1 mobilenetv2 https huggingface co docs transformers model doc mobilenet v2 from google inc released with the paper mobilenetv2 inverted residuals and linear bottlenecks https arxiv org abs 1801 04381 by mark sandler andrew howard menglong zhu andrey zhmoginov liang chieh chen 1 mobilevit https huggingface co docs transformers model doc mobilevit from apple released with the paper mobilevit light weight general purpose and mobile friendly vision transformer https arxiv org abs 2110 02178 by sachin mehta and mohammad rastegari 1 mobilevitv2 https huggingface co docs transformers model doc mobilevitv2 from apple released with the paper separable self attention for mobile vision transformers https arxiv org abs 2206 02680 by sachin mehta and mohammad rastegari 1 mpnet https huggingface co docs transformers model doc mpnet from microsoft research released with the paper mpnet masked and permuted pre training for language understanding https arxiv org abs 2004 09297 by kaitao song xu tan tao qin jianfeng lu tie yan liu 1 mpt https huggingface co docs transformers model doc mpt from mosaiml released with the repository llm foundry https github com mosaicml llm foundry by the mosaicml nlp team 1 mra https huggingface co docs transformers model doc mra from the university of wisconsin madison released with the paper multi resolution analysis mra for approximate self attention https arxiv org abs 2207 10284 by zhanpeng zeng sourav pal jeffery kline glenn m fung vikas singh 1 mt5 https huggingface co docs transformers model doc mt5 from google ai released with the paper mt5 a massively multilingual pre trained text to text transformer https arxiv org abs 2010 11934 by linting xue noah constant adam roberts mihir kale rami al rfou aditya siddhant aditya barua colin raffel 1 musicgen https huggingface co docs transformers model doc musicgen from meta released with the paper simple and controllable music generation https arxiv org abs 2306 05284 by jade copet felix kreuk itai gat tal remez david kant gabriel synnaeve yossi adi and alexandre d fossez 1 mvp https huggingface co docs transformers model doc mvp from ruc ai box released with the paper mvp multi task supervised pre training for natural language generation https arxiv org abs 2206 12131 by tianyi tang junyi li wayne xin zhao and ji rong wen 1 nat https huggingface co docs transformers model doc nat from shi labs released with the paper neighborhood attention transformer https arxiv org abs 2204 07143 by ali hassani steven walton jiachen li shen li and humphrey shi 1 nezha https huggingface co docs transformers model doc nezha from huawei noah s ark lab released with the paper nezha neural contextualized representation for chinese language understanding https arxiv org abs 1909 00204 by junqiu wei xiaozhe ren xiaoguang li wenyong huang yi liao yasheng wang jiashu lin xin jiang xiao chen and qun liu 1 nllb https huggingface co docs transformers model doc nllb from meta released with the paper no language left behind scaling human centered machine translation https arxiv org abs 2207 04672 by the nllb team 1 nllb moe https huggingface co docs transformers model doc nllb moe from meta released with the paper no language left behind scaling human centered machine translation https arxiv org abs 2207 04672 by the nllb team 1 nougat https huggingface co docs transformers model doc nougat from meta ai released with the paper nougat neural optical understanding for academic documents https arxiv org abs 2308 13418 by lukas blecher guillem cucurull thomas scialom robert stojnic 1 nystr mformer https huggingface co docs transformers model doc nystromformer from the university of wisconsin madison released with the paper nystr mformer a nystr m based algorithm for approximating self attention https arxiv org abs 2102 03902 by yunyang xiong zhanpeng zeng rudrasis chakraborty mingxing tan glenn fung yin li vikas singh 1 oneformer https huggingface co docs transformers model doc oneformer from shi labs released with the paper oneformer one transformer to rule universal image segmentation https arxiv org abs 2211 06220 by jitesh jain jiachen li mangtik chiu ali hassani nikita orlov humphrey shi 1 openllama https huggingface co docs transformers model doc open llama from s jol https huggingface co s jol released in open llama https github com s jol open llama 1 opt https huggingface co docs transformers master model doc opt from meta ai released with the paper opt open pre trained transformer language models https arxiv org abs 2205 01068 by susan zhang stephen roller naman goyal mikel artetxe moya chen shuohui chen et al 1 owl vit https huggingface co docs transformers model doc owlvit from google ai released with the paper simple open vocabulary object detection with vision transformers https arxiv org abs 2205 06230 by matthias minderer alexey gritsenko austin stone maxim neumann dirk weissenborn alexey dosovitskiy aravindh mahendran anurag arnab mostafa dehghani zhuoran shen xiao wang xiaohua zhai thomas kipf and neil houlsby 1 owlv2 https huggingface co docs transformers main model doc owlv2 from google ai released with the paper scaling open vocabulary object detection https arxiv org abs 2306 09683 by matthias minderer alexey gritsenko neil houlsby 1 pegasus https huggingface co docs transformers model doc pegasus from google released with the paper pegasus pre training with extracted gap sentences for abstractive summarization https arxiv org abs 1912 08777 by jingqing zhang yao zhao mohammad saleh and peter j liu 1 pegasus x https huggingface co docs transformers model doc pegasus x from google released with the paper investigating efficiently extending transformers for long input summarization https arxiv org abs 2208 04347 by jason phang yao zhao and peter j liu 1 perceiver io https huggingface co docs transformers model doc perceiver from deepmind released with the paper perceiver io a general architecture for structured inputs outputs https arxiv org abs 2107 14795 by andrew jaegle sebastian borgeaud jean baptiste alayrac carl doersch catalin ionescu david ding skanda koppula daniel zoran andrew brock evan shelhamer olivier h naff matthew m botvinick andrew zisserman oriol vinyals jo o carreira 1 persimmon https huggingface co docs transformers model doc persimmon from adept released in a blog post https www adept ai blog persimmon 8b by erich elsen augustus odena maxwell nye sa nak ta rlar tri dao curtis hawthorne deepak moparthi arushi somani 1 phobert https huggingface co docs transformers model doc phobert from vinai research released with the paper phobert pre trained language models for vietnamese https www aclweb org anthology 2020 findings emnlp 92 by dat quoc nguyen and anh tuan nguyen 1 pix2struct https huggingface co docs transformers model doc pix2struct from google released with the paper pix2struct screenshot parsing as pretraining for visual language understanding https arxiv org abs 2210 03347 by kenton lee mandar joshi iulia turc hexiang hu fangyu liu julian eisenschlos urvashi khandelwal peter shaw ming wei chang kristina toutanova 1 plbart https huggingface co docs transformers model doc plbart from ucla nlp released with the paper unified pre training for program understanding and generation https arxiv org abs 2103 06333 by wasi uddin ahmad saikat chakraborty baishakhi ray kai wei chang 1 poolformer https huggingface co docs transformers model doc poolformer from sea ai labs released with the paper metaformer is actually what you need for vision https arxiv org abs 2111 11418 by yu weihao and luo mi and zhou pan and si chenyang and zhou yichen and wang xinchao and feng jiashi and yan shuicheng 1 pop2piano https huggingface co docs transformers model doc pop2piano released with the paper pop2piano pop audio based piano cover generation https arxiv org abs 2211 00895 by jongho choi and kyogu lee 1 prophetnet https huggingface co docs transformers model doc prophetnet from microsoft research released with the paper prophetnet predicting future n gram for sequence to sequence pre training https arxiv org abs 2001 04063 by yu yan weizhen qi yeyun gong dayiheng liu nan duan jiusheng chen ruofei zhang and ming zhou 1 pvt https huggingface co docs transformers model doc pvt from nanjing university the university of hong kong etc released with the paper pyramid vision transformer a versatile backbone for dense prediction without convolutions https arxiv org pdf 2102 12122 pdf by wenhai wang enze xie xiang li deng ping fan kaitao song ding liang tong lu ping luo ling shao 1 qdqbert https huggingface co docs transformers model doc qdqbert from nvidia released with the paper integer quantization for deep learning inference principles and empirical evaluation https arxiv org abs 2004 09602 by hao wu patrick judd xiaojie zhang mikhail isaev and paulius micikevicius 1 rag https huggingface co docs transformers model doc rag from facebook released with the paper retrieval augmented generation for knowledge intensive nlp tasks https arxiv org abs 2005 11401 by patrick lewis ethan perez aleksandara piktus fabio petroni vladimir karpukhin naman goyal heinrich k ttler mike lewis wen tau yih tim rockt schel sebastian riedel douwe kiela 1 realm https huggingface co docs transformers model doc realm html from google research released with the paper realm retrieval augmented language model pre training https arxiv org abs 2002 08909 by kelvin guu kenton lee zora tung panupong pasupat and ming wei chang 1 reformer https huggingface co docs transformers model doc reformer from google research released with the paper reformer the efficient transformer https arxiv org abs 2001 04451 by nikita kitaev ukasz kaiser anselm levskaya 1 regnet https huggingface co docs transformers model doc regnet from meta platforms released with the paper designing network design space https arxiv org abs 2003 13678 by ilija radosavovic raj prateek kosaraju ross girshick kaiming he piotr doll r 1 rembert https huggingface co docs transformers model doc rembert from google research released with the paper rethinking embedding coupling in pre trained language models https arxiv org abs 2010 12821 by hyung won chung thibault f vry henry tsai m johnson sebastian ruder 1 resnet https huggingface co docs transformers model doc resnet from microsoft research released with the paper deep residual learning for image recognition https arxiv org abs 1512 03385 by kaiming he xiangyu zhang shaoqing ren jian sun 1 roberta https huggingface co docs transformers model doc roberta from facebook released together with the paper roberta a robustly optimized bert pretraining approach https arxiv org abs 1907 11692 by yinhan liu myle ott naman goyal jingfei du mandar joshi danqi chen omer levy mike lewis luke zettlemoyer veselin stoyanov 1 roberta prelayernorm https huggingface co docs transformers model doc roberta prelayernorm from facebook released with the paper fairseq a fast extensible toolkit for sequence modeling https arxiv org abs 1904 01038 by myle ott sergey edunov alexei baevski angela fan sam gross nathan ng david grangier michael auli 1 rocbert https huggingface co docs transformers model doc roc bert from wechatai released with the paper rocbert robust chinese bert with multimodal contrastive pretraining https aclanthology org 2022 acl long 65 pdf by huisu weiweishi xiaoyushen xiaozhou tuoji jiaruifang jiezhou 1 roformer https huggingface co docs transformers model doc roformer from zhuiyitechnology released together with the paper roformer enhanced transformer with rotary position embedding https arxiv org abs 2104 09864 by jianlin su and yu lu and shengfeng pan and bo wen and yunfeng liu 1 rwkv https huggingface co docs transformers model doc rwkv from bo peng released on this repo https github com blinkdl rwkv lm by bo peng 1 seamlessm4t https huggingface co docs transformers main model doc seamless m4t from meta ai released with the paper seamlessm4t massively multilingual multimodal machine translation https dl fbaipublicfiles com seamless seamless m4t paper pdf by the seamless communication team 1 segformer https huggingface co docs transformers model doc segformer from nvidia released with the paper segformer simple and efficient design for semantic segmentation with transformers https arxiv org abs 2105 15203 by enze xie wenhai wang zhiding yu anima anandkumar jose m alvarez ping luo 1 segment anything https huggingface co docs transformers model doc sam from meta ai released with the paper segment anything https arxiv org pdf 2304 02643v1 pdf by alexander kirillov eric mintun nikhila ravi hanzi mao chloe rolland laura gustafson tete xiao spencer whitehead alex berg wan yen lo piotr dollar ross girshick 1 sew https huggingface co docs transformers model doc sew from asapp released with the paper performance efficiency trade offs in unsupervised pre training for speech recognition https arxiv org abs 2109 06870 by felix wu kwangyoun kim jing pan kyu han kilian q weinberger yoav artzi 1 sew d https huggingface co docs transformers model doc sew d from asapp released with the paper performance efficiency trade offs in unsupervised pre training for speech recognition https arxiv org abs 2109 06870 by felix wu kwangyoun kim jing pan kyu han kilian q weinberger yoav artzi 1 speecht5 https huggingface co docs transformers model doc speecht5 from microsoft research released with the paper speecht5 unified modal encoder decoder pre training for spoken language processing https arxiv org abs 2110 07205 by junyi ao rui wang long zhou chengyi wang shuo ren yu wu shujie liu tom ko qing li yu zhang zhihua wei yao qian jinyu li furu wei 1 speechtotexttransformer https huggingface co docs transformers model doc speech to text from facebook released together with the paper fairseq s2t fast speech to text modeling with fairseq https arxiv org abs 2010 05171 by changhan wang yun tang xutai ma anne wu dmytro okhonko juan pino 1 speechtotexttransformer2 https huggingface co docs transformers model doc speech to text 2 from facebook released together with the paper large scale self and semi supervised learning for speech translation https arxiv org abs 2104 06678 by changhan wang anne wu juan pino alexei baevski michael auli alexis conneau 1 splinter https huggingface co docs transformers model doc splinter from tel aviv university released together with the paper few shot question answering by pretraining span selection https arxiv org abs 2101 00438 by ori ram yuval kirstain jonathan berant amir globerson omer levy 1 squeezebert https huggingface co docs transformers model doc squeezebert from berkeley released with the paper squeezebert what can computer vision teach nlp about efficient neural networks https arxiv org abs 2006 11316 by forrest n iandola albert e shaw ravi krishna and kurt w keutzer 1 swiftformer https huggingface co docs transformers model doc swiftformer from mbzuai released with the paper swiftformer efficient additive attention for transformer based real time mobile vision applications https arxiv org abs 2303 15446 by abdelrahman shaker muhammad maaz hanoona rasheed salman khan ming hsuan yang fahad shahbaz khan 1 swin transformer https huggingface co docs transformers model doc swin from microsoft released with the paper swin transformer hierarchical vision transformer using shifted windows https arxiv org abs 2103 14030 by ze liu yutong lin yue cao han hu yixuan wei zheng zhang stephen lin baining guo 1 swin transformer v2 https huggingface co docs transformers model doc swinv2 from microsoft released with the paper swin transformer v2 scaling up capacity and resolution https arxiv org abs 2111 09883 by ze liu han hu yutong lin zhuliang yao zhenda xie yixuan wei jia ning yue cao zheng zhang li dong furu wei baining guo 1 swin2sr https huggingface co docs transformers model doc swin2sr from university of w rzburg released with the paper swin2sr swinv2 transformer for compressed image super resolution and restoration https arxiv org abs 2209 11345 by marcos v conde ui jin choi maxime burchi radu timofte 1 switchtransformers https huggingface co docs transformers model doc switch transformers from google released with the paper switch transformers scaling to trillion parameter models with simple and efficient sparsity https arxiv org abs 2101 03961 by william fedus barret zoph noam shazeer 1 t5 https huggingface co docs transformers model doc t5 from google ai released with the paper exploring the limits of transfer learning with a unified text to text transformer https arxiv org abs 1910 10683 by colin raffel and noam shazeer and adam roberts and katherine lee and sharan narang and michael matena and yanqi zhou and wei li and peter j liu 1 t5v1 1 https huggingface co docs transformers model doc t5v1 1 from google ai released in the repository google research text to text transfer transformer https github com google research text to text transfer transformer blob main released checkpoints md t511 by colin raffel and noam shazeer and adam roberts and katherine lee and sharan narang and michael matena and yanqi zhou and wei li and peter j liu 1 table transformer https huggingface co docs transformers model doc table transformer from microsoft research released with the paper pubtables 1m towards comprehensive table extraction from unstructured documents https arxiv org abs 2110 00061 by brandon smock rohith pesala robin abraham 1 tapas https huggingface co docs transformers model doc tapas from google ai released with the paper tapas weakly supervised table parsing via pre training https arxiv org abs 2004 02349 by jonathan herzig pawe krzysztof nowak thomas m ller francesco piccinno and julian martin eisenschlos 1 tapex https huggingface co docs transformers model doc tapex from microsoft research released with the paper tapex table pre training via learning a neural sql executor https arxiv org abs 2107 07653 by qian liu bei chen jiaqi guo morteza ziyadi zeqi lin weizhu chen jian guang lou 1 time series transformer https huggingface co docs transformers model doc time series transformer from huggingface 1 timesformer https huggingface co docs transformers model doc timesformer from facebook released with the paper is space time attention all you need for video understanding https arxiv org abs 2102 05095 by gedas bertasius heng wang lorenzo torresani 1 trajectory transformer https huggingface co docs transformers model doc trajectory transformers from the university of california at berkeley released with the paper offline reinforcement learning as one big sequence modeling problem https arxiv org abs 2106 02039 by michael janner qiyang li sergey levine 1 transformer xl https huggingface co docs transformers model doc transfo xl from google cmu released with the paper transformer xl attentive language models beyond a fixed length context https arxiv org abs 1901 02860 by zihang dai zhilin yang yiming yang jaime carbonell quoc v le ruslan salakhutdinov 1 trocr https huggingface co docs transformers model doc trocr from microsoft released together with the paper trocr transformer based optical character recognition with pre trained models https arxiv org abs 2109 10282 by minghao li tengchao lv lei cui yijuan lu dinei florencio cha zhang zhoujun li furu wei 1 tvlt https huggingface co docs transformers model doc tvlt from unc chapel hill released with the paper tvlt textless vision language transformer https arxiv org abs 2209 14156 by zineng tang jaemin cho yixin nie mohit bansal 1 ul2 https huggingface co docs transformers model doc ul2 from google research released with the paper unifying language learning paradigms https arxiv org abs 2205 05131v1 by yi tay mostafa dehghani vinh q tran xavier garcia dara bahri tal schuster huaixiu steven zheng neil houlsby donald metzler 1 umt5 https huggingface co docs transformers model doc umt5 from google research released with the paper unimax fairer and more effective language sampling for large scale multilingual pretraining https openreview net forum id kxwdl1cwoai by hyung won chung xavier garcia adam roberts yi tay orhan firat sharan narang noah constant 1 unispeech https huggingface co docs transformers model doc unispeech from microsoft research released with the paper unispeech unified speech representation learning with labeled and unlabeled data https arxiv org abs 2101 07597 by chengyi wang yu wu yao qian kenichi kumatani shujie liu furu wei michael zeng xuedong huang 1 unispeechsat https huggingface co docs transformers model doc unispeech sat from microsoft research released with the paper unispeech sat universal speech representation learning with speaker aware pre training https arxiv org abs 2110 05752 by sanyuan chen yu wu chengyi wang zhengyang chen zhuo chen shujie liu jian wu yao qian furu wei jinyu li xiangzhan yu 1 upernet https huggingface co docs transformers model doc upernet from peking university released with the paper unified perceptual parsing for scene understanding https arxiv org abs 1807 10221 by tete xiao yingcheng liu bolei zhou yuning jiang jian sun 1 van https huggingface co docs transformers model doc van from tsinghua university and nankai university released with the paper visual attention network https arxiv org abs 2202 09741 by meng hao guo cheng ze lu zheng ning liu ming ming cheng shi min hu 1 videomae https huggingface co docs transformers model doc videomae from multimedia computing group nanjing university released with the paper videomae masked autoencoders are data efficient learners for self supervised video pre training https arxiv org abs 2203 12602 by zhan tong yibing song jue wang limin wang 1 vilt https huggingface co docs transformers model doc vilt from naver ai lab kakao enterprise kakao brain released with the paper vilt vision and language transformer without convolution or region supervision https arxiv org abs 2102 03334 by wonjae kim bokyung son ildoo kim 1 vision transformer vit https huggingface co docs transformers model doc vit from google ai released with the paper an image is worth 16x16 words transformers for image recognition at scale https arxiv org abs 2010 11929 by alexey dosovitskiy lucas beyer alexander kolesnikov dirk weissenborn xiaohua zhai thomas unterthiner mostafa dehghani matthias minderer georg heigold sylvain gelly jakob uszkoreit neil houlsby 1 visualbert https huggingface co docs transformers model doc visual bert from ucla nlp released with the paper visualbert a simple and performant baseline for vision and language https arxiv org pdf 1908 03557 by liunian harold li mark yatskar da yin cho jui hsieh kai wei chang 1 vit hybrid https huggingface co docs transformers model doc vit hybrid from google ai released with the paper an image is worth 16x16 words transformers for image recognition at scale https arxiv org abs 2010 11929 by alexey dosovitskiy lucas beyer alexander kolesnikov dirk weissenborn xiaohua zhai thomas unterthiner mostafa dehghani matthias minderer georg heigold sylvain gelly jakob uszkoreit neil houlsby 1 vitdet https huggingface co docs transformers model doc vitdet from meta ai released with the paper exploring plain vision transformer backbones for object detection https arxiv org abs 2203 16527 by yanghao li hanzi mao ross girshick kaiming he 1 vitmae https huggingface co docs transformers model doc vit mae from meta ai released with the paper masked autoencoders are scalable vision learners https arxiv org abs 2111 06377 by kaiming he xinlei chen saining xie yanghao li piotr doll r ross girshick 1 vitmatte https huggingface co docs transformers model doc vitmatte from hust vl released with the paper vitmatte boosting image matting with pretrained plain vision transformers https arxiv org abs 2305 15272 by jingfeng yao xinggang wang shusheng yang baoyuan wang 1 vitmsn https huggingface co docs transformers model doc vit msn from meta ai released with the paper masked siamese networks for label efficient learning https arxiv org abs 2204 07141 by mahmoud assran mathilde caron ishan misra piotr bojanowski florian bordes pascal vincent armand joulin michael rabbat nicolas ballas 1 vits https huggingface co docs transformers model doc vits from kakao enterprise released with the paper conditional variational autoencoder with adversarial learning for end to end text to speech https arxiv org abs 2106 06103 by jaehyeon kim jungil kong juhee son 1 vivit https huggingface co docs transformers model doc vivit from google research released with the paper vivit a video vision transformer https arxiv org abs 2103 15691 by anurag arnab mostafa dehghani georg heigold chen sun mario lu i cordelia schmid 1 wav2vec2 https huggingface co docs transformers model doc wav2vec2 from facebook ai released with the paper wav2vec 2 0 a framework for self supervised learning of speech representations https arxiv org abs 2006 11477 by alexei baevski henry zhou abdelrahman mohamed michael auli 1 wav2vec2 conformer https huggingface co docs transformers model doc wav2vec2 conformer from facebook ai released with the paper fairseq s2t fast speech to text modeling with fairseq https arxiv org abs 2010 05171 by changhan wang yun tang xutai ma anne wu sravya popuri dmytro okhonko juan pino 1 wav2vec2phoneme https huggingface co docs transformers model doc wav2vec2 phoneme from facebook ai released with the paper simple and effective zero shot cross lingual phoneme recognition https arxiv org abs 2109 11680 by qiantong xu alexei baevski michael auli 1 wavlm https huggingface co docs transformers model doc wavlm from microsoft research released with the paper wavlm large scale self supervised pre training for full stack speech processing https arxiv org abs 2110 13900 by sanyuan chen chengyi wang zhengyang chen yu wu shujie liu zhuo chen jinyu li naoyuki kanda takuya yoshioka xiong xiao jian wu long zhou shuo ren yanmin qian yao qian jian wu michael zeng furu wei 1 whisper https huggingface co docs transformers model doc whisper from openai released with the paper robust speech recognition via large scale weak supervision https cdn openai com papers whisper pdf by alec radford jong wook kim tao xu greg brockman christine mcleavey ilya sutskever 1 x clip https huggingface co docs transformers model doc xclip from microsoft research released with the paper expanding language image pretrained models for general video recognition https arxiv org abs 2208 02816 by bolin ni houwen peng minghao chen songyang zhang gaofeng meng jianlong fu shiming xiang haibin ling 1 x mod https huggingface co docs transformers model doc xmod from meta ai released with the paper lifting the curse of multilinguality by pre training modular transformers http dx doi org 10 18653 v1 2022 naacl main 255 by jonas pfeiffer naman goyal xi lin xian li james cross sebastian riedel mikel artetxe 1 xglm https huggingface co docs transformers model doc xglm from facebook ai released with the paper few shot learning with multilingual language models https arxiv org abs 2112 10668 by xi victoria lin todor mihaylov mikel artetxe tianlu wang shuohui chen daniel simig myle ott naman goyal shruti bhosale jingfei du ramakanth pasunuru sam shleifer punit singh koura vishrav chaudhary brian o horo jeff wang luke zettlemoyer zornitsa kozareva mona diab veselin stoyanov xian li 1 xlm https huggingface co docs transformers model doc xlm from facebook released together with the paper cross lingual language model pretraining https arxiv org abs 1901 07291 by guillaume lample and alexis conneau 1 xlm prophetnet https huggingface co docs transformers model doc xlm prophetnet from microsoft research released with the paper prophetnet predicting future n gram for sequence to sequence pre training https arxiv org abs 2001 04063 by yu yan weizhen qi yeyun gong dayiheng liu nan duan jiusheng chen ruofei zhang and ming zhou 1 xlm roberta https huggingface co docs transformers model doc xlm roberta from facebook ai released together with the paper unsupervised cross lingual representation learning at scale https arxiv org abs 1911 02116 by alexis conneau kartikay khandelwal naman goyal vishrav chaudhary guillaume wenzek francisco guzm n edouard grave myle ott luke zettlemoyer and veselin stoyanov 1 xlm roberta xl https huggingface co docs transformers model doc xlm roberta xl from facebook ai released together with the paper larger scale transformers for multilingual masked language modeling https arxiv org abs 2105 00572 by naman goyal jingfei du myle ott giri anantharaman alexis conneau 1 xlm v https huggingface co docs transformers model doc xlm v from meta ai released with the paper xlm v overcoming the vocabulary bottleneck in multilingual masked language models https arxiv org abs 2301 10472 by davis liang hila gonen yuning mao rui hou naman goyal marjan ghazvininejad luke zettlemoyer madian khabsa 1 xlnet https huggingface co docs transformers model doc xlnet from google cmu released with the paper xlnet generalized autoregressive pretraining for language understanding https arxiv org abs 1906 08237 by zhilin yang zihang dai yiming yang jaime carbonell ruslan salakhutdinov quoc v le 1 xls r https huggingface co docs transformers model doc xls r from facebook ai released with the paper xls r self supervised cross lingual speech representation learning at scale https arxiv org abs 2111 09296 by arun babu changhan wang andros tjandra kushal lakhotia qiantong xu naman goyal kritika singh patrick von platen yatharth saraf juan pino alexei baevski alexis conneau michael auli 1 xlsr wav2vec2 https huggingface co docs transformers model doc xlsr wav2vec2 from facebook ai released with the paper unsupervised cross lingual representation learning for speech recognition https arxiv org abs 2006 13979 by alexis conneau alexei baevski ronan collobert abdelrahman mohamed michael auli 1 yolos https huggingface co docs transformers model doc yolos from huazhong university of science technology released with the paper you only look at one sequence rethinking transformer in vision through object detection https arxiv org abs 2106 00666 by yuxin fang bencheng liao xinggang wang jiemin fang jiyang qi rui wu jianwei niu wenyu liu 1 yoso https huggingface co docs transformers model doc yoso from the university of wisconsin madison released with the paper you only sample almost once linear cost self attention via bernoulli sampling https arxiv org abs 2111 09714 by zhanpeng zeng yunyang xiong sathya n ravi shailesh acharya glenn fung vikas singh 1 want to contribute a new model we have added a detailed guide and templates to guide you in the process of adding a new model you can find them in the templates templates folder of the repository be sure to check the contributing guidelines contributing md and contact the maintainers or open an issue to collect feedbacks before starting your pr to check if each model has an implementation in flax pytorch or tensorflow or has an associated tokenizer backed by the tokenizers library refer to this table https huggingface co docs transformers index supported frameworks these implementations have been tested on several datasets see the example scripts and should match the performance of the original implementations you can find more details on performance in the examples section of the documentation https github com huggingface transformers tree main examples learn more section description documentation https huggingface co docs transformers full api documentation and tutorials task summary https huggingface co docs transformers task summary tasks supported by transformers preprocessing tutorial https huggingface co docs transformers preprocessing using the tokenizer class to prepare data for the models training and fine tuning https huggingface co docs transformers training using the models provided by transformers in a pytorch tensorflow training loop and the trainer api quick tour fine tuning usage scripts https github com huggingface transformers tree main examples example scripts for fine tuning models on a wide range of tasks model sharing and uploading https huggingface co docs transformers model sharing upload and share your fine tuned models with the community citation we now have a paper https www aclweb org anthology 2020 emnlp demos 6 you can cite for the transformers library bibtex inproceedings wolf etal 2020 transformers title transformers state of the art natural language processing author thomas wolf and lysandre debut and victor sanh and julien chaumond and clement delangue and anthony moi and pierric cistac and tim rault and r mi louf and morgan funtowicz and joe davison and sam shleifer and patrick von platen and clara ma and yacine jernite and julien plu and canwen xu and teven le scao and sylvain gugger and mariama drame and quentin lhoest and alexander m rush booktitle proceedings of the 2020 conference on empirical methods in natural language processing system demonstrations month oct year 2020 address online publisher association for computational linguistics url https www aclweb org anthology 2020 emnlp demos 6 pages 38 45	nlp natural-language-processing pytorch language-model tensorflow bert language-models pytorch-transformers nlp-library transformer model-hub pretrained-models jax flax seq2seq speech-recognition hacktoberfest python machine-learning deep-learning	ai
dandelion	dandelion core dandelion http dandelion github io is the core of all modules that helps you to manage your assets in your application see the documentation here http dandelion github io components core http dandelion github io components core build status https dandelion ci cloudbees com job dandelion core build badge icon https dandelion ci cloudbees com job dandelion core build the dandelion team http dandelion github io team		front_end
modori	modori modori is a natural language processing web api using python flask and konlpy dependencies the whole project is written in python it is tested on python 2 7 and python 3 4 both worked fine you must install konlpy please refer to http konlpy org http konlpy org regarding the installation and api also you need flask micro web framework pip install flask quickstart run the following commands to bootstrap your environment git clone https github com kwk236 modori cd modori pip install r requirements dev txt python server py you will see a pretty welcome screen		ai
QBitNinja	warning this project is not maintained anymore while https api qbit ninja and https tapi qbit ninja you can t expect those to work reliably if all you want is to broadcast a transaction use https blockstream info tx push if you want to build on bitcoin please use https github com dgarage nbxplorer instead this is lightweight works on linux and do not require indexing the whole blockchain qbit ninja an open source and powerful blockchain api showcase you can see the api documentation on api qbit ninja http api qbit ninja and on apiary http docs qbitninja apiary io you can try the api in net with the nuget package http www nuget org packages qbitninja client public servers mainnet api qbit ninja http api qbit ninja testnet tapi qbit ninja http tapi qbit ninja how to setup your own pre requisite download and install net framework 4 7 1 dev pack https www microsoft com net download thank you net471 developer pack download and install visual studio 2017 https visualstudio microsoft com downloads you need to enable net development and asp net web development download and install bitcoin core 0 16 2 https bitcoincore org bin bitcoin core 0 16 2 bitcoin 0 16 2 win64 setup exe and wait it is synchronized get a microsoft azure account in azure create one app resource group then you need to create storage resource azure bus resource web app resource azure vm with 1 data disk of 1 tb attachd to it i advise you d1 v2 setup the indexer the indexer is the application which will listener your full node and index everything into your azure storage you can run it through the qbitninja listener console project assuming your bitcoin node is fully synched bash git clone https github com metacosa qbitninja then edit qbitninja listener console app config your qbitninja listener console app config file should looks like appsettings add key azure accountname value azurestorageaccountname add key azure key value azurestoragekey add key rpcconnectionstring value default add key network value mainnet add key chain value btc add key node value 127 0 0 1 add key servicebus value endpoint sb example servicebus windows net sharedaccesskeyname rootmanagesharedaccesskey sharedaccesskey mysecretkey appsettings azure accountname and azure key are in the azure portal in the settings of your azure storage resource network can be mainnet testnet regtest chain can be btc or ltc or any chain node represented the p2p connection to your bitcoin node do not forget to whitelist the indexer in your node s settings typically adding whitelist 127 0 0 1 in bitcoin conf servicebus connection string to your service bus namespace in shared access policies inside azure rpcconnectionstring optional but needed for more reliably broadcast transactions example of rpcconnectionstring default assume your are running bitcoind locally with default settings cookiefile c path to cookie if you run bitcoind in a different data directory with default authentication you need to set the path to it server http 127 0 0 1 29292 if you run bitcoind rpc on a different port than default myuser password if you run bitcoind with rpcuser and rpcpassword server http 127 0 0 1 29292 myuser password if you run bitcoind rpc with rpcuser and rpcpassword on a different port than default server http 127 0 0 1 29292 cookiefile c path to cookie if you run bitcoind rpc with rpcuser and rpcpassword in a different data directory with default authentication be careful you need to compile qbitninja in preferably in release mode for the configuration to be effective because qbitninja will ultimately use the qbitninja listener console exe config file which is in the same folder as qbitninja listener console exe for its configuration do not forget to disable secure transfer required in your storage configuration https github com metacosa qbitninja issues 97 issuecomment 499482539 one you have setup everything build qbitninja listener console in release mode and run qbitninja listener console exe init you can repeat the same operation on multiple machine to index faster once it finished run qbitninja listener console exe we advise you to the windows task scheduler to run qbitninja listener console exe listen and bitcoind exe automatically even when the user is not logged on or when the virtual machine reboot setup the front the front is a web application which will query your azure storage for blocks transactions balances indexed by the indexer you can run find it in the qbitninja project the easiest is deploy via visual studio 2017 download your web app profile by going into your web app resource settings in azure and clicking on get publish profile open the solution under visual studio 2017 setup the web config exactly how you set up the app config in the previous step right click on the qbitninja project and click on publish click on new profile in the new window click on the bottom left import profiles and select your downloaded publish profile click on publish how to build via visual studio recommended download and install visual studio 2017 https visualstudio microsoft com downloads you need to enable net development and asp net web development download and install net framework 4 7 1 dev pack https www microsoft com net download thank you net471 developer pack open the solution and build by command line download and install build tools for visual studio 2017 https visualstudio microsoft com downloads build tools for visual studio 2017 then use msbuild exe powershell msbuild exe restore msbuild exe p configuration release unity in order for the api to work in unity with net 4 6 for android devices you should qbitninjaclient setcompression false because it s missing the dll monoposixhelper from the build qbitninjaclient client new qbitninjaclient http api qbit ninja nbitcoin network main because https with httpclient seems to not work correctly scripting runtime version select experimental net 4 6 equivalent license this project is released under the terms of the mit license see license license		blockchain
frontend-test	superformula front end developer coding test be sure to read all of this document carefully and follow the guidelines within context use html css and javascript to implement the following mock up you will need to leverage an open api for restaurant data to fill in the details and functionality as described below you are only required to complete the desktop views unless otherwise instructed superformula front end test mockup mockup png use this sketch file to see button states colors and responsive design source sketch file superformula fe test 264388d sketch requirements yelp api you can ask us and we will provide you a yelp api key to use for your pr page structure main filter navigation open now client side filter price client side filter categories cuisines server side search filter section restaurant item image use first item in photos cuisine categories use first item in categories rating price range open closed restaurant name learn more open modal to show more details detail view restaurant name rating map optional if time allows section review item image name rating text functionality the filter navigation needs to be able to perform real time filtering on both client side data as well as server side queries yelp s businesses search endpoint requires a location please use las vegas categories can be pre filled from the categories endpoint https www yelp com developers documentation v3 all categories the items should always show 4 6 items per row depending on viewport size use your own judgement for when to change per breakpoints please see the yelp documentation https www yelp com developers documentation v3 for more details tech stack js oriented use react do not use any react boilerplate such as create react app feel free to use a preprocessor like sass scss less but do not use any css frameworks or libraries bonus also create mobile version included in sketch comp write clear documentation on how the app was designed and how to run the code provide proper unit tests provide components in storybook https storybook js org with tests use yelp s graph ql https www yelp com developers graphql guides intro endpoint write concise and clear commit messages provide an online demo of the application include subtle animations to focus attention describe optimization opportunities when you conclude what we care about use any libraries that you would normally use if this were a real production app please note we re interested in your code the way you solve the problem not how well you can use a particular library or feature we re interested in your method and how you approach the problem just as much as we re interested in the end result here s what you should strive for good use of current html css and javascript performance best practices solid testing approach extensible code q a where should i send back the result when i m done fork this repo and send us a pull request when you think you are done there is no deadline for this task unless otherwise noted to you directly what if i have a question just create a new issue in this repo and we will respond and get back to you quickly		front_end
JuFengGuo	jufengguo answer gifutils gluttonoussnake jsbasis js livetelecast python python thesportscourse dom youdontknowjs js time 20190930 guojufeng xing org1	guojufeng vuejs typescript reactjs nodejs expressjs mangodb javascript html5 css3	front_end
riscv-fw-infrastructure	this repository moved to new house at chips alliance https github com chipsalliance the new riscv fw infrastructure is at https github com chipsalliance riscv fw infrastructure the old repo master branch is still located in this repo n solid https repository images githubusercontent com 223527603 c9c00100 0d80 11ea 9d4c c7d223c8fc95		os
robot-collab	roco dialectic multi robot collaboration with large language models codebase for paper roco dialectic multi robot collaboration with large language models mandi zhao https mandizhao github io shreeya jain https www linkedin com shuran song https www cs columbia edu shurans arxiv https arxiv org abs 2307 04738 project website https project roco github io img src method jpeg alt method width 800 setup setup conda env and package install conda create n roco python 3 8 conda activate roco install mujoco and dm control pip install mujoco 2 3 0 pip install dm control 1 0 8 if you have m1 macbook like me and would like to visualize the task scenes locally download the macos compatible dmg file from mujoco release page https github com deepmind mujoco releases inside it should have a mujoco app file that you can drag into your application folder so it becomes just like other apps in your mac you could then open up the app and drag xml files in it find more informationa in the official documentation https mujoco readthedocs io en latest programming getting started install other packages pip install r requirements txt acquire openai claude api keys this is required for prompting gpts or claude llms you don t necessarily need both of them put your key string somewhere safely in your local repo and provide a file path something like roco openai key json and load them in the scripts example code snippet import openai openai api key your openai key import anthropic client anthropic client api key your claude key streamed client completion stream usage run multi robot dialog on the packgrocery task using the latest gpt 4 model conda activate roco roco python run dialog py task pack llm gpt 4 contact please direct to mandi zhao https mandizhao github io if you are interested in contributing or collaborating please feel free to reach out i m more than happy to chat and brainstorm together cite misc mandi2023roco title roco dialectic multi robot collaboration with large language models author zhao mandi and shreeya jain and shuran song year 2023 eprint 2307 04738 archiveprefix arxiv primaryclass cs ro		ai
cerebri	cerebri build https github com cognipilot cerebri actions workflows build yml badge svg https github com cognipilot cerebri actions workflows build yml see documentation https cognipilot org		os
python-web-dev-21-2	read me this repository contains the code for the second class in andrew pinkham s python web development series titled building backend web applications and apis with django the series is published by pearson and may be bought on informit or viewed on safari books online the series is for intermediate programmers new to web development or django andrew pinkham https andrewsforge com python web development https pywebdev com informit https pywebdev com buy 21 2 safari books online https pywebdev com safari 21 2 andrew may be reached at jambon software for consulting and training jambon software https www jambonsw com table of contents changes made post recording changes made post recording technical requirements technical requirements getting started instructions getting started instructions docker setup docker setup local setup local setup walking the repository walking the repository extra problems extra problems testing the code testing the code deploying the code deploying the code changes made post recording 1 for security purposes a commit has been added at the end of every branch which secures the application using basic login functionality the content of that commit will be discussed in the third python web development class i hope you re looking forward to it 2 test dependencies have been included in all branches so that the docker image may be built once and used on all branches 3 the docker image has been updated to use python 3 7 1 from 3 7 0 4 pre commit and black have been updated to more recent versions 5 the site uses django s staticfilesstorage instead of the manifeststaticfilesstorage shown in lesson 7 by error up to table of contents table of contents technical requirements python 3 6 with sqlite3 support pip 10 a virtual environment e g venv virtualenvwrapper optional docker 17 12 with docker compose or if unavailable postgresql 10 python https www python org downloads pip https pip pypa io en stable installing venv https docs python org 3 library venv html virtualenvwrapper https virtualenvwrapper readthedocs io en latest install html docker https www docker com get started docker compose https docs docker com compose postgresql https www postgresql org all other technical requirements are installed by pip using the requirement files included in the repository this includes django 2 1 django 2 1 https docs djangoproject com en 2 1 up to table of contents table of contents getting started instructions for a full guide to using this code please refer to lesson 2 of the class this lesson demonstrates how to get started locally as well as how to use the docker setup if you are unable to run docker on your machine skip to the local setup local setup section docker setup the use of docker images allows us to avoid installing all of our dependencies including postgesql locally furthermore as discussed in the videos it helps with parity between our development and production environments our docker containers expect the existence of an environment file to generate it on nix systems please invoke the build docker env sh script shell build docker env sh on windows please invoke the batch file build docker env if you run into problems please refer to the videos for why we use this and what is needed in the event these scripts do not work to run the docker containers use the command below shell docker compose up if you wish to run the servers in the background use the d d etached flag as demonstrated below shell docker compose up d to turn off the server use control c in the terminal window if running in the background use the command below shell docker compose down to remove all of the assets created by docker to run the server use the command below shell docker compose down volumes rmi local the volumes flag may be shortened to v up to table of contents table of contents local setup use pip to install your development dependencies console python3 m pip install r requirements development txt if you have checked out to an earlier part of the code note that you will need to use requirements txt instead of requirements development txt you will need to define the secret key environment variable if you would like to use postgresql locally you will need to set database url shell export secret key head c 75 dev urandom base64 tr dc a za z0 9 head c 50 replace the variables in below export database url postgres user password server 5432 db name please be advised that if you are running code in lesson 2 you should expect to see errors lesson 2 changes the database structure but avoids making migrations until the very last moment what s more database settings change in lesson 2 8 errors are therefore normal up to table of contents table of contents walking the repository to make perusing the code in this repository as simple as possible the project defines its own gitconfig file with custom commands aliases to enable the commands you must first point your local git configuration at the file provided either of the two commands below should work shell relative path git config local include path gitconfig absolute path nix only git config local include path builtin pwd gitconfig this will enable the following git commands git next move to the next example commit git prev move to the previous example commit git ci shortcut for git commit git co shortcut for git checkout git st shortcut for git status git ll shortcut for git log oneline these commands can be used on any of the two branches in this repository which are listed below class material contains the code and material seen in the videos as well as solutions to exercises mentioned in lessons 5 and 6 see section below to review with tests includes material above as well as the tests used by me to verify the code works up to table of contents table of contents extra problems at the end of lessons 5 and 6 i leave you with several optional exercises in lesson 5 create new newslink objects in api via post method consider viewsets vs apiview subclasses simplify postserializer with hyperlinkedrelatedfield as seen on newslinkserializer use viewsets and routers to simplify post handling in api viewsets https www django rest framework org api guide viewsets apiview http www cdrf co 3 7 rest framework views apiview html hyperlinkedrelatedfield https www django rest framework org api guide relations hyperlinkedrelatedfield in lesson 6 use createview updateview and deleteview to create views for startup and post objects using startupform and postform create a view to create new newslink objects and associate them automatically with startup objects expand this view to handle updating newslink objects allow for newslink objects to be deleted createview http ccbv co uk projects django 2 0 django views generic edit createview updateview http ccbv co uk projects django 2 0 django views generic edit updateview deleteview http ccbv co uk projects django 2 0 django views generic edit deleteview below are a few other tasks to test your knowledge create links in the templates to enable easier navigation across the site create a welcome page for the api see the use of defaultrouter defaultrouter https www django rest framework org api guide routers defaultrouter the solutions to all of the tasks above can be found in the class material git branch or on github on github https github com jambonrose python web dev 21 2 up to table of contents table of contents testing the code all of the tests used to build the code can be found in the with tests branch on github with tests branch https github com jambonrose python web dev 21 2 tree with tests the branch mostly emulates a test driven development approach commits prefixed with test write a test that will fail while commits with lesson numbers then fix the failing tests from previous test commits to run the tests locally use manage py shell from root of project cd src python3 manage py test tests may also be run in docker shell from root of project docker compose run rm django python manage py test be advised that running tests in lesson 2 is tricky you will generally need to create migrations before being able to run the tests and there are a few commits that break the project s ability to run tests such as when changing the database settings we will cover material about how to test django in the next python web development class i hope you re looking forward to it up to table of contents table of contents deploying the code the project follows 12 factor app principles and is configured to be deployed to heroku to start you will need to sign up for heroku unless you already have an account make sure you have installed the heroku cli 12 factor app https 12factor net heroku https www heroku com sign up for heroku https signup heroku com heroku cli https devcenter heroku com articles heroku cli download and install the following instructions are for nix systems and will need to be adapted for windows ensure your app is ready for deployment shell docker compose run rm django python manage py check deploy settings config settings production from the command line create a new app shell heroku create heroku will give your app a random name such as infinite tundra 77435 assign this name to a variable to be able to use commands below shell export app infinite tundra 77435 replace with your actual app name your git repository should now have a new remote branch named heroku if it is missing you can add it manually shell heroku git remote a app create a postgresql database for your app shell heroku addons create a app heroku postgresql hobby dev configure your app to use production settings shell heroku config set a app django settings module config settings production heroku config set a app secret key head c 75 dev urandom base64 tr dc a za z0 9 head c 50 heroku config set a app pythonhashseed random heroku config set a app web concurrency 2 you may now deploy your app shell git push heroku class material master you can also deploy the other branch to heroku using git push heroku with tests master to create a new user use the command below shell heroku run a app python src manage py createsuperuser to access the remote shell to create data on the fly use the command below shell heroku run a app python src manage py shell plus to see the app online you may use the command below shell heroku open a app for more about what we ve just done please see heroku s getting started with python documentation if lesson 7 does not cover as much material as you d like you may be interested in chapter 29 of django unleashed getting started with python https devcenter heroku com articles getting started with python introduction django unleashed https django unleashed com up to table of contents table of contents	django django-application django-example django-extensions django-rest-framework django-sample python python3 whitenoise heroku herokuapp	front_end
lodestar-frontend	build container https github com rht labs lodestar frontend workflows build 20container badge svg coverage https sonarcloud io api project badges measure project rht lodestar frontend metric coverage https sonarcloud io dashboard id rht lodestar frontend lodestar frontend quickstart first things first the following technologies are used in this application 1 react https reactjs org docs getting started html 1 react hooks https reactjs org docs hooks intro html 1 provider consumer and react contexts https reactjs org docs context html 1 patternfly https www patternfly org v4 documentation react overview release notes 1 typescript https www typescriptlang org docs 1 jest https jestjs io docs en tutorial react 1 react testing library https testing library com docs react testing library intro 1 cypress http cypress io 1 react router https reacttraining com react router style gts is used as the styleguide for the application code that does not conform to the styleguide will fail pr tests before submitting a pr confirm that your code passes the style check by running npm run check additionally you can attempt to automatically fix any style errors in your code by running npm run fix pro tip commit any changes you have before running any script that may modify your source files general architecture this application follows a typical three tier application design these three terms can be generally divided into services which manage communication with external data sources e g rest apis contexts which contain business logic and presentation which contains the user interface and associated presentation state contexts are the mediator between the presentation and the services providing data data manipulation should be handled to the extent reasonable in the context layer services should only return data with known interfaces that is it is the services responsibility to serialize data from an external source s format into an interface that is recognized within the application by extension contexts should not work with unknown data types nor should they be responsible for preparing data to be sent to an external service once data has reached a context its shape should be known in the application the context and presentation layer uses this known data to perform their respective roles in order to facilitate serialization of common interfaces across services a shared serializer interface has been created which can be extended for new data types as they become available services can define serializers for their service within their own service module or within the serializers directory as a high level overview data flow through the system typically follows this pattern front end data flow documentation fe architecture png raw true the redux documentation has an excellent list of questions to ask https redux js org faq organizing state when determining if something should be considered global or component state tests unit tests are contained within the source code rather than in a separate root test directory typically tests will be included in a directory tests at the level of the code they are testing as contexts are responsible for most data manipulation operations that occur in the application special care should be taken to ensure high test coverage of the contexts integration tests are contained in their own root level directory e2e organization the application is separated into several main components all of these components are located in folders within src src components src components contains shared components that can be reused across the application these include custom components built for lodestar specifically or wrapped components from a third party library in this application components should be stateless as much as possible they should not contain business logic src context src context contains the application contexts contexts hold all of the global state and business logic for lodestar for the react docs on contexts see here https reactjs org docs context html contexts serve as the central nervous system for the application they handle the dirty business of interacting with services managing global state and handling business logic all business logic should flow through a context feature context the feature context and its associated feature component provide a convenient api for protecting features that require certain roles the component is written to mirror major parts of the api of the parallel drive react feature flags implementation https github com paralleldrive react feature toggles any changes to the external api of either of the feature components should be done after studying the api exposed by the parallel drive library currently features are derived from the session data on session context service provider context the service provider context acts as a dependency injector for asynchronous services into the application by default the service provider provides the production services you can switch to faked services by passing an environment variable react app use faked true src routes src routes holds container level ui components effectively anything that would be considered a separate screen will likely have a place in the routes folder essentially the components in src routes equate to templates in other design patterns routes orchestrate smaller components into a more meaningful whole in some cases routes may be responsible for making some calls to contexts such as asking the context to fetch data that the template needs to render properly importantly though the route should delegate data processing and service calls to the context if you are attempting to make a service call from a route take a step back and consider how to move that call into the context src schemas src schemas contains the data models that are shared across the application a schema is typically a class with a public interface that encapsulates data the schema may contain some convenience methods for data routine small tasks such as combining firstname and lastname into fullname schemas should not contain significant business logic schemas should be ignorant of the services that return them src services src services encapsulate the dirty details of reaching out to an external service and returning results services ought not contain significant business logic rather they should be concerned with implementing calls to api s each service is contained in a folder at the root of the folder is an eponymous file this file contains the public interface of the service any implementer of that interface lives in a folder called implementations this structure allows the contexts to quickly switch between different implementations of the same service development logging this repository reimplements several common methods from the console interface any output that needs to be logged must be logged through this interface the logger can be imported from src utilities logger index ts bad typescript async function myasynchronousfunction try await myasynchronoustask catch e console error e handle myerror good typescript import logger from src utilities logger async function myasynchronousfunction try await myasynchronoustask catch e logger instance error e handle myerror use good judgment when creating console logs as a rule any error or unexpected behavior that is caught in the application should be logged with logger instance error do not silence errors debug and info are additional log levels to choose from debug is the noisiest level with info being less noisy logging config variables the logger can be set with values in config json available log types are defined in src utilities logger index ts log verbosity is an enum defined in src utilities logger logger ts to set this value set the config value defined in config example json to the desired log verbosity feeback system user feedback is generated through the feedbackcontext which can be imported from src context feedback context feedback context this exposes the following methods showloader hideloader showalert msg string value string timed boolean true hidealert example usage import the context into your component like so javascript import react usecontext from react import feedbackcontext from src context feedback context feedback context const feedbackcontext usecontext feedbackcontext once properly imported the methods can be implemented where needed as follows javascript feedbackcontext showloader feedbackcontext showalert this is my alert message error or success defaults to true feedbackcontext hidealert feedbackcontext hideloader loaders should be applied to all async operations and page transitions alerts should be utilized for all user interactions outside of navigation saves launches creates the react stuff this project was bootstrapped with create react app https github com facebook create react app available scripts in the project directory you can run npm start markdown link check disable runs the app in the development mode open http localhost 3000 http localhost 3000 to view it in the browser markdown link check enable the page will reload if you make edits you will also see any lint errors in the console npm test launches the test runner in the interactive watch mode see the section about running tests https facebook github io create react app docs running tests for more information regenerate test snapshots using npm run test updatesnapshot watchall false npm run e2e launches the e2e test runner in the interactive watch mode see this section about the test runner https docs cypress io guides core concepts test runner html overview for more information sensitive env variables should be set on the command or by export do not check in suggested export cypress base url fe env url export cypress sso user e2e export cypress sso password export cypress sso url sso url export cypress sso client id client id npm run e2e for running the e2e test locally export cypress base url http localhost 3000 export cypress sso user e2e export cypress sso password export cypress sso url https sso host auth realms realm name protocol openid connect token export cypress sso client id client id npm run e2e npm run build builds the app for production to the build folder it correctly bundles react in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes your app is ready to be deployed see the section about deployment https facebook github io create react app docs deployment for more information environment env variables for the app are listed in env sample read from an env file at build time openshift container platform build and deployment for use in an openshift container platform environment this appliction is managed by the use of helm templates see the development readme deployment readme md for details on how to spin up a deployment for developing on openshift node build the node agent is going to install the npm dependecies run the tests build the application package the output the dockerfile and publish this package to nexus bash npm install npm run test ci npm run build ci npm run package npm run publish fields required to be populated to allow launch an engagement cannot be launched until the following fields are populated for that engagement variable section description client name basic the name of the client for this engagement project name basic the name of this engagements project residency date start basic the start date of the engagement residency date end basic the start date of the engagement labs el point of contact the name and email for the engagement lead labs technical lead point of contact the name and email for the technical lead client contact point of contact the name and email for the client contact cloud provider openshift cluster which cloud provider is being utilized provider region openshift cluster which region is this cloud hosted open shift version openshift cluster which version of openshift is required desired subdomain openshift cluster what is the desired path for the cluster persistent storage openshift cluster what are the storage requirements cluster size openshift cluster what is the size of the cluter configuration variables for local deployments because environment variables are compiled into the built source code of the frontend at build time it is not possible to dynamically change these values at load time on the client side in order to allow for dynamic updating of configuration variables these values must be loaded through a network request from the client side from a static file served separate from the client javascript configuration is set using a json file stored in config config json of the public directory the json file is loaded via network request on page load once the configuration has loaded the web application renders this allows a volume to be mounted to config config json and provide dynamic configuration variables to the client depending on the environment in which the frontend is deployed an example config json can be seen in public config config example json public config config example json for more descriptions of each variable please see the runtime configuration variables runtime configuration variables section below runtime configuration variables depending on the type of deployment the way of setting these variables may vary however done the following values are options that can be set to succesfully run the frontend variable type description required default baseurl string target url for the deployment of this frontend app yes n a authbaseurl string uri for sso integration ending in auth for keycloak rhsso yes n a realm string realm for sso integration yes n a clientid string identification of the client application for sso integration yes n a backendurl string uri for backend https github com rht labs lodestar backend git apis yes n a disablelaunch boolean flag to toggle launch functionality on off no false supportemailaddress string e mail address to display on the support page etc yes n a analyticstrackingcode string google analystics tracking code no ua faked allowversionoverride boolean used to override the overall lodestar version to emulate other versions of the app no false learn more you can learn more in the create react app documentation https facebook github io create react app docs getting started		front_end
StudSysCppCLI	studsyscppcli a personal project implementing a command line user interface student administration program this was all the same repository with the library before i diverged them so a lot of issues in this repository relate to the library it is programmed with c entirely and can be compiled on linux with g it allows the following a student to login and browse each module they are registered for each module site has learning resources and also exams that can be taken grades info and student lecturer details as of version 1 0 the only exam format allowed is a multiple choice exam with each question having equal weights also only supports single answers his may be updated with further versions a lecturer to login and browse the module s they are registered as course leader for with this they can create messages resources and exams they can also edit exams that are already created viewing results for each student who took the exam an administrator to login and do the following create module course lecturer student delete module course lecturer student version 1 0 does not currently allow editing these or only in a very limited manner they have to be removed and then created with updated info the system has methods to update each entity however so it would be very easy to have it in later versions how to login 1 on the welcomescreen follow the instructions to login as lecturer or student 2 press e and then enter your e mail address and press enter 3 press p and then enter your password note that you will not be able to see the password that you type for security press enter to insert password into the system 4 press s to submit the details and you should be logged in problems in version 1 0 that i plan to work on 1 when you re in the middle of input for example entering a name there is no way of cancelling i plan to implement a way so that you can type c or cancel instead of a name to make this work i may have to read in everything as a string and not int or float as any string entered into cin returns 0 i can t check if it s 0 a user wants to cancel because they may genuinely want to type a 0 2 there are no features currently implemented for tasks this can be circumvented by using announcements 3 after saving an exam that was created successfully the user is left on the prompt they already used to save the exam in the first place which could mean they could go editing saving an exam that was already saved this is a known bug and is going to be fixed asap also after cancelling this prompt to update the list of exams you need to go back to the module homepage and go back into exams this bug fix should be in the next release 4 you cannot delete an exam that was created this will be fixed in future versions 5 this list isn t exhaustive with more testing i may add more information storage database to store artifacts related to the system such as students lecturers modules exams etc a mysql server is used to store the info dependencies this program uses boost s libraries so you will need to have boost libraries installed the libstudsys library that can be obtained from https github com edwardul99 studsyslibrary as mysql server is used this program requires the mysql c connector version 8 0 it can be downloaded with this link https dev mysql com downloads connector cpp 1 in the operating system drop down pick linux generic 2 choose in os version drop down choose glib 2 12 64 bit my os is 64 bit which i developed it in but you could try it with 32 bit but it s not guaranteed to work 3 ensure you install the correct mysql c connector can t install above on raspberry pi for example but there are plenty of installation guides on google a mysql server configured as of database setup in the readme of studsyscpplibrary also works with mariadb server 10 0 this is what you ll need on raspberry pi how to compile linux obtain the latest studsys library release from github com edwardul99 studsyscpplibrary or build the library using instructions from its readme copy the library to a folder in the root of the cli directory recommended a folder called lib obtain the headers from the studsys library and put them in headers studsys in cli root dir sources ensure there is a symbolic link headers pointing to cli root dir headers if not in the sources directory run ln s headers headers compile with the make file provided by typing make in the root directory run the command export ld library path path to root of lib ld library path if you don t have access to the database to create databases or users or don t know the host contact the database administrator to create a database if not already exists the admin will then be able to give you the ip address to the database server to login to how to run from root of the download directory type program name d database name u user name p pass word h host or program name login file example login file is user user name pass pass host hostname ip database database studsysdbadmin very limited program to administrate the database compile using g studsysdbadmin cpp l mysqlcppconn o studsysdbadmin only command line argument at the moment is c which clears the entire database administration use hidden command a from the welcomescreen if using a config file the property admin enabled true must be present the default value if false if this is missing using command line arguments instead of config file admin is always enabled however command line arguments other than config file will be removed soon as a config file gives greater flexibility username can be defined in the config file passed in with f with the parameter admin user or with the au flag password can be defined in the config file passed in with f with the parameter admin pass or with the ap flag logging errors can be logged if the stud logs environment variable is set to the directory of where to store the logs if this is not set no logging of errors will take place on linux the variable must be exported using export stud logs log path	administration	server
pyimagesearchcode	pyimagesearchcode deep learning for computer vision with python codes those codes were programmed from the book deep learning for computer vision with python and i try various method so that i can successfully push those folders and subfolders here thanks my god		ai
NeuralClimb	neuralclimb this is a small project being developed to detect and identify holds on a rock climbing wall see the current progress pdf for more information about the project		ai
dtype-next	dtype next next generation high performance clojure toolkit clojars project https clojars org cnuernber dtype next latest version svg https clojars org cnuernber dtype next build status https travis ci com cnuernber dtype next svg branch master https travis ci com cnuernber dtype next checkout the overview https cnuernber github io dtype next overview html api documentation https cnuernber github io dtype next java api documentation https cnuernber github io dtype next javadoc index html and sample https github com cnuernber dtype next blob master java test java jtest main java clojure cheatsheet https cnuernber github io dtype next cheatsheet html new functionality efficient 1d convolutions correlations gaussian correlations https cnuernber github io dtype next tech v3 datatype convolve html numeric gradient elemwise difference https cnuernber github io dtype next tech v3 datatype gradient html variable rolling windows https cnuernber github io dtype next tech v3 datatype rolling html var variable rolling window indexes description dtype next provides a unified pathway for dealing with contiguous containers of primitive datatypes such as ints and floats in addition it defines the basis for array programming as found in apl or numpy and a deep java interface hierarchy with default methods to allow implementing new array s painless this interface hierarchy integrates with java streams spliterators and various members of the java util function package in addition it extends these concepts to native heap based containers there are namespaces to allow elementwise operations across scalars and arrays highly optimized reductions across index spaces and algorithms that operate in index space for use when multiple buffers share an index space this library forms the numeric basis that underlies the tech ml dataset https github com techascent tech ml dataset system it also defines a language independent abi which allows zerocopy to c based systems such as numpy https github com clj python libpython clj opencv https github com techascent tech opencv julia https github com cnuernber libjulia clj tvm https github com techascent tvm clj and neanderthal https github com uncomplicate neanderthal additional targets of this library small runtime footprint this is harder than it looks but the main thing is that the system needs to produce the right answers with as little type specific code as necessary full native memory support malloc free memset memcpy just the basics but guaranteed to be available graal native support support for jdk 8 through jdk 17 jdk 16 is no longer supported for jdk 17 usage please see project clj for required flags blogpost https techascent com blog next gen native html example examples clj ffi and involved example https github com cnuernber avclj of using the ffi architecture across jna jdk 16 and graalnative native test in order to get mmap working on the native test i had to grab the larray so from the uberjar and load it manually not a big issue at the end of the day but i was having problems getting graal native to package resources use the scripts to get graal and compile test the code is located under native test so far reader writing copying all work for native and jvm heap datasets tensors work graal native https github com lread clj graal docs https github com borkdude clojure rust graalvm https github com epiccastle spire https github com babashka babashka sql pods test dependencies intel math kernel library use your system package manager to install libmkl rt license copyright 2020 chris nuernberger this program and the accompanying materials are made available under the terms of the eclipse public license 2 0 which is available at http www eclipse org legal epl 2 0	graal-native clojure hpc	os
food-app	food app a new flutter project getting started this project is a starting point for a flutter application a few resources to get you started if this is your first flutter project lab write your first flutter app https docs flutter dev get started codelab cookbook useful flutter samples https docs flutter dev cookbook for help getting started with flutter development view the online documentation https docs flutter dev which offers tutorials samples guidance on mobile development and a full api reference		server
Azure-Computer-Vision-in-a-day-workshop	azure computer vision in a day workshop img src workshopheader jpg in this technical workshop you will receive a thorough introduction to azure computer vision and azure vision studio you will be taught how to utilize the new capabilities of azure computer vision 4 to analyze images including its multimodal features florence moreover you will be able to investigate pre existing solution accelerators and best practices for prototyping and deploying end to end use cases finally the workshop will conclude with a q a session and a wrap up note some features are currently in preview and the api may change in the future https learn microsoft com en us azure cognitive services computer vision 1 workshop agenda morning 9 00 12 00 br focus introduction and images processing with azure computer vision br br azure computer vision presentation 30 min br azure azure vision studio 30 min br intro workshop 15 min br azure computer vision setup 15 min br image analysis using azure computer vision 15 min br coffee pause 20 min br dense captioning of images 15 min br background removal with azure computer vision 15 min br gradio webapp example using the background removal from azure computer vision 10 min br image retrieval with azure computer vision images et text vectors embeddings 15 min br afternoon 14 00 16 00 br focus visual search usecase with azure computer vision br br recap 20 min br fashion visual search images analysis 20 min br fashion visual search images embeddings 20 min br fashion visual search search using an image or a prompt we will build a webapp as well 15 min br fashion visual search images clustering 15 min br question conclusion and qna session 30 min br br legend presentation hands on lab 2 notebooks azure computer vision setup a href 00 setup azure computer vision ipynb 00 setup azure computer vision a image analysis using azure computer vision a href 01 image analysis ipynb 01 image analysis a dense captioning of images a href 02 captioning and dense captioning ipynb 02 captioning and dense captioning a background removal with azure computer vision a href 03 background removal ipynb 03 background removal a gradio webapp example using the background removal from azure computer vision a href 04 20 20gradio 20app 20for 20background 20removal ipynb 04 gradio app for background removal a image retrieval with azure computer vision images et text vectors embeddings a href 05 image retrieval ipynb 05 image retrieval a visual search demo based on a fashion images datasets using azure computer vision a href 06 fashion visual search images analysis ipynb 06 fashion visual search images analysis a visual search demo images vectors embeddings a href 07 fashion visual search images embeddings ipynb 07 fashion visual search images embeddings a visual search demo search using an image reference or a text a href 08 fashion visual search search using an image or a prompt ipynb 08 fashion visual search search using an image or a prompt a visual search demo images clustering using images vectors embeddings a href 09 fashion visual search images clustering ipynb 09 fashion visual search images clustering a visual search demo gradio webapp a href 10 fashion visual search gradio app ipynb 10 fashion visual search gradio app a note you need to enter your azure computer vision endpoint and key in this env file a href azure env azure env a 3 azure computer vision presentation a href azure computer vision 4 pdf azure computer vision powepoint presentation a 4 documentation https learn microsoft com en us azure cognitive services computer vision 5 vision studio https portal vision cognitive azure com 6 azure computer vision 4 demos videos a href https www youtube com playlist list ply4moyaxz3vmaa ie5wrunicz9sif74ex azure computer vision 4 florence demos videos a br br created 19 apr 2023 br updated 04 may 2023 serge retkowsky serge retkowsky microsoft com https www linkedin com in serger	azure azure-ai azure-cognitive-services azure-computer-vision microsoft computer-vision image-processing	ai
windy-afternoon	description weihang chen s blog introduction qq ai lab github ahangchen https github com ahangchen https www zhihu com people meng li feng lin activities cweihang foxmail com star https github com ahangchen windy afternoon 2019 https zhuanlan zhihu com p 42936891 https zhuanlan zhihu com p 54161673 ml papers https github com ahangchen gdlnotes cvpr 2018 tfusion ml papers reid tfusion md sqlite android bug android sqlite sqlite zai android shang de yi ge bug md pytorch ml pratice torch best practice md paper list jianming lv weihang chen qing li can yang unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns cvpr 2018 ccf a paper http openaccess thecvf com content cvpr 2018 papers lv unsupervised cross dataset person cvpr 2018 paper pdf code https github com ahangchen tfusion jianming lv jiajie zhong weihang chen qinzhe xiao zhenguo yang and qing li homepage augmentation by predicting links in heterogenous networks cikm 2018 ccf b	android machine-learning linux ubuntu	ai
NLP-Cube	downloads https pepy tech badge nlpcube https pepy tech project nlpcube downloads https pepy tech badge nlpcube month https pepy tech project nlpcube month weekly https img shields io pypi dw nlpcube svg daily https img shields io pypi dd nlpcube svg version https badge fury io py nlpcube svg python 3 https img shields io badge python 3 blue svg https www python org downloads release python 360 github stars https img shields io github stars adobe nlp cube svg style social label star maxage 2592000 https github com adobe nlp cube stargazers news 05 august 2021 we are releasing version 3 0 of nlpcube and models and introducing flavours flavours this is a major update but we did our best to maintain the same api so previous implementation will not crash the supported language list is smaller but you can open an issue for unsupported languages and we will do our best to add them other options include fixing the pip package version 1 0 8 pip install nlpcube 0 1 0 8 15 april 2019 we are releasing version 1 1 models check all supported languages below languages both 1 0 and 1 1 models are trained on the same ud2 2 corpus http hdl handle net 11234 1 2837 however models 1 1 do not use vector embeddings thus reducing disk space and time required to use them some languages actually have a slightly increased accuracy some a bit decreased by default nlp cube will use the latest at this time 1 1 models to use the older 1 0 models just specify this version in the load call cube load en 1 0 en for english or any other language code this will download if not already downloaded and use this specific model version same goes for any language version you want to use if you already have nlp cube installed and want to use the newer 1 1 models type either cube load en 1 1 or cube load en latest to auto download them after this calling cube load en without version number will automatically use the latest ones from your disk hr nlp cube nlp cube is an opensource natural language processing framework with support for languages which are included in the ud treebanks http universaldependencies org list of all available languages below use nlp cube if you need sentence segmentation tokenization pos tagging both language independent uposes and language dependent xposes and attrs lemmatization dependency parsing example input this is a test output is 1 this this pron dt number sing prontype dem 4 nsubj 2 is be aux vbz mood ind number sing person 3 tense pres verbform fin 4 cop 3 a a det dt definite ind prontype art 4 det 4 test test noun nn number sing 0 root spaceafter no 5 punct 4 punct spaceafter no if you just want to run it here s how to set it up and use nlp cube in a few lines quick start tutorial examples 1 20nlp cube 20quick 20tutorial ipynb for advanced users that want to create and train their own models please see the advanced tutorials in examples starting with how to locally install nlp cube examples 2 20advanced 20usage 20 20nlp cube 20local 20installation ipynb simple pip installation update version install or update nlp cube with bash pip3 install u nlpcube api usage to use nlp cube programmatically in python follow this tutorial examples 1 20nlp cube 20quick 20tutorial ipynb the summary would be python from cube api import cube import the cube object cube cube verbose true initialize it cube load en device cpu select the desired language it will auto download the model on first run text this is the text i want segmented tokenized lemmatized and annotated with pos and dependencies document cube text call with your own text string to obtain the annotations the document object now contains the annotated text one sentence at a time to print the third words s pos in the first sentence just run print document sentences 0 2 upos 0 is the first sentence and 2 is the third word each token object has the following attributes index word lemma upos xpos attrs head label deps space after for detailed info about each attribute please see the standard conll format flavours previous versions on nlp cube were trained on individual treebanks this means that the same language was supported by multiple models at the same time for instance you could parse english en text with en ewt en esl en lines etc the current version of nlpcube combines all flavours of a treebank under the same umbrella by jointly optimizing a conditioned model you only need to load the base language for example en and then select which flavour to apply at runtime text from cube api import cube import the cube object cube cube verbose true initialize it cube load en device cpu select the desired language it will auto download the model on first run text this is the text i want segmented tokenized lemmatized and annotated with pos and dependencies parse using the default flavour in this case ewt document cube text call with your own text string to obtain the annotations or you can specify a flavour document cube text flavour en lines webserver usage the current version dropped supported since most people preferred to implement their one nlpcube as a service cite if you use nlp cube in your research we would be grateful if you would cite the following paper nlp cube end to end raw text processing with neural networks http www aclweb org anthology k18 2017 boro tiberiu and dumitrescu stefan daniel and burtica ruxandra proceedings of the conll 2018 shared task multilingual parsing from raw text to universal dependencies association for computational linguistics p 171 179 october 2018 or in bibtex format inproceedings boro dumitrescu burtica 2018 k18 2 author boro tiberiu and dumitrescu stefan daniel and burtica ruxandra title nlp cube end to end raw text processing with neural networks booktitle proceedings of the conll 2018 shared task multilingual parsing from raw text to universal dependencies month october year 2018 address brussels belgium publisher association for computational linguistics pages 171 179 abstract we introduce nlp cube an end to end natural language processing framework evaluated in conll s multilingual parsing from raw text to universal dependencies 2018 shared task it performs sentence splitting tokenization compound word expansion lemmatization tagging and parsing based entirely on recurrent neural networks written in python this ready to use open source system is freely available on github for each task we describe and discuss its specific network architecture closing with an overview on the results obtained in the competition url http www aclweb org anthology k18 2017 languages and performance for comparison the performance of 3 0 models is reported on the 2 2 ud corpus but distributed models are obtained from ud 2 7 results are reported against the test files for each language available in the ud 2 2 corpus using the 2018 conll eval script please see more info about what each metric represents here http universaldependencies org conll18 evaluation html notes version 1 1 of the models no longer need the large external vector embedding files this makes loading the 1 1 models faster and less ram intensive all reported results here are end 2 end e g we test the tagging accuracy on our own segmented text as this is the real use case conll results are mostly reported on gold or pre segmented text leading to higher accuracy for the tagger parser etc language model token sentence upos xpos alltags lemmas uas las chinese zh 1 0 93 03 99 10 88 22 88 15 86 91 92 74 73 43 69 52 zh 1 1 92 34 99 10 86 75 86 66 85 35 92 05 71 00 67 04 zh 3 0 95 88 87 36 91 67 83 54 82 74 85 88 79 15 70 08 english en 1 0 99 25 72 8 95 34 94 83 92 48 95 62 84 7 81 93 en 1 1 99 2 70 94 94 4 93 93 91 04 95 18 83 3 80 32 en 3 0 98 95 75 00 96 01 95 71 93 75 96 06 87 06 84 61 french fr 1 0 99 68 94 2 92 61 95 46 90 79 93 08 84 96 80 91 fr 1 1 99 67 95 31 92 51 95 45 90 8 93 0 83 88 80 16 fr 3 0 99 71 93 92 97 33 99 56 96 61 90 79 89 81 87 24 german de 1 0 99 7 81 19 91 38 94 26 80 37 75 8 79 6 74 35 de 1 1 99 77 81 99 90 47 93 82 79 79 75 46 79 3 73 87 de 3 0 99 77 86 25 94 70 97 00 85 02 82 73 87 08 82 69 hungarian hu 1 0 99 8 94 18 94 52 99 8 86 22 91 07 81 57 75 95 hu 1 1 99 88 97 77 93 11 99 88 86 79 91 18 77 89 70 94 hu 3 0 99 75 91 64 96 43 99 75 89 89 91 31 86 34 81 29 italian it 1 0 99 89 98 14 86 86 86 67 84 97 87 03 78 3 74 59 it 1 1 99 92 99 07 86 58 86 4 84 53 86 75 76 38 72 35 it 3 0 99 92 98 13 98 26 98 15 97 34 97 76 94 07 92 66 romanian ro rrt ro 1 0 99 74 95 56 97 42 96 59 95 49 96 91 90 38 85 23 ro 1 1 99 71 95 42 96 96 96 32 94 98 96 57 90 14 85 06 ro 3 0 99 80 95 64 97 67 97 11 96 76 97 55 92 06 87 67 spanish es 1 0 99 98 98 32 98 0 98 0 96 62 98 05 90 53 88 27 es 1 1 99 98 98 40 98 01 98 00 96 6 97 99 90 51 88 16 es 3 0 99 96 97 17 96 88 99 91 94 88 98 17 92 11 89 86	embeddings parse nlp-cube language-pipeline tokenization sentence-splitting part-of-speech-tagger lemmatization dependency-parser dependency-parsing universal-dependencies machine-translation information-extraction	ai
Android-Java-Kotlin-	moblie development android java kotlin xamarin c net	kotlin-android java-android xamarin	front_end
fxrtos-lite	a href https scan coverity com projects eremex fxrtos lite img alt coverity scan build status src https scan coverity com projects 23951 badge svg a a href https github com eremex fxrtos lite actions img alt build status src https github com eremex fxrtos lite actions workflows build yml badge svg a description fx rtos lite is small kernel intended to be used with microcontrollers it is implemented as a c99 static library containing os services features api includes the following services threads software timers one shot and periodic semaphores mutexes with priority ceiling message queues memory block pools events condition variables barriers arbitrary sized memory pools rwlocks supported hardware and toolchains lite version supports only two most common cpu architectures arm cortex m3 armv7 m architecture may also be used on armv8 m risc v rv32i profile current configurations release platform async cortex m3 gnu tools zip minimal os using interrupt driven routines instead of threads for cortex m3 mcu s standard cortex m3 zip regular configuration for cortex m3 mcu s standard riscv32i gnu tools zip regular configuration for risc v rv32i isa arm version supports gcc keil mdk and iar ewarm compilers risc v version supports only gcc at now host system may be either windows or linux mac should also work but untested no external dependencies required except the compiler getting started this repository contains non configured version of the kernel represented as a set of components it is useful if you want to contribute to os in case if you just need a kernel to use in your embedded application consider using preconfigured kernels available for arm and risc v how to build the library from preconfigured sources download and unpack appropriate release archive from releases https github com eremex fxrtos lite releases ensure supported compiler is available via path set gcc prefix as compiler prefix if you use gcc i e riscv none embed enter directory where build bat and makefile are located run build bat or make command for those who do not want to mess with toolchains and source code we provide prebuilt binaries while binary version may be sufficient for most users it lacks configuration and optimization options how to build the library from scratch set environment variables fxrtos dir as path to kernel root folder gcc prefix as compiler prefix if you use gcc i e arm none eabi for arm fxdj as path to fx dj py https github com eremex fx dj dependency injection tool enter directory for target core e g cores standard cortex m3 run build bat on windows or make src and then make lib on linux mac arm only if dependency building finished with errors before retry you need to remove src directory how to use the os may be linked to the project as a binary or it can be added as set of source files in ide demonstrations of use are located in fxrtos examples https github com eremex fxrtos examples repo limitations please note that lite version is a soft realtime or best effort kernel it is not intended for deterministic hard realtime operation advanced features such as deterministic timers low latency deferred interrupt processing multiprocessing support privilege levels separation and security are available only in full version please contact eremex sales department for further details when developing latency critical applications using lite edition the following limitations should been taken into account broadcasting or notify all synchronization objects such as condvar or event more waiting threads results in longer latency since notification process is non preemptive possible solutions do not use broadcasting objects or limit maximum number of waiting threads priority based notification policy with synchronization objects i e posting semaphore and releasing the most prioritized waiting thread uses linear search with scheduling disabled this means that n waiting threads results in unbounded o n scheduling and interrupt latency possible solutions use fifo notification policy or limit maximum number of waiting threads for any synchronization object message queue flush releases up to n threads where n is queue length possible solutions do not use queue flushing or limit queue length to reasonable value timers implementation uses sorted queues and linear search resulting in o n latency depending on number of active timers in the system possible solutions limit maximum number of software timers and do not use timeslicing support for questions on using fx rtos contact authors via telegram group https t me fxrtos	rtos embedded kernel fx-rtos	os
esp-azure	esp azure iot sdk table of contents introduction introduction getting started get started creating an azure iot device create device monitoring results monitoring troubleshooting troubleshooting 2021 update a name 2021 update a since this library has been published microsoft has created newer versions of the azure sdk for usage with the espressif esp32 this new library is better suited for microcontrollers great for composition with your own network stack and officially supported by microsoft the first one azure iot middleware for freertos https github com azure azure iot middleware freertos is based on esp idf and freertos and it has samples https github com azure samples iot middleware freertos samples for iot hub and iot central using the device provisioning service dps the second one is based on azure iot for c library for arduino https github com azure azure sdk for c arduino and also has samples for iot hub if you can avoid using this library for any new projects introduction a name introduction a the esp azure iot sdk is based on azure iot c sdk https github com azure azure iot sdk c and enables users to connect their esp32 based devices to the azure iot hub it provides some examples which can help understand most common use cases getting started a name get started a hardware you will basically just need a development host and an esp32 development board https www espressif com en products hardware development boards to get started development host setup this project is to be used with espressif s iot development framework esp idf https github com espressif esp idf follow these steps to get started setup esp idf development environment by following the steps here https docs espressif com projects esp idf en latest get started index html in a separate folder clone the esp azure project as follows please note the recursive option which is required to clone the various git submodules required by esp azure bash git clone recursive https github com espressif esp azure git note that if you ever change the branch or the git head of either esp idf or esp azure ensure that all the submodules of the git repo are in sync by executing git submodule update init recursive setting up azure iot hub create an azure iot hub by following the documentation here https docs microsoft com en us azure iot hub iot hub create through portal note when selecting the pricing and scale tier there is also an option to select f1 free tier which should be sufficient for basic evaluation copy the iot hub connection string primary key from the azure iot hub this will be required later the screenshot below will help you locate it doc static connection string png connection string primary key sample hostname azure iot hub name azure devices net sharedaccesskeyname iothubowner sharedaccesskey base64 encoded access key setting up azure cli install azure cli https docs microsoft com en us cli azure install azure cli view azure cli latest from your terminal execute the az command to verify that the installation was successful output will be like this az welcome to azure cli use az h to see available commands or go to https aka ms cli install the azure iot cli extension using az extension add name azure cli iot ext after that you should be able to use azure cli to manage your iot device a list of useful azure clis can be found here doc azure cli iot hub md creating an azure iot device a name create device a login to azure cli using az login create a new device using az iot hub device identity create n iothub name d device id get connection string for your device using az iot hub device identity show connection string n iothub name d device id device connection string sample hostname azure iot hub name azure devices net deviceid azure iot device id sharedaccesskey base64 encoded shared access key this will be required in the examples monitoring results a name monitoring a to see various events and the data being exchanged between the device and iot hub from your command line run the following command az iot hub monitor events n iothub name login connection string primary key note the single quotes for the connection string without them the command wont work as desired to monitor activity on your esp device run make monitor troubleshooting a name troubleshooting a 1 some common problems can be fixed by disabling the firewall 2 you can try with the followings if your build fails git submodule update init recursive check the compiler version and verify that it is the correct one for your esp idf version check if the idf path is set correctly clean the project with make clean and if required using rm rf build sdkconfig sdkconfig old 3 ensure that the device connection string received from azure iot hub are correct	component cloud	server
Free-Web-Development-eBooks	free web development books this is a backup edition of techbookhunter s curated collection of free web development related ebooks available on the internet the original repository https github com techbookhunter free web development books no longer exists please feel free to share learn and contribute if you want to contribute to this list send a pull request all contributors will be recognized and appreciated disclaimer the contributor s cannot be held responsible for any misuse of the data list of books you can find all the books listed below in book book folder of this repo angular test driven development second edition download book angular angular 20test driven 20development 20 20second 20edition pdf angularjs essentials download book angular angularjs 20essentials pdf angularjs test driven development download book angular angularjs 20test driven 20development pdf become a ninja with angular 2 download book angular become 20a 20ninja 20with 20angular 202 pdf beginning html and css download book html 20 20css beginning 20html 20and 20css pdf beginning javascript download book javascript beginning 20javascript pdf bootstrap 4 by example download book bootstrap bootstrap 204 20by 20example pdf bootstrap site blueprints volume ii download book bootstrap bootstrap 20site 20blueprints 20volume 20ii pdf bootstrap site blueprints download book bootstrap bootstrap 20site 20blueprints pdf coffeescript application development cookbook download book uncategorized coffeescript 20application 20development 20cookbook pdf css and documents download book html 20 20css css 20and 20documents pdf css framework alternatives explore five lightweight alternatives to bootstrap and foundation with project examples download book html 20 20css css 20framework 20alternatives 20 20explore 20five 20lightweight 20alternatives 20to 20bootstrap 20and 20foundation 20with 20project 20examples pdf css3 the missing manual third edition download book html 20 20css css3 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unnamed_app	lvdc intelligent system at lvdc from pos to command centre from command centre to activity equipment management equipment management		server
front-end-developer-roadmap	front end developer roadmap front end developer roadmap images front end roadmap png list of the tutorials html css https www youtube com watch v ub1o30fr ee index 1 list plillgf rfqbztasqiqdvm1r5mlrqq79cu javascript basics https www youtube com watch v le urjbheve list plwkjhjtqvabk2qrztwszcin38jc ndhw5 javascript dom manipulation https www youtube com watch v ealkqob9fu0 list plwkjhjtqvablllk6r2dngjuvwb cfncuo es6 https www youtube com watch v 1mglwu69iju list plwkjhjtqvabljtmmes0c cel2lde er f advanced javascript course https www youtube com watch v wgycebgucck list plz1xpaff8ixbiu78ql158l kln9cvh5fg npm https www youtube com watch v jhdhasskmb0 sass https www youtube com watch v st5b7hnmljg index 1 list pl4cuxegkcc9iewigam3gtju 7ia3w2wza webpack 4 https www youtube com watch v deyxi 6c2u4 git https www youtube com watch v vr y 2zwrie list plwkjhjtqvabkfiqhnnaxpophh9tswmxif react vs vue vs angular https www youtube com watch v kmx1mfemm3e flow https www youtube com watch v xwmuaubxcdq typescript https www youtube com watch v ray 3siqt e react https www youtube com watch v mhkgqaoc7bc list ploycgnoiygabj2gqsldrjgvxtqfdxkm5b redux https www youtube com watch v 1w oq i1xb8 list ploycgnoiygabj2gqsldrjgvxtqfdxkm5b index 15 rxjs https www youtube com watch v t9wou11uu6u list pl55riy5tl51phpagycrn9ubnlvxf8rgvi vue 2 https www youtube com watch v z6hqqgvgi4y angular 6 https www youtube com watch v uovytwpk3ia list plyxzs 5yyqlqcmhqdyw3yo5v79c7eate angular 6 another tutorial https www youtube com watch v fliurgp7rci list pllbnbe 1fcppuqk2troeyhasvru0nxaee ngrx https www youtube com watch v f97icoaeknu additional clean code https www youtube com watch v b9c5gmms7ks list plwkjhjtqvabkk24eapurzmq0 kw5u9pjh data structures https www youtube com watch v gj5qbhegoeo list plwkjhjtqvabkso ibgiip48n o jqa9pj design patterns https www youtube com watch v bgu7feiwkzc list plwkjhjtqvabnztkai3bqcyxknfwn c704 contribution feel free to use figma https www figma com file xzgsbmmowarvvvolw9rsnov2 front end roadmap or just svg images front end roadmap svg version to fork the image		front_end
DevOps-Eval	p align center img src images devops eval logo png style width 50 id title icon p p align center a devops domain knowledge evaluation benchmark for large language models p p align center a href https huggingface co datasets devops eval devopseval exam target blank hugging face a a href data target blank data a a href resources tutorial md target blank tutorial zh a br a href https github com luban agi devops eval readme zh md a a href https github com luban agi devops eval readme md english p devops eval is a comprehensive evaluation suite specifically designed for foundation models in the devops field it consists of xxxx multi choice questions spanning 8 diverse disciplines as shown below we hope devops eval could help developers especially in the devops field track the progress and analyze the important strengths shortcomings of their models img src images devops diagram zh jpg style zoom 80 news 2023 09 30 devops eval br br table of contents leaderboard leaderboard results on validation split results on validation split data data how to evaluate how to evaluate how to submit how to submit todo todo licenses licenses citation citation leaderboard coming soon br br results on validation split coming soon br br data coming soon br br how to evaluate coming soon br br how to submit coming soon br br todo br br licenses mit license https img shields io badge license mit blue svg https lbesson mit license org this work is licensed under a mit license https lbesson mit license org br br citation please cite our paper if you use our dataset br br		ai
iotSDR	iotsdr getting started guide this small guide will help you setup your board to get started with the jupyter notebook pre requisites 1 iotsdr 7010 or 7020 board 2 computer with compatible browser google chrome 3 ethernet cable 100m or 1g 4 micro usb data cable or power adapter of 5v for power jack 5 micro sd card class 10 with preloaded image 16gb recommended microsd card setup to flash iotsdr micro sd card follow the below steps 1 download the appropriate iotsdr image for your board 2 click here for iotsdr7010 https bit ly 2pep3y4 or iotsdr7020 https bit ly 3cxja33 4 use etcher utility etcher https www balena io etcher 5 following the etcher instructions write the image to a blank micro sd card 16gb recommended if you are facing issues you can find the detailed instructions here https github com embedinn iotsdr blob master iotsdr usd card md board setup set the boot jumper to the sd position this sets the board to boot linux from the micro sd card image images iot jpg insert the micro sd card with respective pre loaded iotsdr image file into the micro sd card slot connect the ethernet cable one end with iotsdr and other with pc connect the usb cable to your pc laptop and to the pwr uart microusb port on the board next to power jack iotsdr board should now power up and get into boot sequence running jupyter on the iotsdr 1 after setting the sd card header position inserting the sd card with pre loaded image file and the usb cable is connected the green led will come on immediately to confirm that the board has power following to that after few seconds the blue done led should light up to show that bit file is loaded in the zynq device after some time the linux os will be booted and ready for use 2 once board setup up is complete you can connect your browser to it to start using jupyter notebook 3 follow the below procedure to configure ethernet connection between host pc and iotsdr before running jupyter notebook on browser 1 assign your computer a static ip address from the pool of 192 168 2 xx except 99 and 1 2 connect the board to your computer s ethernet port 3 ping to 192 168 2 99 to confirm that you are connected 4 now open chrome browser and write http 192 168 2 99 9090 lab in the search bar 5 enter password window will appear the password is xilinx note by default the board currently have a fixed ip 192 168 2 99 address mode running application on jupyter notebook after the password tab window two folder of the notebooks on the left of webpage will appear and the files can be accessed and user can run the demo projects image images demo png contact us if you are facing any issues feel free to contact us at info embedinn com or at discord https discord gg kzxs8gsv7d	iotsdr python jupyter	server
LM-Infinite	lm infinite introduction this is reproduction of the paper lm infinite simple on the fly length generalization for large language models https arxiv org abs 2308 16137 in pytorch the work is done by chi han https glaciohound github io qifan wang wenhan xiong yu chen heng ji sinong wang in this paper the authors propose a simple method called lm infinite to improve the length generalization of large language models to as long as 128k tokens without any additional training or parameter updates the key idea is to use 1 a lambda shaped attention pattern so that each token only attends to the nearest l pretrain tokens as well as a few starting tokens and 2 a distance limit l pretrain so that the attention distance is capped at l pretrain the proposed method is compatible with multiple state of the art language models including but not limited to llama llama 2 gpt j mpt 7b series lm infinite is also computational efficient with only o n time complexity tada tada tada now a drop in replacement for huggingface transformers we have implemented the lm infinite method as a drop in replacement for huggingface transformers after you load the transformers models and if it is a llama model an mpt model or a gpt j model you can run the following codes to enable lm infinite for llama model from models llama import convert llama model model convert llama model model 4096 10 for mpt model from models mpt 7b import convert mpt model model convert mpt model model 4096 10 for gpt j model from models gpt j import convert gpt j model model convert gpt j model model 4096 10 then you can use the model as usual requirements python 3 11 pytorch 2 0 1 datasets 2 14 4 tokenizers 0 13 3 transformers 4 32 1 sentencepiece 0 1 99 evaluate 0 4 0 rouge score 0 1 2 protobuf 3 20 3 accelerate 0 22 0 deepspeed 0 10 2 tqdm 4 66 1 einops 0 6 1 a detailed list of python packages from an anaconda perspective can be found in requirements txt some packages were installed by conda and some by pip my commands to install the requirements in anaconda pip environment are as follows conda install pytorch torchvision torchaudio pytorch cuda 11 8 c pytorch c nvidia conda install c conda forge sentencepiece einops cudatoolkit dev tqdm ipython datasets evaluate rouge score protobuf accelerate pip install transformers deepspeed directory structure license readme md requirements txt configs zero3 efficient config json config for deepspeed acceleration data generation metrics py get data py dataset loading and preprocessing passkey retrieval create passkey data py create passkey data sh passkey retrieval accuracy py split pile file py split the pile dataset into task specific files models constant py a constant function model get llama2 convert llama weights to hf py convert llama 2 weights to huggingface format download llama2 sh get model py gpt j py lambda attention py efficient implementation of lambda attention llama py model base py mpt 7b py scripts combine evaluate generation py combine results py eval downstream tasks py evaluate on passkey retrieval task eval generation py evaluate generation metrics eval ppl deepspeed py evaluate perplexity utils arguments py utils py visualization plot nll py position pca py relative attention explosion py usage data preparation for datasets you need to prepared a corpus dataset if you download the the original pile source https pile eleuther ai to pile path test jsonl zst and pile path val jsonl zst run the following commands to extract the compressed dataset cd pile path zstd d test jsonl zst zstd d val jsonl zst then run the following commands to split the dataset into task specific files cd repository root mkdir p pile path val mkdir p pile path test python data split pile file py pile path val jsonl pile path val python data split pile file py pile path test jsonl pile path test however the official pile does not seem to be available for download anymore so you probably need to figure out another source e g https huggingface co datasets arxiv dataset or https openwebtext2 readthedocs io en latest alternatively you can also use your own corpus both two options require you to edit data get data py data get data py model preparation for backbone models the paper uses llama 2 llama gpt j and mpt 7b the last 3 models are directly available on the fly from huggingface model hub so not action is needed beforehand the llama 2 download key needs to be requested from meta ai request form https ai meta com resources models and libraries llama downloads then run the following command bash models get llama2 download llama2 sh and follow prompts to download the checkpoints to path to llama2 checkpoints then run python models get llama2 convert llama weights to hf py input dir path to llama2 checkpoints model size 7b output dir path to llama2 checkpoints llama 2 7b hf to convert the llama 2 7b checkpoints to huggingface format evaluation the codes requires a log dir to store the logs and results please select a directory with enough space perplexity evaluating the perplexity of llama 2 model on arxiv test set trial llama2 infinite arxiv mkdir p log dir trial cuda visible devices 0 master port shuf i 29500 65535 n 1 pythonpath deepspeed include localhost cuda visible devices master port master port scripts eval ppl deepspeed py deepspeed config configs zero3 efficient config json model path to llama2 checkpoints llama 2 7b hf tokenizer path path to llama2 checkpoints use lambda attention local branch 4096 global branch 100 limit distance 4096 dataset the pile dataset group arxiv split test dataset dir pile path max length 32770 log dir log dir trial a brief explanation of the arguments model the path or name to model pass decapoda research llama 7b hf to use llama mosaicml mpt 7b to use mpt 7b and eleutherai gpt j 6b to use gpt j 6b tokenizer path the path to the tokenizer remove this argument if not using llama 2 use lambda attention use lambda attention required for lm infinite local branch the local branch size 2048 for llama mpt 7b and gpt j required for lm infinite global branch the global branch size range 10 100 gives generally similar effect required for lm infinite limit distance the distance limit 2048 for llama mpt 7b and gpt j required for lm infinite dataset the dataset name see data get data py data get data py to figure how to use custom datasets if you want to evaluate on vanilla models without lm infinite simply remove the use lambda attention local branch 4096 global branch 100 limit distance 4096 argument set if you want only to evaluate on a subset of the test set you can use the start data from argument to specify the starting index of the test set and or max data num to specify the number of examples after that index generation generating evaluation from llama 2 model on arxiv test set trial llama2 infinite generate arxiv mkdir p log dir trial cuda visible devices 0 master port shuf i 29500 65535 n 1 pythonpath deepspeed include localhost cuda visible devices master port master port scripts eval generation py deepspeed config configs zero3 efficient config json model path to llama2 checkpoints llama 2 7b hf tokenizer path path to llama2 checkpoints use lambda attention local branch 4096 global branch 100 limit distance 4096 dataset the pile dataset group arxiv split test dataset dir pile path max length 33000 max generation length 100 evaluate metrics evaluate positions 4096 8192 12288 16384 log dir log dir trial evaluation on passkey retrieval task first create the dataset with the following command and put to passkey data echo passkey data mkdir p passkey data for max length in 2048 3072 4096 5120 6144 7168 8192 10240 12288 14335 16384 do echo max length python data passkey retrieval create passkey data py token length max length dump file path passkey data max length tokenizer path path to llama2 checkpoints done evaluating the passkey retrieval task on arxiv test set with llama 2 model max length 4096 trial llama2 infinite passkey max length mkdir p log dir trial cuda visible devices 0 master port shuf i 29500 65535 n 1 pythonpath deepspeed include localhost cuda visible devices master port master port scripts eval downstream tasks py deepspeed config configs zero3 efficient config json model path to llama2 checkpoints llama 2 7b hf tokenizer path path to llama2 checkpoints use lambda attention local branch 4096 global branch 100 limit distance 4096 dataset passkey retrieval dataset dir passkey data dataset group max length max generation length 10 evaluate metrics log dir log dir trial citation article han2023lminfinite title lm infinite simple on the fly length generalization for large language models author han chi and wang qifan and xiong wenhan and chen yu and ji heng and wang sinong journal arxiv preprint arxiv 2308 16137 year 2023	language-model long-context model-diagnostics	ai
stream-site	preview image https pub rachni com img chrome 2016 12 28 22 38 36 png rachni an nginx rtmp module front end authors joel bethke joel bethke gmail com andrew may credits special thanks to hdxx for helping sort out some installation dependency issues this site aims to be a easy to use front end for the nginx rtmp module a few more screenshots can be found here http imgur com a q90jg there is no demo currently but i am working on standing something up current features account system lets users sign up for the site with email verification to activate accounts allows for password resets other account features are planned see github issues private stream keys each user is assigned a private streaming key this is used to connect to the ingest server anyone attempting to connect without a valid account streamkey will be denied on demand recording allows anyone to start recording a stream to the server for playback later recorded videos are stored indefinitely future plans to only let the streamer start stop their own recording in site recording playback all recorded videos are viewable playable from the site itself download is allowed on live notifications will allow any user to subscribe to another channel and receive an email notification when they go live currently implemented simply future enhancements planned stream api api for grabbing stream info possibly other functions useful for making widgets on another site for current viewers live channels can also be used to start stop recording if authorized built on mdl https getmdl io index html sass http sass lang com video js https github com videojs video js player planned features see github issues https github com fenrirthviti stream site issues page for details or to make any feature requests config information this site uses and requires linux i m using the exec function in a few directives which does not work on windows you can probably get around this but i do not intend to run this on windows so i have not tested any flavor of linux that offers the below packages should be fine nginx with nginx http flv module https github com winshining nginx http flv module http ssl module and http xslt module postgresql php7 i believe it should run on php5 4 but i have not tested mail function is required note the following packages were included in my build of php7 but you may need to install them manually php pgsql php xml php curl javascript ffmppeg for live screenshots recording remuxing this is the config string i used for nginx prefix etc nginx user nginx group nginx sbin path usr sbin nginx conf path etc nginx nginx conf error log path var log nginx error log http log path var log nginx access log pid path var run nginx pid lock path var run nginx lock with http ssl module add dynamic module path to nginx http flv module with http xslt module with openssl path to openssl all nginx conf files can be found in src nginx note this is a fairly complex nginx setup please make sure you read all the conf files and make the necessary directory config changes to them note many of the administration features will require manual database manipulation currently there is not much that will need to be done but be aware there is no admin console currently it is planned for future updates but has not been a priority installation install nginx with nginx http flv module http ssl module and http xslt module see above verify all config files are updated to the paths you want to use check every file there is a lot to configure copy the config files from src nginx to your nginx config directory default etc nginx if you used my config line and restart nginx install pgsql and set up your database user import the sql files from src pgsql to your database this will set up the two required tables make sure you update line 18 in subscribers sql line 19 of chat sql and line 23 of users sql to your database user account edit lib database class php with your db info edit inc config php to your liking copy everything but src and scss to your server if you wish to use sass to edit the site layouts colors all the files are in src css and the main file is scss application css otherwise just copy css to use the pre compiled versions css site css is required either way the nginx user needs to be able to access the following execute rights to rtmp sslive sh and rtmp convert sh write rights to var log rachni full access to your recordings folder configured in rtmp conf and main conf	nginx-rtmp stream php7 javascript	front_end
MMDAgent-EX	mmdagent ex an extended version of mmdagent for research development and fun of dialogue system on multiple mobile cloud environment top web site https mmdagent lee lab org what is here this page contains related tools and materials for mmdagent ex it also has a issue based communication channel to share feature requests plans bug reports and other technical issues tools mit a tiny script to make and update content index file how to use it https mmdagent ex dev docs create mit		front_end
PaulAtkins88.github.io	paul atkins website created for introduction to information technology rmit assignment 1 view site by visiting https paulatkins88 github io		server
sensorbee	build status https travis ci org sensorbee sensorbee svg branch master https travis ci org sensorbee sensorbee coverage status https coveralls io repos github sensorbee sensorbee badge svg branch master https coveralls io github sensorbee sensorbee branch master this project has been discontinued sensorbee lightweight stream processing engine for iot see http sensorbee io for details		server
frontend-resources	frontend resources a name top a a collection of all the resources i use to keep up with the latest in front end web development tom mcgurl tom mcgurl https twitter com tom mcgurl front end web development is rapidly growing and changing sometimes it s tough to keep up with all of the libraries and frameworks out there as a lead javascript developer at my company i m often approached by co workers and friends who are looking to get into front end development or polish their skills i decided to make this repo to list all of the great resources i use everyday to keep up to date and to learn about all the awesome things happening on the web inspired by all the awesome lists on github https github com bayandin awesome awesomeness also see the repositories section repos table of contents podcasts podcasts learning resources tutorials learning twitter twitter blogs blogs books books videos videos repositories repositories style and best practice guides style and best practice guides things to check out things to check out no particular order what i m learning about this week what im learning this week meetups meetups road map road map podcasts a name podcasts a podcasts are an amazing way to get the latest news and info on all the awesome things happening in web development especially if you re a commuter this is where i get most of my news keep up with the happenings in the javascript community a name jj a javascript jabber http devchat tv js jabber weekly every wednesday an awesome podcast for everything javascript and my personal favorite don t skip over the early episodes even though they may not be current they have awesome info on things like backbone coffeescript ember javascript objects and many more hosted consistently by charles max wood of devchat tv http devchat tv check out his other podcasts they are all awesome other co hosts are also great see the twitter section twitter shoptalk http shoptalkshow com weekly another great podcast hosted by dave rupert and chris coyier of css tricks they cover a lot of different areas and have some design focused episodes as well as javascript episodes the web platform podcast http thewebplatform libsyn com another great podcast that covers all sorts of frontend technologies they cover things from web components to web rtc and much more a name aia a adventures in angular http devchat tv adventures in angular weekly every thursday an amazing angular podcast from the same guys that do javascript jabber a must listen to for anyone getting into or already using angular nodeup http nodeup com nodejs podcast that covers a wide range of node related topics and covers the future of nodejs as well as tips for nodejs developers frontside the podcast https frontsidethepodcast simplecast fm the hosts do a lot of work in emberjs so there are some really fantastic discussions on the emberjs framework they also cover a wide range of topics from discussions about being a developer current tech trends and news a name ngair a angular air https ng air github io this podcast can also be watched in video form on youtube this is another fantastic angular podcast codewinds http codewinds com podcast html it covers jsconf 2014 modular javasctipt isomorphic apps reactjs and more codewinds is back after a short hiatus and now has plans for weekly episodes ember land http ember land an podcast all about ember this is a technical podcast that talks about ember features the ember community and ember news ember weekend https emberweekend firebaseapp com episodes our first foray an weekly emberjs podcast the hosts chase mccarthy and jonathan jackson discuss ember projects they are working on ember news resourses and more the changelog https changelog com podcast the changelog is another great developer podcast it covers a wide range of topics including recently typscript reactjs ember and the rust programming language react podcast http reactpodcast com by monthly a new podcast centering around facebook s reactjs 5 minutes of javascript https fivejs codeschool com weekly an awesome podcast with updates on what s going on in the js community in 5 minutes they cover the past weeks js news javascript air https javascriptair com weekly a fantastic podcast about all things javascript they also do live streaming of the podcast so users can tweet in questions really great topics react 30 https react30 com a podcast dedicated to react and from the same team as javascript air this podcast covers some awesome reactjs react topics in 30 minutes frontend happy hour http frontendhappyhour com a group of developers discuss all things front end over drinks this is a great podcast and the topics are awesome and super informative they cover things such as the future of javascript frameworks keeping up with frontend trends and more react native radio https devchat tv react native radio a podcast that focuses on react native react native this is a great resource for anyone interested in or working with react native elm town https elmtown github io a podcast dedicated to the elm programming language a great resource for anyone interested in or working with elm space dojo http podcast crater io a roundtable podcast about javascript this podcast has some episodes covering awesome topics such as nativescript redux elm and react full stack radio http www fullstackradio com a podcast for developers interested in building great software products webostie this podcast covers the full stack but has some great frontend topics this is a fantastic podcast for those interested in not only the frontend but how it connects to the entire stack jamstack radio http www heavybit com library podcasts jamstack radio a podcast all about the jamstack a stack for building fast and secure apps and websites jam stands for javascript apis and markdown you can read more about the jamstack here https jamstack org learning resources tutorials a name learning a here are some of the great websites that offer tutorials egghead io https egghead io a name eio a a super awesome site that specializes in small bite sized video tutorials i direcet anyone who asks me for great angular tutorials to go here they have videos covering things such as angular core javascript react es6 d3 and nodejs a must for any front end developer many of the hosts and guests from the podcasts above contribute tutorials to this site exercism io http exercism io exercism io is an awesome site with exercises for many different languages they have a javascript track as well as an esnext track you work on one exercise at a time and when you are finished you submit your answer you can then view your solution on the site where you can comment others can comment and you can iterate on your solution you can also view how others solved the same problem so you can see the many ways to tackle a problem i ve been doing the elm elixir clojure and javascript tracks and it s been a ton of functions you can check out my exercism repos here but the real fun is doing it for yourself elm https github com tommcgurl elm exercism elixir https github com tommcgurl elixir exercism clojure https github com tommcgurl clojure exercism pluralsight http www pluralsight com tag developer pagesize 48 sort new a name ps a this site has tons of material on everything development many of the hosts and guests from the podcasts above contribute tutorials to this site scotch io https scotch io this is a great site with many tutorials on everything front end they cover the entire mean mean mongodb expressjs angular and nodejs stack they have a great reactjs series they do an article each week on the top code pens http codepen io great site to bookmark on your phone github https github com this one should be obvious but there are a ton of tutorial repos here just search for what you are interested in many time the repo will link out to a blog to go along with the tutorial codrops http tympanus net codrops great web design tutorials and inspirations learn es6 harmony http learnharmony org this site lets you try out the latest features of es6 es6 right in the browser learn the new features while trying them out yourself try out arrow functions object destructuring method shorthand template strings classes and more functional programming tutorial http reactive extensions github io learnrx this interactive tutorial is a great way to learn the basics of functional programming this tutorial takes you through using functions such as map filter and reduce to make your code more concise and durable functional programming provides developers with the tools to abstract common collection operations into reusable composable building blocks dashing d3 https www dashingd3js com table of contents this is an interactive tutorial on the charting library d3 it is a nice tutorial that goes over the basics and builds off them to teach you d3 in a fun interactive way javascript 30 https javascript30 com this is a 30 day vanilla javascript challenge build 30 things in 30 days with 30 tutorials homepage warriorjs https github com olistic warriorjs a fun way to learn javascript you play a game by writing javascript to control your character your character has to climb a tall tower where on each floor you must write code instructing your character to attack rescue captives and reach the stairs twitter here are some of the people groups that i follow on twitter that post some great front end news and articles overall my suggestion is just search and follow the people who s blogs articles you ve read or who s names you know from podcasts tutorials videos conferences and the community in general eggheadio https twitter com eggheadio posts when new videos come out javascriptdaily https twitter com javascriptdaily the name says it all javascript news reactjsnews https twitter com reactjsnewsnk shares reactjs news tutorials and creations codrops https twitter com codrops they post when new articles come out and when they release their web design development news collective embersherpa https twitter com embersherpa tweets about emberjs resources news and the community emberweekly https twitter com emberweekly emberjs news articles tips and code angularjs https twitter com angularjs another self explanitory one angular news jashkenas https twitter com jashkenas jeremy ashkenas jeremy ashkenas created backbonejs underscorejs and coffeescript follow jeremy for for tweets about the above and javascript in general check out the javascript jabber jj episode with jeremy on backbone wycats https twitter com wycats yehuda katz yehuda is a member of the emberjs ruby on rails jquery and now rust core teams yehuda tweets about ember and javascript also check out yahuda many of the podcasts podcasts i ve listed above danwahlin https twitter com danwahlin dan tweets about javascript news and articles he also has a great blog with some good tutorials and posts check out his awesome blog https weblogs asp net dwahlin see the blog section blogs john papa https twitter com john papa john is featured on the adventures in angular podcast http devchat tv adventures in angular see above he tweets about javascript tidbits and especially angularjs he also has a blog http www johnpapa net see the blog section blogs wardbell https twitter com wardbell ward built breezejs http www getbreezenow com he also does a lot of work in angular and tweets about that ward is featured in many of the podcasts podcasts above check out his javascript jabber jj episode on the mean stack mean also check out the adventures in angular aia episodes he co hosts on testing with angular scotch io https twitter com scotch io tweet when new articles com out great way to keep up on the sites tutorials bennadel https twitter com bennadel ben is always posting things about javascript he also has a great blog http www bennadel com see the blog section blogs josepheames https twitter com josepheames one of the host of javascript jabber jj and adventures in angular aia one of the organizers of ng conf http www ng conf org tweets about angular and javascript in general he teaches pluralsight ps cources as well js dev https twitter com js dev aaron frost aaron frost is a co host on many javascript jabber jj and adventures in angular aia episodes he is also an organizer of ng conf http www ng conf org he also has video tutorials on egghead io eio auser https twitter com auser ari lerner ari is the author of the popular ng book ngbook a lot of posts about angularjs news updates and of course ng book tomdale https twitter com tomdale tom helped create emberjs and is part of the core team tom tweets about emberjs and javascript eisenbergeffect https twitter com eisenbergeffect rob eisenberg rob is most famous for his javascript framwork durandal http durandaljs com rob now works on a new framework called aurelia aurelia he also worked on angular 2 for a while before leaving and releasing aurelia he also has a blog http eisenbergeffect bluespire com see the blog section blogs floydophone https twitter com floydophone pete hunt pete hunt is most famous for his work at facbook he is part of the reactjs team he tweets about reactjs flux and all things related ryanflorence https twitter com ryanflorence ryan florence is a huge advocate for reactjs and started the popular react router https github com rackt react router ryan posts about javascript reactjs updates news and his reactjs training courses https reactjs training com mjackson https twitter com mjackson michael jackson works with ryan florence above on the react router https github com rackt react router he posts about javascript as well as his reactjs training courses with ryan florence reactjs training courses https reactjs training com stevenewcomb https twitter com stevenewcomb steve newcomb is currently most famous for being the ceo and founder of famo us follow steve for updates on famous as well as some awesome javascript posts ericelliott https twitter com ericelliott lang en eric is a fantastic person to follow for javascript goodies he tweets frequently and his tweets always contain awesome javascript info and resources i m constantly retweeting his stuff becuase it s realy awesome dan abramov https twitter com dan abramov a name danabramov a dan abramov is the creator of redux redux and some awesome react components and libraries dan tweets about javascript react and of course redux stackjs https twitter com stackjs javascript stacks is another great resource for javascript resourses and info they post about cool new libraries code pens jsbin jsfiddles and other javascript tidbits this is another frequent tweeter echojs https twitter com echojs community driven news site entirely focused on javascript development html5 and front end news echojs is a great resource that tweets all things js news blogs and repos follow frameworks and libraries emberjs https twitter com emberjs reactjs https twitter com reactjs gruntjs https twitter com gruntjs gulpjs https twitter com gulpjs nodejs https twitter com nodejs official iojs https twitter com official iojs ionicframework https twitter com ionicframework and so on just search for things you use in your day to day development blogs medium https medium com search q javascript medium is a place to share your stories and ideas medium com about i ve linked above to the javascript section of medium i just did a search for javascript this site is great and many awesome developers post and link to articles here it s also a great place to get started with spreading your ideas without having to buy your own domain or setup a blog site john pappa s blog evangalist on the loose http www johnpapa net blogs about everyting javascript and angular he has some great posts ben nadel s blog http www bennadel com ben has some awesome posts and tutorials you can learn a lot aobut javascript on this site you ll also always be able to spot him in all of his great photos from different conferences dan wahlin s blog https weblogs asp net dwahlin another awesome blog for javascript tutorials he covers everything from angularjs to core javascript such as es6 rob eisenberg s blog http eisenbergeffect bluespire com rob discusses his new framework aurelia aurelia he also compares aurelia to other frameworks such as angular 2 and his experiences working on angular 2 and why he decided to leave books here are some of the books i ve found super useful elequent javascript http eloquentjavascript net this book is fantastic there is an online version and a paperback version http www amazon com gp product 1593275846 ref as li qf sp asin il tl ie utf8 camp 1789 creative 9325 creativeasin 1593275846 linkcode as2 tag marijhaver 20 linkid vpxxxsryc5cog5r5 i prefer the onine once becuase it has interactive excersises at the end of each chapter realy great resource to learn the ins and outs of javascript ng book https www ng book com a name ngbook a an awesome angular book this was the first book i read when i started angular and it was extremely helpful it s constantly being updated written by ari lerner co runner of ng newsletter http www ng newsletter com see twitter section twitter the print version of the book is available through amazon http www amazon com ng book the complete book angularjs dp 099134460x ref tmm pap title 0 ie utf8 qid 1389643028 sr 8 1 and createspace https www createspace com 4572262 ng book homepage essential javascript http www amazon com effective javascript specific software development dp 0321812182 this book goes deep into the workings of javascript and helps provide a better understanding when working with the language also check out the javascript jabber episode with the author david herman http devchat tv js jabber 044 jsj book club effective javascript with david herman you don t know js https github com getify you dont know js a name ydkjs a this is an open source book series that dives deep into the core mechanisms of the javascript language the readme you can read them all online for free or even buy the print copies these books cover everything from the super basics in the book up going https github com getify you dont know js blob master up 20 20going readme md you dont know js up going all the way to es6 beyond https github com getify you dont know js blob master es6 20 20beyond readme md you dont know js es6 beyond videos conference videos 2015 ng conf 2015 https www youtube com user ngconfvideos this is the youtube page for all the ng conf videos march 5 6 salt lake city utah ember conf 2015 https www youtube com watch v o12 90dm qs list ple7tqudrkcyacwiups0cjpyt6tjub4xxu this is the youtube page for all the videos from ember conf 2015 march 3 4 portland oregon reactjs conf 2015 https www youtube com watch v kvz p zi6w4 list plb0iamt7 gs1cbw4qonlqztyv1taw0scr this is the youtube page for the videos from reactjs conf 2015 january 28 29 facebook hq ca react rally 2015 https www youtube com playlist list plud4kd wl zzhhy g8hpnztvx m35loxq this is the youtube playlist for the react rally videos august 24 25 salt lake city ut ng europe 2014 https www youtube com channel ucegup3tjjfmsem 1y8ivisq youtube page for ng europe videos jsconf 2015 https www youtube com channel uczovcacnddcfgdf41p z0ia youtube page for all of the jsconf videos fluent conf 2015 https www youtube com watch v 2ksxo2 lfl0 list pl055epbe6d5zqihe7na5f6iq bznjuwvs youtube playlist for all the fluent conf 2015 talks some really influential members of the javascript community talk all things web and javascript 2016 reactjs conf 2016 https www youtube com playlist list plb0iamt7 gs0m8q95ric2lom6nc77q1iy youtube playlist for all of the reactjs conf 2016 videos february 22 23 san francisco ca react rally 2016 https www youtube com watch v mad4oly9yg list plud4kd wl zysfu3tiysb4wqffqzo ejq youtube playlist for all the react rally videos august 25 26 salt lake city ut elm conf 2016 https www youtube com playlist list plgljm3byamph2zuz1nbkhqyeawe4sn0cd youtube playist for all the elm conf videos september 15 st louis missouri other js must watch https github com bolshchikov js must watch this is a list of must watch videos devoted to javascript the readme this repo has some fantastic javascript videos it is sorted by date from 2009 2015 the lastest entry is the two part series using ecmascript 6 today repositories there are also a ton of github repos that are great to watch some are dedicated to collecting all of the resources for a given topic these are where i got the inspiration for this these are amazing ways to find a material on a particular framework or library here are some of the ones i use awesome react https github com enaqx awesome react a collection of awesome react tools resources videos and shiny things the readme awesome angularjs https github com gianarb awesome angularjs a list of awesome angularjs services directives utilities and resources the readme awesome nodejs https github com sindresorhus awesome nodejs a curated list of delightful node js packages and resources the readme frontend development https github com dypsilon frontend dev bookmarks an amazing huge list of awesome frontent development resources a huge list of frontend development resources i collected over time sorted from general knowledge at the top to concrete problems at the bottom dypsilon https github com dypsilon awesome javascript https github com sorrycc awesome javascript a collection of awesome browser side javascript libraries resources and shiny things the readme awesome ember https github com nmec awesome ember a curated list of awesome ember js things the readme react primer draft https github com mikechau react primer draft a great repo for getting started with building single page applications with reactjs does a great job covering all the aspects of react including component life cycle hooks state properties mixins and more angular2 education https github com timjacobi angular2 education a curated list of helpful material to get started with education on angular 2 the readme this is an awesome collection of resourses for leanring angular 2 ajs awesome react native https github com jondot awesome react native a curated list of awesome articles tutorials and resources dealing with react native the readme front end developer interview questions https github com h5bp front end developer interview questions this repos is a fantastic resource to keep bookmarked it is a list of common questions that someone can ask when interviewing for a front end developer position this is useful for not only the people asking the questions but also for those learning javascript the quesetions focus on some common areas of knowlege for javascript html and css that a front end developer should have experience with this is a good list to go throught to see where you stand on these topics and to get an idea of where you have room for improvement for example if you see a question that you don t know how to answer you now have a topic you can look into to brush up on your skills es6 features https github com lukehoban es6features readme a name es6features a a repo covering the features of the es6 now es2015 es6 standard it contains examples of all of the new functionaity that are well commented for understanding this is a great place to start looking at es6 and thinking about using babel babel so that you can start writing es6 today js stack from scratch https github com verekia js stack from scratch a step by step guide to building a modern javascript stack from scratch this repo walks users through creating a modern web application using the latest javascript trends awesome elm https github com isruslan awesome elm a great curated list of elm resources public apis https github com toddmotto public apis this is a currated list of free to use json apis this list is an awesome place to look for ideas on what kind of app you could build the list is organized into categories such as games comics calendar music news and much more style and best practice guides style guides are a useful way to get everyone on the same page when it comes to code style style and best practice guides help make code more standard readable and shareable all of which make code collaboration and onboarding easier there are plenty of benefits to using a styleguide amongst a team or even for your own benefit javascript style guide https github com airbnb javascript this repo is a javascript style guide from the folks at airbnb this guide will help you take advantages of the benefits listed above angular styleguide https github com johnpapa angular styleguide this is an awesome styleguide for angular john s styleguide is widely adopted and will get you writing your angular code in a way that follows best practice and makes it easier to test build and understand the benefit of this style guide is that like any styleguide more adoption means more people writing code in a way that others are already familiar with notable mention todd motto s angular styleguide https github com toddmotto angularjs styleguide this styleguide is very similar to john s but differs in a few places follow whichever one works best for you since they are similar enough you will still get the benefits discussed above git style guide https github com agis git style guide this guide covers popular practices for working with git getting into open source can be intimidating i m still working on it myself style guides like this help make contributing to open source via github less intimidating by guiding you on best practices this guide covers things like branch naming commits merging and more things to check out no particular order reactjs http facebook github io react a name react a awesome javascript library from the facebook team has some great concepts and is a great alternative for any existing view layer in your mvc applicatoin angular https angularjs org and angular 2 https angular io a name ajs a fantastic javascript framework and currently the one i use the most extremely popular and a must know right now emberjs http emberjs com a framework for creating ambitious web applications emberjs homepage nodejs http nodejs org and now io js https iojs org as well a name node a i m sure almost everyone knows about node at this point but iojs is the latest fork of node and created in the interest of ensureing nodejs s future moving forward i don t think i can explain it any better than the website lead in a must know for front end developers it s javascript that can run on the server you can use this to run a javscript task runner like grunt or gulp see below to make you development way more productive see mean stack mean npm https www npmjs com node package manager npm comes with nodejs npm is amazing it is used to manage project dependecies using a little file called package json npm is quickly becoming a core element in front end web development and in my opinion a driving force for javascripts success with tools like browserify http browserify org and systemjs https github com systemjs systemjs you can now use npm to manage your browser and nodejs dependencies javascript task runners you can use task runners to do things like start a server watch for changes in html css and js and refresh your browser when you save pre process your sass or less and generate css create sprite sheets from images so much more there are many options and i ve listed some below try them all and choose the one that works best for you your team grunt http gruntjs com an awesome javascript task runner that will make you super productive grunt take a configuration approach in which you configure your tasks using a gruntfile gulp http gulpjs com an alternative to grunt that favors code over configuration files with gulp you write code that pipes your output into the next process and is easy to grok for those that prefer straight code over a config files see also brocolli https github com broccolijs broccoli and brunch http brunch io yeoman http yeoman io a scaffolding tool that can generate a project scaffold for you yeoman is awesome for hitting the ground running on any project generate out the boilerplate code for your next project with some of the great open source generators out there here are some of my favorites angular fullstack https github com daftmonk generator angular fullstack angular https github com yeoman generator angular react webpack https github com newtriks generator react webpack bower http bower io a package manager for the web bower io bower is a package manager for all of your client side browser dependencies use bower to manage your angular jquery or bootstrap versions for example ionic http ionicframework com a name ionic a a front end sdk written with angular and ui toolkit for creating awesome hybrid mobile apps uses cordova to wrap app in a deployable native application super super awesome see next entry ng cordova http ngcordova com angular directives and wrappers around the cordova api mean stack http en wikipedia org wiki mean a name mean a mean stack is a full stack solution that stands for mongodb link expressjs link angular ajs nodejs node with mean you can use javascript across your entire stack mongodb uses json for document storage node is javascript on the server express is a library for setting up a rest endpoint with node and angular is the javascript framework for your client firebase https www firebase com firebase is a backend as a service firebase let s you store and sync data in real time react native http facebook github io react native a name react native a build native applcations using react and javascript this is truely an awesome concept this does not create a wrapper around a uiwebview but instead uses react components to render native applivation views it supports both ios and android check out my post react native post below on it aurelia http aurelia io a name aurelia a aurelia is a new framework from rob eisenberg http eisenbergeffect bluespire com aurelia uses the latest javascript concepts such as es6 or now es2015 web components and object observe also has adaptive binding definitely worth checking out ecmascript6 a k a es6 now es2015 https github com lukehoban es6features a name es6 a es6 is the latest standard for javascript the link above documents the features that make up es6 es6 is expected to be ratified in june 2015 lukehoban es6features you can start writing your applications in es6 today by using a transpiler the most popular right now are babel see bellow google s traceur https github com google traceur compiler babel https babeljs io a name babel a babel is a javascript compiler it allows you to use next generation javascript standards such as es6 es2015 and later javascript standards today it does this by transpiling your es6 code into es5 javascript so that it can work in browsers today materialize http materializecss com a name materialize a materialize is an awesome styling library for material design you can think of it like bootstrap for material design i ve been using it recently for prototyping as well as a react application demo and i m very happy with it try it out on your next hack to style it up json server https github com typicode json server a name json server a get a full fake rest api with zero coding in less than 30 seconds seriously the readme md this is an awesome module for setting up a mock backend using json focus working on your frontend and iterate on your backend models easily check out egghead io https egghead io s tutorial creating demo apis with json server https egghead io lessons nodejs creating demo apis with json server reapp http reapp io reapp is to reactjs what ionic is to angular reapp let s you build awesome mobile apps with reactjs it contains a set of react components known as react ui kit it uses webpack and babel out of the box immutablejs https facebook github io immutable js immutability in javascript is a popular conversation right now in the community this is facebook s library for creating immutable collections the site does a good job explaining the benefits of immutable data jspm http jspm io jspm is a browser package manager that handles loading all types of modules amd commonjs and es6 in the same was as the es6 module loader https github com moduleloader es6 module loader this is a great option for developers looking to use es6 modules today rxjs https github com reactive extensions rxjs tree master doc a name rxjs a rxjs is a library for that helps writing asynchronus and event based programs rxjs makes it easy to write reactive programs in javascript check out egghead io s rxjs section https egghead io technologies rx to learn the basics webpack http webpack github io docs what is webpack html a name webpack a webpack is a module bundler similar to browserify webpack is well suited for large single page apps that need to break up their bundles into diffrent modules webpack 2 is on the horizon the new website https webpack js org is up and contains new documentation https webpack js org concepts flow https code facebook com posts 1505962329687926 flow a new static type checker for javascript flow is a static type checker for javascript much like typescript http www typescriptlang org it is often used in conjunction with react becuase it supports jsx check out the javascript jabber podcast episode on flow http devchat tv js jabber 163 jsj flow with jeff morrison and avik chaudhuri ampersandjs http ampersandjs com a modular mvc framework similar to backbonejs it s modularity allows you to pull in only the components that you need try using the ampersand collection https github com ampersandjs ampersand collection as the store for your reactjs things to check out no particular order app react router http rackt github io react router an awesome router built for reactjs extremely useful when building apps with react similar to other routers and inspired by the amazing emberjs router redux http rackt github io redux a name redux a a state container for javascript apps redux works really well with react apps becuase it builds of the ideas of flux it s state maintenence allows for live code editing and even a time traveling debugger making the developer experience awesome check out this video https www youtube com watch v xssnoqynths from react europe 2015 where the creator dan abramov danabramov demonstrates his live reloading and time traveling debugging egghead io egghead io has an awesome and free series on redux https egghead io series getting started with redux by dan abramov danabramov definitely check it out he does a great job explaining redux with some testing and es6 along the way devdocs io devdocs io devdocs is a fantastic open source web app for offline documentation similar to the os x app dash but web based they use local storage to allow users to save their favorite front end library framework documentation for organized searchable and offline viewing they even recently added the react native docs elm http elm lang org a name elm a elm is a functional programming language that compiles to javascript its architecture makes it easy to build great web apps elm was the inspiration behind the redux architecture definitely check out elm if you are interested in functional programming and super fast rendering inspired by reacts virtual dom virtual dom https github com matt esch virtual dom a javascript dom model supporting element creation diff computation and patch operations for efficient re rendering docs virtual dom is a collection of modules designed to provide a declarative way of representing the dom for your app docs virtual dom was inspired by the notion of virtual dom from reactjs and basically seperates out that aspect into its own library this concept has gained a ton of popularity since it was implemented and widely introduced to the javascript community by reactjs check this library out and see if it could work in your existing app as a replacement for your view layer electron http electron atom io build cross platform desktop apps with web technologies website electron allows you to build desktop applications using javascript html and css electron was used to build github s atom editor https atom io as well as the popular chat client slack a pretty impressive app electron allows you to build awesome native desktop applications and the ones i ve seen made with it so far are really great scotch io has a tutorial on building an app with angular and electron https scotch io tutorials creating desktop applications with angularjs and github electron cyclejs http cycle js org a functional and reactive javascript framework for cleaner code website cyclejs is a framework that uses observables from rxjs rxjs and combines them with a reactive programming library in order to simplify your application architecture egghead io egghead io currently has a free series on cyclejs https egghead io series cycle js fundamentals css modules https github com css modules css modules css modules lets developers easily scope and compose styles a css module is a css file in which all class names and animation names are scoped locally by default readme glen maddern one of the authors has an awesome blog post on css modules http glenmaddern com articles css modules mobx https mobxjs github io mobx simple scalable state management mobx is a library for managing state within your applications it is similar to redux redux but has some key differences including the fact that is is built on observables and uses a functional reactive programming frp pattern here is a blog post from formidable labs on why they chose to use mobx over redux https formidable com blog 2016 06 02 why we chose mobx over redux for spectacle editor exponentjs https getexponent com exponent is a platform for building native ios and android apps using react native react native it allows you to write a react native app without ever having to open xcode or android studio it also has a nice application generator to get you started ignite https infinite red ignite ignite is a react native application generator ignite helps you get started building react native react native applications by scaffolding out an application it comes with a lot out of the box and is a great starting point for anyone looking to build a react native application yarn https code facebook com posts 1840075619545360 a package manager for javascript from facebook yarn manages your npm and bower packages in an intelligent way it supports local caching of packages shoutem ui tookit https shoutem github io ui an open source ui toolkit for building react native react native apps this is a very polished toolkit with with awesome ui elements and themes jamstack https jamstack org jamstack stands for javascript apis and markup this is a term being used a lot lately to refer to fast secure apps bundled and served over a cdn there is also a podcast below dedicated to this stack react native express http www reactnativeexpress com an amazing react native tutorial that covers everything from modern js with es6 to creating an uber clone the guide has live examples as well as exercises i highly recommend this for anyone looking to work with react native svelte https svelte technology svelte is not a javascript framework in the way we think of them today instead of interpreting application code at run time it interprets it at build time this basically means that you aren t hindered by the performance cost of framework code and in theory your application can load faster you can read the introductory blog post here https svelte technology blog frameworks without the framework you can also check out this blog post http react etc net entry react vs svelte the javascript build time framework comparing svelt to react preact https github com developit preact preact is a fast 3kb alternative to react with the same es2015 api docs as stated preact is a reactjs alternative it has a smaller footprint since it only supports modern es6 data fns https github com date fns date fns data fns is toolset for manipulating javascript dates it is built to be modular so you include only what you need data fns is like lodash for dates it has 140 functions for all occasions https date fns org docs docs jest http facebook github io jest jest is a testing framework created by facebook jest works especially well for testing reactjs react code jest has been around for a bit but after some recent updates has really gained momentum and community support definitely check it out for testing your react components react bits https github com vasanthk react bits an awesome collection of react patterns tips tricks and techniques this repo collects some of the best react libraries around it has awesome resources for things like gotchas style guides and anti patterns create react native app https github com react community create react native app create react native apps with no build configuration repo readme this project is an awesome way to get started building applications with react native it includes the expo library expo io formerly exponent which is a fantastic library for cross platform react native components much like create react app the build process is abstracted away allowing developers to concentrate on building applications with javascript rather than spending time setting up a build process and dealing with cross platform compatibility between ios and android apollo client http dev apollodata com a graphql client library that works with all the popular frontend frameworks as well as native applications apollo makes it easy to fetch only the data you need when interacting with a graphql endpoint works out of the box with redux react and react router draftjs https draftjs org an awesome rich text editor by facebook framework for react applications draft js is great for doing rich text editing it has a great plugin ecosystem everything is customizable i found draftjs super easy to use definitely check it out if you are looking for a rich text editor prettier https github com prettier prettier prettier is an opinionated formatter for javascript that has gained popularity recently it supports all the latest language features such as es2017 jsx and flow this differs from jshint and eslint in that it doesn t require a large configuration it instead takes javascript as input transforms it and outputs the formatted js react vr https facebook github io react vr build vr websites and interactive 360 experiences with react website react vr lets you create awesome virtual experiences using the react framework similar to the way react native works react vr let s you code javascript that renders vr uis instead of plain dom meetups a friend of mine has been going to some meetups lately and invited me along to one meetups are awesome they are a great way to network meet other people who share your interests in development and hear some awesome talks javascript meetup map http javascript meetup com this is a map from meetup com http www meetup com of all the javascript related meetups available just enter your zipcode and find a meetup in your area what i m learning this week i ve decided to add a section covering what topics i m looking into on a weekly basis i m putting it here for now until i move some of this over to a blog week of january 17 it s been quite a while since i ve updated this so i apologize i ve been keeping busy though it s 2016 now and 2015 was a great year for javascript we saw react gain popularity while angular s popularity waned a bit in the wake of angular 2 while it looks like angular 2 is finally upon us having recently gone beta we have yet to see how the community will take to it i ll definitely write more on that as things pan out over that last few months i ve had the opportunity to work on some brilliant apps at work i was lucky enough to work on our mobile app which is still being written using react native with redux i more recently have been working on another project at work a responsive web app using react with redux between those two apps and some hack week projects at work i ve definitely gotten to dive into redux a bit more and can say that i m definitely enjoying it it s not without its quirks but it definitely helps me understand my application in a way that other architectures don t in my free time i ve been doing something different i ve really started to dig into the elm programming language elm and i have to say i m really enjoying it it s been extremely fun and challenging learning a new language being fully functional and statically typed elm is very different than what i m used to with javascript that said those differences seem to be what make it so great i m even enjoying writing tests in fact elm makes writing unit tests so easy that i ve actively been practicing tdd while working with it i m planning on putting up a tutorial on building a small app with elm soon in the meantime if anyone is interested in elm the github repo awesome elm https github com isruslan awesome elm has some wonderful resources for getting started october 4 i recently listened to the javascript jabber episode on the elm language https devchat tv js jabber 175 jsj elm with evan czaplicki and richard feldman with evan czaplicki the creator and richard feldman it was an awesome and inciteful episode shortly after i was lucky enough that my company was able to bring in richard feldman to give a talk on his experience with using elm elm for his day to day web app development i decided to give it a shot and i have really liked it so far at first it took a while to get used to the syntax and i m still far from fully understanding or writing it well but after going through the docs a ton things are finally starting to come together the documentation has been fantastic i m trying it out by building a small application one that i already started in react so far it s been going pretty well the static typing has allowed the compiler to catch any issues and it makes it really easy to write nice clean code once i m finished with the demo i will be posting it on my github in the form of a tutorial definitely check out elm it has a fantastic architecture and its functional style makes writing code clean and fun it s also the inspiration for the popular redux architecture redux september 20 the new job now has me commuting by train instead of by car this presents me with some great time to work on some fun projects i picked up a macbook air 11 that has worked out great as a travel machine one of the limitations though is that i am not connected to the internet while i m on the trian so everything i do has to work offline so i have a setup that has been working for me which i will describe i plan on extending this into an actual blog post at some point soon for some background the app i m currently working on uses reactjs as a view layer and ampersandjs models as the model layer i m also writing in es6 via the babel transpiler using webpack and the webpack dev server for a ui library i m using materialize materialize okay so the first issue was documentation i needed a way to view docs offline for that i use both dash an os x app for offline docs and devdocs io devdocs io that i mentioned above next i needed a way to make requests for data so that i could model out my stores for this i used json server json server which is an awsome and easy way to serve up json data next it s important that you aren t trying to use a cdn for any of your apps resources since the request for those files will fail when offline instead try to contain everything within the app either via bower or npm all npm and bower dependencies should be installed prior to trying to work offline one thing i did was create a seperate project where i install all of the libraries frameworks i think i will use and add them to a massive package json bower json then when i need them for one of my apps i pull them out of that project rather than trying to fetch them from a server august 30 it s been a while since my last post and i m happy to say that the break was due to me switching jobs i ve started an exciting and awesome new role that has me working and experimenting with a lot of exciting front end technology this past week in particular i ve been working on writing my own yeoman generators it s been super fun and simple i decided to write a generator to make things easier while i m working on the react version of my brew book application i found myself spending a lot of time typing out the react class components this led me to create a generator to generate out a react component class it s folder and corresponding sass file after using yeoman for so long it was awesome to finally write my own generator it s super simple since it s just node with ejs for the templating i m hoping publish one of my generators soon july 5 this week i ve been looking more into webpack webpack and babel babel i plan on using webpack for the reactjs react version of my brewbook app i also think it s time i started writing es6 so i ll be using babel as a transpiler i started reading some great articles on using webpack with reactjs and babel setting up a single page react web app with react router and webpack http jmfurlott com tutorial setting up a single page react web app with react router and webpack is a great article on getting started with webpack and the babel loader i also started es6 and beyond https github com getify you dont know js tree master es6 20 26 20beyond from the awesome you don t know js series ydkjs it is an amazing resource that really covers the new es6 features in depth i can t wait to start using these features june 21 this week i published my first two npm packages i published the modules i was working on last week ampfire collection https www npmjs com package ampfire collection and ampfire model https www npmjs com package ampfire model it was a pretty awesome experience publishing something to npm for the first time the npm site https www npmjs com has a great guide on how to publish a package https docs npmjs com getting started publishing npm packages so i just followed that it was a super easy processes essentially you just create a commonjs http www commonjs org module and export the api that you want then your package json dictates all of the info for your package including the name that it is exposed as to consumers of the package you can even add some keywords to your package json to make it more discoverable on npm i plan on updated the packages with some tests and even a demo soon i definitely recommend checking out the guide above and try publishing any libraries you may have created that you think others could benefit from june 14 this week i ve been taking a look at ampersandjs it s a nice modular alternative to backbonejs and i think it will work nicely as the store component to a flux application i plan on using ampersand collections and models as the stores for reactjs version of brewbook since ampersandjs models and collections emit events just like backbonejs models and collections they are easy to integrate with a react view layer in a flux application i also started modifying firebase s backbonefire https github com firebase backbonefire library so that will work with ampersandjs models and collections as apposed to backbone models and collections i m calling it ampfire https github com tommcgurl ampfire right now i have it broken into two parts ampfire model and ampfire collection i wanted to follow ampersands modularity once i m done it will be easy to integrate your ampersand stores with firebase may 17 i ve fallen a bit behind in updating this post section but i m going to try and keep it up to date i ve been working recently on a new repo experiment i have decided to start posting something i ve been working on it s basically my version of the popular todomvc my plan is to make a craft beer catalogging app in a few of the popular frameworks libraries i ve began posting my first version it uses the ionic framework ionic which uses angular the repo is called brewbook ionic https github com tommcgurl brewbook ionic next i plan on implementing it in react with flux april 19 this week i thanks to a friend i had the opportunity to attend the april code genius https genius engineering photos it was an awesome meetup hosted by the folks at genius http genius com jeremey ashkenas gave an awesome terminator themed talk on transpilers and how coffeescript compiles to javascript james somers https twitter com jsomers from genius gave an awesome and hilarious talk on genius image annotation feature in their chrome extension https chrome google com webstore detail genius beta ccaokncpmmjiakalbcfdbfmpcaiddjdn hl en us utm source chrome ntp launcher dave desandro https twitter com desandro gave a really great talk on his carousel library flickity http flickity metafizzy co and how physics isn t as intimidating as some think it was a great experience and i m already signed up to go to the may code genius http code genius com with jacob thorton https twitter com fat the creator of bootstrap bower and more it will definitely be a good time april 12 this week i continued to work a bit on the users demo react native i ve also been working with ionic ionic a bit again if you haven t checked out ionic yet definitely take some time to give it a look there is a great video from ng conf https www youtube com watch v wvr11fvceu4 from the guys at ionic that shows off some of the awesome stuff you can do with it i plan on putting a repo up soon with a demo application the web platform podcast http thewebplatform libsyn com had a great episode the past week http thewebplatform libsyn com 39 famous mobile performance mixed mode with steve newcomb of famo us steve covered a ton of stuff including their plans for mixed mode the future of the company and much more definitely check this episode out angular air had a great episode on testing with angular https plus google com events cb42tejb88eliamj65o2ogh12qo with guests julie ralph of protractor andres dominquez and zan thrash they discuss compare and contrast the different forms of javascript testing april 5 this week i do not have much to update since i was on vacation during that time however i managed to start my react native user s demo discussed in last week s post the repo is users demo react native https github com tommcgurl users demo react native you can read the details of it in last weeks post so far i ve managed to complete a good portion of the application most of the basic functionality is complete and i hope to finalize the functionality and spruce it up a bit in my next commits i m thinking about extending it a bit further than originally planned in order to display the use of flux and more crud operations so far i m very pleased with react native and it has been really fun to use during my vacation flight to austin tx i read some awesome articles on es6 and i started looking into the transpiler babel babel i plan on using this in future development so that i can begin working with es2015 it already has support for jsx and react and has plugins for all the major build systems also the codewinds podcast came back finally i listend to the two most recent episodes episode 14 http codewinds com podcast 014 html simply covers the reason for the long hiatus in episode 15 http codewinds com podcast 015 html talks to michael jackson and ryan florence about reactjs march 29 a name react native post a this past week facebook released react native http facebook github io react native i decided to try it out and was very impressed for those that have not heard react native is a way to build native applications using react and javascript this does not involve the uiwebview it instead uses the fact that react is decoupled from the dom to allow components to render to native views such as ios only platform supported at the moment i followed the getting started guide and the walkthrough for creating a listview movie application i was extremely impressed at how easy it was to use there were a few things that are unique to react native as expected but they are easy to pick up once you have some experience with reactjs i decided to start a demo repo where i expand upon the movies application it s called users demo react native https github com tommcgurl users demo react native i am using the random user api http randomuser me from randomapi https twitter com randomapi to show a master detail view of users i will be posting each commit as a step to making the application march 22 this week i m taking a look at angular 2 0 https angular io i have yet to catch up on all the ng conf videos but plan on doing that by the end of the week scotch io posted an article on the best news from ng conf 2015 https scotch io bar talk the best news from angulars ng conf 2015 a few weeks ago that does a great job summarizing the major points from the conference i got a chance to listen to adventures in angular episode 34 http devchat tv adventures in angular 034 aia live from ng conf 2015 where the guys discuss the conference and it definitely got me excited i also listened to the angular air episode 6 https plus google com events cmhq7mqarkoful8u7iusbjsdjtk with the ionic team they talk about their excitement for angular 2 and how they are working closely with the angular team while working on ionic 2 as someone who loves to use ionic i m super excited to see what it s next version has in store check out this blog post http ionicframework com blog angular 2 ionic from the ionic blog http ionicframework com blog that goes into more detail on angular 2 and ionic 2 angular 2 definitely looks like it packs some changes and i have to take a deeper look before commenting on them on the surface it looks like it has some serious preformance improvements which i m always down for i do like the idea that angular 2 0 will embrace es6 and web components i think this is great for the future of web and for pushing standards i ve started using customer elements one part of the web component spec at my job and it helped me solve a unique problem with some ease and elegance i m definitely excited to check out and start using angular 2 road map here are a few things i m planning on adding to this repo a where to start guide i have had many questions directed my way in the form of all of these frameworks and technologies are great but where do i start i would like this section to help guide developers that may be new to some of this stuff down the right path to connecting the dots i will explain how some of the things to check out things to check out no particular order can be used to create an actual application an tutorial section specifically for beginners sometimes its tough to find the right tutorials to jump in to a new library or framework i plan on adding links to tutorials guided towards developers trying out a libarary or framework for the first time		front_end
frontend-boilerplate	logo frontend boilerplate svg build status https travis ci com mirabeau nl frontend boilerplate svg branch master https travis ci com mirabeau nl frontend boilerplate coverage status https coveralls io repos github mirabeau nl frontend boilerplate badge svg branch master https coveralls io github mirabeau nl frontend boilerplate branch master david https img shields io david mirabeau nl frontend boilerplate svg maxage 2592000 https github com mirabeau nl frontend boilerplate known vulnerabilities https snyk io test github mirabeau nl frontend boilerplate badge svg https snyk io test github mirabeau nl frontend boilerplate we re hiring https www werkenbijmirabeau nl en this project is a highly opinionated boilerplate that can be used to quickly kickstart a new web development project it s built on modern tools such as gulp babel and browsersync using the boilerplate will help you stay productive by eliminating bikeshedding and by encouraging testing table of contents table of contents table of contents features features kick start kick start getting started ef b8 8f getting started set up your development environment set up your development environment creating new components creating new components creating new templates creating new templates linting and testing linting and testing building and uploading building and uploading extending the boilerplate extending the boilerplate how to configure bundles how to configure bundles how to including external dependencies how to including external dependencies how to run conditionerjs instead of vanilla how to run conditionerjs ef b8 8f instead of vanilla using the component library using the component library using the custom nunjucks component tag using the custom nunjucks component tag templates templates includedata plugin includedata plugin features a live reloading server with browsersync https browsersync io automated build process that includes but is not limited to scss compilation javascript transpiling and image optimization create documentation easy and almost automatically by simply adding a yml file to your component folders linting with eslint https eslint org and stylelint https github com stylelint stylelint sourcemaps custom nunjucks tags that allow external json loading into components keeping your data and views clean and separate kick start 1 clone this repo 2 run npm run dev and point your browser to localhost 3000 3 lint and test by running npm run codequality and npm run test 4 build by running npm run dist 5 upload by adding a env file to your project root with your sftp credentials and run npm run upload getting started this boilerplate utilizes gulp heavily to automate tasks and manage frontend dependencies if you re new to gulp here s a starter guide https css tricks com gulp for beginners set up your development environment 1 make sure you have nodejs installed in your machine for detailed instructions see installing node https nodejs org en download package manager 2 clone the project to your computer using git github com mirabeau nl frontend boilerplate git 3 navigate to your new folder and run this command to install the project specific dependencies npm install 4 done you can now start your development server by running npm run dev this command will start a local server located at http localhost 3000 note optionally you can run the development server in https mode without certificates by passing the https flag if you have to use certificates you can pass them in config js under browsersync it s important to realize that this boilerplate distinguishes between components and templates a component located in src components is just that a component it could be a menu or a list of items a template located in src templates is a collection of components what you ll see when opening your brand new server in a browser is a list of all your components and templates there is no hello world located in the index file open up home njk to see the components come together creating new components components are located in the src components folder as you can see we ve included 2 components that you can edit to your liking creating a new component is as simple as creating a new folder and adding a componentname njk file scss files of the same name should be added to the same folder and must also be called in src static scss all scss if your component utilizes javascript you can load it using html data attributes for example div class foo data module folder modulename will initialize src folder modulename js on your foo div the best way to get acquainted with the boilerplate is playing around with it try adding new components and changing old ones creating new templates frontend boilerplate uses nunjucks as a templating language elements that should return on every page such as title and meta can be found in src layout default njk other templates will extend from this default template mock data all assets that don t belong directly to the interface but are used for mocking content can be placed in the src mock folder some examples are audio video images and json data for network requests the mock folder is watched when running npm run dev and it s content is copied to the dist mock folder when added or changed linting and testing the codestyle task supports md css scss js json files and automatically formats the code the codequality task supports scss js files you can update the codestyle with npm run codequality or use the build in functionality of your ide which auto formats your code on save the linting tools currently check javascript code linting using prettier and eslint and scss file code linting using prettier stylelint you can run the linters with npm run codequality unit tests are done with mochajs javascript files in the component folder ending with spec js will be run through mocha run tests by executing npm run test building and uploading running npm run dist will compile and build in your src folder and pipe it to the dist folder this folder can then be uploaded to your server by running npm run upload in order to upload you ll need to add an env file to your project root with your sftp credentials upload host ftp example org upload user username upload private key key upload path example path if you want to generate a static website instead of a component library you can run npm run dist static this command will get all your templates and move them to the root of the dist folder so you can run a server directly in dist or upload to a static hosting like netlify or amazon s3 make sure that you have an index file in the root of templates folder automated deployment using pipelines in this example we use bitbucket pipelines it s very simple to enable automatic deployments using the sftp connection in pipelines put the environment variables in the bitbucket repository settings under repository variables create a bitbucket pipelines yml with the following content in this case we will start a deployment whenever changes are pushed to develop you can change the branch when necessary image node 14 17 4 pipelines branches develop step name deploy develop branch to the test environment script npm install npm run codequality npm run codestyleisvalid upload host bitbucket variable host upload path bitbucket variable path upload user bitbucket variable user upload private key bitbucket variable key npm run upload push some changes to develop and you re done extending the boilerplate how to configure bundles by default the boilerplate uses browserify to bundle all direct childs of the src static js in their own bundle to import a whole directory into your bundle make it importable via an index js file export default require spec js mode hash resolve reduce strip ext how to including external dependencies to include external dependencies in your procect you can either install them as runtime dependency using npm i save or import them directly from a vendor folder how to run conditionerjs instead of vanilla if you want to run conditionerjs https github com rikschennink conditioner 1 run npm i conditioner js save 2 remove the manual init code block in main js 3 uncomment the conditioner init code block in main js 4 enjoy using the component library components are visible in the component library when they contain a yaml yml file the yaml file should have the same name as the component s njk file and contains the following parameters title title of the component shown in the library description component description text demo div style width 100 height 200px background f2f2f2 div implementation implementation instructions note that demo should at least contain as this gets replaced with the component s html the component is rendered for each you provide within the demo parameter components can be nested either with or without a sub folder sub folders can go to any depth nav nav njk nav yml anothernav njk anothernav yml footernav footernav njk footernav yml using the custom nunjucks component tag a custom nunjucks tag is available as a convenience for loading external json data into a component component component name if the component has an accompanying json file with the same name its contents will be loaded and provided automatically scoped to just the component you can also augment the data set by providing a pojo plain old javascript object as the second parameter component component name cookies are delicious this is especially useful for overriding the default data for a component with template data component component name componentname where componentname is an object from the template s data set finally you can also provide an alternative data source as the second parameter this is great for component variations for example first the component directory is looked in for the provided path component component name component name variation json after that the path is looked for from the project base component component name data my custom data json you can also provide both a data path and inline data component component name component name variation json override something if the component resides in a nested folder simply write out the path to it for example component my sub folder component name templates like components templates can be nested inside of sub folders to any depth also like components templates that have an accompanying json file will have it automatically loaded and provided as template data includedata plugin in case the above isn t enough at some point the includedata plugin https github com vincentleung nunjucks includedata is available for even more freedom in including data from external json files refer to its documentation for details	frontend-bootstrap bootstrap html css javascript boilerplate frontend-starter-kit nunjucks stylelint codestyle browsersync gulp scaffold	front_end
doc	documentation of lel a libre euro lingua alliance the recent results of alpaca are impressive it was shown that it is realistically possible even without a supercomputer and a multi million budget to train competitive chatgpt style large language models unfortunately the existing instruction following models are not open source https opensource org osd and do not support multiple languages on the long run we strive to provide these so called instruction following large language models aka chatgpt style models for the european languages our goal is to provide models code training data and documentation that is 1 free open source https opensource org osd and permissive mit license https en wikipedia org wiki mit license 2 transparent 3 open for agile contribution and participation see contribution joining our community contribution joining our community 4 state of the art 5 up to date our ressources documentation repository https github com lel a doc multilingual alpaca prompt https github com lel a doc blob main alpaca prompt md ideas links and findings https github com lel a doc blob main ideas links findings md cleaned german alpaca dataset https github com lel a geralpacadatacleaned euroinstructproject https github com lel a euroinstructproject instruction datasets from existing german english and other european datasets hugging face organization https huggingface co lel a rough planning this section plans the first steps and outlines the individual ventures in the future first milestone in the first step we want to train evaluate and publish a german and english generative pre trained transformer gpt model of relatively small size this model will not yet have the so called instruction following capabilities the concrete steps towards this goal are 1 provide a clean and appropriate english and german text corpus 2 identify training method training code model type and hyperparameters 3 find a way to get the necessary computation power and storage 4 start monitor and maintain the training 5 evaluate the results on reference tasks 6 publish the final model and results second milestone add instruction following capabilities by fine tuning the gpt model from before this could be done in the same style as alpaca an openai api access might be necessary for this which would add additional costs identify training method training code and hyperparameters find a way to get the necessary computation power and storage start monitor and maintain the training evaluate the results on reference tasks publish the final model and results outlook add more european languages add different programming languages use more training data in general improve quality of training data determine and monitor bias take countermeasures if necessary train larger models retrain existing models to keep them current update training method training code model type and hyperparameters when new research is published add multimodal capabilities impediments and risks 1 we do not have sufficient computing power neither the hardware nor the money to rent see 5 https github com lel a doc issues 5 2 the licensing implications of using the openai api self instruct alpaca style training are not entirely clear see 6 https github com lel a doc issues 6 3 the llama training code is not open sourced see 4 https github com lel a doc issues 4 relevant links llama blog introducing llama a foundational 65 billion parameter large language model https ai facebook com blog large language model llama meta ai arxiv llama open and efficient foundation language models https arxiv org abs 2302 13971 llama model card https github com facebookresearch llama blob main model card md github chatllama https github com juncongmoo chatllama alpaca blog alpaca a strong replicable instruction following model https crfm stanford edu 2023 03 13 alpaca html github stanford alpaca an instruction following llama model https github com tatsu lab stanford alpaca arxiv self instruct aligning language model with self generated instructions https arxiv org abs 2212 10560 training data common crawl https commoncrawl org oscar https oscar project github io documentation gc4 corpus https german nlp group github io projects gc4 corpus html online language modelling dataset pipeline https github com huggingface olm datasets cleaned alpaca dataset https github com gururise alpacadatacleaned contribution joining our community our commitment to open source means that we are enabling in fact encouraging all interested parties to contribute and become part of our community contribution and feedback is encouraged and always welcome we communicate via a slack channel to get access to the slack please reach out to omar at huggingface co or philipp at huggingface co to be added licensing copyright c 2023 by the lel a team licensed under the mit license the license you may not use this file except in compliance with the license you may obtain a copy of the license by reviewing the file license https raw githubusercontent com lel a doc main license in the repository	llama alpaca llm nlp chat gpt europe german multilingual english language large-language-models	ai
ibu-devops-engineering-on-aws-cloud-group-11	devops aws academy project for course devops engineering on aws cloud group members merjema muji hanah sijer i rania weiss		cloud
robustlearn	contributors contributors shield contributors url forks forks shield forks url stargazers stars shield stars url issues issues shield issues url mit license license shield license url project logo br div align center a href https github com microsoft robustlearn img src https wjdcloud blob core windows net tools roblearn png alt logo width 400 a strong robustlearn strong a unified library for research on robust machine learning div latest research in robust machine learning including adversarial backdoor attack and defense out of distribution ood generalization and safe transfer learning hosted projects rift iccv 2023 adversarial robustness generalization ood code rift improving generalization of adversarial training via robust critical fine tuning https arxiv org abs 2308 02533 diversify iclr 2023 ood code diversify out of distribution representatio n learning for time series classification https arxiv org abs 2209 07027 drm kdd 2023 ood code drm domain specific risk minimization for out of distribution generalization https arxiv org abs 2208 08661 ddlearn kdd 2023 ood code ddlearn generalizable low resource activity recognition with diverse and discriminative representation learning https arxiv org abs 2306 04641 sdmix imwut 2022 ood code sdmix semantic discriminative mixup for generalizable sensor based cross domain activity recognition http arxiv org abs 2206 06629 marc acml 2022 long tail code marc margin calibration for long tailed visual recognition https arxiv org abs 2112 07225 fedclip ieee data engineering bulletin 2023 ood largemodel code fedclip fedclip fast generalization and personalization for clip in federated learning https arxiv org abs 2302 13485 chatgpt robustness arxiv 2023 ood adversarial largemodel code chatgpt robust on the robustness of chatgpt an adversarial and out of distribution perspective https arxiv org abs 2302 12095 stay tuned for more upcoming projects you can clone or download this repo then go to the project folder that you are interested to run and develop your research related repos transfer learning transferlearning everything for transfer domain adaptation and more https github com jindongwang transferlearning semi supervised learning usb unified semi supervised learning benchmark https github com microsoft semi supervised learning torchssl a unified ssl library https github com torchssl torchssl prompt benchmark for large language models promptbench adverarial robustness of prompts of llms https github com microsoft promptbench evlauation of large language models llm eval https llm eval github io federated learning personalizedfl library for personalized federated learning https github com microsoft personalizedfl enhancement of large language models llm enhance https llm enhance github io contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments trademarks this project may contain trademarks or logos for projects products or services authorized use of microsoft trademarks or logos is subject to and must follow microsoft s trademark brand guidelines https www microsoft com en us legal intellectualproperty trademarks usage general use of microsoft trademarks or logos in modified versions of this project must not cause confusion or imply microsoft sponsorship any use of third party trademarks or logos are subject to those third party s policies contributors shield https img shields io github contributors microsoft robustlearn svg style for the badge contributors url https github com microsoft robustlearn graphs contributors forks shield https img shields io github forks microsoft robustlearn svg style for the badge forks url https github com microsoft robustlearn network members stars shield https img shields io github stars microsoft robustlearn svg style for the badge stars url https github com microsoft robustlearn stargazers issues shield https img shields io github issues microsoft robustlearn svg style for the badge issues url https github com microsoft robustlearn issues license shield https img shields io github license microsoft robustlearn svg style for the badge license url https github com microsoft robustlearn blob main license txt		ai
web-hv	web hierarchy viewer a web based tool for inspecting ui of an in development android app launch app https google github io web hv development to run the project locally checkout the code and start a local web server https gist github com willurd 5720255 in the root directory this tool only uses html5 javascript apis and doesn t have any server side component see contributing md on how to submit patches dependencies jquery https github com jquery jquery javascript autocomplete https github com pixabay javascript autocomplete jszip http stuartk com jszip pako https github com nodeca pako jsbn http www cs students stanford edu tjw jsbn features inspect ui of running apps debug apps run commands on view nodes and change properties at runtime save hierarcy to disk for offline viewing and sharing this is not an officially supported google product	android development-tools	front_end
moments	moments a new flutter project getting started this project is a starting point for a flutter application a few resources to get you started if this is your first flutter project lab write your first flutter app https flutter dev docs get started codelab cookbook useful flutter samples https flutter dev docs cookbook for help getting started with flutter view our online documentation https flutter dev docs which offers tutorials samples guidance on mobile development and a full api reference		front_end
taxinomitis	machine learning for kids what is this this is the source code for the api and website behind machine learning for kids https machinelearningforkids co uk it s a tool aimed at children which introduces machine learning by providing them with hands on experiences for training simple machine learning systems and building things with them it provides an easy to use guided environment for training machine learning models for classifying text numbers or recognising images this builds on existing efforts to introduce and teach coding to children by adding these models to scratch https scratch mit edu about a widely used educational coding platform allowing children to create projects and build games with the machine learning models that they ve trained it s currently running at https machinelearningforkids co uk the code build status https travis ci org ibm taxinomitis svg branch master https travis ci org ibm taxinomitis this started as a personal side project by dale lane https github com dalelane for use by a couple of local schools it s grown far beyond what i expected all of this is a long winded way of saying that i never expected to share this code with anyone let alone open source it it definitely has many of the embarrassing hallmarks of a hobby project tinkered with in evenings and weekends please keep that in mind when you look through the code and bear with me while i try and tidy things up the project worksheets they are in a separate repository so that they can be updated more frequently without re deploying the application they re all ms word documents so if you d like to make improvements or even provide a new project worksheet that would be fantastic they are managed in a separate repository if you d like to report a problem with one of the project worksheets submit changes or suggest or contribute a new project worksheet please do that in the taxinomitis docs https github com ibm taxinomitis docs repository		ai
info	phys220 math220 cpsc220 course information course syllabus syllabus phys220 math220 cpsc220 2017f pdf welcome to the phys220 math220 cpsc220 course information readme file refer to this information throughout the semester for helpful tutorials references and tips please read the getting started gettingstarted md guide for orientation including how to create accounts on slack github and cocalc and manage the homework classwork work flow getting started click here first gettingstarted md primary course links try here first cocalc https cocalc com br collaborative calculation in the cloud free ubuntu linux servers that we will use for the course slack scststudents https scststudents slack com channel phys220 2017f br professional communication tool industry standard chat application that we will use for the course python scipy tutorials http www scipy lectures org br official scipy tutorials primary python resource we will use for the course course reference slides try here next check back throughout the semester for updates to these slides as they are live documents that are continually being improved 1 linux bash overview http slides com profdressel linux bash overview 1 vim overview http slides com profdressel vim overview 1 git overview http slides com profdressel git overview 1 ipython jupyter overview http slides com profdressel jupyter overview 1 numpy pandas overview http slides com profdressel numpy and pandas overview 1 python classes objects overview http slides com profdressel python objects overview 1 julia overview http slides com profdressel julia overview other resources read as needed coding standards chapman coding standards chapman 20coding 20standards pdf br chapman university cpsc coding standards use for your code python style guide official google https google github io styleguide pyguide html br official google style guide for python use for your code github resources github hello world https guides github com activities hello world br tutorial of a simple example that demonstrates the github workflow github flow guide https guides github com introduction flow br understand the flow of how github works for source code management mastering markdown https guides github com features mastering markdown br learn concise syntax for writing beautifully formatted html easily in readme files and jupyter notebooks git resources the git parable http tom preston werner com 2009 05 19 the git parable html br understand the logic behind why git is the way it is trygit tutorial https try github io levels 1 challenges 1 br interactive tutorial for using git git tutorial http git scm com docs gittutorial br official git tutorial git user manual http git scm com docs user manual html br official git manual latex resources latex tutorial https www latex tutorial com br friendly tutorial of how latex works and how to use it overleaf com https www overleaf com br alternative online collaborative latex editor with many convenient templates learn latex in y minutes https learnxinyminutes com docs latex br quick overview of the essential syntax latex wikibook https en wikibooks org wiki latex br more comprehensive reference textbook linux bash resources introductory linux bash tutorial http linuxcommand org lc3 learning the shell php br user friendly introductory tutorial of how to use the linux command line linux bash tutorial http ryanstutorials net linuxtutorial br alternative introductory tutorial bash guide for beginners http tldp org ldp bash beginners guide html br introductory scripting with bash shell scripting tutorial http www shellscript sh br intermediate scripting with bash vim resources tip copy the vimrc config file from this repository to your linux home directory interactive vim tutorial http openvim com br friendly step by step tutorial of how to use vim productively to edit code vim cheat sheet http vim rtorr com br concise summary of key commands vim reference card http tnerual eriogerg free fr vimqrc pdf br alternative concise summary of key commands vim for python http www fullstackpython com vim html br recommended vim configuration for editing python code python resources python scipy tutorials http www scipy lectures org br official scipy tutorial learning python scipy https lectures quantecon org py learning python html br excellent short introduction to scientific python interactive python tutorial http www learnpython org br interactive tutorial for basic python as a refresher learn python in y minutes https learnxinyminutes com docs python br concise summary of basic syntax official python tutorial https docs python org tutorial br official basic python tutorial matlab resources getting started with matlab http www mathworks com help matlab getting started with matlab html br official matlab tutorial learn matlab in y minutes https learnxinyminutes com docs matlab br concise summary of basic syntax python vs matlab http www pyzo org python vs matlab html br comparison of scientific python and matlab matlab python dictionary http mathesaurus sourceforge net matlab numpy html br conversion chart between scientific python and matlab julia resources learning julia https lectures quantecon org jl learning julia html br excellent short introduction to julia for scientific programming introduction to julia http ucidatascienceinitiative github io introtojulia br uc irvine introduction to julia julia documentation http julia readthedocs io en latest manual introduction br official julia documentation learn julia in y minutes https learnxinyminutes com docs julia br concise summary of basic syntax matlab python julia r comparison http sebastianraschka com articles 2014 matrix cheatsheet html br comparison of the most common data science programming languages sage resources sage tutorial https doc sagemath org html en tutorial br official tutorial for the sage computational engine free perks github developer pack https education github com pack br free stuff just for being a student peruse at your leisure		server
moko-time	project deprecated use https github com kotlin kotlinx datetime start from kotlin 1 4 0 moko time img logo png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https api bintray com packages icerockdev moko moko time images download svg https bintray com icerockdev moko moko time latestversion kotlin version https img shields io badge kotlin 1 3 70 orange mobile kotlin time this is a kotlin multiplatform library that supports time and timers table of contents features features requirements requirements versions versions installation installation usage usage samples samples set up locally setup locally contributing contributing license license features timer for recurrent delayed operations getcurrentmilliseconds in common code requirements gradle version 5 6 4 android api 16 ios version 9 0 versions kotlin 1 3 50 0 1 0 kotlin 1 3 60 0 2 0 kotlin 1 3 70 0 3 0 installation root build gradle groovy allprojects repositories maven url https dl bintray com icerockdev moko project build gradle groovy dependencies commonmainapi dev icerock moko time 0 3 0 usage timer create the timer for a recurring operation kotlin var iteration 0 val timer timer intervalmilliseconds 5000 iteration println iteration iteration true return true to repeat operation after interval timer start call block after intervalmilliseconds timer stop manually stop repeating timer create the timer for a single run a delayed operation kotlin val timer timer intervalmilliseconds 5000 println printed after 5 seconds false we not need repeating just single run timer start call block after intervalmilliseconds current milliseconds kotlin val milliseconds long getcurrentmilliseconds println now milliseconds milliseconds samples please see more examples in the sample directory sample set up locally the time directory time contains the time library the sample directory sample contains sample apps for android and ios plus the mpp library connected to the apps for local testing use the publishtomavenlocal sh script so that sample apps use the locally published version contributing all development both new features and bug fixes is performed in the develop branch this way master always contains the sources of the most recently released version please send prs with bug fixes to the develop branch documentation fixes in the markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master on release for more details on contributing please see the contributing guide contributing md license copyright 2019 icerock mag inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license	android kotlin ios kotlin-native kotlin-multiplatform moko time	front_end
building-computer-vision-apps-artificial-neural-networks	apress source code this repository accompanies building computer vision applications using artificial neural networks https www apress com 9781484258866 by shamshad ansari apress 2020 comment cover cover image 9781484258866 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates clone instruction clone repo with all history git clone https github com apress building computer vision apps artificial neural networks git this will download all prior commit history the overall size will be of the order of 1 4gb if this causes any error clone only the latest commit using the command git clone https github com apress building computer vision apps artificial neural networks git depth 1 contributions see the file contributing md for more information on how you can contribute to this repository book value proposition contains real examples that you can implement and modify to build useful computer vision systems gives line by line explanations of computer vision working code examples explains training neural networks involving large numbers of images on cloud infrastructure such as amazon aws google cloud platform and microsoft azure about the book apply computer vision and machine learning concepts in developing business and industrial applications using a practical step by step approach the book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples section 1 covers the basics of image and video processing with code examples of how to manipulate and extract useful information from the images you will mainly use opencv with python to work with examples in this section section 2 describes machine learning and neural network concepts as applied to computer vision you will learn different algorithms of the neural network such as convolutional neural network cnn region based convolutional neural network r cnn and yolo in this section you will also learn how to train tune and manage neural networks for computer vision section 3 provides step by step examples of developing business and industrial applications such as facial recognition in video surveillance and surface defect detection in manufacturing the final section is about training neural networks involving a large number of images on cloud infrastructure such as amazon aws google cloud platform and microsoft azure it walks you through the process of training distributed neural networks for computer vision on gpu based cloud infrastructure by the time you finish reading building computer vision applications using artificial neural networks and working through the code examples you will have developed some real world use cases of computer vision with deep learning what you will learn employ image processing manipulation and feature extraction techniques work with various deep learning algorithms for computer vision train manage and tune hyperparameters of cnns and object detection models such as r cnn ssd and yolo build neural network models using keras and tensorflow discover best practices when implementing computer vision applications in business and industry train distributed models on gpu based cloud infrastructure who this book is for data scientists analysts and machine learning and software engineering professionals with python programming knowledge about the author shamshad sam ansari is a distinguished data scientist inventor and author he has several technology publishing in his name he has co authored 4 us patents related to ai in healthcare sam is the founder president and ceo of accure an ai automation company he enjoys working with software engineers data scientists devops and business analysts and solving real world scientific and business problems he has worked with 4 tech startups in his 20 years of career prior to starting accure he worked for apixio a healthcare ai startup ibm and orbit solutions he has technical expertise in the area of computer vision machine learning ai cognitive science nlp and big data he architected designed and developed an ai automation platform called momentum that allows to build ai solutions without writing code he is passionate about teaching and mentoring he has spent more than 10 000 hours in teaching and mentoring students from across the world sam holds a bachelor s degree in engineering from bit sindri and master s degree in computer science from indian institute of information technology and management iiitm kerala		ai
Front-End-Projects	front end projects front end projects https socialify git ci tusharkesarwani front end projects image description 1 descriptioneditable a 20place 20for 20developers forks 1 issues 1 language 1 name 1 owner 1 pulls 1 stargazers 1 theme light p align center a href https frontendprojects netlify app img src https forthebadge com images badges check it out svg a p div align center p open source love svg1 https badges frapsoft com os v1 open source svg v 103 https github com ellerbrock open source badges prs welcome https img shields io badge prs welcome brightgreen svg style flat visitors https api visitorbadge io api visitors path tusharkesarwani 2ffront end projects 20 countcolor 23263759 style flat github forks https img shields io github forks tusharkesarwani front end projects github repo stars https img shields io github stars tusharkesarwani front end projects github contributors https img shields io github contributors tusharkesarwani front end projects github last commit https img shields io github last commit tusharkesarwani front end projects github repo size https img shields io github repo size tusharkesarwani front end projects github https img shields io github license tusharkesarwani front end projects github issues https img shields io github issues tusharkesarwani front end projects github closed issues https img shields io github issues closed raw tusharkesarwani front end projects github pull requests https img shields io github issues pr tusharkesarwani front end projects github closed pull requests https img shields io github issues pr closed tusharkesarwani front end projects p div open source program this project is a part of following open source programs div align center ssoc 2 0 https raw githubusercontent com tusharkesarwani front end projects main img ssoc png div div align center swoc 3 0 https raw githubusercontent com tusharkesarwani front end projects main img 9630b803 7d9b 4b19 ae68 cdbfc16c8254 png div h2 align center tech stacks used h2 p align center a href https www w3schools com html target blank rel noreferrer img src https raw githubusercontent com devicons devicon master icons html5 html5 original wordmark svg alt html5 width 100 height 100 a a href https www w3schools com css target blank rel noreferrer img src https upload wikimedia org wikipedia commons thumb d d5 css3 logo and wordmark svg 1200px css3 logo and wordmark svg png alt css3 width 100 height 100 a a href https dart dev target blank rel noreferrer a a href https developer mozilla org en us docs web javascript target blank rel noreferrer img src https cdn cdnlogo com logos j 69 javascript svg alt js width 80 height 80 a p h2 align center how to get started h2 you can refer to the following articles on basics of git and github and also contact the project mentors in case you are stuck forking a repo https help github com en github getting started with github fork a repo cloning a repo https docs github com en repositories creating and managing repositories cloning a repository how to create a pull request https opensource com article 19 7 create pull request github getting started with git and github https towardsdatascience com getting started with git and github 6fcd0f2d4ac6 h2 align center how to contribute h2 take a look at the existing issues or create your own issues wait for the issue to be assigned to you after which you can start working on it fork the repo and create a branch for any issue that you are working upon create a pull request which will be promptly reviewed and suggestions would be added to improve it add screenshots to help us know what this script is all about h2 a href https github com tusharkesarwani front end projects blob main contributing md contributing guidelines a h2 links portfolio https img shields io badge my portfolio 000 style for the badge logo ko fi logocolor white https portfolio of tushar netlify app linkedin https img shields io badge linkedin 0a66c2 style for the badge logo linkedin logocolor white https www linkedin com in tushar104 h2 align center stars chart h2 stargazers over time stargazers over time https starchart cc tusharkesarwani front end projects svg https starchart cc tusharkesarwani front end projects h2 align center project admin h2 table align center tr td align center a href https github com tusharkesarwani img src https avatars githubusercontent com u 92527686 v 4 width 100px alt br sub b tusharkesarwani b sub a td tr table h2 align center project mentors h2 table align center tr td align center a href https github com tusharkesarwani img src https avatars githubusercontent com u 92527686 v 4 width 100px alt br sub b tusharkesarwani b sub a td tr table h2 align center project contributors h2 table align center tr td a href https github com tusharkesarwani front end projects graphs contributors align center img src https contrib rocks image repo tusharkesarwani front end projects a td tr table hr h1 align center happy coding h1 show some nbsp by giving the star to this repo p align right a href https github com tusharkesarwani front end projects back to top a p	cool-projects css3 front-end-development github html5 javascript open-source open-source-project project web-development	front_end
geektime	machine learning from scratch https time geekbang org column intro 100085501		ai
featherdatabase	featherbase database client that improves productivity of devops and engineering teams	database nextjs reactjs	server
MLTS	mlts machine learning toolkit for seo initial demo notebook here https github com mltseo mlts blob master demos ipynb what are the problems needs what are the particular problems in the community that could be solved via machine learning generating better titles generating descriptions for pages summarization generating alt text from images need to get from the community create a twitterbot what is the overall flow data getting data cleaning and feature extraction iteration and updating optimization models train predict roles developing use cases evangelism community analytics per britney analytics https ga beacon appspot com ua xxxxx x gist id pixel coding tutorials documentation readability unit tests linting design data needs link data analytics scraping ranking data anonymous performance data proposed structure most folders include a todo txt with some suggested items to start with apis holds glue for various seo apis data holds datagetter classes for apis and hosted datasets docs holds the documentation for the repo models holds various models that can be used to train on npl glue for nlp libraries testing unit testing and ci tutorials holds ipython tutorials in pytorch and tensorflow config py holds api keys and configuration data main py the main application file requirements txt python libraries needed to install via pip original concept gist source https gist github com jroakes e84180a6ebafce11cecc9554421a9ac3		ai
IIT	iit introduction to information technology		server
tiny-helpers	checkl https api checklyhq com v1 badges checks 315d1e86 eca6 436f 874e 639f20c85d62 style flat square theme default responsetime false tiny helpers dev a collection of useful online web development tools screenshot of tiny helpers dev screenshot jpg contributing make sure you have a recent version of node js installed https nodejs org en we recommend at least version v12 14 after installing node js you ll have the node but also the npm https www npmjs com command available npm is node js package manager additionally please have a look at the contributing md contributing md including further information about what counts as a tiny helper fork and clone this repository head over to your terminal and run the following command git clone git github com your username tiny helpers git cd tiny helpers npm ci npm run helper add add a new helper npm run helper add will ask a few questions and create a file in helpers commit the changes and open a pull request https help github com en github collaborating with issues and pull requests creating a pull request run the project locally this project uses vercel s routing configuration the route doesn t work locally to start navigate to localhost 8080 home after running npm run dev npm run dev contributors sparkles table only 10 td cell per tr row please tr td align center a href https github com stefanjudis img src https avatars3 githubusercontent com u 962099 v 4 width 75 alt stefanjudis br sub b stefan judis b sub a td td align center a href https github com fhemberger img src https avatars3 githubusercontent com u 153481 v 4 width 75 alt fhemberger br sub b frederic hemberger b sub a td td align center a href https github com nikitahl img src https avatars3 githubusercontent com u 12826823 v 4 width 75 alt nikitahl br sub b nikita hlopov b sub a td td align center a href https github com akx img src https avatars2 githubusercontent com u 58669 v 4 width 75 alt akx br sub b aarni koskela b sub a td td align center a href https github com 0xadada img src https avatars2 githubusercontent com u 51207 v 4 width 75 alt 0xadada br sub b 0xadada b sub a td td align center a href https github com mrassili img src https avatars0 githubusercontent com u 25288435 v 4 width 75 alt mrassili br sub b marouane r b sub a td td align center a href https github com ben1980 img src https avatars1 githubusercontent com u 919260 v 4 width 75 alt ben1980 br sub b benjamin mahr b sub a td td align center a href https github com mannil img src https avatars0 githubusercontent com u 640208 v 4 width 75 alt mannil br sub b alexander lichter b sub a td td align center a href https github com canrau img src https avatars0 githubusercontent com u 5196971 v 4 width 75 alt canrau br sub b can rau b sub a td td align center a href https github com bjuretko img src https avatars2 githubusercontent com u 30392977 v 4 width 75 alt bjuretko br sub b benedict juretko b sub a td tr tr td align center a href https github com kilian img src https avatars3 githubusercontent com u 41970 v 4 width 75 alt kilian br sub b kilian valkhof b sub a td td align center a href https github com philnash img src https avatars3 githubusercontent com u 31462 v 4 width 75 alt philnash br sub b phil nash b sub a td td align center a href https github com remy img src https avatars0 githubusercontent com u 13700 v 4 width 75 alt remy br sub b remy sharp b sub a td td align center a href https github com sjeiti img src https avatars0 githubusercontent com u 785706 v 4 width 75 alt sjeiti br sub b ron valstar b sub a td td align center a href https github com tomtasche img src https avatars2 githubusercontent com u 128734 v 4 width 75 alt tomtasche br sub b thomas taschauer b sub a td td align center a href https github com axe312ger img src https avatars1 githubusercontent com u 1737026 v 4 width 75 alt axe312ger br sub b benedikt r tsch b sub a td td align center a href https github com nicokoenig img src https avatars0 githubusercontent com u 16404104 v 4 width 75 alt nicokoenig br sub b nico k nig b sub a td td align center a href https github com bkmxer img src https avatars1 githubusercontent com u 18457056 v 4 width 75 alt bkmxer br sub b anton ilchuk b sub a td td align center a href https github com austinpray img src https avatars1 githubusercontent com u 2192970 v 4 width 75 alt austinpray br sub b austin pray b sub a td td align center a href https github com caseymhunt img src https avatars3 githubusercontent com u 2065615 v 4 width 75 alt caseymhunt br sub b casey hunt b sub a td tr tr td align center a href https github com christiangrieger img src https avatars0 githubusercontent com u 8712219 v 4 width 75 alt christiangrieger br sub b christian grieger b sub a td td align center a href https github com stof img src https avatars0 githubusercontent com u 439401 v 4 width 75 alt stof br sub b christophe coevoet b sub a td td align center a href https github com crgeary img src https avatars0 githubusercontent com u 3949335 v 4 width 75 alt crgeary br sub b christopher geary b sub a td td align center a href https github com dylanatsmith img src https avatars1 githubusercontent com u 6905903 v 4 width 75 alt dylanatsmith br sub b dylan smith b sub a td td align center a href https github com innocenzi img src https avatars3 githubusercontent com u 16060559 v 4 width 75 alt innocenzi br sub b enzo innocenzi b sub a td td align center a href https github com loilo img src https avatars3 githubusercontent com u 1922624 v 4 width 75 alt loilo br sub b florian reuschel b sub a td td align center a href https github com c1rrus img src https avatars3 githubusercontent com u 358435 v 4 width 75 alt c1rrus br sub b james nash b sub a td td align center a href https github com cherry img src https avatars2 githubusercontent com u 856748 v 4 width 75 alt cherry br sub b james ross b sub a td td align center a href https github com jankohlbach img src https avatars0 githubusercontent com u 43587328 v 4 width 75 alt jankohlbach br sub b jan kohlbach b sub a td td align center a href https github com gnclmorais img src https avatars0 githubusercontent com u 385232 v 4 width 75 alt gnclmorais br sub b gon alo morais b sub a td tr table	eleventy show-on-my-website	front_end
Natural-Language-Processing-with-Flair	natural language processing with flair a href https www packtpub com product natural language processing with flair 9781801072311 utm source github utm medium repository utm campaign 9781801072311 img src https static packt cdn com products 9781801072311 cover smaller alt natural language processing with flair height 256px align right a this is the code repository for natural language processing with flair https www packtpub com product natural language processing with flair 9781801072311 utm source github utm medium repository utm campaign 9781801072311 published by packt a practical guide to understanding and solving nlp problems with flair what is this book about flair is an easy to understand natural language processing nlp framework designed to facilitate training and distribution of state of the art nlp models for named entity recognition part of speech tagging and text classification flair is also a text embedding library for combining different types of embeddings such as document embeddings transformer embeddings and the proposed flair embeddings this book covers the following exciting features gain an understanding of core nlp terminology and concepts get to grips with the capabilities of the flair nlp framework find out how to use flair s state of the art pre built models build custom sequence labeling models embeddings and classifiers learn about a novel text classification technique called tars discover how to build applications with flair and how to deploy them to production if you feel this book is for you get your copy https www amazon com natural language processing flair understanding dp 1801072310 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders the code will look like the following from flair data import sentence from flair tokenization import spacetokenizer tokenizer spacetokenizer s sentence some nice text use tokenizer tokenizer print s this repository contains jupyter notebooks containing code snippets from the book natural language processing with flair published by packt publishing each notebook belongs to one chapter in the book before running the notebooks make sure to install flair version 0 11 by running pip install flair 0 11 contributing feel free to suggest code changes to the book as part of opening an issue or a pull request following is what you need for this book this book is for data scientists machine learning engineers and ml practitioners in both academia and industry a fundamental understanding of machine learning concepts and working knowledge of python programming is assumed prior experience implementing ml dl models with tensorflow or pytorch will be beneficial you ll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed software and hardware list chapter software required os required 1 10 flair 0 11 windows mac os x and linux any 1 10 flask 2 0 3 windows mac os x and linux any detailed instructions about how to set up your local environment and install the python packages can be found in chapter 1 introduction to flair we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781801072311 colorimages pdf related products other books you may enjoy mastering spacy packt https www packtpub com product mastering spacy 9781800563353 utm source github utm medium repository utm campaign 9781800563353 amazon https www amazon com dp b093q3xmf9 distributed machine learning with python packt https www packtpub com product distributed machine learning with python 9781801815697 utm source github utm medium repository utm campaign 9781801815697 amazon https www amazon com dp b09trv6ztz get to know the author tadej magajna is a former lead machine learning engineer former data scientist and now a software engineer at microsoft he currently works in a team responsible for language model training and building language packs for keyboards such as microsoft swiftkey he has a master s degree in computer science he started his career as a 15 year old at a local media company as a web developer and progressed toward more complex problems he has tackled problems such as nlp market research public transport bus and train capacity forecasting and finally language model training in his current role currently he is based in his hometown of ljubljana slovenia		ai
udagram-elasticbeanstalk	udagram rest api udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images getting setup installing project dependencies this project uses npm to manage software dependencies npm relies on the package json file located in the root of this repository after cloning open your terminal and run bash npm install tip npm i is shorthand for npm install installing useful tools 1 postbird https github com paxa postbird postbird is a useful client gui graphical user interface to interact with our provisioned postgres database we can establish a remote connection and complete actions like viewing data and changing schema tables columns ect 2 postman https www getpostman com downloads postman is a useful tool to issue and save requests postman can create get put post etc requests complete with bodies it can also be used to test endpoints automatically we ve included a collection udacity c2 restapi postman collection json which contains example requsts running the server locally to run the server locally in developer mode open terminal and run bash npm run dev developer mode runs off the typescript source any saves will reset the server and run the latest version of the codebase	typescript elasticbeanstalk nodejs	cloud
diy-llm-qa-bot	image https raw githubusercontent com databricks industry solutions github main profile solacc logo wide png cloud https img shields io badge cloud aws azure blue logo googlecloud style for the badge poc https img shields io badge poc 10 days green style for the badge https databricks com try databricks diy llm qa bot accelerator the goal of this solution accelerator is to show how we can leverage a large language model in combination with our own data to create an interactive application capable of answering questions specific to a particular domain or subject area the core pattern behind this is the delivery of a question along with a document or document fragment that provides relevant context for answering that question to the model the model will then respond with an answer that takes into consideration both the question and the context p img src https brysmiwasb blob core windows net demos images bot flow png width 500 p to assemble this application i e the q a bot we will need to assemble a series of documents that are relevant to the domain we wish to serve we will need to index these to enable rapid search given a user question we will then need to assemble the core application which combines a question with a document to form a prompt and submits that prompt to a model in order to generate a response finally we ll need to package both the indexed documents and the core application component as a microservice to enable a wide range of deployment options we will tackle these three steps across the following three notebooks p 01 build document index 02 assemble application 03 deploy application p vladimir kolovski databricks com zhe wang databricks com jingting lu databricks com bryan smith databricks com copy 2023 databricks inc all rights reserved the source in this notebook is provided subject to the databricks license https databricks com db license source all included or referenced third party libraries are subject to the licenses set forth below library description license source langchain building applications with llms through composability mit https pypi org project langchain tiktoken fast bpe tokeniser for use with openai s models mit https pypi org project tiktoken faiss cpu library for efficient similarity search and clustering of dense vectors mit https pypi org project faiss cpu openai building applications with llms through composability mit https pypi org project openai getting started although specific solutions can be downloaded as dbc archives from our websites we recommend cloning these repositories onto your databricks environment not only will you get access to latest code but you will be part of a community of experts driving industry best practices and re usable solutions influencing our respective industries img width 500 alt add repo src https user images githubusercontent com 4445837 177207338 65135b10 8ccc 4d17 be21 09416c861a76 png to start using a solution accelerator in databricks simply follow these steps 1 clone solution accelerator repository in databricks using databricks repos https www databricks com product repos 2 attach the runme notebook to any cluster and execute the notebook via run all a multi step job describing the accelerator pipeline will be created and the link will be provided the job configuration is written in the runme notebook in json format 3 execute the multi step job to see how the pipeline runs 4 you might want to modify the samples in the solution accelerator to your need collaborate with other users and run the code samples against your own data to do so start by changing the git remote of your repository to your organization s repository vs using our samples repository learn more you can now commit and push code collaborate with other users via git and follow your organization s processes for code development the cost associated with running the accelerator is the user s responsibility project support please note the code in this project is provided for your exploration only and are not formally supported by databricks with service level agreements slas they are provided as is and we do not make any guarantees of any kind please do not submit a support ticket relating to any issues arising from the use of these projects the source in this project is provided subject to the databricks license license all included or referenced third party libraries are subject to the licenses set forth below any issues discovered through the use of this project should be filed as github issues on the repo they will be reviewed as time permits but there are no formal slas for support		ai
paho.mqtt.embedded-c	eclipse paho mqtt c c client for embedded platforms this repository contains the source code for the eclipse paho http eclipse org paho mqtt c c client library for embedded platorms it is dual licensed under the epl and edl see about html and notice html for more details you can choose which of these licenses you want to use the code under the edl allows you to embed the code into your application and distribute your application in binary or source form without contributing any of your code or any changes you make back to paho see the edl for the exact conditions there are three sub projects 1 mqttpacket simple de serialization of mqtt packets plus helper functions 2 mqttclient high er level c client plus 3 mqttclient c high er level c client pretty much a clone of the c client the mqttpacket directory contains the lowest level c library with the smallest requirements this supplies simple serialization and deserialization routines they serve as a base for the higher level libraries but can also be used on their own it is mainly up to you to write and read to and from the network the mqttclient directory contains the next level c library this networking code is contained in separate classes so that you can plugin the network of your choice currently there are implementations for linux arduino and mbed arm mbed was the first platform for which this was written where the conventional language choice is c which explains the language choice i have written a starter porting guide http modelbasedtesting co uk 2014 08 25 porting a paho embedded c client the mqttclient c directory contains a c equivalent of mqttclient for those platforms where c is not supported or the convention as far as possible it is a direct translation from mqttclient build requirements compilation cmake builds for the various packages have been introduced along with travis ci configuration for automated build testing the basic method of building on linux is mkdir build paho cd build paho cmake make the travis build sh file has the full build and test sequence for linux usage and api see the samples directories for examples of intended use doxygen config files for each package are available in the doc directory runtime tracing the mqttclient api has debug tracing for mqtt packets sent and received turn this on by setting the mqtt debug preprocessor definition reporting bugs this project uses github issues here github com eclipse paho mqtt embedded c issues https github com eclipse paho mqtt embedded c issues to track ongoing development and issues more information discussion of the paho clients takes place on the eclipse mattermost paho channel https mattermost eclipse org eclipse channels paho and the eclipse paho dev mailing list https dev eclipse org mailman listinfo paho dev general questions about the mqtt protocol are discussed in the mqtt google group https groups google com forum hl en us fromgroups forum mqtt more information is available via the mqtt community http mqtt org	eclipseiot mqtt iot embedded internet-of-things	server
wad	wad web application development		front_end
StudentAI	studentai studentai is an ai chatbot app designed to enhance students learning experiences through personalized interactions the app uses openai s api to provide students with comprehensive and informative answers to their questions features personalized learning experiences tailored to each student s needs access to a vast knowledge base covering multiple subjects and topics ability to answer questions in a comprehensive and informative way user friendly interface designed for seamless interactions chatbot with openai s api integration for accurate responses getting started to get started with studentai you will need to 1 install the flutter sdk 2 clone the studentai repository 3 create a file named secrets dart in the lib data directory and copy the following code into it dart this file is intended for development purposes only please ensure that you add it to the gitignore file before pushing your source code anywhere const string devapikey your api key wiredash secrets create account in https wiredash io to use its feedback sdk const string label1 label id const string label2 label id const string label13 label id const string projectid project id const string secretkey secret key this application stores all its secrets in the secrets dart file please ensure that you keep this file secure and out of version control systems like git it is also recommended that you don t hardcode secrets in your code and instead use encrypted environment variables 4 run flutter pub get to install the dependencies 5 run flutter run to start the app usage once the app is running you can start chatting with studentai by typing in your questions or requests studentai will use its knowledge base and openai s api to provide you with personalized learning experiences api key before starting to use the app add your api key to the app here are some ways to get your api key get your api key from the official openai account https beta openai com account api keys data all data used in this app to create cards forms and prompts come through our api studentai api for more information about the api please check the following repository studentai api https github com avadhkumar geek studentai api examples here are some examples of how you can use studentai ask studentai questions about your homework or studies get studentai to generate mcqs compare topics create study plans etc have studentai help you with your research get studentai to provide you with summaries of complex topics screenshots screenshot 2023 06 27 11 06 18 620 com student ai https github com avadhkumar geek studentai assets 1390862 d52165a9 8072 4f4f 8c3a f10ef7bcb6bf screenshot 2023 06 27 11 07 21 077 com student ai https github com avadhkumar geek studentai assets 1390862 5d17dfb3 f333 4aeb b0b3 48d025cde5fe contributing we welcome contributions from the community please feel free to create a pull request for bug fixes feature requests or any other improvements license studentai is licensed under the gpl license v3 license contact if you have any questions or feedback please feel free to contact us at avadhkachhadiya gmail com references sandeepscet https github com sandeepscet prompt apps	ai-chatbot educational-project llm openai	ai
FreeRtos-Labs	freertos labs freertos labs of rtos course http www vlsiacademy org rtos1 html feel free to use these codes in anyway		os
kubeone	kubermatic kubeone p align center img src docs img kubeone logo text png gh light mode only width 700px img src docs img kubeone logo text dark png gh dark mode only width 700px p kubeone report card https goreportcard com badge github com kubermatic kubeone https goreportcard com report github com kubermatic kubeone kubermatic kubeone automates cluster operations on all your cloud on prem edge and iot environments kubeone can install high available ha master clusters as well single master clusters getting started all user documentation for the latest stable version is available at the kubeone docs website docs information about the support policy natively supported providers supported kubernetes versions and supported operating systems can be found in the compatibility document docs compatibility for a quick start you should check the following documents architecture docs concepts to get familiar with the concepts of kubeone getting kubeone docs getting kubeone to download and install kubeone locally provisioning docs provisioning to provision the kubernetes cluster installing kubeone the fastest way to install kubeone is to use the installation script bash curl sfl get kubeone io sh the installation script downloads the release archive from github installs the kubeone binary in your usr local bin directory and unpacks the example terraform configs addons and helper scripts in your current working directory for other installation methods check the getting kubeone guide docs getting kubeone on our documentation website features easily deploy your highly available cluster on any infrastructure kubeone works on any infrastructure out of the box all you need to do is to provision the infrastructure and let kubeone know about it kubeone will take care of setting up a production ready highly available cluster native support for the most popular providers kubeone natively supports the most popular providers including aws azure digitalocean gcp hetzner cloud nutanix openstack vmware cloud director and vmware vsphere the natively supported providers enjoy additional features such as integration with terraform and kubermatic machine controller kubernetes conformance certified kubeone is a kubernetes conformance certified installer with support for all upstream supported upstream supported versions kubernetes versions declarative cluster definition define all your clusters declaratively in a form of a yaml manifest you describe what features you want and kubeone takes care of setting them up integration with terraform the built in integration with terraform allows you to easily provision your infrastructure using terraform and let kubeone take all the needed information from the terraform state integration with cluster api kubermatic machine controller and operating system manager manage your worker nodes declaratively by utilizing the cluster api cluster api and kubermatic machine controller machine controller create remove upgrade or scale your worker nodes using kubectl kubermatic operating system manager operating system manager is responsibile for managing user data for worker machines in the cluster getting involved we very appreciate contributions if you want to contribute or have an idea for a new feature or improvement please check out our contributing guide contributing guide if you want to get in touch with us and discuss about improvements and new features please create a new issue on github or connect with us over slack kubeone channel k8s slack kubeone on kubernetes slack k8s slack reporting bugs if you encounter issues please create a new issue on github github issue or talk to us on the kubeone slack channel k8s slack kubeone when reporting a bug please include the following information kubeone version or git commit that you re running kubeone version description of the bug and logs from the relevant kubeone command if applicable steps to reproduce the issue expected behavior if you re reporting a security vulnerability please follow the process for reporting security issues security vulnerability changelog see the list of releases changelog to find out about feature changes upstream supported versions https kubernetes io docs setup release version skew policy supported versions cluster api https github com kubernetes sigs cluster api machine controller https github com kubermatic machine controller operating system manager https github com kubermatic operating system manager docs https docs kubermatic com kubeone docs architecture https docs kubermatic com kubeone v1 7 architecture docs concepts https docs kubermatic com kubeone v1 7 architecture concepts docs compatibility https docs kubermatic com kubeone v1 7 architecture compatibility docs getting kubeone https docs kubermatic com kubeone v1 7 getting kubeone docs provisioning https docs kubermatic com kubeone v1 7 tutorials creating clusters contributing guide https github com kubermatic kubeone blob main contributing md k8s slack kubeone https kubernetes slack com messages cnev2umt7 k8s slack http slack k8s io github issue https github com kubermatic kubeone issues security vulnerability https github com kubermatic kubeone blob main contributing md reporting a security vulnerability changelog https github com kubermatic kubeone releases	kubernetes cluster-api	server
secretflow	div align center img src docs static logo light png div circleci https dl circleci com status badge img gh secretflow secretflow tree main svg style svg https dl circleci com status badge redirect gh secretflow secretflow tree main p align center a href readme zh cn md a a href readme md english a p secretflow is a unified framework for privacy preserving data intelligence and machine learning to achieve this goal it provides an abstract device layer consists of plain devices and secret devices which encapsulate various cryptographic protocols a device flow layer modeling higher algorithms as device object flow and dag an algorithm layer to do data analysis and machine learning with horizontal or vertical partitioned data a workflow layer that seamlessly integrates data processing model training and hyperparameter tuning div align center img src docs static secretflow arch svg div documentation secretflow https www secretflow org cn docs secretflow en getting started https www secretflow org cn docs secretflow en getting started index html user guide https www secretflow org cn docs secretflow en user guide index html api reference https www secretflow org cn docs secretflow en api index html tutorial https www secretflow org cn docs secretflow en tutorial index html secretflow related projects kuscia https github com secretflow kuscia a lightweight privacy preserving computing task orchestration framework based on k3s scql https github com secretflow scql a system that allows multiple distrusting parties to run joint analysis without revealing their private data spu https github com secretflow spu a provable measurable secure computation device which provides computation ability while keeping your private data protected heu https github com secretflow heu a high performance homomorphic encryption algorithm library yacl https github com secretflow yacl a c library that contains cryptography network and io modules which other secretflow code depends on install please check installation md docs getting started installation md deployment please check deployment md docs getting started deployment md learn pets we also provide a curated list of papers and secretflow s tutorials on privacy enhancing technologies pets please check awesome pets md docs awesome pets awesome pets md contributing please check contributing md contributing md disclaimer non release versions of secretflow are prohibited from using in any production environment due to possible bugs glitches lack of functionality security issues or other problems	differential-privacy homomorphic-encryption machine-learning privacy-preserving private-set-intersection secure-multiparty-computation trusted-execution-environment data-analysis federated-learning split-learning	ai
CVND-udacity	computer vision nanodegree this repository contains project files for udacity s computer vision nanodegree program which i enrolled on 24 april 2018 projects facial keypoint detection p1 facial keypoints https github com vmelan cvnd udacity tree master p1 facial keypoints use image processing techniques and deep learning techniques to detect faces in an image and find facial keypoints such as the position of the eyes nose and mouth on a face automatic image captioning p2 image captioning https github com vmelan cvnd udacity tree master p2 image captioning combine cnn and rnn knowledge to build a deep learning model that produces captions given an imput image landmark detection tracking slam p3 slam https github com vmelan cvnd udacity tree master p3 slam implement graph slam simultaneous localization and mapping for a 2 dimensional world	computer-vision-nanodegree udacity deep-learning computer-vision	ai
Natural-Language-Processing-Tutorials	natural language process tutorials natural language processing tutorials nlp with python julia and javascripts contents nlp with python natural language processing with spacy natural language processing with textblob natural language processing with polyglot natural language processing with textacy nlp with javascript natural language processing with compromise js natural language processing with natural js natural language processing with sentiment js nlp with julia natural language processing with textanalysis jl textsummarization jl		ai
stereo-vision	readme this readme would normally document whatever steps are necessary to get your application up and running performance result youtube video https youtu be fdmsz2lurbg gpx result kitti road 2011 09 26 drive 0029 groundtruth blue vs visualodom red https github com willsapgreen stereo vision blob master result kitti road 2011 09 26 drive 0029 jpg groundtruth blue and visualodom red what is this repository for stereo vision is a tool which contains the following computer vision and machine learning developments and will continue integrating more developments stereomapper a qt gui to read kitti dataset and visualize the disparity map visual odometry the original resource is from http www cvlibs net software libviso the version here only contains the playback function but with the memory leak fix libviso2 a library for computing the six dof motion of a moving mono stereo camera the original resource is from http www cvlibs net software libviso libelas a library for computing disparity maps from rectified graylevel stereo pairs how do i get set up system setup git clone the repo git submodule update dependencies opencv 3 1 0 qt creator 4 2 1 database setup download the kitti raw data http www cvlibs net datasets kitti unzip the calibration zip file to calib folder unzip the sync zip file to sync folder unzip the tracklets zip file to tracklets folder system executation load stereomapper pro in stereomapper run qmake build stereomapper project and run the project click scan button and open the folder contains calib sync and tracklets folders contribution guidelines who do i talk to andreas geiger geiger kit edu		ai
ML_prep	machine learning interview prep goal to prepare those looking to transition into a mle role brought to you by insight data science https insightfellows com ai apply for our 7 week ai fellowship today currently this repo contains phone screen ml interview prep https docs google com document d 148hyuhe5p0k0xk6t4jf zosjfwrviofm5ssupifyumk edit usp sharing questions algorithms validation methods stats practical coding sessions i recommend setting aside 90 minutes before starting any of these challenges in order to simulate the conditions a coding sessions during an ml onsite view solutions only once you have completed your solution and feel free to fork this repo to add your solutions code review checklist https docs google com document u 2 d 1ilvzve9kszvh lc0hdar3kx1eytqiyzubdft0t08poi edit cs practical grading rubric https docs google com document d 1re3i15ukdrtd21bxdyk3wacchejzkfgb gw8drp zqm edit usp sharing ml jeopardy https docs google com presentation d 1oqwr6qbnsaraxzb zqwqntdapzm3hjlahwazn8xtv5i edit usp sharing a guide to github collaborating https zhampel github io insight github guide version control with git github https docs google com presentation d 1vb3rpqynek73jjevm9td zdrlmfpygwphdg4drc8f2k edit slide id g3c383738fa 0 81 dockerized data science https drive google com file d 1y 080q2ukev9lyc5efsjw9pg3w4aq8ux view usp sharing a presentation from manifold ai https www manifold ai on using docker for ml models system design ml system design flashcards https quizlet com 88ii2t x 1jqt i 2p7fwa huge thanks goes out to chip huyen s https github com chiphuyen machine learning systems design ml repo for assisting ml terminology falshcards https quizlet com 498570175 flashcards great to use for ml charades shoutout to google developers https developers google com machine learning glossary for assisting system design outline https docs google com document d 1kdlpxhgc43vb8a i4fqn4rl85doo dgsxsedipzmnxy edit system design workshop https docs google com presentation d 1nf 2yfn3tth asopqxagsv kv gcsiazqyf kzrer8w edit ts 5ea8346d slide id g70cecd63af 0 24 cloud computing resources aws aws in plain english https www expeditedssl com aws in plain english aws basics https docs google com presentation d 1sxse3g6kdoqqohh 8az2dsa1syfy2agpae7tvg b2u edit usp sharing aws security https docs google com presentation d 1wy62ioy9qdviej4ccbcwx6ocdb5dnxamjeuzcbljvw8 edit usp sharing things to know when getting started setting up your deep learning ami https docs google com document d 1y29xi4wa 2buwuuicldavs3t5upq3ixvvg vnb uqgi edit usp sharing instructions for connecting atom ide to ec2 https docs google com document d 1lit8hsuh88nm6hrmywlndccdcixqxw4l4sy05xrcgbw edit pli 1 instructions for connecting to jupyter on an ec2 instance https docs google com document d 1udtuwj7bm7t7svidwgzfdz5mksxvelahgtru8seqcbo edit usp sharing mounting s3 bucket to ec2 instance https medium com rachaelcreager mounting an s3 bucket to ubuntu ec2 instance 8973885ed2c2 boto s3 bucket example https github com srirambaskaran aws ml training blob master logistic regression py intro to sagemaker https docs google com presentation d 1kdhwm njna2zpiiae1mhzl0tgx00xil0ij0jldjfhai edit slide id g4a17de6c9c 0 155 sagemaker overview https docs google com presentation d 1t4dpwnhttnvsnxrsavothymewvuhs 7kmduhhmqowt4 edit slide id g7179c43c49 0 77 sagemaker training example https github com jdurago drug prediction gnn sagemaker inference example https github com jdurago drug prediction gnn inference behavioral questions to ask hiring managers https docs google com document d 1gqv8xtx0onm yxv85pqivplj5zogsr6mwlb7autw7vy edit usp sharing before accepting an offer negotiation tips swe for mles data structures and algorithms https docs google com presentation d 1wqezqd1b2eeu4tzthq 3kwfykomhrzpdcjd4vgj0g8q edit slide id g88fc7667a8 0 22 object oriented programming https docs google com presentation d 1ogcpusibpl5zuahqdoowokohxg8usi4 evv4jaymtus edit slide id p oop w python review https docs google com presentation d 15x06pbhktcgqph ne63 d4ncyy2ez9mkg1l4vouyizq edit slide id g8a56287b0d 0 59 intro to dynamic programming https docs google com presentation d 1s6ze6 xrred8jtxr3zq4iht7mpx1apif9efyux9zm0e edit slide id g7e91418eb9 0 310 intro to data for mles intro to data challenges https docs google com presentation d 1nzfnkm4qgboip6tkytryg2oo0i ycl95mbik s1i4u edit slide id g81eed5f95c 0 188 intro to data modeling https docs google com presentation d 1dpdiwbrgdabsdyqar hgaspsmspeiced tfulkz6u3m edit slide id g866ef379cb 0 138 intro to distributed systems https docs google com presentation d 1 0xp cbnzrjw5ck3pk v8svwfeueapikhtpxkobvcbi edit slide id p intro to devops https docs google com presentation d 102mr rbid1ctsupsug5v8 142azoqtj5vwk78ze8es4 edit slide id g70ef162819 0 0		ai
fend-office-hours	front end web developer office hours support documents this repository is aimed at giving a detailed overview of what was spoken about in the office hours for the front end nanodegree we have organized each repository by topics of interest so you can browse by topics archival note this repository is deprecated therefore we are going to archive it however learners will be able to fork it to their personal github account but cannot submit prs to this repository if you have any issues or suggestions to make feel free to utilize the https knowledge udacity com forum to seek help on content specific issues submit a support ticket along with the link to your forked repository if learners are blocked for other reasons here are the links for the retail consumers https udacity zendesk com hc en us requests new and enterprise learners https udacityenterprise zendesk com hc en us requests new ticket form id 360000279131		front_end
alibaba-rsocket-broker	p align center img src https raw githubusercontent com wiki alibaba alibaba rsocket broker img logo png alt logo width 30 p p align center a href https gitter im alibaba rsocket broker community utm source badge utm medium badge utm campaign pr badge img alt gitter src https badges gitter im alibaba rsocket broker community svg a a href https repo1 maven org maven2 com alibaba rsocket img alt maven src https img shields io maven central v com alibaba rsocket alibaba rsocket spring boot starter a a href https github com alibaba alibaba rsocket broker img alt github repo size src https img shields io github repo size alibaba alibaba rsocket broker a a href https github com alibaba alibaba rsocket broker issues img alt open issues src https img shields io github issues raw alibaba alibaba rsocket broker svg a a href https github com alibaba alibaba rsocket broker actions workflows maven yml img alt build status src https img shields io github workflow status alibaba alibaba rsocket broker java 20ci 20with 20maven a a href https www apache org licenses license 2 0 txt img alt apache license 2 src https img shields io badge license asf2 blue svg a a href https github com alibaba alibaba rsocket broker blob master readme en md img alt src https img shields io badge en readme brightgreen a p alibaba rsocket broker rsocket rpc pub sub streaming control plane prometheus metrics zipkin rsocket collector chaos observability rsocket broker alibaba rsocket broker wiki https github com alibaba alibaba rsocket broker wiki alibaba rsocket broker https github com alibaba rsocket broker rsocket by example http rsocketbyexample info github discussions https github com alibaba alibaba rsocket broker discussions rsocket broker rsocket broker broker broker broker broker broker zero copy broker rsocket broker structure https github com alibaba alibaba rsocket broker raw master alibaba rsocket broker structure png rsocket broker rsocket metadatapush broker broker service to service rsocket 4 load balancing broker broker circuit breakers back pressure distributed messaging rsocket rsocket sdk rsocket sdk stack https github com alibaba alibaba rsocket broker wiki rsocket sdk stack alibaba rsocket service common rsocket annotation reactive alibaba rsocket core rsocket alibaba rsocket spring boot starter spring boot starter for rsocket rsocket alibaba broker spring boot starter spring boot starter for rsocket broker alibaba rsocket broker alibaba rsocket broker alibaba broker registry client spring boot starter rsocket broker alibaba broker config client spring boot starter rsocket broker rsocket broker gateway http rsocket broker http http rsocket rsocket broker gateway grpc rsocket broker grpc grpc rsocket jdk 11 rsocket broker server java 11 broker client java 8 maven 3 5 x node 16 rsocket broker vaadin 23 0 node 16 example accountservice protobuf protobuf maven plugin protobuf ide mvn dskiptests package protobuf ide example example rsocket broker jbang https www jbang dev download jbang rsocket broker alibaba rsocket broker rsocket broker docker compose rsocket broker rsocket broker docker compose up d rsocket broke ide rsocket broker alibabarsocketbrokerserver main rsocket broker rsocket responder requester rsocketresponderserver main rsocket responder reactive rsocketrequesterapp main rsocket requester reactive service idea example http get http localhost 8181 user 2 curl http localhost 8181 user 2 example example rsocket reactive responder requester reactive broker rsocket maven module user service api java public interface userservice mono user findbyid integer id rsocket responder rsocketservice annotation java rsocketservice serviceinterface userservice class service public class userserviceimpl implements userservice override public mono user findbyid integer id return mono just new user 1 nick id mysql reactive r2dbc mysql spring cloud rsocket r2dbc mysql demo https github com linux china spring cloud function demo rsocket requester proxy reactive spring bean bean public userservice userservice autowired upstreammanager upstreammanager return rsocketremoteservicebuilder client userservice class upstreammanager upstreammanager build rsocket requester http rest api java restcontroller public class portalcontroller autowired userservice userservice getmapping user id public mono user user pathvariable integer id return userservice findbyid id https github com alibaba rsocket broker rsocket broker simple example references rsocket http rsocket io rsocket java sdk https github com rsocket rsocket java spring rsocket https docs spring io spring docs current spring framework reference web reactive html rsocket spring boot rsocket starter https docs spring io spring boot docs current reference htmlsingle boot features rsocket project reactor http projectreactor io reactive foundation https reactive foundation	alibaba rsocket broker rpc service-mesh iot grpc	server
Aws-Networking	aws cloud engineering		cloud
blockchain	blockchain https img shields io badge author jonluo brightgreen svg https img shields io badge platform ubuntu16 04 brightgreen svg https img shields io badge go v1 10 3 blue svg https img shields io badge node v9 2 0 blue svg https img shields io badge npm v6 4 0 blue svg https img shields io badge docker v18 06 0 blue svg https img shields io badge docker nbsp compose v1 22 0 blue svg 1 0 2 0 3 0 simple demo 200 p2p proof of work proof of stake eth solidity eth dapp web btc ipfs hyperledger fabric chaincode chaincode app fabric sdk go app fabric 1 2 0 chaincode node app fabric sdk node app fabric in action fabric fabric sourcecode debugging fabric fabric ledgersdata fabric fabric1 4 multi machine deployment fabric1 4 fabric go sdk examples fabric sdk go star http blog csdn net csolo article details 52858236 https www imooc com article details id 28738 https blog csdn net s lisheng article details 78022645 btc https github com bitcoin bitcoin eth https github com ethereum go ethereum eos https github com eosio eos eos litecoin https github com litecoin project litecoin qtum https github com qtumproject qtum bytom btm https github com bytom bytom neo https github com neo project neo zilliqa https github com zilliqa zilliqa cardano ada https github com input output hk cardano sl metaverse https github com mvs org metaverse nebulas https github com nebulasio go nebulas architecture jpg gzh jpg		blockchain
EmbedBooks	h1 align center h1 p align center a href http img wangkeyan cn kycgqrcode jpg img src https img shields io badge e5 85 ac e4 bc 97 e5 8f b7 e7 a7 91 e5 b2 a9 e6 88 90 e6 9e 9c brightgreen svg alt a a href https www zhihu com people talk iot img src https img shields io badge e7 9f a5 e4 b9 8e e7 a7 91 e5 b2 a9 blue svg alt a a href https blog csdn net u014070965 img src https img shields io badge csdn e7 a7 91 e5 b2 a9 orange svg alt csdn a a href https github com imkeyan embedbooks img src https img shields io badge pdf e5 b5 8c e5 85 a5 e5 bc 8f e7 bb 8f e5 85 b8 lightgrey svg alt pdf a p pdf linux star vipwky http img wangkeyan cn wxqrcode jpg c kycg http img wangkeyan cn kycgqrcode jpg 00 00 01 c 01 c 02 02 03 03 04 04 4 1 arm 4 1 arm 4 2 risc v 4 2 risc v 4 3 x86 4 3 x86 05 05 5 1 5 1 5 2 linux 5 2 linux 5 3 rtos 5 3 rtos 06 06 07 07 08 08 09 09 10 10 11 11 12 12 13 c 13 c 14 14 15 15 15 1 mcu 15 1 mcu 15 2 15 2 15 3 ar vr 15 3 ar vr 15 4 15 4 15 5 15 5 16 18 17 donate 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1jqiyqfrneu lbn5kbkmksa udgu unix 1 api 3 https pan baidu com s 1pgr8lhoxbgbywdfs52krta ner6 unix 2 2 https pan baidu com s 11y72ifcfaqk5wdxxq5tulg qt5i 5 3 rtos freertos https pan baidu com s 1i7zyrduft m0u7imbjbbia f3qq freertos stm32 https pan baidu com s 1g8ih0jnfenlwiwqrfwqd2a 28pd stm32f407 freertos v1 1 https pan baidu com s 10a1eekbzvyo2opc2crezzw htrf https pan baidu com s 127vnpqsoyl0jxfxigtypdw 6fk7 06 https pan baidu com s 1uk5m6ihxsetesbkaw5ypia eyik https pan baidu com s 1w phjauzmidmrm5i910dgw asfu tcpip lwip https pan baidu com s 1ax30x2qnxuh785m xtl6iw 32w6 tcp ip https pan baidu com s 1hus4ifwxhcjlgyzj 8kpnq 6p6c tcp ip 1 2 3 https pan baidu com s 1lgohvdta8 vb5cxqliskxq x395 https pan baidu com s 1spu6o l042zq8s1fqngrzg 2qex tcpip 5 https pan baidu com s 15 ebv8wbhhtf8gxcn6vpla awdf http https pan baidu com s 12nl zlr9neectn rayf9nq gyhs mqtt https pan baidu com s 1dkkl7e0hbyxrap0xycyfdw z3kv 07 stm32 https pan baidu com s 1pkbqudat62qj8pwqiuykcq 7nqv 08 c https pan 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3 https pan baidu com s 1csx7zjt l6kxoplkdpa6uq h6qw 15 15 1 mcu 15 2 15 3 ar vr 15 4 15 5 16 17 https pan baidu com s 1crqymelixad u3tquh9 ja 3yn5 tcp udp https pan baidu com s 1jyrilpkljas8r4dirkojja md8p sscom v3 3 https pan baidu com s 1duia8hdbw5qnofyt pxa w 6mkd 18 35 https pan baidu com s 1t gkecrfokyzlsojp9mvog yetg https pan baidu com s 1zpjrl2qjb0yp5c u4meebq qvpi 2019 2020 https pan baidu com s 1 1hzbwo7znd1qo4vitwmrw v8n2 2019 2020 https pan baidu com s 1xx32yvwzajllcuf6vqthrg tqas https pan baidu com s 1zk0itvldzmddmli4idxwjw rdim https pan baidu com s 13 yapyhq3bykv36fqiud5q tapx 1510335354 qq com	c arm linux rtos	os
line-liff-v2-starter	liff starter liff starter is a good starter template can help you understand how to integrate liff into your own development environment you can check the source code and modify it to implement some cool stuff with liff api getting start install dependencies sh npm ci you can run local server with sh npm start build deploy deploy the app using deploy to netlify button deploy to netlify https www netlify com img deploy button svg https app netlify com start deploy repository https github com line line liff v2 starter 1 click deploy to netlify above 2 on the create new app page in netlify fill in the required information 3 click deploy app build and deploy the app with netlify cli tools 1 install netlify cli tool from npm sh npm install netlify cli g 2 run following command to build project sh liff id your liff id npm run build 3 make sure you have signed in your netlify account sh netlify login 4 deploy to netlify sh netlify deploy 5 create your site name and choose the source path dist to deploy 6 you can see the stating draft site url once you confirm it you can deploy it to production stie sh netlify deploy prod demo site you can also check official site before you trying it https liff starter netlify app		front_end
dialtone	dialtone this is the home for dialtone dialpad s design system it includes the resources needed to create consistent predictable interfaces that conform to dialpad s design principles language styles and best practices install dialtone install it via npm npm install dialpad dialtone import dialtone css less less import node modules dialpad dialtone lib dist css dialtone min css javascript js import dialpad dialtone lib dist css dialtone min css add dialtone s theme class to the body light mode html body class dialtone theme light body dark mode html body class dialtone theme dark body this will define the dialtone css variables for your desired theme it is required to do this for dialtone to function building dialtone locally to build dialtone locally visit our installation instructions https dialpad design guides getting started build dialtone locally contributing if you re interested in contributing to dialtone please read our contributing docs https github com dialpad dialtone blob master github contributing md before submitting a pull request requesting features reporting bugs requesting a feature or reporting a bug please do so at the below links request feature https dialpad atlassian net secure createissue jspa issuetype 10901 pid 12428 report bug https dialpad atlassian net secure createissue jspa issuetype 10878 pid 12428 please also feel free to contact us via the dialtone slack channel https dialpad slack com messages dialtone with any questions	css less vuejs design-system	os
embedded-system-design-nptel	embedded system design nptel course esd nptel hompage https user images githubusercontent com 70762571 167149390 ee8152f2 24d1 413f 89cf 4de2b0ae539c png reference 1 dr konstantinos tatas http staff fit ac cy com tk 2 peter marwedel https www marwedel eu peter	embedded-systems embedded-system-design nptel nptel-assignments nptel-courses swayam	os
OpenNMT-tf	ci https github com opennmt opennmt tf workflows ci badge svg https github com opennmt opennmt tf actions query workflow 3aci codecov https codecov io gh opennmt opennmt tf branch master graph badge svg https codecov io gh opennmt opennmt tf pypi version https badge fury io py opennmt tf svg https badge fury io py opennmt tf documentation https img shields io badge docs latest blue svg https opennmt net opennmt tf gitter https badges gitter im opennmt opennmt tf svg https gitter im opennmt opennmt tf utm source badge utm medium badge utm campaign pr badge forum https img shields io discourse status server https 3a 2f 2fforum opennmt net 2f https forum opennmt net opennmt tf opennmt tf is a general purpose sequence learning toolkit using tensorflow 2 while neural machine translation is the main target task it has been designed to more generally support sequence to sequence mapping sequence tagging sequence classification language modeling the project is production oriented and comes with backward compatibility guarantees https github com opennmt opennmt tf blob master changelog md key features modular model architecture models are described with code to allow training custom architectures and overriding default behavior for example the following instance defines a sequence to sequence model with 2 concatenated input features a self attentional encoder and an attentional rnn decoder sharing its input and output embeddings python opennmt models sequencetosequence source inputter opennmt inputters parallelinputter opennmt inputters wordembedder embedding size 256 opennmt inputters wordembedder embedding size 256 reducer opennmt layers concatreducer axis 1 target inputter opennmt inputters wordembedder embedding size 512 encoder opennmt encoders selfattentionencoder num layers 6 decoder opennmt decoders attentionalrnndecoder num layers 4 num units 512 attention mechanism class tfa seq2seq luongattention share embeddings opennmt models embeddingssharinglevel target the opennmt https opennmt net opennmt tf package overview html package exposes other building blocks that can be used to design multiple input features https opennmt net opennmt tf package opennmt inputters parallelinputter html mixed embedding representation https opennmt net opennmt tf package opennmt inputters mixedinputter html multi source context https opennmt net opennmt tf package opennmt inputters parallelinputter html cascaded https opennmt net opennmt tf package opennmt encoders sequentialencoder html or multi column https opennmt net opennmt tf package opennmt encoders parallelencoder html encoder hybrid sequence to sequence models https opennmt net opennmt tf package opennmt models sequencetosequence html standard models such as the transformer are defined in a model catalog https github com opennmt opennmt tf blob master opennmt models catalog py and can be used without additional configuration find more information about model configuration in the documentation https opennmt net opennmt tf model html full tensorflow 2 integration opennmt tf is fully integrated in the tensorflow 2 ecosystem reusable layers extending tf keras layers layer https www tensorflow org api docs python tf keras layers layer multi gpu training with tf distribute https www tensorflow org api docs python tf distribute and distributed training with horovod https github com horovod horovod mixed precision training with tf keras mixed precision https www tensorflow org guide mixed precision visualization with tensorboard https www tensorflow org tensorboard tf function graph tracing that can be exported to a savedmodel https opennmt net opennmt tf serving html and served with tensorflow serving https github com opennmt opennmt tf tree master examples serving tensorflow serving or python https github com opennmt opennmt tf tree master examples serving python compatibility with ctranslate2 ctranslate2 https github com opennmt ctranslate2 is an optimized inference engine for opennmt models featuring fast cpu and gpu execution model quantization parallel translations dynamic memory usage interactive decoding and more opennmt tf can automatically export https opennmt net opennmt tf serving html ctranslate2 models to be used in ctranslate2 dynamic data pipeline opennmt tf does not require to compile the data before the training instead it can directly read text files and preprocess the data when needed by the training this allows on the fly tokenization https opennmt net opennmt tf tokenization html and data augmentation by injecting random noise model fine tuning opennmt tf supports model fine tuning workflows model weights can be transferred to new word vocabularies e g to inject domain terminology before fine tuning on in domain data contrastive learning https ai google research pubs pub48253 to reduce word omission errors source target alignment sequence to sequence models can be trained with guided alignment https arxiv org abs 1607 01628 and alignment information are returned as part of the translation api opennmt tf also implements most of the techniques commonly used to train and evaluate sequence models such as automatic evaluation during the training multiple decoding strategy greedy search beam search random sampling n best rescoring gradient accumulation scheduled sampling checkpoint averaging and more see the documentation https opennmt net opennmt tf to learn how to use these features usage opennmt tf requires python 3 7 or above tensorflow 2 6 2 7 2 8 2 9 2 10 2 11 2 12 or 2 13 we recommend installing it with pip bash pip install upgrade pip pip install opennmt tf see the documentation https opennmt net opennmt tf installation html for more information command line opennmt tf comes with several command line utilities to prepare data train and evaluate models for all tasks involving a model execution opennmt tf uses a unique entrypoint onmt main a typical opennmt tf run consists of 3 elements the model type the parameters described in a yaml file the run type such as train eval infer export score average checkpoints or update vocab that are passed to the main script onmt main model type model config config file yml auto config run type run options for more information and examples on how to use opennmt tf please visit our documentation https opennmt net opennmt tf library opennmt tf also exposes well defined and stable apis https opennmt net opennmt tf package overview html from high level training utilities to low level model layers and dataset transformations for example the runner class can be used to train and evaluate models with few lines of code python import opennmt config model dir data wmt ende checkpoints data source vocabulary data wmt ende joint vocab txt target vocabulary data wmt ende joint vocab txt train features file data wmt ende train en train labels file data wmt ende train de eval features file data wmt ende valid en eval labels file data wmt ende valid de model opennmt models transformerbase runner opennmt runner model config auto config true runner train num devices 2 with eval true here is another example using opennmt tf to run efficient beam search with a self attentional decoder python decoder opennmt decoders selfattentiondecoder num layers 6 vocab size 32000 initial state decoder initial state memory memory memory sequence length memory sequence length batch size tf shape memory 0 start ids tf fill batch size opennmt start of sentence id decoding result decoder dynamic decode target embedding start ids start ids initial state initial state decoding strategy opennmt utils beamsearch 4 more examples using opennmt tf as a library can be found online the directory examples library https github com opennmt opennmt tf tree master examples library contains additional examples that use opennmt tf as a library nmt wizard docker https github com opennmt nmt wizard docker uses the high level opennmt runner api to wrap opennmt tf with a custom interface for training translating and serving for a complete overview of the apis see the package documentation https opennmt net opennmt tf package overview html additional resources documentation https opennmt net opennmt tf forum https forum opennmt net gitter https gitter im opennmt opennmt tf	neural-machine-translation tensorflow python opennmt machine-translation deep-learning natural-language-processing	ai
IoT_Sec_Tutorial	iot sec tutorial iot		server
owq	owq lessons learned from activation outliers for weight quantization in large language models this is the code for the paper owq lessons learned from activation outliers for weight quantization in large language models https arxiv org abs 2306 02272 owq preserves few weak columns as fp16 while compressing other weight coulmns to 3 4 bits owq achieves substantial quality improvements with only negligible storage and computation overhead effectively preserving the benefits of low precision acceleration the current release supports following features implementation of the owq algorithm recon py https github com xvyaward owq blob main owq recon py 3 4 bit weight quantization of opt llama and bloom families opt py https github com xvyaward owq blob main opt py llama py https github com xvyaward owq blob main llama py bloom py https github com xvyaward owq blob main bloom py evaluating the perplexity of quantized models opt py https github com xvyaward owq blob main opt py llama py https github com xvyaward owq blob main llama py bloom py https github com xvyaward owq blob main bloom py evaluating the zero shot accuracy of quantized models zeroshot py https github com xvyaward owq blob main zeroshot py supports 3 bit packed weight save load 1 5 file size of fp16 checkpoint efficient 3 bit matrix fp16 vector product cuda kernel for owq owq kernel https github com xvyaward owq tree main owq kernel features we are working on integrating all models opt llama bloom into single file efficient matrix matrix multiplication cuda kernel for owq efficient w4a16 cuda kernel table of contents install install usage measuring perplexity usage zero shot zero shot 3 bit cuda kernel 3 bit cuda kernels install we highly recommend to use docker image that supports cuda if you use anaconda instead you need to setup cuda for kernel use 0 a using docker docker run it gpus all ipc host v local storage docker container storage pytorch pytorch 2 0 0 cuda11 7 cudnn8 devel install git apt update apt install git y 0 b using anaconda instead of docker conda create n owq python 3 10 y conda activate owq 1 clone the owq repository git clone https github com xvyaward owq cd owq 2 install all the dependencies pip install r requirements txt 3 install 3 bit cuda kernel 3bit w x fp16 a cd owq kernel python setup cuda py install torch tested on v2 0 0 cu117 transformers tested on v4 29 2 datasets tested on v2 12 0 experiments were conducted on a single nvidia a100 gpu with 80gb memory we also confirmed that reconstruction using owq works on rtx 3090 gpu 24gb memory for 30b models we have tested 3 bit cuda kernel on the nvidia a100 gpu and a6000 gpu usage running owq measuring the perplexity ppl opt example here we use opt 1 3b model as an example you can replace the model argument opt 1 3b among opt 125m opt 350m opt 2 7b opt 6 7b opt 13b opt 66b owq using 3 01 bit 3 bit quantization few fp16 weight columns python opt py facebook opt 1 3b c4 wbits 3 target bit 3 01 owq using 4 01 bit 4 bit quantization few fp16 weight columns python opt py facebook opt 1 3b c4 wbits 4 target bit 4 01 please refer to scripts for more examples below are the example for the other options fp16 rtn gptq measuring the ppl of the full precision fp16 model python opt py facebook opt 1 3b c4 wbits 16 4 bit round to nearest rtn quantization python opt py facebook opt 1 3b c4 wbits 4 nearest gptq with 3 bit quantization python opt py facebook opt 1 3b c4 wbits 3 tuning minmax the above usage examples for opt models can be used same for other model families as well llama owq using 3 01 bit 3 bit quantization few fp16 weight columns python llama py llama model location c4 wbits 3 target bit 3 01 bloom owq using 3 01 bit 3 bit quantization few fp16 weight columns python bloom py bigscience bloom 1b1 c4 wbits 3 target bit 3 01 to run other bloom models replace bloom 1b1 with one of bloom 560m bloom 1b7 bloom 3b bloom 7b1 bloom zero shot here we give an example of measuring zero shot accuracy on lambada openai and piqa tasks using opt 125m model current version only supports measuring zeroshot accuracy from the saved model you need checkpoint file before measuring the zero shot accuracy making checkpoint file of owq reconstruction python opt py facebook opt 125m c4 wbits 3 target bit 3 05 no eval save checkpoint file measuring zero shot accuracy python zeroshot py facebook opt 125m load checkpoint file batch size 8 task lambada openai piqa 3 bit cuda kernels benchmark kernel performance benchmark performance for the matrix multiplication cd owq kernel python test kernel py benchmark language generation with 3 bit packed model opt llama example of opt 66b language generation single token save compressed model python opt py facebook opt 66b c4 wbits 3 target bit 3 01 no eval save checkpoint file packing benchmark generating a 128 token sequence with the saved model cuda visible device 0 python opt py facebook opt 66b c4 load pack3 checkpoint file packing benchmark 128 benchmark fp16 baseline note that the model will be split across all listed gpus cuda visible devices 0 1 2 python opt py facebook opt 66b c4 benchmark 128 if you save quantized model with packing option this gives 3 bit packed checkpoint with name pack3 checkpoint file together with fake quantized model checkpoint file please note that our 3 bit kernels are currently only optimized for a100 or a6000 gpus and may thus yield suboptimal performance on smaller models or on other gpus reference gptq accurate post training compression for generative pretrained transformers https arxiv org abs 2210 17323 this code is based on gptq https github com ist daslab gptq our zero shot experiment codes are based on eleutherai lm evaluation harness https github com eleutherai lm evaluation harness thanks to meta ai for releasing powerful llm opt https arxiv org abs 2205 01068 and llama https arxiv org abs 2302 13971 cite if you find our code or owq useful for your research please consider citing article lee2023owq title owq lessons learned from activation outliers for weight quantization in large language models author lee changhun and jin jungyu and kim taesu and kim hyungjun and park eunhyeok journal arxiv preprint arxiv 2306 02272 year 2023		ai
AWS-Cloud-Resume	aws cloud resume cloud resume challenge this repository contains the code for my cloud resume challenge project which is an initiative to showcase my skills in developing and deploying a modern web application using aws services check out my blog post on what the technologies used and how i completed the challenge https pendingusername github io the architecture of the project is as follows the frontend of the web application is built using html css and javascript and is hosted on amazon s3 amazon cloudfront is used to distribute the web application and improve its performance the web application interacts with a restful api built using aws lambda and amazon api gateway the api is responsible for handling user requests and retrieving and storing data in amazon dynamodb iam is used to manage access to aws resources for users and services acm is used to manage ssl tls certificates for secure communication between the web application and the api dynamodb is used to store and retrieve data for the web application github actions is used to automatically build and deploy the web application to s3 and the api to lambda route 53 is used to route user requests to the cloudfront distribution aws firewall manager is used to manage security rules for the web application deployment the deployment process for the project is fully automated using github actions and terraform when changes are pushed to the github repository github actions builds and deploys the web application to s3 and the api to lambda terraform is used to manage the aws resources and automate the infrastructure deployment process lean more here https www terraform io		cloud
Qix	prs welcome https img shields io badge prs welcome brightgreen svg style flat square http makeapullrequest com github stars https img shields io github stars ty4z2008 qix svg style plastic github forks https img shields io github forks ty4z2008 qix svg color blue style plastic about me weibo jun http weibo com ty4z2008 twitter https twitter com ty4z2008 e mail ty4z2008 gmail com scale system channel https t me scalesystem https t me scalesystem note there may be some incorrect information in the article i hope i can correct error with you you can contact me with email or pr pull request welcome blush my translation node mysql document translate node mysql offcial document https github com felixge node mysql blob master readme md node mysql chinese document https github com ty4z2008 qix blob master node md machine learning and deep learning resources chapter 1 https github com ty4z2008 qix blob master dl md chapter 2 https github com ty4z2008 qix blob master dl2 md golang learning resources chapter 1 https github com ty4z2008 qix blob master golang md postgresql database resources chapter 1 https github com ty4z2008 qix blob master pg md distributed system resources chapter 1 https github com ty4z2008 qix blob master ds md database system resources chapter 1 https github com ty4z2008 qix blob master db md additional notes dear friends in order to respect to the efforts of authorship in the reading process when you find that resource the authorship is incorrect i also want you to submit feedback https github com ty4z2008 qix issues thanks buddy license mit license https github com ty4z2008 qix blob master license md	machine-learning distributed-systems postgresql deep-learning go awesome distributed-computing distributed-database	ai
xone	p align center br img src https nextapps de img logo xone svg alt xone javascript development environment width 50 br br a target blank href https www npmjs com package xone img src https img shields io npm v xone svg a img src https img shields io badge status beta orange svg a target blank href https travis ci org nextapps de xone img src https travis ci org nextapps de xone svg branch master a a target blank href https coveralls io github nextapps de xone branch master img src https coveralls io repos github nextapps de xone badge svg branch master a a target blank href https github com nextapps de xone issues img src https img shields io github issues nextapps de xone svg a a target blank href https github com nextapps de xone blob master license md img src https img shields io npm l xone svg a p h1 h1 h3 mobile application development kit mvc framework h3 xone provides you a lightweight full stack environment on top of node js to develop beautiful applications for every use based on html5 and javascript and enables the optimal integration of an universal codebase into a wide range of systems e g mobile devices tablets desktops browser environments notice actually this is a beta state of this repository some breaking changes could possibly be introduced in upcoming versions do not use for production until this message has been removed license license md installation doc install md api doc api md work in progress codebase structure doc code structure md global app configuration doc app config md basic examples mvc pattern routes events templates dev tools doc xone md persistent models doc xone model md core library doc xone core md dependency management calculation doc xone deps md amd loader build plugin doc xone amd md validations doc xone validate md demo tutorial todo app https nextapps de github io xone announcement xone v1 0 0 stable xone will get some major changes this was required to open capabilities for upcoming features we are working now like xone native the plan is to release a final architecture until v 1 0 0 and then also to be done with most of the breaking changes v0 9 0 beta coming in q1 2018 xone native cordova based wrapper enables native features without changing anything on your codebase like native transitions native filesystem storage native image library native media player native device access camera photo library statusbar etc native map provides default cordova based project build configurations which already including all required plugins as well as platform configurations available source code of demo apps native demo app through app stores including technical demos components demo demo apps xone developer tools documentation 90 test coverage img src doc xone processing model png alt width 50 align right style float right padding 0 0 20px 20px v0 8 0 beta in progress xone non blocking ui processing model controllable via util process async util process paint util process promise util process queue util process stack util process asap util process run pseudo thread runner instance fully transfer to asynchronous module definition replaces the poorly closure compiler dependency system replace most of global namespaces of the framework to constructors and also changing capitalized style for the new amd namespacing note by using amd you can internally customize naming of all references of xone conflicts with global namespace is not possible in amd app controller name new controller name or controller new name app view name new view name or view new name app event query new event query or event new query app route route new route route or route new route app mapper name is now a part of view model and or route see view mappings model mappings payload mappings app model name new model name or model new name app worker name new worker name or worker new name app handler you are free to use any style like before app helper you are free to use any style like before app setting get key setting get key app plugin filesystem filesystem core util package e g util array merge alternatively of calling the method new it is also possible to use create as well as register singular naming coding convention is still valid new constructors can either be instantiated via the builtin method new or via the classical new keyword the new concept of using constructors opens a lot of nice features they will coming soon and also adds some missing oop styles user interface components slider side drawer side menu internal app status notification adjustable statusbar visibility and webview overlays statusbar per screen view provides package definitions packages can group or import html templates javascript css styles and assets provide dependency management of whole modules packages through the keyword import package and export package worker to de compress contents in background lzip img src doc xone gui png alt width 35 align right style float right padding 0 0 20px 20px xone development user interface browser replacement for any console commands model supports now virtual properties everywhere not only in views model can now define a structural schema to save in storage this also improves extraction of nested models new templating implementation and compiler allows inserting javascript allows nesting of for loops and if conditionals better handling of virtual and mapped properties ultra fast through new pre compilation strategy and usage of model bind caching instead of view bind cache v0 7 0 beta support for web components support for web templates support for native filesystem implements web animation api virtual dom integration inferno xone native adapter for cordova based apps popups alerts confirmations status filesystem storage native transition action sheet notifications reminder calendar native image library native map new view controller engine new layout controller the old was completely removed simplified project structure build extensions css auto prefixer manage hooks manage optional dependencies improved persistent storing of models improved auto resizing layout improved dependency management lazy image loader with offline filesystem cache user interface components navigation bar toolbar scrollpane vertical horizontal split lists tables grid layout pull to refresh swipe back views view manager controls components render payloads those features will become deprecated for now unsupported using the config rack layout controller any calls to app layout old view helpers slides pulls popups alerts build xone bundle js custom location of xone installation features basically xone is a cherry picked mixture of common programming paradigm used by major technologies like ruby on rails mustache handlebars node js mithril google closure and combines them into a lightweight application development environment for this purpose xone has an easy to learn high level interface without forcing you to implement any low level framework cryptofied code as you would do with angular or react that also preserves huge flexibility to your codebase for upcoming technologies ports or any other major changes manage and execute production development benchmark and test environments developing seamlessly in a web browser environment saves you tons of time provides a dynamic template system which is readable for designers therefore you will get the most lightweight footprint for your codebase provides an advanced declarative mvc architecture for your app e g persistent models routes controller events views handlers mapper uses closure compiler to achieve optimal dead code elimination xone libraries compiles with your codebase the used code remains in a single javascript build file others build system dependency management environment management platform management unit test integration benchmark integration simple fast and natural usage out of the box without loosing any framework magics targeting products hybrid apps progressive web apps apps based on cordova phonegap ionic electron single page application browser apps xone basically is available in 3 different versions 1 xone project development environment initial project created by xone create provides you a scalable development stack for modern web based applications on top of node js is intended for a compilation build 2 xone extern bundle standalone xone bundle js cdn rawgit com nextapps de xone master dist xone bundle js alternatively use this bundle to add xone as a dependency to an already existing build compiler system compatible with closure compiler advanced mode is intended for a compilation build 3 xone extern library standalone xone lib js cdn rawgit com nextapps de xone master dist xone lib js xone lib min js cdn rawgit com nextapps de xone master dist xone lib min js skips the build part completely and make xone using like a jquery library in your web based project is not intended for a compilation xone comparison 1 project 2 bundle 3 library features by default xone project environment xone bundle standalone xone library standalone final project filesize smallest small normal core library img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 mvc framework img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 render engine img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 unit tests img src http nextapps de img icon check png v 2 debugging tools img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 environments img src http nextapps de img icon check png v 2 global app configuration img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 build compile project img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 manage platforms img src http nextapps de img icon check png v 2 dynamic templates html json img src http nextapps de img icon check png v 2 dependency management img src http nextapps de img icon check png v 2 initial codebase img src http nextapps de img icon check png v 2 cli tools img src http nextapps de img icon check png v 2 support closure compiler advanced mode img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 dead code removal img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 is not strictly bound by conventions img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 does not require node js img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 use as a standalone library like jquery underscore img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 use as a framework like bootstrap angular img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 use as a dev environment like sencha meteor img src http nextapps de img icon check png v 2 installation xone project bash npm install g xone note to make the xone cli globally available you have to install xone as a global npm module also in addition to any local installation if you want to keep simple as most as possible you can also use a local custom versions of xone as well as using the cli without any global installations read further note xone project binaries typically has to be installed via xone create or xone install and comes with its own pre defined folder structure followed by some conventions you can pick one of the two stand alone github com nextapps de xone tree master dist versions optionally to skip as many conventions as possible e g xone acts like an extern javascript plugin windows alternatively in the root of your project you can use the local cli shortcut app instead for xone e g bash my project app build note the options create and install both are not available over the shortcut app macos linux on a linux machine you may use bash sudo npm install g xone if the global xone identifier is not registered properly try one of these lines bash hash xone hash r if you also like to make the shortcut app in your terminal available you need to add the xone bin folder to the path of your system export path path path to node modules xone bin alternatively you can use the local cli fallback like bash bash xone build create new xone project create a new project inside the directory workspace my project bash workspace xone create my project works but it is generally not recommended to use whitespaces in a project folder name bash workspace xone create my project read further doc install md update existing xone project to update xone of an already existing project you basically need 2 steps 1 fetch and install latest version via npm bash npm install g xone 2 install update to a xone project automatically fetches sources from npm modules bash workspace my project xone install note this will not overwrite any of your project files only projects xone library files located in app lib xone are updated read further doc install md build xone project un compiled sources located in workspace my project app bash my project xone build production build located in workspace my project public www note we recommended to use production builds for any external public release and use the sources only for developing testing and may some other internal purposes to skip the build integration of xone you have to use the standalone version instead run xone project local webserver bash my project xone server open your preferred webrowser and goto http localhost app or http localhost public www optionally you can pass custom host and port bash my project xone server localhost 8080 open your preferred webrowser and goto http localhost 8080 run xone project local filesystem open app index html from sources or public www index html from production build in your preferred browser deploy xone project cordova web etc use production builds located in workspace my project public to move forward into your cordova based projects or upload to a webserver manage platforms xone provides custom platform injections to perform platform specific production builds therefore all those builds only includes necessary code and dependencies for their related platform show currently defined platforms bash my project xone platform perform platform specific compilation bash my project xone compile android compiled files remains in workspace my project app perform platform specific builds bash my project xone build android build destination workspace my project public android add custom platforms you can add unlimited custom platforms e g create a platform webapp bash my project xone platform add webapp my project xone build webapp build destination workspace my project public webapp build xone standalone bash my project xone build bundle build destination workspace my project app lib xone dist xone bundle js bash my project xone build lib build destination workspace my project app lib xone dist xone lib js bash my project xone build lib min app js build destination workspace my project app js xone lib min js note the order of passed parameters cannot be changed actually using xone standalone library html doctype html html head meta charset utf 8 head body end of body load xone script src js xone lib min js script your app code script src js app js script body html configure build tool xone build uses the google closure compiler all xone libraries also supports compilation in advanced mode the build properties can be configured in xone json the closure compiler also provides a simple dependency management system provide require you should make use of to improve dead code removal note actually xone supports 2 different versions of closure compiler 1 requires java 2 requires javascript node it is recommended to have a java jre properly installed on your machine to unlock some benefits of the closure compiler java version to change the type of the compiler you need to change the value of the field closure compiler lib type from js into jar in xone json accordingly note if you have less experience with the closure compiler you can optionally set the compilation level to simple on the field closure compiler level within the xone config file comparison closure compiler java vs closure compiler javascript features by default closure compiler java closure compiler javascript dependency management img src http nextapps de img icon check png v 2 img src http nextapps de img icon check png v 2 auto sort dependencies img src http nextapps de img icon check png v 2 strict dependencies entry point img src http nextapps de img icon check png v 2 pretty print compilation img src http nextapps de img icon check png v 2 build performance normal slow compression ratio best good memory consumption normal extreme generating docs jsdoc bash my project xone docs docs will be generated in docs api xone render engine the render engine provides an optional fast mode to get the most out of performance all internal processes of xone takes advantage when fast mode is enabled e g render templates animate elements toggle views when it runs in fast mode the render engine has a very closed infrastructure you should keep notice 1 css styles change styles get styles 2 css classes change classes get classes 3 html content change contents get contents note when fast mode is enabled it should not be mixed by any other external manipulations of the same category listed above we recommended to disable the fast mode when style issues occurs by any direct style manipulations e g when using an extern library note the fast mode is enabled by default in a xone project may change in future and is disabled by default in the xone extern library performance comparison native vs jquery vs xone https jsperf com xone style performance https jsperf com xone dom selector performance restrictions when fast mode is enabled the problem js var display get style direct display document getelementbyid my div style display console log display block set style xone core setstyle my div display none get style xone display core getstyle my div display console log display none get style direct display document getelementbyid my div style display console log display block instead do this js var display set style xone core setstyle my div display none do on next refresh core paint function get style direct display document getelementbyid my div style display console log display none development environments default environments are production live build development local test local benchmark local change environment in app manifest js json env development platform www or just adding parameters to the url url http localhost 9000 env test platform android debug true note you are also able to override any config attribute by passing url parameters respectively custom environments show currently defined environments bash my project xone env add custom environments bash my project xone env add offline build destination workspace my project app config offline js todo x provide standalone version dependency less x create install cli x use npm modules for system tasks supports all platforms improve environment mappings x improve worker integration x provide plugin management x replace closure dependency loader x improve dependency management x improve platform management support hooks x support animations x support asynchronous module definitions x improve versioning improve migration x improve benchmark integration part 1 2 improve benchmark integration part 2 2 x refactor api add documentation add demos examples add jsdoc add unit tets x core tests x amd tests x async tests x paint tests x model tests mvc tests test xone standalone x platform support x macos x windows x linux features manage development environments production development test benchmark manage process environments rack webapp cordova native local browser manage platform environments android ios windows webkit mozilla msie model view controller supports persistent model management full mvc managed environment routes events handlers controller interfaces adapter models mapper views payloads threads worker stubs mocks translations javascript core library codebase dependency management supports dom pattern in json format template system static content dynamic content partials locales build system template compiler local webserver layout manager multi platform injections event delegation nosql persistent storage gzip storage data compression flexicache auto scale auto clean multi language support unit test integration versioning migrations dev task hooks compile build deploy process xone library hooks mvc storage handlers events debug mode debugger tools performance tools analytics statistic tools benchmark integration closure compiler integration advanced mode less css integration css compressor integration jsdoc integration provides the most possible performance while ensuring maximum crossplatform support compatible with almost each library out there also can be mixed with similar technologies like angular or react img src http nextapps de img xone workflow png	open-source javascript javascript-framework application-framework mobile-development mvc-framework sdk html5 webapp progressive-web-app hybrid-apps pwa cordova android ios desktop-application	front_end
I-T	i t information technology		server
stanford-cs-229-machine-learning	machine learning cheatsheets for stanford s cs 229 available in https github com afshinea stanford cs 229 machine learning tree master ar english https github com afshinea stanford cs 229 machine learning tree master en espa ol https github com afshinea stanford cs 229 machine learning tree master es https github com afshinea stanford cs 229 machine learning tree master fa fran ais https github com afshinea stanford cs 229 machine learning tree master fr https stanford edu shervine l ko teaching cs 229 cheatsheet machine learning tips and tricks portugu s https github com afshinea stanford cs 229 machine learning tree master pt t rk e https github com afshinea stanford cs 229 machine learning tree master tr ti ng vi t https github com afshinea stanford cs 229 machine learning tree master vi https github com afshinea stanford cs 229 machine learning tree master zh https github com afshinea stanford cs 229 machine learning tree master zh tw goal this repository aims at summing up in the same place all the important notions that are covered in stanford s cs 229 machine learning course and include refreshers in related topics that highlight the key points of the prerequisites of the course cheatsheets for each machine learning field as well as another dedicated to tips and tricks to have in mind when training a model all elements of the above combined in an ultimate compilation of concepts to have with you at all times content vip cheatsheets a href https github com afshinea stanford cs 229 machine learning blob master en cheatsheet supervised learning pdf img src https stanford edu shervine teaching cs 229 illustrations cover en 001 png alt illustration width 220px a a href https github com afshinea stanford cs 229 machine learning blob master en cheatsheet unsupervised learning pdf img src https stanford edu shervine teaching cs 229 illustrations cover en 002 png alt illustration width 220px a a href https github com afshinea stanford cs 229 machine learning blob master en cheatsheet deep learning pdf img src https stanford edu shervine teaching cs 229 illustrations cover en 003 png alt illustration width 220px a a href https github com afshinea stanford cs 229 machine learning blob master en cheatsheet machine learning tips and tricks pdf img src https stanford edu shervine teaching cs 229 illustrations cover en 004 png alt illustration width 220px a supervised learning unsupervised learning deep learning tips and tricks vip refreshers a href https github com afshinea stanford cs 229 machine learning blob master en refresher probabilities statistics pdf img src https stanford edu shervine teaching cs 229 illustrations cover en 005 png alt illustration width 220px a a href https github com afshinea stanford cs 229 machine learning blob master en refresher algebra calculus pdf img src https stanford edu shervine teaching cs 229 illustrations cover en 006 png 1 alt illustration width 220px a probabilities and statistics algebra and calculus super vip cheatsheet a href https github com afshinea stanford cs 229 machine learning blob master en super cheatsheet machine learning pdf img src https stanford edu shervine teaching cs 229 illustrations cover en 007 png alt illustration width 400px a all the above gathered in one place website this material is also available on a dedicated website https stanford edu shervine teaching cs 229 so that you can enjoy reading it from any device translation would you like to see these cheatsheets in your native language you can help us translating them on this dedicated repo https github com shervinea cheatsheet translation authors afshine amidi https twitter com afshinea ecole centrale paris mit and shervine amidi https twitter com shervinea ecole centrale paris stanford university	cheatsheet machine-learning data-science supervised-learning unsupervised-learning deep-learning ml-cheatsheet cs229	ai
mint-ui	div align center a href http mint ui netlify com img width 200 src packages components public mintui logo svg a div div align center ui components for web a react implementation npm version https img shields io npm v turtlemint mint ui npm size https badgen net bundlephobia minzip turtlemint mint ui w3c validation https img shields io w3c validation html targeturl https 3a 2f 2fmint ui netlify com netlify status https img shields io netlify f808dfbe b589 4cca 8149 3a169f9f44bb npm https img shields io npm l turtlemint mint ui div features a modern and fitting ui design system for scalable web applications saas driven component apis boasts of composite ui a rendering engine that lays out any nth level deep nested hierarchical structure a set of high quality react components out of the box written in typescript with predictable static types the whole package of development and design resources and tools environment support modern browsers and internet explorer 9 with polyfills img src https raw githubusercontent com alrra browser logos master src edge edge 48x48 png alt ie edge width 24px height 24px http godban github io browsers support badges br ie edge img src https raw githubusercontent com alrra browser logos master src firefox firefox 48x48 png alt firefox width 24px height 24px http godban github io browsers support badges br firefox img src https raw githubusercontent com alrra browser logos master src chrome chrome 48x48 png alt chrome width 24px height 24px http godban github io browsers support badges br chrome img src https raw githubusercontent com alrra browser logos master src safari safari 48x48 png alt safari width 24px height 24px http godban github io browsers support badges br safari img src https raw githubusercontent com alrra browser logos master src opera opera 48x48 png alt opera width 24px height 24px http godban github io browsers support badges br opera ie9 ie10 ie11 edge last 2 versions last 2 versions last 2 versions last 2 versions install bash npm i turtlemint mint ui save bash yarn add turtlemint mint ui usage jsx import button from turtlemint mint ui reactdom render button title sample button btnstyle primary mountnode development use gitpod a free online dev environment for github open in gitpod https gitpod io button open in gitpod svg https gitpod io https github com turtlemint mint ui or clone locally bash git clone git github com turtlemint mint ui git cd mint ui yarn install yarn workspace turtlemint mint ui run dev it should spin up a browser automatically if not open your browser and visit http 127 0 0 1 5555 contributing prs welcome https img shields io badge prs welcome brightgreen svg style flat square http makeapullrequest com we welcome all contributions please read our contributing md https github com turtlemint mint ui blob master contributing md first you can submit any ideas as pull requests https github com turtlemint mint ui pulls or as github issues https github com turtlemint mint ui issues	react reactjs ui-components ui-design ui ui-library design-system react-components react-hooks typescript-library turtlemint-ui turtlemint gitpod modern	os
OptiGuide	optiguide large language models for supply chain optimization supply chain operations traditionally involve a variety of complex decision making problems over the last few decades supply chains greatly benefited from advances in computation which allowed the transition from manual processing to automation and cost effective optimization nonetheless business operators still need to spend substantial efforts in explaining and interpreting the optimization outcomes to stakeholders motivated by the recent advances in large language models llms we study how this disruptive technology can help bridge the gap between supply chain automation and human comprehension and trust thereof we design optiguide a framework that accepts as input queries in plain text and outputs insights about the underlying optimization outcomes our framework does not forgo the state of the art combinatorial optimization technology but rather leverages it to quantitatively answer what if scenarios e g how would the cost change if we used supplier b instead of supplier a for a given demand importantly our design does not require sending proprietary data over to llms which can be a privacy concern in some circumstances we demonstrate the effectiveness of our framework on a real server placement scenario within microsoft s cloud supply chain along the way we develop a general evaluation benchmark which can be used to evaluate the accuracy of the llm output in other scenarios this repository includes the following material to open source the optiguide project research code base for the optiguide framework in the optiguide optiguide py optiguide optiguide py file a demo notebook of the optiguide framework notebook optiguide example ipynb notebook optiguide example ipynb benchmarks dataset in benchmark benchmark for evaluating language models for supply chain applications benchmark utils for future evaluation create a github release for the benchmark citation please cite the paper if you use this code in your own work latex article li2023large title large language models for supply chain optimization author li beibin and mellou konstantina and zhang bo and pathuri jeevan and menache ishai journal arxiv preprint arxiv 2307 03875 year 2023 setup for the optiguide code installation once python is installed just run pip install optiguide to install optiguide here is the pypi page https pypi org project optiguide for more information tutorial option 1 run from colab directly 1 check the colab notebook open in colab https colab research google com assets colab badge svg https colab research google com drive 13emjocy79mhyeuyicsmbem63ks7mzi33 usp sharing 2 create a oai config list file inside the colab environment so that it can connect to the openai service json model gpt 4 api key your api key comes here api type azure api base the api base url api version 2023 03 15 preview 1 now you can run the colab code tutorial option 2 run locally 1 install python and python packages pip install optiguide 2 install and setup gurobi optimizer from gurobi s official website https www gurobi com downloads gurobi software and you can get a trial license for academic use make sure the license is setup correctly 3 run optiguide example you can setup the openai api key and secret and then test the code with jupyter notebook example notebook optiguide example ipynb example notebook oai config list file json model gpt 4 api key your api key comes here api type azure api base the api base url api version 2023 03 15 preview for benchmark evaluation 1 install python and python packages pip install optiguide gurobipy requests beautifulsoup4 2 download the gurobi examples python download py implementation optiguide implementation we re implemented optiguide for research purposes using autogen https github com microsoft autogen a framework that enables the development of llm applications with multiple agents that can converse with each other to solve tasks evaluation implementation we simplified the evaluation process by using gurobi and gurobi examples as detailed below benchmark dataset we have gathered several python applications from gurobi s example code https www gurobi com documentation current examples python examples html these applications are copyrighted by gurobi the specific examples we are using include diet py facility py netflow py tsp py workforce1 py subsequently we manually crafted questions and provided the ground truth answer code for each question these labeled questions and answers can be found in the benchmark macro benchmark macro folder to download the dataset from gurobi and automatically edit the source code execute python download py the source code for the benchmarked applications will then be saved in the benchmark application benchmark application folder ensure you have the necessary packages installed using pip install requests beautifulsoup4 additionally we developed a coffee benchmark application coffee py application to showcase the optiguide framework as described in our paper we acknowledge that both human and machine may contain errors while generating this benchmark if you identify any inaccuracies feel free to open an issue or submit a pull request for correction responsible ai considerations the integration of large language models llms into supply chain management has improved efficiency and offered human like understanding however it s crucial to actively consider the ethical and responsible aspects of ai to ensure its application is both beneficial and free from unexpected consequences since we use publicly available language models our framework naturally adopts both the strengths and weaknesses of the underlying model aware of these challenges we ve implemented a safeguard mechanism to ensure the fairness and safety of the results yet despite these safeguards the complex nature of ai and its inherent flaws can give rise to issues like model biases adversarial attacks and other anomalies researchers must critically assess and interpret these ai models maintaining a comprehensive understanding of both the advantages and challenges of llms by responsibly utilizing the capabilities of these models we can achieve groundbreaking progress in supply chain practices and optimization all while upholding ethical and unbiased principles prohibitions due to the specialized nature of this repository which includes both benchmark data and source code designed for evaluating data oriented models and pipelines we expressly prohibit the following 1 scraping the content of this repository for the purpose of training machine learning models deep learning architectures data science algorithms large language models or any other data driven computational models utilizing this repository s content for such purposes could introduce bias and invalidate the evaluation metrics of the trained models you are however permitted to use this repository for the evaluation of models and pipelines 2 violating the licensing terms in this repository contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments trademarks this project may contain trademarks or logos for projects products or services authorized use of microsoft trademarks or logos is subject to and must follow microsoft s trademark brand guidelines https www microsoft com en us legal intellectualproperty trademarks usage general use of microsoft trademarks or logos in modified versions of this project must not cause confusion or imply microsoft sponsorship any use of third party trademarks or logos are subject to those third party s policies		ai
Awesome_Prompting_Papers_in_Computer_Vision	awesome prompting papers in computer vision a curated list of prompt based papers in computer vision and vision language learning awesome prompting papers in computer vision awesome prompting papers in computer vision keywords keywords vision prompt vision prompt vision language prompt vision language prompt language interactable prompt language interactable prompt vision language instruction tuning vision language instruction tuning more resources more resources keywords task tag e g https img shields io badge object detection 759cbc style flat square https img shields io badge vqa 759cbc style flat square abbreviation tag e g https img shields io badge clip cd6155 style flat square characteristic tag some characteristic makes this paper unique e g https img shields io badge nas bc9575 style flat square https img shields io badge unsupervised bc9575 style flat square bold font we highlight some pilot work that may contribute to the prevalence of visual prompting vision prompt this section collects papers prompting pretrained vision foundation models e g vit for parameter efficient adaptation learning to prompt for continual learning paper https arxiv org abs 2112 08654 code https github com google research l2p cvpr 2022 https img shields io badge continual learning 759cbc style flat square visual prompt tuning paper https arxiv org pdf 2203 12119 pdf code https github com kmnp vpt eccv 2022 https img shields io badge vpt cd6155 style flat square dualprompt complementary prompting for rehearsal free continual learning paper https arxiv org pdf 2204 04799 pdf code https github com google research l2p eccv 2022 https img shields io badge continual learning 759cbc style flat square adaptformer adapting vision transformers for scalable visual recognition paper https arxiv org abs 2205 13535 code https github com shoufachen adaptformer neurips 2022 https img shields io badge action recognition 759cbc style flat square scaling shifting your features a new baseline for efficient model tuning paper https arxiv org abs 2210 08823 code https github com dongzelian ssf neurips 2022 p2p tuning pre trained image models for point cloud analysis with point to pixel prompting paper https arxiv org abs 2208 02812 code https github com wangzy22 p2p neurips 2022 https img shields io badge 3d point cloud tasks 759cbc generative visual prompt unifying distributional control of pre trained generative models paper https arxiv org abs 2209 06970 code https github com chenwu98 generative visual prompt neurips 2022 https img shields io badge image generation 759cbc visual prompting via image inpainting paper code https github com amirbar visual prompting neurips 2022 https img shields io badge visual in context learning 759cbc https img shields io badge image generation 759cbc decorate the newcomers visual domain prompt for continual test time adaptation paper https arxiv org abs 2212 04145 aaai 2023 lpt long tailed prompt tuning for image classification paper https openreview net forum id 8povaeo8ie iclr 2023 diversity aware meta visual prompting paper https arxiv org abs 2303 08138 code https github com shikiw dam vp cvpr 2023 semantic prompt for few shot image recognition paper https arxiv org abs 2303 14123 cvpr 2023 https img shields io badge few shot learning 759cbc visual prompt tuning for generative transfer learning paper https arxiv org abs 2210 00990 code https github com google research generative transfer cvpr 2023 https img shields io badge image generative tasks 759cbc cora adapting clip for open vocabulary detection with region prompting and anchor pre matching paper https arxiv org abs 2303 13076 code https github com tgxs002 cora cvpr 2023 https img shields io badge open vocabulary detection 759cbc images speak in images a generalist painter for in context visual learning paper https arxiv org abs 2212 02499 code https arxiv org abs 2212 02499 cvpr 2023 https img shields io badge image generation 759cbc https img shields io badge in context learning 759cbc pivot prompting for video continual learning paper https arxiv org abs 2212 04842 cvpr 2023 https img shields io badge continual learning 759cbc learning expressive prompting with residuals for vision transformers paper https arxiv org abs 2303 15591 cvpr 2023 https img shields io badge semantic segmentation 759cbc blackvip black box visual prompting for robust transfer learning paper https arxiv org abs 2303 14773 code https github com changdaeoh blackvip cvpr 2023 https img shields io badge black box optimization 759cbc visual prompt multi modal tracking paper https arxiv org abs 2303 10826 code https github com jiawen zhu vipt cvpr 2023 https img shields io badge object training 759cbc a la carte prompt tuning apt combining distinct data via composable prompting paper https arxiv org abs 2302 07994 cvpr 2023 https img shields io badge continual learning 759cbc understanding and improving visual prompting a label mapping perspective paper https arxiv org abs 2211 11635 code https github com optml group ilm vp cvpr 2023 visual query tuning towards effective usage of intermediate representations for parameter and memory efficient transfer learning paper https arxiv org abs 2212 03220 code https github com andytu28 vqt cvpr 2023 explicit visual prompting for low level structure segmentations low level segmentation paper https arxiv org abs 2303 10883 code https github com nifangbaage explicit visual prompt cvpr 2023 https img shields io badge low level segmentation 759cbc understanding and improving visual prompting a label mapping perspective paper https arxiv org abs 2211 11635 code https github com optml group ilm vp cvpr 2023 arxiv papers exploring visual prompts for adapting large scale models paper https arxiv org pdf 2203 17274 pdf code https github com hjbahng visual prompting arxiv 2022 03 vision transformer adapter for dense predictions paper https arxiv org pdf 2205 08534 pdf code https github com czczup vit adapter arxiv 2022 05 https img shields io badge object detection 759cbc style flat square https img shields io badge instance segmentaion 759cbc style flat square neural prompt search paper https arxiv org abs 2206 04673 code https github com davidzhangyuanhan noah arxiv 2022 06 https img shields io badge noah cd6155 style flat square https img shields io badge nas bc9575 style flat square convolutional bypasses are better vision transformer adapters paper https arxiv org abs 2207 07039 code https github com jieshibo petl vit arxiv 2022 07 conv adapter exploring parameter efficient transfer learning for convnets paper https arxiv org pdf 2208 07463 pdf arxiv 2022 08 prompt vision transformer for domain generalization paper https arxiv org pdf 2208 08914 pdf arxiv 2022 08 https img shields io badge domain generalization 759cbc style flat square prompt matched semantic segmentation paper https arxiv org abs 2208 10159 arxiv 2022 08 https img shields io badge segmentation 759cbc style flat square visual prompt tuning for test time domain adaptation paper https arxiv org abs 2210 04831 arxiv 2022 10 visual prompting for adversarial robustness paper https arxiv org abs 2210 06284 arxiv 2022 10 https img shields io badge adversarial robustness 759cbc style flat square prompt generation networks for efficient adaptation of frozen vision transformers paper https arxiv org abs 2210 06466 code https github com jochemloedeman pgn arxiv 2022 10 towards a unified view on visual parameter efficient transfer learning paper https arxiv org abs 2210 00788 code https github com bruceyo v petl arxiv 2022 10 https img shields io badge action recognition 759cbc style flat square multitask vision language prompt tuning paper https arxiv org abs 2211 11720 code https github com sincerass mvlpt arxiv 2022 11 https img shields io badge mvlpt cd6155 style flat square https img shields io badge multitask learning bc9575 style flat square vision language prompt this section collects papers prompting pretrained vision language foundation models e g clip for parameter efficient adaptation learning transferable visual models from natural language supervision paper https arxiv org abs 2103 00020 code https github com openai clip icml 2021 https img shields io badge clip cd6155 style flat square learning to prompt for vision language models paper https arxiv org abs 2109 01134 code https github com kaiyangzhou coop ijcv 2022 https img shields io badge coop cd6155 style flat square prompt distribution learning paper https arxiv org pdf 2205 03340 pdf cvpr 2022 conditional prompt learning for vision language models paper https arxiv org pdf 2203 05557 pdf code https github com kaiyangzhou coop cvpr 2022 https img shields io badge cocoop cd6155 style flat square denseclip language guided dense prediction with context aware prompting paper https arxiv org pdf 2112 01518 pdf code https github com raoyongming denseclip cvpr 2022 https img shields io badge detection 759cbc style flat square https img shields io badge segmentation 759cbc style flat square bridge prompt towards ordinal action understanding in instructional videos paper https arxiv org pdf 2203 14104 pdf code https github com ttlmh bridge prompt cvpr 2022 https img shields io badge action recognition 759cbc style flat square https img shields io badge action segmentation 759cbc style flat square pointclip point cloud understanding by clip paper https arxiv org pdf 2112 02413 pdf code https github com zrrskywalker pointclip cvpr 2022 https img shields io badge point cloud 759cbc style flat square vl adapter parameter efficient transfer learning for vision and language tasks paper https arxiv org pdf 2112 06825 pdf code https github com ylsung vl adapter cvpr 2022 https img shields io badge vqa 759cbc style flat square https img shields io badge videoqa 759cbc style flat square https img shields io badge captioning 759cbc style flat square a good prompt is worth millions of parameters low resource prompt based learning for vision language models paper https arxiv org abs 2110 08484 acl 2022 https img shields io badge vqa 759cbc https img shields io badge captioning 759cbc can language understand depth paper https arxiv org pdf 2207 01077 pdf code https github com adonis galaxy depthclip acm mm 2022 https img shields io badge depthclip cd6155 style flat square https img shields io badge depth estimation 759cbc expanding language image pretrained models for general video recognition paper https arxiv org abs 2208 02816 code https aka ms x clip eccv 2022 https img shields io badge action recognition 759cbc style flat square tip adapter training free adaption of clip for few shot classification paper https arxiv org pdf 2207 09519 pdf code https github com gaopengcuhk tip adapter eccv 2022 ordinalclip learning rank prompts for language guided ordinal regression paper https arxiv org abs 2206 02338 neurips 2022 https img shields io badge ordinal regression 759cbc style flat square test time prompt tuning for zero shot generalization in vision language models paper https arxiv org pdf 2209 07511 pdf code https azshue github io tpt neurips 2022 learning to decompose visual features with latent textual prompts paper https openreview net forum id wtcud6hrozr iclr 2023 plot prompt learning with optimal transport for vision language models paper https openreview net forum id zqwryboxynh code https github com chengy12 plot iclr 2023 visual language prompt tuning with knowledge guided context optimization paper https arxiv org abs 2303 13283 code https github com htyao89 kgcoop cvpr 2023 https img shields io badge image classification 759cbc open set fine grained retrieval via prompting vision language evaluator paper https openaccess thecvf com content cvpr2023 papers wang open set fine grained retrieval via prompting vision language evaluator cvpr 2023 paper pdf cvpr 2023 https img shields io badge open set retrieval 759cbc multimodal prompting with missing modalities for visual recognition paper https arxiv org abs 2303 03369 code https github com yilunlee missing aware prompts cvpr 2023 efficient multimodal fusion via interactive prompting paper https arxiv org abs 2304 06306 cvpr 2023 https img shields io badge multimodal classification 759cbc hierarchical prompt learning for multi task learning paper https openaccess thecvf com content cvpr2023 papers liu hierarchical prompt learning for multi task learning cvpr 2023 paper pdf code https github com lynlynlyn hipro cvpr 2023 https img shields io badge multitask learning 759cbc text visual prompting for efficient 2d temporal video grounding paper https arxiv org abs 2303 04995 cvpr 2023 https img shields io badge video grounding 759cbc vop text video co operative prompt tuning for cross modal retrieval paper https arxiv org abs 2211 12764 code https github com bighuang624 vop cvpr 2023 https img shields io badge text video retrieval 759cbc maple multi modal prompt learning paper https arxiv org abs 2210 03117 code https github com muzairkhattak multimodal prompt learning cvpr 2023 texts as images in prompt tuning for multi label image recognition paper https arxiv org abs 2211 12739 code https github com guozix tai dpt cvpr 2023 https img shields io badge multi label recognition 759cbc vita clip video and text adaptive clip via multimodal prompting paper https arxiv org abs 2304 03307 code https github com talalwasim vita clip cvpr 2023 https img shields io badge action recognition 759cbc lasp text to text optimization for language aware soft prompting of vision language models paper https arxiv org abs 2210 01115 code https www adrianbulat com lasp cvpr 2023 pi tuning transferring multimodal foundation models with optimal multi task interpolation paper https arxiv org abs 2304 14381 code https github com tencentarc pi tuning icml 2023 pouf prompt oriented unsupervised fine tuning for large pre trained models paper https arxiv org abs 2305 00350 code https github com korawat tanwisuth pouf icml 2023 rethinking the openness of clip paper https arxiv org abs 2206 01986 code https github com lancopku clip openness acl 2023 https img shields io badge repe cd6155 style flat square promptstyler prompt driven style generation for source free domain generalization paper https arxiv org abs 2307 15199 code https promptstyler github io iccv 2023 https img shields io badge promptstyler cd6155 style flat square bar arxiv papers colorful prompt tuning for pre trained vision language models paper https arxiv org abs 2109 11797 arxiv 2021 08 https img shields io badge cpt cd6155 style flat square https img shields io badge grounding 759cbc style flat square actionclip a new paradigm for video action recognition paper https arxiv org abs 2109 08472 code https github com sallymmx actionclip arxiv 2021 09 https img shields io badge action recognition 759cbc style flat square clip adapter better vision language models with feature adapters paper https arxiv org abs 2110 04544 code https github com gaopengcuhk clip adapter arxiv 2021 10 amortized prompt lightweight fine tuning for clip in domain generalization paper https arxiv org abs 2111 12853 arxiv 2021 11 https img shields io badge domain generalization bc9575 style flat square prompting visual language models for efficient video understanding paper https arxiv org abs 2112 04478 code https github com ju chen efficient prompt arxiv 2021 12 task https img shields io badge action recognition 759cbc style flat square task https img shields io badge action localization 759cbc style flat square task https img shields io badge retrieval 759cbc style flat square unsupervised prompt learning for vision language models paper https arxiv org pdf 2204 03649 pdf code https github com tonyhuang2022 upl arxiv 2022 04 https img shields io badge upl cd6155 style flat square https img shields io badge unsupervised bc9575 style flat square prompt aligned gradient for prompt tuning paper https arxiv org abs 2205 14865 code https github com beierzhu prompt align arxiv 2022 05 parameter efficient image to video transfer learning paper https arxiv org pdf 2206 13559 pdf arxiv 2022 06 https img shields io badge st adapter cd6155 style flat square task https img shields io badge action recognition 759cbc style flat square dualcoop fast adaptation to multi label recognition with limited annotations paper https arxiv org abs 2206 09541 arxiv 2022 06 task https img shields io badge multilabel recognition 759cbc style flat square prompt tuning for generative multimodal pretrained models paper https arxiv org abs 2208 02532 code https github com ofa sys ofa arxiv 2022 06 https img shields io badge vqa 759cbc style flat square https img shields io badge captioning 759cbc style flat square https img shields io badge referring expression 759cbc style flat square https img shields io badge visual entailment 759cbc style flat square prompt tuning with soft context sharing for vision language models paper https arxiv org pdf 2208 13474 pdf arxiv 2022 08 https img shields io badge multi task learning 759cbc style flat square cpl counterfactual prompt learning for vision and language models paper https arxiv org abs 2210 10362 code https github com eric ai lab cpl arxiv 2022 10 https img shields io badge retrieval 759cbc style flat square https img shields io badge vqa 759cbc style flat square understanding and mitigating overfitting in prompt tuning for vision language models paper https arxiv org abs 2211 02219 code https github com machengcheng2016 subspace prompt learning arxiv 2022 10 https img shields io badge object detection 759cbc style flat square https img shields io badge semantic segmentation 759cbc style flat square unified vision and language prompt learning paper https arxiv org abs 2210 07225 arxiv 2022 10 multi prompt alignment for multi source unsupervised domain adaptation paper https arxiv org abs 2209 15210 arxiv 2022 10 https img shields io badge domain adaptation 759cbc style flat square prompt pre training with twenty thousand classes for open vocabulary visual recognition paper https arxiv org abs 2304 04704 code https github com amazon science prompt pretraining arxiv 2023 04 language interactable prompt language interactable prompter develops zero few shot capabilities by prompting several independent foundational models vlms llms vms etc with the language interface one of the most attractive applications is multimodal chatbot https github com zjr2000 awesome multimodal assistant multimodal few shot learning with frozen language models paper https arxiv org abs 2106 13884 neurips 2021 https img shields io badge vqa 759cbc an empirical study of gpt 3 for few shot knowledge based vqa paper https arxiv org pdf 2109 05014 pdf code https github com microsoft pica aaai 2022 https img shields io badge vqa 759cbc visualgpt data efficient adaptation of pretrained language models for image captioning paper https arxiv org pdf 2102 10407 pdf code https github com vision cair visualgpt cvpr 2022 https img shields io badge captioning 759cbc socratic models composing zero shot multimodal reasoning with language paper https arxiv org pdf 2204 00598 pdf code https socraticmodels github io code iclr 2023 https img shields io badge captioning 759cbc style flat square https img shields io badge retrieval 759cbc style flat square https img shields io badge visual dialog 759cbc style flat square bar arxiv papers visual chatgpt talking drawing and editing with visual foundation models paper https arxiv org abs 2303 04671 code https github com microsoft taskmatrix demo https huggingface co spaces microsoft visual chatgpt arxiv 2023 03 https img shields io badge visual chatgpt cd6155 style flat square https img shields io badge multimodal chatbot 759cbc https img shields io badge llms chatgpt 759cbc chameleon plug and play compositional reasoning with large language models paper https arxiv org abs 2304 09842 code https github com lupantech chameleon llm arxiv 2023 04 https img shields io badge chameleon cd6155 style flat square https img shields io badge multimodal chatbot 759cbc https img shields io badge llms gpt4 759cbc clipcap clip prefix for image captioning paper https arxiv org abs 2111 09734 code https github com rmokady clip prefix caption arxiv 2021 11 https img shields io badge captioning 759cbc flamingo a visual language model for few shot learning paper https arxiv org abs 2204 14198 arxiv 2022 04 https img shields io badge vqa 759cbc https img shields io badge captioning 759cbc language models can see plugging visual controls in text generation paper https arxiv org pdf 2205 02655 pdf code https github com yxuansu magic arxiv 2022 05 https img shields io badge magic cd6155 style flat square https img shields io badge captioning 759cbc zero shot video question answering via frozen bidirectional language models paper https arxiv org pdf 2206 08155 pdf arxiv 2022 06 https img shields io badge videoqa 759cbc visual clues bridging vision and language foundations for image paragraph captioning paper https arxiv org pdf 2206 01843 pdf arxiv 2022 06 https img shields io badge captioning 759cbc vision language instruction tuning the goal of vision language instruction tuning is to train a model that can effectively understand instructions for general purpose multimodal tasks visual instruction tuning paper https arxiv org abs 2304 08485 code https github com haotian liu llava demo https llava hliu cc arxiv 2023 04 minigpt 4 enhancing vision language understanding with advanced large language models paper https arxiv org abs 2304 10592 code https github com vision cair minigpt 4 demo minigpt 4 github io arxiv 2023 04 otter a multi modal model with in context instruction tuning paper https arxiv org abs 2305 03726 code https github com luodian otter demo otter cliangyu com arxiv 2023 05 multimodal gpt a vision and language model for dialogue with humans paper https arxiv org abs 2305 04790 code https github com open mmlab multimodal gpt arxiv 2023 05 instructblip towards general purpose vision language models with instruction tuning paper https arxiv org abs 2305 06500 code https github com salesforce lavis tree main projects instructblip arxiv 2023 05 more resources promptpapers https github com thunlp promptpapers a comprehensive curated list for prompting papers mainly in natural language processing awesome multimodal assistant https github com zjr2000 awesome multimodal assistant a curated list for vision language instruction tuning and llm based chatbot	prompt-learning adapter few-shot-learning prompt-tuning zero-shot-learning visual-prompt parameter-efficient-tuning	ai
VisualLinkerScript	visuallinkerscript a tool to help in creating visualizing and editing gnu linker scripts commonly used in embedded system design		os
Puzzlr-Backend	puzzlr backend this is the backend rest api for the puzzlr ios android application me gaurav sheni https github com gsheni teddy beachley and brad naumann built for our csc331 software engineering class at wfu it uses a mean stack mongodb express node js and currently runs on heroku setup you must first have node js https nodejs org en installed once you ve installed node and npm clone the project and in the root directory run the following npm install this will install the required npm modules next run the following to start the express server npm start navigate to http localhost 3000 http localhost 3000 to see the running web instance		os
2019-conferences	2019 web development conferences a list of 2019 web development conferences a list of 2018 conferences https github com ryanburgess 2018 conferences you can also add all conferences directly into your calendar by importing the ics file into google calendar etc the ics file can be downloaded here https rawgit com ryanburgess 2019 conferences master 2019 conferences ics but it s recommended to add it via url if your client supports that thus you will dynamically get all updates conference list conference date where ng atlanta https ng atl org 9 12 january 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america atlanta covalence http www covalenceconf com 16 16 january 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america san francisco ca new adventures https newadventuresconf com 2019 23 25 january 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 gb svg height 16 alt great britain united kingdom of great britain and northern ireland nottingham forward js https forwardjs com 24 24 january 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america san francisco ca react iran http reactiran com 31 31 january 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 ir svg height 16 alt iran iran islamic republic of tehran iran pause fest https www pausefest com au 6 8 february 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 au svg height 16 alt australia australia melbourne australia c t webdev https ctwebdev de 6 8 february 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 de svg height 16 alt germany germany k ln germany js conf hawaii https www jsconfhi com 7 8 february 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america hawaii frontfest https frontfest es 9 9 february 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 es svg height 16 alt spain spain madrid spain frontend developer love http www frontenddeveloperlove com 13 15 february 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 nl svg height 16 alt netherlands netherlands amsterdam netherlands vue js amsterdam https www vuejs amsterdam 14 15 february 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 nl svg height 16 alt netherlands netherlands amsterdam netherlands agent conf https www agent sh 21 24 february 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 at svg height 16 alt austria austria dornbirn lech austria ng india https www ng ind com 23 23 february 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 in svg height 16 alt india india gurgaon india reactfoo https reactfoo in 2019 1 2 march 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 in svg height 16 alt india india bengaluru india aea seattle https aneventapart com event seattle 2019 4 6 march 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america seattle midwest php https 2019 midwestphp org 8 9 march 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america minneapolis mn js kongress https js kongress com 11 12 march 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 de svg height 16 alt germany germany munich t3chfest https t3chfest uc3m es 14 15 march 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 es svg height 16 alt spain spain madrid emberconf https emberconf com 18 20 march 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america portland oregon upfront https upfrontconf com 22 22 march 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 gb svg height 16 alt great britain united kingdom of great britain and northern ireland manchester u k vueconf us http us vuejs org 25 27 march 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america tampa fl reactathon https www reactathon com 30 31 march 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america san francisco ca perf matters https perfmattersconf com 2 3 april 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america redwood city ca mainxchange https mainxchange de 3 3 april 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 de svg height 16 alt germany germany wuerzburg by jsheroes https jsheroes io 11 12 april 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 ro svg height 16 alt romania romania cluj napoca romania smashingconf san fran https www smashingconf com sf 2019 16 17 april 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america san francisco frontconf https frontconf com 27 27 april 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 de svg height 16 alt germany germany munich southerndev http southerndev co 27 27 april 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america augusta the lead developer https newyork2019 theleaddeveloper com 30 30 april 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america new york ny uenoland https ueno land 2 4 may 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america brooklyn longhorn php https longhornphp com 2 4 may 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america san francisco ca reactjs girls conference https reactjsgirls com 2 3 may 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 gb svg height 16 alt great britain united kingdom of great britain and northern ireland london uk aea boston https aneventapart com event boston 2019 6 8 may 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america boston fullstack tech radar day https fullstackradar tikalk com 15 15 may 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 il svg height 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height 16 alt canada canada toronto aea washington d c https aneventapart com event washington dc 2019 29 june 1 july 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america washington d c jscamp barcelona https jscamp tech 18 19 july 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 es svg height 16 alt spain spain barcelona mid atlantic developer conference 2019 https www middevcon com 1 2 august 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america baltimore md jsconf us https 2019 jsconf us 12 14 august 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america carslbad ca codercruise 2019 https www codercruise com 19 23 august 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america bahamas abstractions https abstractions io 21 23 august 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america pittsburgh pa aea chicago https aneventapart com event chicago 2019 26 28 august 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america chicago smashingconf freiburg https www smashingconf com freiburg 2019 9 10 september 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 de svg height 16 alt germany germany freiburg strange loop https thestrangeloop com 12 14 september 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america st louis react day new york https reactnewyork com 13 13 september 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america new york ny we love speed 2019 https www welovespeed com 2019 20 20 september 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 fr svg height 16 alt france france lille jsdayie https www jsday org 20 20 september 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 ie svg height 16 alt ireland ireland dublin ireland view source conference https 2019 viewsourceconf org 30 september 1 october 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 nl svg height 16 alt netherlands netherlands amsterdam nl grace hopper https ghc anitab org 1 4 october 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america orlando fl nordic js http nordicjs com 10 11 october 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 se svg height 16 alt sweden sweden stockholm dotnetos https conf dotnetos org 10 11 october 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 pl svg height 16 alt poland poland warsaw connect tech http connect tech 16 18 october 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america atlanta ga devfest nantes https devfest gdgnantes com 21 22 october 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 fr svg height 16 alt france france nantes fr php world 2019 https world phparch com 22 25 october 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america washington d c thunderplains https thunderplainsconf com 22 22 october 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america oklahoma city ok aea denver https aneventapart com event denver 2019 28 30 october 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america denver reactiveconf https reactiveconf com 30 october 1 november 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 cz svg height 16 alt czech republic czechia prague czech republic the big elixir https www thebigelixir com 7 8 november 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america new orleans la vueconf toronto https vuetoronto com 11 12 november 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 ca svg height 16 alt canada canada toronto canada dotcss https 2019 dotcss io 4 4 december 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 fr svg height 16 alt france france paris france dotjs https 2019 dotjs io 5 6 december 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 fr svg height 16 alt france france paris france devternity https devternity com 6 7 december 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 lv svg height 16 alt latvia latvia riga aea san francisco https aneventapart com event san francisco 2019 9 11 december 2019 img src https cdnjs cloudflare com ajax libs flag icon css 3 2 1 flags 4x3 us svg height 16 alt usa united states of america san francisco contributing 1 fork it 2 create your feature branch git checkout b my new feature 3 add your conference to list json 4 run npm install to install local dependencies 5 run npm run build to build the readme and generate the ics file 6 commit your changes git commit am add some feature 7 push to the branch git push origin my new feature 8 create new pull request		front_end
gimci	gimci https avatars2 githubusercontent com u 22726552 v 3 s 200 gimci npm gimci https badge fury io js gimci svg https badge fury io js gimci open source love https badges frapsoft com os mit mit svg v 102 js natural language processing module for korean processing text in korean with the character based metric costs not only the great amount of calculation but it also reaps the quality of search off here you have a letter based processing mechanism with the newly devised romanization rule hangul roman gimci is designed to boost the overall performance of natural language processing models of korean and improve understanding of the korean writing system in a remarkably different way motivation hangul characters are each composed of two to three letters constructing block initial medial final traditional langauge processing models of hangul handled the text with the unit of character meaning it considered the total possibilities of11172 initial 19 medial 21 final 28 when dealing with the next character to come this is a tremendous amount if you scale up the volume to be processed now if we can at all treat it by the unit of letter the problem may shrink excessively rough calculation dictates that we have 40 standard 24 combined 16 possibilities to look ahead quite a jump in the overall performance but how could we possibly achieve this an idea of constructing a wholly new set of romanization began here it later turned out that the new rule also shed a light to a relationship between each letter of hangul get started gimci is implemented in javascript with the view of making i runnable at both web web browsers and native operating systems start by installing via npm node js npm install save gimci live demo live demo link https www youtube com watch v ccocd131fb8 api api readme https github com gimci gimci blob temp docs apis md		ai
it-academy-python-autumn	it academy python autumn web development course for it academy		front_end
reed-me	reed me d myreads a book tracking web app myreads web app is created using react js as a part of the udacity s react nanodegree program about this is a single page app navigation happens without the need to refresh pages and it represents a virtual bookcase to store your books and track what you re reading myreads lets you manage your digital bookshelf it supports three shelves currently reading want to read read additionally you can search and add books to any shelf live demo https my reads 8c75aa netlify com development a static example of the css and html markup was provided and a backend api to communicate with a backend server from udacity for book information and long term storage then i added interactivity to the app by refactoring the static code into react components following dot do one thing rule starter code https github com udacity reactnd project myreads starter backend server the provided file booksapi js contains following methods to perform necessary operations on the backend getall to get all the books from the api update update shelf information of the book search search book in the database features i needed to add in terms of ui the main page shows 3 shelves for books each book is shown on the correct shelf along with its title and all of its authors the search page has a search input field as the user types into the search field books that match the query are displayed on the page the search works correctly when a book does not have a thumbnail or an author to test this try searching for poetry and biography each book in the main page or in the results of a search has a control that allows users to move it between shelves the control is tied to each book instance the shelf the book is in is reflected through its control on all pages any changes in book shelves should be reflected on all pages ie if a book s shelf is changed on the search page then it should appear in the responding shelf on the main page in terms of state component state is passed down from parent components to child components the state variable is not modified directly but by using setstate function books have the same state on both the search page and the main application page if a book is on a bookshelf that is reflected in both locations ui and url are in sync done using react router screenshots screenshot1 screenshot2 installation clone the repository change directories and use npm to install the dependencies clone download this repo run npm installor yarn install in the project directory to install dependencies start the project can be run with npm start then it can be viewed in the browser at http localhost 3000 important the backend api is built by udacity and only a fixed set of search terms are supported supported search terms can be found in search terms md that list of terms are the only terms that will work with the api		server
AbslutPositionSys_Action_B	abslutpositionsys action b my graduate design to fork a ops for robocon compitition or indoor 2d move device which is a embeded sys on stm32f405 calc and output current position info in the coordinate system created on the start point dbug log 05 05 2019 modify as5045 cycle counter to zero simplly set pid parament for temperature control dbug log 05 04 2019 get sin graph form as5045 and linen it successfully count tonggle freq from as5045 data and int for position ok waitting for pid control for imu device dbug log 05 02 2019 modify dmp driver to tow mpu6050 find that mpu6050 with dmp can t init at a not horizental flat dbug log 05 01 2019 find bug pwm configor modified and test ok midify usart1 rx interrupt to recive instructions use mpu6050 dmp driver for one sensor successful dbug log 04 30 2019 add tim2 interrupt to provide a time base for imu calc test ok modify heat res from gpio to pwm drive mode to get a smoth control test ok dbug log 04 29 2019 successfully read the sensor as5045 s data it means all hardware is ready then fit them togather dbug log 04 27 2019 successfully read origin data from mpu6050 imu sensor waitting for data processing and as5045	stm32f405 position-system robocon-compition as5045-magnetic-encoder	os
day3-instant-flask-web-development-book-app	day3 instant flask web development book app sample app built in instant flask web development book		front_end
programming-book-3	programming book 3 go to the project pages https evanli github io programming book 3 to see the books programming book series programming book https github com evanli programming book algorithm https github com evanli programming book algorithm back end https github com evanli programming book back end database https github com evanli programming book database front end https github com evanli programming book front end git https github com evanli programming book git programming book 2 https github com evanli programming book 2 c https github com evanli programming book 2 c go https github com evanli programming book 2 go javascript https github com evanli programming book 2 javascript node js https github com evanli programming book 2 nodejs programming book 3 https github com evanli programming book 3 python https github com evanli programming book 3 python machine learning https github com evanli programming book 3 machine learning deep learning https github com 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download 7 09m https github com evanli programming book 3 raw master machine learning hands 20on 20machine 20learning 20with 20scikit 20learn 20and 20tensorflow pdf mlapp machine learning a probabilistic perspective kevin p murphy machine learning mlapp 20 20machine 20learning a 20probabilistic 20perspective 20 20kevin 20p 20murphy pdf download 25 69m https github com evanli programming book 3 raw master machine learning mlapp 20 20machine 20learning a 20probabilistic 20perspective 20 20kevin 20p 20murphy pdf prml pattern recognition and machine learning 2006 machine learning prml pattern recognition and machine learning 2006 pdf download 10 85m https github com evanli programming book 3 raw master machine learning prml pattern recognition and machine learning 2006 pdf prml machine learning prml pdf download 6 39m https github com evanli programming book 3 raw master machine learning prml pdf python machine learning python machine learning python 20machine 20learning 20 20python pdf 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books	ai
CloudComputingProjects_CS441	cloudcomputingprojects cs441 contains my projects hws from the course engineering distributed objects for cloud computing at uic		cloud
jeos	eos blockchain java sdk eosio java sdk with eos rpc api maven central https img shields io maven central v io jafka jeos svg https maven badges herokuapp com maven central io jafka jeos javadocs https javadoc io badge io jafka jeos svg https javadoc io doc io jafka jeos dependency release version maven http repo1 maven org maven2 io jafka jeos http repo1 maven org maven2 io jafka jeos xml dependencies dependency groupid io jafka groupid artifactid jeos artifactid version 0 9 15 version dependency dependencies gradle compile group io jafka name jeos version 0 9 15 latest snapshot version xml repositories repository id oss sonatype org snapshot id url http oss sonatype org content repositories snapshots url releases enabled false enabled releases snapshots enabled true enabled snapshots repository repositories dependencies dependency groupid io jafka groupid artifactid jeos artifactid version 1 0 snapshot version dependency dependencies java samples with rpc transfer eos with java https gist github com adyliu b35ec8551c05f82a1d7307395d4910da create account with java https gist github com adyliu 6d30650cf2f6d0a703d5b547db484d31 java samples without rpc local only create eos key with java without rpc https gist github com adyliu 63d93895d07678d3d80a52dfbcb18976 sign transaction offline https gist github com adyliu 492503b94d0306371298f24e15481da4 rpc api create eos account with rpc api https github com adyliu jeos wiki create eos account with rpc api create eos account with rpc api pdf https github com adyliu jeos wiki create eos account with rpc api pdf	eos eos-blockchain eos-sdk eos-java eos-rpc eos-java-client	blockchain
447HW2	447hw2 repo to house the website and database files for software engineering homework 2 to get started make sure python3 venv and flask are set up to start the front end navigate to the react flask app directory in a terminal and run npm start to start the back end navigate to the react flask app directory in a terminal and run npm run start api the front end page should automatically start up and display the records in the database the database should be connected to the flask backend already		server
chess-llm	chessllm this is a project to play chess against large language models llms currently it only supports the openai text completion api and has only been tested on gpt 3 5 turbo instruct because this is the only model i ve seen that plays chess well this code is very bare bones it s just enough to make things run maybe in the future i ll extend it to make it better if you re reading this and this message is still here you can play a version of this bot running under my api key by clicking here https lichess org user gpt35 turbo instruct friend what is this this is a chess engine that plays chess by prompting gpt 3 5 turbo instruct to do this it passes the entire game state as a pgn and the model plays whatever move the language model responds with so in order to respond to a standard king s pawn opening i prompt the model with white garry kasparov black magnus carlsen result 1 2 1 2 whiteelo 2900 blackelo 2800 1 e4 and then it responds e5 which means my engine should return the move e5 installing this project has minimal dependencies just python chess and requests to run the uci engine or some additional dependencies to the lichess bot in the lichess bot folder pip install r requirements txt pip install r lichess bot requirements txt if you want to run the bot getting started add your openai key put your key in a file called openai api key uci engine if you already have a uci compliant chess engine downloaded and want to play against the model you can pass uci engine py lichess bot the lichess bot directory is a fork of the lichess bot https github com lichess bot devs lichess bot project with a few hacks so that my bot talks a bit more and explains what it s doing if you want to make a lichess bot you ll first need to create a new account then get an api key and turn it into a bot the steps to do this are described in the lichess bot readme lichess bot readme md once you ve done that put your api key as the first line of config yml next steps i highly doubt i ll do any of these things but here are some things i may want to do or you can do search what happens if instead of predicting the top 1 move you predict different moves and take the best how do you choose best resign if lost how can you detect if the game is lost and then just resign if it s obviously over other models right now this works for just openai s text completion models it would be great to hook this up to other models if any of them become reasonably good at chess license gpl v3 this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation version 3 this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details you should have received a copy of the gnu general public license along with this program if not see http www gnu org licenses		ai
computer-vision-tasks	computer vision tasks	stereo-calibration motion-tracking background-subtraction	ai
Data-Engineering-Database-Dev-Querying-Data-with-T-SQL-from-SQL-Server	data engineering database dev querying data with t sql from sql server data engineering database dev querying data with t sql from sql server		server
awesome-design-systems	awesome design systems awesome https awesome re badge flat2 svg https awesome re a curated list of bookmarks resources and articles about design systems focused on developers p align center a href https github com klaufel awesome design systems readme img src media awesome design systems cover svg alt awesome design systems a p contents design systems design systems ui design tools ui design tools design tokens design tokens pattern library pattern library testing testing books books talks talks design systems a design system is an ever evolving collection of reusable components guided by rules that ensure consistency and speed by being the single source of truth for any product development design systems of some known companies atlassian design guidelines https atlassian design end to end design language to create straightforward and beautiful experiences firefox photon design system https design firefox com photon launch recognizable enjoyable firefox products and features faster github primer https primer style open source it to allow the community to design and build their own projects gitlab design system pajamas https design gitlab com resources components and design guidelines behind gitlab google material design https material io design create intuitive and beautiful products with material design ibm carbon https www carbondesignsystem com carbon is ibm s open source design system for products and experiences shopify polaris https polaris shopify com our design system helps us work together to build a great experience for all of shopify s merchants see more design systems here https github com alexpate awesome design systems design systems articles a design system governance process https bradfrost com blog post a design system governance process a guide to collaborating on design systems https www invisionapp com inside design collaborating on design systems building a design system start with a map https blog prototypr io building a design system start with map 909aa4baf41f building your design system https www designbetter co design systems handbook building design system design systems are for people https publication design systems design systems are for people a484620b6988 design systems vs pattern libraries vs style guides what s the difference https www uxpin com studio blog design systems vs pattern libraries vs style guides whats difference how spotify organises work in figma to improve collaboration https spotify design articles 2020 04 20 how spotify works in figma how to build design systems https medium muz li how to build design systems 3431560f51fb what is a design system everything you need to know https uxmisfit com 2019 03 26 what is a design system everything you need to know your sketch library is not a design system https bradfrost com blog post your sketch library is not a design system more resources design system checklist https designsystemchecklist com build better design systems an open source checklist to help you plan build and grow your design system design systems survey https designsystemssurvey seesparkbox com designers and developers from more than 20 industries tell us about their design systems and their uses design system https design systems resources for the design systems community super friendly https superfriendlydesign systems we help in house teams make better digital products with design systems ui design tools design tools figma https www figma com helps teams create test and ship better designs from start to finish cross platform invision https www invisionapp com the digital product design platform powering the world s best user experiences sketch https www sketch com a design toolkit built to help you create your best work from your earliest ideas through to final artwork for macos adobexd https www adobe com products xd html share your story with designs that look and feel like the real thing wireframe animate prototype collaborate and more it s all right here all in one ui ux design tool marvel https marvelapp com marvel has everything you need to bring ideas to life and transform how you create digital products with your team placing the power of design in everyone s hands uxpin https www uxpin com design and manage your entire ux ui project in one tool penpot https penpot app penpot is the first open source design and prototyping platform meant for cross domain teams see design tools plugins here https github com lisadziuba awesome design tools blob master awesome design plugins md integrations abstract https www abstract com design collaboration without the chaos for sketch and xd on macos avocode https avocode com helps you share design files discuss changes and code websites mobile apps newsletters faster invision design system manager https www invisionapp com design system manager powers creative and consistent design at scale with a central place to manage design and coded components zeplin https zeplin io the better way to share organize and collaborate on designs built with developers in mind accessibility a11y accessibility for developeres https www invisionapp com inside design accessibility for developers 5 simple ways developers can help improve and enforce website accessibility stark https www getstark co empowers you to design with accessibility in mind from conception of brand to fruition of product contrast checker colorblind simulation and color suggestions the a11y project https a11yproject com a community driven effort to make web accessibility easier see resources https a11yproject com resources section design tools articles adobe xd vs sketch vs figma vs invision how to pick the best design software in 2020 https www freecodecamp org news adobe xd vs sketch vs figma vs invision design tokens design tokens w3c community group https www w3 org community design tokens view repository on github here https github com design tokens community group designtokens dev https www designtokens dev ship your design tokens without managing infrastructure diez https diez org free open source developer toolkit for expressing visual styles that can be shared across codebases native platforms and teams design tokens validator https animaapp github io design token validator site validate your design tokens against the design token community group spec plugins abstract connect https github com michaelzaporozhets abstractconnect a design tokens extractor for devs using abstract javascript zeplin json export tokens https extensions zeplin io berk zeplin json extension design tokens zeplin extension to generate your tokens in json format tools figmagic https github com mikaelvesavuori figmagic generate design tokens export graphics and extract design token driven react components from your figma documents style dictionary https github com amzn style dictionary a style dictionary uses design tokens to define styles once and use those styles on any platform or language superposition https superposition design extract design tokens from websites and use them in code and in your design tool use the design system you already have theo https github com salesforce ux theo theo is an abstraction for transforming and formatting design tokens articles a designer s guide to the figma api https medium com danhollick a designers guide to the figma api 64f2785969d8 building a visual studio code theme with style dictionary https dbanks design blog vs code theme with style dictionary design tokens with figma https blog prototypr io design tokens with figma aef25c42430f documenting design tokens https dbanks design blog documenting design tokens how to manage your design tokens with style dictionary https medium com didoo how to manage your design tokens with style dictionary 98c795b938aa manage design tokens with typescript and styled components https www erikverweij dev blog manage design tokens with typescript and styled components theo design tokens using node sass importer for any build method https basalt io blog theo design tokens using node sass importer for any build method tokenize it https blog prototypr io tokenize it 2a544ef1413b tokens in design systems https medium com eightshapes llc tokens in design systems 25dd82d58421 see more design tokens info here https github com sturobson awesome design tokens coding tools backlight https backlight dev with collaboration between developers and designers at heart backlight is a very complete coding platform where teams build document publish scale and maintain design systems pattern library styleguides and documentation stencil https stenciljs com toolchain for building reusable scalable design systems zeroheight https zeroheight com create beautiful living styleguides and document all your design system resources in one place learn about this https medium com zeroheight zeroheight 3 0 b6643c347596 develop isolated components backlight https backlight dev collaborative platform to build design systems on the code side empower your front end with an all in one solution to manage components quick start speed up collaboration pattern lab https patternlab io pattern lab helps you and your team build thoughtful pattern driven user interfaces using atomic design principles react styleguidist https react styleguidist js org isolated react component development environment with a living style guide storybook https storybook js org build bulletproof ui components faster storybook is an open source tool for developing ui components in isolation for react vue and angular it makes building stunning uis organized and efficient styled system https styled system com styled system is a collection of utility functions that add style props to your react components and allows you to control styles based on a global theme object storybook addons accessibility https github com storybookjs storybook tree master addons a11y test component compliance with web accessibility standards actions https github com storybookjs storybook tree master addons actions get ui feedback when an action is performed on an interactive element backgrounds https github com storybookjs storybook tree master addons backgrounds switch backgrounds to view components in different settings console https github com storybookjs storybook addon console show console output like logs errors and warnings in the storybook docs https github com storybookjs storybook tree master addons docs document component usage and properties in markdown knobs https github com storybookjs storybook tree master addons knobs interact with component inputs dynamically in the storybook ui links https github com storybookjs storybook tree master addons links link stories together to build demos and prototypes with your ui components source https github com storybookjs storybook tree master addons storysource view a story s source code to see how it works and paste into your app storyshots https github com storybookjs storybook tree master addons storyshots take a code snapshot of every story automatically with jest viewport https github com storybookjs storybook tree master addons viewport build responsive components by adjusting storybook s viewport size and orientation more info to storybook here https github com lauthieb awesome storybook pattern libraries articles dependency discovery in storybook https medium com storybookjs discover dependencies in storybook 49264d361e21 design systems workflow in storybook https blog hichroma com design systems in storybook 2b2be06e394b how design systems use storybook https medium com storybookjs how design systems use storybook 2ed735ad07a9 how packaging makes it dead simple to share ui components https blog hichroma com how packaging makes it dead simple to share ui components 29912593539d storybook design system https github com storybookjs design system storybook docs sneak peek https medium com storybookjs storybook docs sneak peak 5be78445094a testing unit regression test chromatic https www chromaticqa com visual testing for react angular and vue chromatic ensures consistency in ui components down to the pixel every commit is automatically tested for visual changes in the cloud testing library https testing library com simple and complete testing utilities that encourage good testing practices books acing the system design interview https www manning com books acing the system design interview book that gives the insights skills and practice needed to ace the toughest system design interview questions by zhiyong tan atomic design https atomicdesign bradfrost com atomic design methodology for creating design systems by brad frost building design systems https www apress com gp book 9781484245132 unify user experiences through a shared design language by sarrah vesselov and taurie davis design systems https www smashingmagazine com design systems book a practical guide to creating design languages for digital products by alla kholmatova smashing magazine front end style guides https www maban co uk projects front end style guides creating and maintaining style guides for websites by anna debenham frontend architecture for design systems http shop oreilly com product 0636920040156 do a modern blueprint for scalable and sustainable websites by micah godbolt introduction to design systems https fem design systems netlify app a practical introduction to design systems by using react figma and storybook by emma bostian modular web design https www amazon com modular web design components documentation dp 0321601351 creating reusable components for user experience design and documentation by nathan curtis talks building accessible interfaces patterns and techniques https vimeo com 331530115 will be building and refactoring common ui components and share a couple of techniques she often uses to build with accessibility in mind by sara soueidan design processes systems in craft https dotall com sessions design processes systems in craft design shouldn t be a siloed practice but a collaborative effort rooted in process how do we get there in this session we ll look at how we can reframe our design approach to be more human centric and systems minded by courtney bradford design systems the state of the web https www youtube com watch v jpmewxisu5e conversation about the role of design systems in modern web development and how they can change the dynamics between designer and developer by adam argyle design advocate at google design systems https youtu be k8mf3adg bm t 4750 women of react conf by neha sharma https twitter com hellonehha how to build a design system uxpin https www youtube com watch v h0mqkrjdaao will teach you how to build a ux design system using the ux pin prototyping design platform introducing design systems into chaos https youtu be fzsi1bk brm shares practical examples on where to begin to set up a design system what to prioritize and how to make a big impact to customers and colleagues to help you introduce systems that bring order to chaos by diana mounter design systems lead at github jina anne designing a design system https youtu be 7hyollo2gc4 will share strategies for how to approach design and build an effective design system how to successfully maintain the system to ensure ongoing usefulness by jina lead designer on the design systems team at salesforce ux level up your design system with styled system https youtu be k8mf3adg bm t 7280 women of react conf by taley a mirza https twitter com taleyamirza maintaining design systems https aneventapart com news post maintaining design systems by brad frost aea video helps you learn how to keep your system and the products it serves in sync and understand how to maintain and evolve your design system to give your users get the best possible experience by brad frost front end designer contributing contributions welcome read the contribution guidelines contributing md first	design design-systems design-system reusable-components figma digital-products storybook atomic-design css design-tokens design-patterns pattern-library pattern-lib awesome awesome-list frontend resources	os
training-glossary	training glossary glossary of modern web development terms content is contained in the wiki https github com snugug training glossary wiki		front_end
FedJudge	fedjudge federated legal large language model large language model llm legal llm a href https github com andrewzhe lawyer llama target blank lawyer llama a a href https github com pku yuangroup chatlaw target blank chatlaw a legal llm legal llm legal llm fedjudge federated legal large language model a href https github com yuelinan c3vg target blank c3vg a a href https huggingface co datasets fedjudge fedjudge court target blank a a href https github com andrewzhe lawyer llama target blank lawyer llama a a href https github com andrewzhe lawyer llama target blank lawyer llama a a href https github com baichuan inc baichuan 7b target blank baichuan 7b a fedjudge llm cost baichuan 7b lora lora fedjudge base llm fl fedjudge base continuual learning methods fedjudge cl cl client e e 1 2 3 a href https arxiv org abs 2309 08173 target blank a details summary summary question baichuan 7b center fedjudge base cl client3 1 2 3 4 96 5 x x x 1 2 3 x 2018 8 24 23 xx xxx 309 2018 xxxx xx 82 59 100 xx xx xx xxx xxx xx xx xx xx xx xx xx xx xx 82 59mg 100ml xx xx xx 1 2 3 4 5 6 7 8 9 10 1 2 1 2 3 1 2 3 4 5 1 2 n n 16 1 2 3 4 details python import torch from transformers import automodelforcausallm autotokenizer from peft import peftmodel tokenizer autotokenizer from pretrained baichuan inc baichuan 7b trust remote code true model automodelforcausallm from pretrained baichuan inc baichuan 7b device map auto trust remote code true peft model peftmodel from pretrained model fedjudge fedjudge base 7b torch dtype torch float32 half peft model peftmodel from pretrained model fedjudge fedjudge cl 7b torch dtype torch float32 half peft model peftmodel from pretrained model fedjudge fedjudge cl client3 7b torch dtype torch float32 half data inputs tokenizer data return tensors pt inputs inputs to cuda 0 pred peft model generate inputs max new tokens 500 repetition penalty 1 1 pred result tokenizer decode pred cpu 0 skip special tokens true print pred result split data 1 x 2023 8 fedjudge base 7b lora x 2023 8 x 2023 9 x 2023 9 fedjudge baichuan2 7b baichuan2 13b https github com yangjianxin1 firefly https github com beyondguo llm tuning https github com andrewzhe lawyer llama https github com liuhc0428 law gpt https github com cshaitao lexilaw https github com pengxiao song lawgpt https github com pku yuangroup chatlaw https github com baichuan inc baichuan 7b article yue2023fedjudge title fedjudge federated legal large language model author yue linan and liu qi and du yichao and gao weibo and liu ye and yao fangzhou journal arxiv preprint arxiv 2309 08173 year 2023 misc fedjudge github author linan yue and qi liu and yichao du and weibo gao and ye liu and fangzhou yao title fedjudge federated legal large language model year 2023 publisher github journal github repository howpublished url https github com yuelinan fedjudge		ai
lightllm	div align center picture img alt lightllm src assets lightllm drawio png width 90 picture div div align center docs https img shields io badge docs latest blue https github com modeltc lightllm blob main docs tokenattention md docker https github com modeltc lightllm actions workflows docker publish yml badge svg https github com modeltc lightllm actions workflows docker publish yml stars https img shields io github stars modeltc lightllm style social https github com modeltc lightllm discord banner https img shields io discord 1139835312592392214 logo discord logocolor white https discord gg wzzfwvsguu license https img shields io github license modeltc lightllm https github com modeltc lightllm blob main license div lightllm is a python based llm large language model inference and serving framework notable for its lightweight design easy scalability and high speed performance lightllm harnesses the strengths of numerous well regarded open source implementations including but not limited to fastertransformer tgi vllm and flashattention features tri process asynchronous collaboration tokenization model inference and detokenization are performed asynchronously leading to a considerable improvement in gpu utilization nopad unpad offers support for nopad attention operations across multiple models to efficiently handle requests with large length disparities dynamic batch enables dynamic batch scheduling of requests flashattention https github com dao ailab flash attention incorporates flashattention to improve speed and reduce gpu memory footprint during inference tensor parallelism utilizes tensor parallelism over multiple gpus for faster inference token attention docs tokenattention md implements token wise s kv cache memory management mechanism allowing for zero memory waste during inference high performance router collaborates with token attention to meticulously manage the gpu memory of each token thereby optimizing system throughput int8kv cache this feature will increase the capacity of tokens to almost twice as much only llama support supported model list bloom https huggingface co bigscience bloom llama https github com facebookresearch llama llama v2 https huggingface co meta llama starcoder https github com bigcode project starcoder qwen 7b https github com qwenlm qwen 7b chatglm2 6b https github com thudm chatglm2 6b baichuan 7b https github com baichuan inc baichuan 7b baichuan 13b https github com baichuan inc baichuan 13b internlm 7b https github com internlm internlm when you start qwen 7b you need to set the parameter eos id 151643 trust remote code chatglm2 needs to set the parameter trust remote code baichuan needs to set the parameter trust remote code internlm needs to set the parameter trust remote code get started requirements the code has been tested with pytorch 1 3 cuda 11 8 and python 3 9 to install the necessary dependencies please refer to the provided requirements txt and follow the instructions as shell pip install r requirements txt container you can use the official docker container to run the model more easily to do this follow these steps pull the container from the github container registry shell docker pull ghcr io modeltc lightllm main run the container with gpu support and port mapping shell docker run it gpus all p 8080 8080 shm size 1g v your local path data ghcr io modeltc lightllm main bin bash alternatively you can build the container yourself shell docker build t image name docker run it gpus all p 8080 8080 shm size 1g v your local path data image name bin bash you can also use a helper script to launch both the container and the server shell python tools quick launch docker py help note if you use multiple gpus you may need to increase the shared memory size by adding shm size to the docker run command installation install from the source code by shell python setup py install the code has been tested on a range of gpus including a100 a800 4090 and h800 if you are running the code on a100 a800 etc we recommend using triton 2 0 0 dev20221202 or triton 2 1 0 if you are running the code on h800 etc it is necessary to compile and install the source code of triton 2 1 0 https github com openai triton tree main from the github repository if the code doesn t work on other gpus try modifying the triton kernel used in model inference install triton package use triton 2 0 0 dev20221202 shell pip install triton 2 0 0 dev20221202 use triton 2 1 0 better performance but the code is under continuous development and may be unstable shell pip install u index url https aiinfra pkgs visualstudio com publicpackages packaging triton nightly pypi simple triton nightly run llama with efficient routers and tokenattention lightllm can be deployed as a service and achieve the state of the art throughput performance launch the server shell python m lightllm server api server model dir path llama 7b host 0 0 0 0 port 8080 tp 1 max total token num 120000 the parameter max total token num is influenced by the gpu memory of the deployment environment a larger value for this parameter allows for the processing of more concurrent requests thereby increasing system concurrency for more startup parameters please refer to api server py lightllm server api server py or apiserverargs md docs apiserverargs md to initiate a query in the shell shell curl http 127 0 0 1 8080 generate x post d inputs what is ai parameters max new tokens 17 frequency penalty 1 h content type application json to query from python python import time import requests import json url http localhost 8080 generate headers content type application json data inputs what is ai parameters do sample false ignore eos false max new tokens 1024 response requests post url headers headers data json dumps data if response status code 200 print response json else print error response status code response text performance service performance we compared the service performance of lightllm and vllm 0 1 2 on llama 7b using an a800 with 80g gpu memory to begin prepare the data as follows shell wget https huggingface co datasets anon8231489123 sharegpt vicuna unfiltered resolve main sharegpt v3 unfiltered cleaned split json launch the service shell python m lightllm server api server model dir path llama 7b tp 1 max total token num 121060 tokenizer mode auto evaluation shell cd test python benchmark serving py tokenizer path llama 7b dataset path sharegpt v3 unfiltered cleaned split json num prompts 2000 request rate 200 the performance comparisons results are presented below vllm lightllm total time 361 79 s br throughput 5 53 requests s total time 188 85 s br throughput 10 59 requests s static inference performance for debugging we offer static performance testing scripts for various models for instance you can evaluate the inference performance of the llama model by shell cd test model python test llama py faq the llama tokenizer fails to load consider resolving this by running the command pip install protobuf 3 20 0 error ptx version 7 4 does not support target sm 89 launch with bash tools resolve ptx version python m lightllm server api server community for further information and discussion join our discord server https discord gg wzzfwvsguu license this repository is released under the apache 2 0 license license acknowledgement we learned a lot from the following projects when developing lightllm faster transformer https github com nvidia fastertransformer text generation inference https github com huggingface text generation inference vllm https github com vllm project vllm flash attention 1 2 https github com dao ailab flash attention openai triton https github com openai triton	deep-learning gpt llama llm model-serving nlp openai-triton	ai

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