├── site ├── assets │ ├── javascripts │ │ └── lunr │ │ │ └── min │ │ │ ├── lunr.jp.min.js │ │ │ ├── lunr.vi.min.js │ │ │ ├── lunr.multi.min.js │ │ │ ├── lunr.th.min.js │ │ │ ├── lunr.hy.min.js │ │ │ ├── lunr.te.min.js │ │ │ ├── lunr.ta.min.js │ │ │ ├── lunr.zh.min.js │ │ │ ├── lunr.ja.min.js │ │ │ ├── lunr.hi.min.js │ │ │ ├── lunr.kn.min.js │ │ │ ├── lunr.sa.min.js │ │ │ ├── lunr.stemmer.support.min.js │ │ │ ├── lunr.ko.min.js │ │ │ ├── lunr.sv.min.js │ │ │ ├── lunr.da.min.js │ │ │ ├── lunr.no.min.js │ │ │ ├── lunr.he.min.js │ │ │ ├── lunr.nl.min.js │ │ │ ├── lunr.de.min.js │ │ │ ├── lunr.du.min.js │ │ │ ├── lunr.ru.min.js │ │ │ ├── lunr.fi.min.js │ │ │ ├── lunr.hu.min.js │ │ │ ├── lunr.pt.min.js │ │ │ ├── lunr.el.min.js │ │ │ ├── lunr.fr.min.js │ │ │ ├── lunr.ro.min.js │ │ │ ├── lunr.it.min.js │ │ │ ├── lunr.es.min.js │ │ │ └── lunr.ar.min.js │ ├── images │ │ └── favicon.png │ ├── css │ │ └── style.css │ └── stylesheets │ │ ├── palette.06af60db.min.css.map │ │ └── palette.06af60db.min.css ├── sitemap.xml.gz ├── sitemap.xml ├── _includes │ └── mathjax.html ├── _config.yml └── _layouts │ └── default.html ├── javascripts └── mathjax_config.js ├── docs ├── Part0 │ ├── Part0-0.md │ └── Part0-1.md ├── _includes │ └── mathjax.html ├── _config.yml ├── index.md ├── Part1 │ ├── Part1-1.md │ └── Part1-2.md ├── assets │ └── css │ │ └── style.css ├── _layouts │ └── default.html ├── vit.md └── stablediffusion.md ├── readme.md ├── mkdocs.yml └── LICENSE /site/assets/javascripts/lunr/min/lunr.jp.min.js: -------------------------------------------------------------------------------- 1 | module.exports=require("./lunr.ja"); -------------------------------------------------------------------------------- /site/sitemap.xml.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Joining-AI/LLM_Interview_Prepare/HEAD/site/sitemap.xml.gz -------------------------------------------------------------------------------- /site/assets/images/favicon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Joining-AI/LLM_Interview_Prepare/HEAD/site/assets/images/favicon.png -------------------------------------------------------------------------------- /site/sitemap.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | -------------------------------------------------------------------------------- /javascripts/mathjax_config.js: -------------------------------------------------------------------------------- 1 | window.MathJax = { 2 | tex2jax: { 3 | inlineMath: [['$', '$'], ['\\(', '\\)']], 4 | displayMath: [['$$', '$$'], ['\\[', '\\]']], 5 | processEscapes: true 6 | }, 7 | "HTML-CSS": { availableFonts: ["TeX"] } 8 | }; 9 | -------------------------------------------------------------------------------- /docs/Part0/Part0-0.md: -------------------------------------------------------------------------------- 1 | [![GitHub stars](https://img.shields.io/github/stars/Joining-AI/LLM_Interview_Prepare?style=social)](https://github.com/Joining-AI/LLM_Interview_Prepare) 2 | # AI面试与职业机会 3 | 特别鸣谢 **[奇绩创坛](https://apply.miracleplus.com/?s=PRteam)** 4 | 5 | ## 创业孵化器 6 | 7 | [奇绩创坛申请](https://apply.miracleplus.com/?s=PRteam) 8 | 9 | [返回](../index.md) 10 | -------------------------------------------------------------------------------- /docs/Part0/Part0-1.md: -------------------------------------------------------------------------------- 1 | [![GitHub stars](https://img.shields.io/github/stars/Joining-AI/LLM_Interview_Prepare?style=social)](https://github.com/Joining-AI/LLM_Interview_Prepare) 2 | # markdown文档格式模板 3 | 本文档主页的面经格式遵循如下模板： 4 | 5 | # 第0部分 6 | ## xx面经 7 | ### n.n xxx (环节名)面 8 | n.n.n 问题? 9 | 10 | - **本环节-某方法篇** 11 | 12 | n.n.n 问题? 13 | 14 | [返回](../index.md) 15 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # [LLM_Interview_Prepare](https://joining-ai.github.io/LLM_Interview_Prepare/) 2 | 3 | maintained by [SJTU Joining AI](https://sjtujoining.com) attribute to Malotru 4 | 5 | 本仓库秉承开源精神，维护了关于大模型面试中常见面试试题和面试经验的整理，同时维护大模型相关的工作与求职机会。 6 | 7 | 鉴于维护团队的背景，本仓库会更加偏重数据工程、Agent工程和Stable Diffusion的版块，我们会在后续的维护中添加相应的优秀实践和教材 8 | 9 | [特此声明]仓库中的试题亦引用自公开社区与平台，包括知乎和牛客网、Github仓库等公开内容，而答案则都经过维护团队的校验 20240727 10 | -------------------------------------------------------------------------------- /docs/_includes/mathjax.html: -------------------------------------------------------------------------------- 1 | 4 | -------------------------------------------------------------------------------- /site/_includes/mathjax.html: -------------------------------------------------------------------------------- 1 | 4 | -------------------------------------------------------------------------------- /docs/_config.yml: -------------------------------------------------------------------------------- 1 | title: LLM Interview Prepare 2 | description: >- 3 | 本仓库秉承开源精神，维护了关于大模型面试中常见面试试题和面试经验的整理，同时维护大模型相关的工作与求职机会。 4 | baseurl: "" # the subpath of your site, e.g. /blog 5 | url: "https://joining-ai.github.io/LLM_Interview_Prepare/" # the base hostname & protocol for your site 6 | 7 | # Build settings 8 | theme: jekyll-theme-minimal 9 | 10 | # Navigation links 11 | nav_links: 12 | - title: "Github Home" 13 | url: "https://github.com/Joining-AI/LLM_Interview_Prepare" 14 | - title: "Synthetic Data" 15 | url: "https://joining-ai.github.io/SyntheticData/" 16 | - title: "Agent" 17 | url: "https://github.com/Joining-AI/JoinQwen" 18 | -------------------------------------------------------------------------------- /site/_config.yml: -------------------------------------------------------------------------------- 1 | title: LLM Interview Prepare 2 | description: >- 3 | 本仓库秉承开源精神，维护了关于大模型面试中常见面试试题和面试经验的整理，同时维护大模型相关的工作与求职机会。 4 | baseurl: "" # the subpath of your site, e.g. /blog 5 | url: "https://joining-ai.github.io/LLM_Interview_Prepare/" # the base hostname & protocol for your site 6 | 7 | # Build settings 8 | theme: jekyll-theme-minimal 9 | 10 | # Navigation links 11 | nav_links: 12 | - title: "Github Home" 13 | url: "https://github.com/Joining-AI/LLM_Interview_Prepare" 14 | - title: "Synthetic Data" 15 | url: "https://joining-ai.github.io/SyntheticData/" 16 | - title: "Agent" 17 | url: "https://github.com/Joining-AI/JoinQwen" 18 | -------------------------------------------------------------------------------- /docs/index.md: -------------------------------------------------------------------------------- 1 | 2 | [![GitHub stars](https://img.shields.io/github/stars/Joining-AI/LLM_Interview_Prepare?style=social)](https://github.com/Joining-AI/LLM_Interview_Prepare) 3 | 4 | 欢迎来到 AIDIY Wiki，这是一个整理和分享 LLM、StableDiffusion 和 ViT 相关面试题目和知识点的平台。 5 | 6 | - **LLM**: 大型语言模型的相关知识点和面试题目。 7 | - **StableDiffusion**: 稳定扩散模型的相关知识点和面试题目。 8 | - **ViT**: 视觉变换器的相关知识点和面试题目。 9 | 10 | 11 | ## 写在前面 12 | 13 | [AI面试与职业机会](Part0/Part0-0.md) 14 | 15 | [markdown文档格式模板](Part0/Part0-1.md) 16 | 17 | ## 基础知识 18 | 19 | [什么是大模型](Part1/Part1-1.md) 20 | 21 | [大模型类别](Part1/Part1-2.md) 22 | 23 | [![GitHub stars](https://img.shields.io/github/stars/Joining-AI/LLM_Interview_Prepare?style=social)](https://github.com/Joining-AI/LLM_Interview_Prepare) -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.vi.min.js: -------------------------------------------------------------------------------- 1 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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-------------------------------------------------------------------------------- 1 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ko=function(){this.pipeline.reset(),this.pipeline.add(e.ko.trimmer,e.ko.stopWordFilter)},e.ko.wordCharacters="[A-Za-z가-힣]",e.ko.trimmer=e.trimmerSupport.generateTrimmer(e.ko.wordCharacters),e.Pipeline.registerFunction(e.ko.trimmer,"trimmer-ko"),e.ko.stopWordFilter=e.generateStopWordFilter("아 휴 아이구 아이쿠 아이고 어 나 우리 저희 따라 의해 을 를 에 의 가 으로 로 에게 뿐이다 의거하여 근거하여 입각하여 기준으로 예하면 예를 들면 예를 들자면 저 소인 소생 저희 지말고 하지마 하지마라 다른 물론 또한 그리고 비길수 없다 해서는 안된다 뿐만 아니라 만이 아니다 만은 아니다 막론하고 관계없이 그치지 않다 그러나 그런데 하지만 든간에 논하지 않다 따지지 않다 설사 비록 더라도 아니면 만 못하다 하는 편이 낫다 불문하고 향하여 향해서 향하다 쪽으로 틈타 이용하여 타다 오르다 제외하고 이 외에 이 밖에 하여야 비로소 한다면 몰라도 외에도 이곳 여기 부터 기점으로 따라서 할 생각이다 하려고하다 이리하여 그리하여 그렇게 함으로써 하지만 일때 할때 앞에서 중에서 보는데서 으로써 로써 까지 해야한다 일것이다 반드시 할줄알다 할수있다 할수있어 임에 틀림없다 한다면 등 등등 제 겨우 단지 다만 할뿐 딩동 댕그 대해서 대하여 대하면 훨씬 얼마나 얼마만큼 얼마큼 남짓 여 얼마간 약간 다소 좀 조금 다수 몇 얼마 지만 하물며 또한 그러나 그렇지만 하지만 이외에도 대해 말하자면 뿐이다 다음에 반대로 반대로 말하자면 이와 반대로 바꾸어서 말하면 바꾸어서 한다면 만약 그렇지않으면 까악 툭 딱 삐걱거리다 보드득 비걱거리다 꽈당 응당 해야한다 에 가서 각 각각 여러분 각종 각자 제각기 하도록하다 와 과 그러므로 그래서 고로 한 까닭에 하기 때문에 거니와 이지만 대하여 관하여 관한 과연 실로 아니나다를가 생각한대로 진짜로 한적이있다 하곤하였다 하 하하 허허 아하 거바 와 오 왜 어째서 무엇때문에 어찌 하겠는가 무슨 어디 어느곳 더군다나 하물며 더욱이는 어느때 언제 야 이봐 어이 여보시오 흐흐 흥 휴 헉헉 헐떡헐떡 영차 여차 어기여차 끙끙 아야 앗 아야 콸콸 졸졸 좍좍 뚝뚝 주룩주룩 솨 우르르 그래도 또 그리고 바꾸어말하면 바꾸어말하자면 혹은 혹시 답다 및 그에 따르는 때가 되어 즉 지든지 설령 가령 하더라도 할지라도 일지라도 지든지 몇 거의 하마터면 인젠 이젠 된바에야 된이상 만큼\t어찌됏든 그위에 게다가 점에서 보아 비추어 보아 고려하면 하게될것이다 일것이다 비교적 좀 보다더 비하면 시키다 하게하다 할만하다 의해서 연이서 이어서 잇따라 뒤따라 뒤이어 결국 의지하여 기대여 통하여 자마자 더욱더 불구하고 얼마든지 마음대로 주저하지 않고 곧 즉시 바로 당장 하자마자 밖에 안된다 하면된다 그래 그렇지 요컨대 다시 말하자면 바꿔 말하면 즉 구체적으로 말하자면 시작하여 시초에 이상 허 헉 허걱 바와같이 해도좋다 해도된다 게다가 더구나 하물며 와르르 팍 퍽 펄렁 동안 이래 하고있었다 이었다 에서 로부터 까지 예하면 했어요 해요 함께 같이 더불어 마저 마저도 양자 모두 습니다 가까스로 하려고하다 즈음하여 다른 다른 방면으로 해봐요 습니까 했어요 말할것도 없고 무릎쓰고 개의치않고 하는것만 못하다 하는것이 낫다 매 매번 들 모 어느것 어느 로써 갖고말하자면 어디 어느쪽 어느것 어느해 어느 년도 라 해도 언젠가 어떤것 어느것 저기 저쪽 저것 그때 그럼 그러면 요만한걸 그래 그때 저것만큼 그저 이르기까지 할 줄 안다 할 힘이 있다 너 너희 당신 어찌 설마 차라리 할지언정 할지라도 할망정 할지언정 구토하다 게우다 토하다 메쓰겁다 옆사람 퉤 쳇 의거하여 근거하여 의해 따라 힘입어 그 다음 버금 두번째로 기타 첫번째로 나머지는 그중에서 견지에서 형식으로 쓰여 입장에서 위해서 단지 의해되다 하도록시키다 뿐만아니라 반대로 전후 전자 앞의것 잠시 잠깐 하면서 그렇지만 다음에 그러한즉 그런즉 남들 아무거나 어찌하든지 같다 비슷하다 예컨대 이럴정도로 어떻게 만약 만일 위에서 서술한바와같이 인 듯하다 하지 않는다면 만약에 무엇 무슨 어느 어떤 아래윗 조차 한데 그럼에도 불구하고 여전히 심지어 까지도 조차도 하지 않도록 않기 위하여 때 시각 무렵 시간 동안 어때 어떠한 하여금 네 예 우선 누구 누가 알겠는가 아무도 줄은모른다 줄은 몰랏다 하는 김에 겸사겸사 하는바 그런 까닭에 한 이유는 그러니 그러니까 때문에 그 너희 그들 너희들 타인 것 것들 너 위하여 공동으로 동시에 하기 위하여 어찌하여 무엇때문에 붕붕 윙윙 나 우리 엉엉 휘익 윙윙 오호 아하 어쨋든 만 못하다\t하기보다는 차라리 하는 편이 낫다 흐흐 놀라다 상대적으로 말하자면 마치 아니라면 쉿 그렇지 않으면 그렇지 않다면 안 그러면 아니었다면 하든지 아니면 이라면 좋아 알았어 하는것도 그만이다 어쩔수 없다 하나 일 일반적으로 일단 한켠으로는 오자마자 이렇게되면 이와같다면 전부 한마디 한항목 근거로 하기에 아울러 하지 않도록 않기 위해서 이르기까지 이 되다 로 인하여 까닭으로 이유만으로 이로 인하여 그래서 이 때문에 그러므로 그런 까닭에 알 수 있다 결론을 낼 수 있다 으로 인하여 있다 어떤것 관계가 있다 관련이 있다 연관되다 어떤것들 에 대해 이리하여 그리하여 여부 하기보다는 하느니 하면 할수록 운운 이러이러하다 하구나 하도다 다시말하면 다음으로 에 있다 에 달려 있다 우리 우리들 오히려 하기는한데 어떻게 어떻해 어찌됏어 어때 어째서 본대로 자 이 이쪽 여기 이것 이번 이렇게말하자면 이런 이러한 이와 같은 요만큼 요만한 것 얼마 안 되는 것 이만큼 이 정도의 이렇게 많은 것 이와 같다 이때 이렇구나 것과 같이 끼익 삐걱 따위 와 같은 사람들 부류의 사람들 왜냐하면 중의하나 오직 오로지 에 한하다 하기만 하면 도착하다 까지 미치다 도달하다 정도에 이르다 할 지경이다 결과에 이르다 관해서는 여러분 하고 있다 한 후 혼자 자기 자기집 자신 우에 종합한것과같이 총적으로 보면 총적으로 말하면 총적으로 대로 하다 으로서 참 그만이다 할 따름이다 쿵 탕탕 쾅쾅 둥둥 봐 봐라 아이야 아니 와아 응 아이 참나 년 월 일 령 영 일 이 삼 사 오 육 륙 칠 팔 구 이천육 이천칠 이천팔 이천구 하나 둘 셋 넷 다섯 여섯 일곱 여덟 아홉 령 영".split(" ")),e.Pipeline.registerFunction(e.ko.stopWordFilter,"stopWordFilter-ko"),e.ko.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}(),e.Pipeline.registerFunction(e.ko.stemmer,"stemmer-ko")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.sv.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Swedish` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){function e(){var e,r=w.cursor+3;if(o=w.limit,0<=r||r<=w.limit){for(a=r;;){if(e=w.cursor,w.in_grouping(l,97,246)){w.cursor=e;break}if(w.cursor=e,w.cursor>=w.limit)return;w.cursor++}for(;!w.out_grouping(l,97,246);){if(w.cursor>=w.limit)return;w.cursor++}o=w.cursor,o=o&&(w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(u,37),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.in_grouping_b(d,98,121)&&w.slice_del()}}function i(){var e=w.limit_backward;w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.find_among_b(c,7)&&(w.cursor=w.limit,w.ket=w.cursor,w.cursor>w.limit_backward&&(w.bra=--w.cursor,w.slice_del())),w.limit_backward=e)}function s(){var e,r;if(w.cursor>=o){if(r=w.limit_backward,w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(m,5))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.slice_from("lös");break;case 3:w.slice_from("full")}w.limit_backward=r}}var a,o,u=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],c=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],l=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],d=[119,127,149],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,t(),w.cursor=w.limit,i(),w.cursor=w.limit,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.da.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Danish` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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52 | CLIP模型 53 | （1）基本思想：文本和图像在特征域进行对齐 54 | 55 | （2）模型结构： 56 | - 文本编码器（BERT） 57 | - 图像编码器（ViT） 58 | - 对上面两个encoder提取的特征计算余弦距离对齐 59 | 60 | （3）训练目标：info-nce-loss (对比学习MoCo的对抗基因) 61 | 62 | （4）训练数据集：400million的图文对齐图片数据 63 |
64 | 65 |
66 | BLIP模型 67 | （1）基本思想：深度嵌入联合编码器，同时使三个视觉语言模型上联合表现最佳：图像对文本对比学习、图像文本互翻译联合条件生成训练。 68 | 69 | （2）模型结构：Multimodal mixture of Encoder-Decoder 70 | - 多模态交叉编码器（ViT） 71 | - 串联文本编码器（BERT） 72 | - 串联Image-grounded text encoder: 在self-attention和FFN中间加一层cross-attention 73 | - 多模态Image-grounded text decoder: 用casual self-attention层（预测下一个token）代替了双向自注意力层（建立当前输入token的表达） 74 | 75 | （3）训练目标： 76 | - ITC loss（视图距离，生成图像和文本编码器距离）； 77 | - ITM loss（视图距离，生成图像和文本编码器）； 78 | - LM loss（语言生成，优化图像和文本编码器在语言建模应用可互式交互生成的效果。 79 | 这三种损失（loss）是在多模态（图文）模型中常用的，用以优化图文对齐和生成性能。以下是对这三种损失的详细介绍： 80 | 81 | 1. **ITC loss (Image-Text Contrastive loss)** 82 | - **目的**：优化图文对齐的编码器，改善图像和文本之间的语义匹配。 83 | - **工作原理**：这种损失函数通常通过对比学习实现，即通过最大化匹配图文对的相似性，同时最小化不匹配对的相似性。实现方式通常涉及计算图像和文本嵌入之间的距离或相似度，并使用如负对数似然这样的方法进行优化。 84 | - **应用**：这种方法在提升图文嵌入在同一向量空间中的表现上非常有效，有助于后续的检索和分类任务。 85 | 86 | 2. **ITM loss (Image-Text Matching loss)** 87 | - **目的**：优化图文匹配能力，用于更细致地判定图像内容与文本描述之间的匹配程度。 88 | - **工作原理**：这种损失通常用于二分类任务，即判断给定的图像和文本是否匹配。模型需要学习区分哪些图文组合是真实相关的，哪些是随机组合的。这通常通过一个有监督的学习过程来完成，其中正例是实际匹配的图文对，而负例是不匹配的图文对。 89 | - **应用**：这有助于模型在更复杂的多模态场景下进行准确的内容理解和生成，常用于自动图文生成、图文同步解读等应用。 90 | 91 | 3. **LM loss (Language Modeling loss)** 92 | - **目的**：主要用于语言生成任务，尤其是在图文交互生成中的应用，通过优化模型的语言生成能力来增强交互性。 93 | - **工作原理**：这种损失函数通常用于评估模型生成的文本与实际文本之间的一致性。在图文模型中，可以使用图像作为上下文来生成描述文本，并通过计算生成文本和真实描述之间的交叉熵损失来优化模型。 94 | - **应用**：这种方法使得模型不仅能够理解图像内容，还能基于理解生成准确、自然的语言描述，常用于图像描述、自动文案生成等任务。 95 | 96 | （4）训练数据集： 97 | - COCO 98 | - Visual Genome 99 | - 网络数据：Conceptual Captions 3M, Conceptual 12M（视图数据），SBU Captions 100 | - 另外进行一个额外的拍摄照片文本对齐的web模型数据 LAION (115M 图片) 101 |
102 | 103 |
104 | BLIP2模型 105 | （1）基本思想：两个阶段，通过利用预训练好的视觉模型和语言模型来提升多模态效果和降低训练成本。 106 | 107 | （2）模型结构： 108 | BLIP-2 由预训练的Image Encoder，预训练的Large Language Model，和一个可学习的 Q-Former 组成。 109 | - Image Encoder：从输入图片中提取视觉特征，尝试了两种网络结构，CLIP 训练的 ViT-L/14和EVA-CLIP训练的 ViT-g/14（去掉了最后一层）。 110 | - Large Language Model：大语言模型进行文本生成，尝试了接入decoder-based LLM 和 encoder-decoder-based LLM两种结构。 111 | - Q-Former：弥补视觉和语言两种模态的modality gap，可以理解为固定图像编码器和固定LLM之间的**信息枢纽**，选取最有用的视觉特征给LLM来生成文本。 112 | 113 | （3） 训练目标： 114 | - ITC loss（偏理解）：图文对比学习，对其图文特征空间； 115 | - ITG loss（偏生成）：确定输入图像生成文本描述，迫使Query提取包含文本信息的特征； 116 | - ITM loss（偏理解）：图文匹配二分类，图文表示的细粒度对齐。 117 | 118 | （4）训练数据 119 | - BLIP运用的数据集 120 | - 利用提出的CapFilt方法从网络图中提取匹配的图文对，然后对文字描述内容进行filter，得到有利于训练的caption 121 |
122 | 123 | 4. BEIT方法 124 | 125 | 126 | ### $常见面经题目$ 127 | 128 | 1. CLIP和BEIT V3的区别; 129 | 2. BEIT V3除了BERT外还有别的特殊的设计吗; 130 | 3. V3和V2的 embbeding有什么不同; 131 | 4. VIT的patch怎么做的; 132 | 5. 224* 224 * 3的图像做成14 * 14的patch的话，最后sequence的长度是多少; 133 | 6. Transformer里的position encoding怎么做的; 134 | 7. 相对位置编码和绝对位置编码有什么区别吗; 135 | 8. 具体实现相对位置编码该怎么做; 136 | 9. CV领域有哪些其他的预训练的模型; 137 | 10. 什么是对比学习; 138 | 11. coco,simclr等对比学习方法是怎么具体做的； 139 | 140 | [![GitHub stars](https://img.shields.io/github/stars/Joining-AI/LLM_Interview_Prepare?style=social)](https://github.com/Joining-AI/LLM_Interview_Prepare) -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.he.min.js: -------------------------------------------------------------------------------- 1 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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r,n,i,s,t=v.cursor;;)if(r=v.cursor,v.bra=r,v.eq_s(1,"ß"))v.ket=v.cursor,v.slice_from("ss");else{if(r>=v.limit)break;v.cursor=r+1}for(v.cursor=t;;)for(n=v.cursor;;){if(i=v.cursor,v.in_grouping(p,97,252)){if(s=v.cursor,v.bra=s,e("u","U",i))break;if(v.cursor=s,e("y","Y",i))break}if(i>=v.limit)return void(v.cursor=n);v.cursor=i+1}}function s(){for(;!v.in_grouping(p,97,252);){if(v.cursor>=v.limit)return!0;v.cursor++}for(;!v.out_grouping(p,97,252);){if(v.cursor>=v.limit)return!0;v.cursor++}return!1}function t(){m=v.limit,l=m;var e=v.cursor+3;0<=e&&e<=v.limit&&(d=e,s()||(m=v.cursor,m=v.limit)return;v.cursor++}}}function c(){return m<=v.cursor}function u(){return l<=v.cursor}function a(){var e,r,n,i,s=v.limit-v.cursor;if(v.ket=v.cursor,(e=v.find_among_b(w,7))&&(v.bra=v.cursor,c()))switch(e){case 1:v.slice_del();break;case 2:v.slice_del(),v.ket=v.cursor,v.eq_s_b(1,"s")&&(v.bra=v.cursor,v.eq_s_b(3,"nis")&&v.slice_del());break;case 3:v.in_grouping_b(g,98,116)&&v.slice_del()}if(v.cursor=v.limit-s,v.ket=v.cursor,(e=v.find_among_b(f,4))&&(v.bra=v.cursor,c()))switch(e){case 1:v.slice_del();break;case 2:if(v.in_grouping_b(k,98,116)){var t=v.cursor-3;v.limit_backward<=t&&t<=v.limit&&(v.cursor=t,v.slice_del())}}if(v.cursor=v.limit-s,v.ket=v.cursor,(e=v.find_among_b(_,8))&&(v.bra=v.cursor,u()))switch(e){case 1:v.slice_del(),v.ket=v.cursor,v.eq_s_b(2,"ig")&&(v.bra=v.cursor,r=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-r,u()&&v.slice_del()));break;case 2:n=v.limit-v.cursor,v.eq_s_b(1,"e")||(v.cursor=v.limit-n,v.slice_del());break;case 3:if(v.slice_del(),v.ket=v.cursor,i=v.limit-v.cursor,!v.eq_s_b(2,"er")&&(v.cursor=v.limit-i,!v.eq_s_b(2,"en")))break;v.bra=v.cursor,c()&&v.slice_del();break;case 4:v.slice_del(),v.ket=v.cursor,e=v.find_among_b(b,2),e&&(v.bra=v.cursor,u()&&1==e&&v.slice_del())}}var d,l,m,h=[new r("",-1,6),new r("U",0,2),new r("Y",0,1),new r("ä",0,3),new r("ö",0,4),new r("ü",0,5)],w=[new r("e",-1,2),new r("em",-1,1),new r("en",-1,2),new r("ern",-1,1),new r("er",-1,1),new r("s",-1,3),new r("es",5,2)],f=[new r("en",-1,1),new r("er",-1,1),new r("st",-1,2),new r("est",2,1)],b=[new r("ig",-1,1),new r("lich",-1,1)],_=[new r("end",-1,1),new r("ig",-1,2),new r("ung",-1,1),new r("lich",-1,3),new r("isch",-1,2),new r("ik",-1,2),new r("heit",-1,3),new r("keit",-1,4)],p=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32,8],g=[117,30,5],k=[117,30,4],v=new n;this.setCurrent=function(e){v.setCurrent(e)},this.getCurrent=function(){return v.getCurrent()},this.stem=function(){var e=v.cursor;return i(),v.cursor=e,t(),v.limit_backward=e,v.cursor=v.limit,a(),v.cursor=v.limit_backward,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.de.stemmer,"stemmer-de"),e.de.stopWordFilter=e.generateStopWordFilter("aber alle allem allen aller alles als also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.du.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Dutch` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");console.warn('[Lunr Languages] Please use the "nl" instead of the "du". The "nl" code is the standard code for Dutch language, and "du" will be removed in the next major versions.'),e.du=function(){this.pipeline.reset(),this.pipeline.add(e.du.trimmer,e.du.stopWordFilter,e.du.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.du.stemmer))},e.du.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.du.trimmer=e.trimmerSupport.generateTrimmer(e.du.wordCharacters),e.Pipeline.registerFunction(e.du.trimmer,"trimmer-du"),e.du.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){for(var e,r,i,o=C.cursor;;){if(C.bra=C.cursor,e=C.find_among(b,11))switch(C.ket=C.cursor,e){case 1:C.slice_from("a");continue;case 2:C.slice_from("e");continue;case 3:C.slice_from("i");continue;case 4:C.slice_from("o");continue;case 5:C.slice_from("u");continue;case 6:if(C.cursor>=C.limit)break;C.cursor++;continue}break}for(C.cursor=o,C.bra=o,C.eq_s(1,"y")?(C.ket=C.cursor,C.slice_from("Y")):C.cursor=o;;)if(r=C.cursor,C.in_grouping(q,97,232)){if(i=C.cursor,C.bra=i,C.eq_s(1,"i"))C.ket=C.cursor,C.in_grouping(q,97,232)&&(C.slice_from("I"),C.cursor=r);else if(C.cursor=i,C.eq_s(1,"y"))C.ket=C.cursor,C.slice_from("Y"),C.cursor=r;else if(n(r))break}else if(n(r))break}function n(e){return C.cursor=e,e>=C.limit||(C.cursor++,!1)}function o(){_=C.limit,f=_,t()||(_=C.cursor,_<3&&(_=3),t()||(f=C.cursor))}function t(){for(;!C.in_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}for(;!C.out_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}return!1}function s(){for(var e;;)if(C.bra=C.cursor,e=C.find_among(p,3))switch(C.ket=C.cursor,e){case 1:C.slice_from("y");break;case 2:C.slice_from("i");break;case 3:if(C.cursor>=C.limit)return;C.cursor++}}function u(){return _<=C.cursor}function c(){return f<=C.cursor}function a(){var e=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-e,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var e;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.slice_del(),w=!0,a())))}function m(){var e;u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.eq_s_b(3,"gem")||(C.cursor=C.limit-e,C.slice_del(),a())))}function d(){var e,r,i,n,o,t,s=C.limit-C.cursor;if(C.ket=C.cursor,e=C.find_among_b(h,5))switch(C.bra=C.cursor,e){case 1:u()&&C.slice_from("heid");break;case 2:m();break;case 3:u()&&C.out_grouping_b(z,97,232)&&C.slice_del()}if(C.cursor=C.limit-s,l(),C.cursor=C.limit-s,C.ket=C.cursor,C.eq_s_b(4,"heid")&&(C.bra=C.cursor,c()&&(r=C.limit-C.cursor,C.eq_s_b(1,"c")||(C.cursor=C.limit-r,C.slice_del(),C.ket=C.cursor,C.eq_s_b(2,"en")&&(C.bra=C.cursor,m())))),C.cursor=C.limit-s,C.ket=C.cursor,e=C.find_among_b(k,6))switch(C.bra=C.cursor,e){case 1:if(c()){if(C.slice_del(),i=C.limit-C.cursor,C.ket=C.cursor,C.eq_s_b(2,"ig")&&(C.bra=C.cursor,c()&&(n=C.limit-C.cursor,!C.eq_s_b(1,"e")))){C.cursor=C.limit-n,C.slice_del();break}C.cursor=C.limit-i,a()}break;case 2:c()&&(o=C.limit-C.cursor,C.eq_s_b(1,"e")||(C.cursor=C.limit-o,C.slice_del()));break;case 3:c()&&(C.slice_del(),l());break;case 4:c()&&C.slice_del();break;case 5:c()&&w&&C.slice_del()}C.cursor=C.limit-s,C.out_grouping_b(j,73,232)&&(t=C.limit-C.cursor,C.find_among_b(v,4)&&C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-t,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del())))}var f,_,w,b=[new r("",-1,6),new r("á",0,1),new r("ä",0,1),new r("é",0,2),new r("ë",0,2),new r("í",0,3),new r("ï",0,3),new r("ó",0,4),new r("ö",0,4),new r("ú",0,5),new r("ü",0,5)],p=[new r("",-1,3),new r("I",0,2),new r("Y",0,1)],g=[new r("dd",-1,-1),new r("kk",-1,-1),new r("tt",-1,-1)],h=[new r("ene",-1,2),new r("se",-1,3),new r("en",-1,2),new r("heden",2,1),new r("s",-1,3)],k=[new r("end",-1,1),new r("ig",-1,2),new r("ing",-1,1),new r("lijk",-1,3),new r("baar",-1,4),new r("bar",-1,5)],v=[new r("aa",-1,-1),new r("ee",-1,-1),new r("oo",-1,-1),new r("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(e){C.setCurrent(e)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var r=C.cursor;return e(),C.cursor=r,o(),C.limit_backward=r,C.cursor=C.limit,d(),C.cursor=C.limit_backward,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.du.stemmer,"stemmer-du"),e.du.stopWordFilter=e.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),e.Pipeline.registerFunction(e.du.stopWordFilter,"stopWordFilter-du")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.ru.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Russian` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.el=function(){this.pipeline.reset(),void 0===this.searchPipeline&&this.pipeline.add(e.el.trimmer,e.el.normilizer),this.pipeline.add(e.el.stopWordFilter,e.el.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.el.stemmer))},e.el.wordCharacters="A-Za-zΑαΒβΓγΔδΕεΖζΗηΘθΙιΚκΛλΜμΝνΞξΟοΠπΡρΣσςΤτΥυΦφΧχΨψΩωΆάΈέΉήΊίΌόΎύΏώΪΐΫΰΐΰ",e.el.trimmer=e.trimmerSupport.generateTrimmer(e.el.wordCharacters),e.Pipeline.registerFunction(e.el.trimmer,"trimmer-el"),e.el.stemmer=function(){function e(e){return s.test(e)}function t(e){return/[ΑΕΗΙΟΥΩ]$/.test(e)}function r(e){return/[ΑΕΗΙΟΩ]$/.test(e)}function n(n){var s=n;if(n.length<3)return s;if(!e(n))return s;if(i.indexOf(n)>=0)return s;var u=new RegExp("(.*)("+Object.keys(l).join("|")+")$"),o=u.exec(s);return null!==o&&(s=o[1]+l[o[2]]),null!==(o=/^(.+?)(ΑΔΕΣ|ΑΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΚ|ΜΑΜ|ΜΑΝ|ΜΠΑΜΠ|ΠΑΤΕΡ|ΓΙΑΓΙ|ΝΤΑΝΤ|ΚΥΡ|ΘΕΙ|ΠΕΘΕΡ|ΜΟΥΣΑΜ|ΚΑΠΛΑΜ|ΠΑΡ|ΨΑΡ|ΤΖΟΥΡ|ΤΑΜΠΟΥΡ|ΓΑΛΑΤ|ΦΑΦΛΑΤ)$/.test(o[1])||(s+="ΑΔ")),null!==(o=/^(.+?)(ΕΔΕΣ|ΕΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΠ|ΙΠ|ΕΜΠ|ΥΠ|ΓΗΠ|ΔΑΠ|ΚΡΑΣΠ|ΜΙΛ)$/.test(o[1])&&(s+="ΕΔ")),null!==(o=/^(.+?)(ΟΥΔΕΣ|ΟΥΔΩΝ)$/.exec(s))&&(s=o[1],/(ΑΡΚ|ΚΑΛΙΑΚ|ΠΕΤΑΛ|ΛΙΧ|ΠΛΕΞ|ΣΚ|Σ|ΦΛ|ΦΡ|ΒΕΛ|ΛΟΥΛ|ΧΝ|ΣΠ|ΤΡΑΓ|ΦΕ)$/.test(o[1])&&(s+="ΟΥΔ")),null!==(o=/^(.+?)(ΕΩΣ|ΕΩΝ|ΕΑΣ|ΕΑ)$/.exec(s))&&(s=o[1],/^(Θ|Δ|ΕΛ|ΓΑΛ|Ν|Π|ΙΔ|ΠΑΡ|ΣΤΕΡ|ΟΡΦ|ΑΝΔΡ|ΑΝΤΡ)$/.test(o[1])&&(s+="Ε")),null!==(o=/^(.+?)(ΕΙΟ|ΕΙΟΣ|ΕΙΟΙ|ΕΙΑ|ΕΙΑΣ|ΕΙΕΣ|ΕΙΟΥ|ΕΙΟΥΣ|ΕΙΩΝ)$/.exec(s))&&o[1].length>4&&(s=o[1]),null!==(o=/^(.+?)(ΙΟΥΣ|ΙΑΣ|ΙΕΣ|ΙΟΣ|ΙΟΥ|ΙΟΙ|ΙΩΝ|ΙΟΝ|ΙΑ|ΙΟ)$/.exec(s))&&(s=o[1],(t(s)||s.length<2||/^(ΑΓ|ΑΓΓΕΛ|ΑΓΡ|ΑΕΡ|ΑΘΛ|ΑΚΟΥΣ|ΑΞ|ΑΣ|Β|ΒΙΒΛ|ΒΥΤ|Γ|ΓΙΑΓ|ΓΩΝ|Δ|ΔΑΝ|ΔΗΛ|ΔΗΜ|ΔΟΚΙΜ|ΕΛ|ΖΑΧΑΡ|ΗΛ|ΗΠ|ΙΔ|ΙΣΚ|ΙΣΤ|ΙΟΝ|ΙΩΝ|ΚΙΜΩΛ|ΚΟΛΟΝ|ΚΟΡ|ΚΤΗΡ|ΚΥΡ|ΛΑΓ|ΛΟΓ|ΜΑΓ|ΜΠΑΝ|ΜΠΡ|ΝΑΥΤ|ΝΟΤ|ΟΠΑΛ|ΟΞ|ΟΡ|ΟΣ|ΠΑΝΑΓ|ΠΑΤΡ|ΠΗΛ|ΠΗΝ|ΠΛΑΙΣ|ΠΟΝΤ|ΡΑΔ|ΡΟΔ|ΣΚ|ΣΚΟΡΠ|ΣΟΥΝ|ΣΠΑΝ|ΣΤΑΔ|ΣΥΡ|ΤΗΛ|ΤΙΜ|ΤΟΚ|ΤΟΠ|ΤΡΟΧ|ΦΙΛ|ΦΩΤ|Χ|ΧΙΛ|ΧΡΩΜ|ΧΩΡ)$/.test(o[1]))&&(s+="Ι"),/^(ΠΑΛ)$/.test(o[1])&&(s+="ΑΙ")),null!==(o=/^(.+?)(ΙΚΟΣ|ΙΚΟΝ|ΙΚΕΙΣ|ΙΚΟΙ|ΙΚΕΣ|ΙΚΟΥΣ|ΙΚΗ|ΙΚΗΣ|ΙΚΟ|ΙΚΑ|ΙΚΟΥ|ΙΚΩΝ|ΙΚΩΣ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΑΔ|ΑΛ|ΑΜΑΝ|ΑΜΕΡ|ΑΜΜΟΧΑΛ|ΑΝΗΘ|ΑΝΤΙΔ|ΑΠΛ|ΑΤΤ|ΑΦΡ|ΒΑΣ|ΒΡΩΜ|ΓΕΝ|ΓΕΡ|Δ|ΔΙΚΑΝ|ΔΥΤ|ΕΙΔ|ΕΝΔ|ΕΞΩΔ|ΗΘ|ΘΕΤ|ΚΑΛΛΙΝ|ΚΑΛΠ|ΚΑΤΑΔ|ΚΟΥΖΙΝ|ΚΡ|ΚΩΔ|ΛΟΓ|Μ|ΜΕΡ|ΜΟΝΑΔ|ΜΟΥΛ|ΜΟΥΣ|ΜΠΑΓΙΑΤ|ΜΠΑΝ|ΜΠΟΛ|ΜΠΟΣ|ΜΥΣΤ|Ν|ΝΙΤ|ΞΙΚ|ΟΠΤ|ΠΑΝ|ΠΕΤΣ|ΠΙΚΑΝΤ|ΠΙΤΣ|ΠΛΑΣΤ|ΠΛΙΑΤΣ|ΠΟΝΤ|ΠΟΣΤΕΛΝ|ΠΡΩΤΟΔ|ΣΕΡΤ|ΣΗΜΑΝΤ|ΣΤΑΤ|ΣΥΝΑΔ|ΣΥΝΟΜΗΛ|ΤΕΛ|ΤΕΧΝ|ΤΡΟΠ|ΤΣΑΜ|ΥΠΟΔ|Φ|ΦΙΛΟΝ|ΦΥΛΟΔ|ΦΥΣ|ΧΑΣ)$/.test(o[1])||/(ΦΟΙΝ)$/.test(o[1]))&&(s+="ΙΚ")),"ΑΓΑΜΕ"===s&&(s="ΑΓΑΜ"),null!==(o=/^(.+?)(ΑΓΑΜΕ|ΗΣΑΜΕ|ΟΥΣΑΜΕ|ΗΚΑΜΕ|ΗΘΗΚΑΜΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΑΜΕ)$/.exec(s))&&(s=o[1],/^(ΑΝΑΠ|ΑΠΟΘ|ΑΠΟΚ|ΑΠΟΣΤ|ΒΟΥΒ|ΞΕΘ|ΟΥΛ|ΠΕΘ|ΠΙΚΡ|ΠΟΤ|ΣΙΧ|Χ)$/.test(o[1])&&(s+="ΑΜ")),null!==(o=/^(.+?)(ΑΓΑΝΕ|ΗΣΑΝΕ|ΟΥΣΑΝΕ|ΙΟΝΤΑΝΕ|ΙΟΤΑΝΕ|ΙΟΥΝΤΑΝΕ|ΟΝΤΑΝΕ|ΟΤΑΝΕ|ΟΥΝΤΑΝΕ|ΗΚΑΝΕ|ΗΘΗΚΑΝΕ)$/.exec(s))&&(s=o[1],/^(ΤΡ|ΤΣ)$/.test(o[1])&&(s+="ΑΓΑΝ")),null!==(o=/^(.+?)(ΑΝΕ)$/.exec(s))&&(s=o[1],(r(s)||/^(ΒΕΤΕΡ|ΒΟΥΛΚ|ΒΡΑΧΜ|Γ|ΔΡΑΔΟΥΜ|Θ|ΚΑΛΠΟΥΖ|ΚΑΣΤΕΛ|ΚΟΡΜΟΡ|ΛΑΟΠΛ|ΜΩΑΜΕΘ|Μ|ΜΟΥΣΟΥΛΜΑΝ|ΟΥΛ|Π|ΠΕΛΕΚ|ΠΛ|ΠΟΛΙΣ|ΠΟΡΤΟΛ|ΣΑΡΑΚΑΤΣ|ΣΟΥΛΤ|ΤΣΑΡΛΑΤ|ΟΡΦ|ΤΣΙΓΓ|ΤΣΟΠ|ΦΩΤΟΣΤΕΦ|Χ|ΨΥΧΟΠΛ|ΑΓ|ΟΡΦ|ΓΑΛ|ΓΕΡ|ΔΕΚ|ΔΙΠΛ|ΑΜΕΡΙΚΑΝ|ΟΥΡ|ΠΙΘ|ΠΟΥΡΙΤ|Σ|ΖΩΝΤ|ΙΚ|ΚΑΣΤ|ΚΟΠ|ΛΙΧ|ΛΟΥΘΗΡ|ΜΑΙΝΤ|ΜΕΛ|ΣΙΓ|ΣΠ|ΣΤΕΓ|ΤΡΑΓ|ΤΣΑΓ|Φ|ΕΡ|ΑΔΑΠ|ΑΘΙΓΓ|ΑΜΗΧ|ΑΝΙΚ|ΑΝΟΡΓ|ΑΠΗΓ|ΑΠΙΘ|ΑΤΣΙΓΓ|ΒΑΣ|ΒΑΣΚ|ΒΑΘΥΓΑΛ|ΒΙΟΜΗΧ|ΒΡΑΧΥΚ|ΔΙΑΤ|ΔΙΑΦ|ΕΝΟΡΓ|ΘΥΣ|ΚΑΠΝΟΒΙΟΜΗΧ|ΚΑΤΑΓΑΛ|ΚΛΙΒ|ΚΟΙΛΑΡΦ|ΛΙΒ|ΜΕΓΛΟΒΙΟΜΗΧ|ΜΙΚΡΟΒΙΟΜΗΧ|ΝΤΑΒ|ΞΗΡΟΚΛΙΒ|ΟΛΙΓΟΔΑΜ|ΟΛΟΓΑΛ|ΠΕΝΤΑΡΦ|ΠΕΡΗΦ|ΠΕΡΙΤΡ|ΠΛΑΤ|ΠΟΛΥΔΑΠ|ΠΟΛΥΜΗΧ|ΣΤΕΦ|ΤΑΒ|ΤΕΤ|ΥΠΕΡΗΦ|ΥΠΟΚΟΠ|ΧΑΜΗΛΟΔΑΠ|ΨΗΛΟΤΑΒ)$/.test(o[1]))&&(s+="ΑΝ")),null!==(o=/^(.+?)(ΗΣΕΤΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΕΤΕ)$/.exec(s))&&(s=o[1],(r(s)||/(ΟΔ|ΑΙΡ|ΦΟΡ|ΤΑΘ|ΔΙΑΘ|ΣΧ|ΕΝΔ|ΕΥΡ|ΤΙΘ|ΥΠΕΡΘ|ΡΑΘ|ΕΝΘ|ΡΟΘ|ΣΘ|ΠΥΡ|ΑΙΝ|ΣΥΝΔ|ΣΥΝ|ΣΥΝΘ|ΧΩΡ|ΠΟΝ|ΒΡ|ΚΑΘ|ΕΥΘ|ΕΚΘ|ΝΕΤ|ΡΟΝ|ΑΡΚ|ΒΑΡ|ΒΟΛ|ΩΦΕΛ)$/.test(o[1])||/^(ΑΒΑΡ|ΒΕΝ|ΕΝΑΡ|ΑΒΡ|ΑΔ|ΑΘ|ΑΝ|ΑΠΛ|ΒΑΡΟΝ|ΝΤΡ|ΣΚ|ΚΟΠ|ΜΠΟΡ|ΝΙΦ|ΠΑΓ|ΠΑΡΑΚΑΛ|ΣΕΡΠ|ΣΚΕΛ|ΣΥΡΦ|ΤΟΚ|Υ|Δ|ΕΜ|ΘΑΡΡ|Θ)$/.test(o[1]))&&(s+="ΕΤ")),null!==(o=/^(.+?)(ΟΝΤΑΣ|ΩΝΤΑΣ)$/.exec(s))&&(s=o[1],/^ΑΡΧ$/.test(o[1])&&(s+="ΟΝΤ"),/ΚΡΕ$/.test(o[1])&&(s+="ΩΝΤ")),null!==(o=/^(.+?)(ΟΜΑΣΤΕ|ΙΟΜΑΣΤΕ)$/.exec(s))&&(s=o[1],/^ΟΝ$/.test(o[1])&&(s+="ΟΜΑΣΤ")),null!==(o=/^(.+?)(ΙΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(Π|ΑΠ|ΣΥΜΠ|ΑΣΥΜΠ|ΑΚΑΤΑΠ|ΑΜΕΤΑΜΦ)$/.test(o[1])&&(s+="ΙΕΣΤ")),null!==(o=/^(.+?)(ΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΛ|ΑΡ|ΕΚΤΕΛ|Ζ|Μ|Ξ|ΠΑΡΑΚΑΛ|ΠΡΟ|ΝΙΣ)$/.test(o[1])&&(s+="ΕΣΤ")),null!==(o=/^(.+?)(ΗΘΗΚΑ|ΗΘΗΚΕΣ|ΗΘΗΚΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΗΚΑ|ΗΚΕΣ|ΗΚΕ)$/.exec(s))&&(s=o[1],(/(ΣΚΩΛ|ΣΚΟΥΛ|ΝΑΡΘ|ΣΦ|ΟΘ|ΠΙΘ)$/.test(o[1])||/^(ΔΙΑΘ|Θ|ΠΑΡΑΚΑΤΑΘ|ΠΡΟΣΘ|ΣΥΝΘ)$/.test(o[1]))&&(s+="ΗΚ")),null!==(o=/^(.+?)(ΟΥΣΑ|ΟΥΣΕΣ|ΟΥΣΕ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΦΑΡΜΑΚ|ΧΑΔ|ΑΓΚ|ΑΝΑΡΡ|ΒΡΟΜ|ΕΚΛΙΠ|ΛΑΜΠΙΔ|ΛΕΧ|Μ|ΠΑΤ|Ρ|Λ|ΜΕΔ|ΜΕΣΑΖ|ΥΠΟΤΕΙΝ|ΑΜ|ΑΙΘ|ΑΝΗΚ|ΔΕΣΠΟΖ|ΕΝΔΙΑΦΕΡ)$/.test(o[1])||/(ΠΟΔΑΡ|ΒΛΕΠ|ΠΑΝΤΑΧ|ΦΡΥΔ|ΜΑΝΤΙΛ|ΜΑΛΛ|ΚΥΜΑΤ|ΛΑΧ|ΛΗΓ|ΦΑΓ|ΟΜ|ΠΡΩΤ)$/.test(o[1]))&&(s+="ΟΥΣ")),null!==(o=/^(.+?)(ΑΓΑ|ΑΓΕΣ|ΑΓΕ)$/.exec(s))&&(s=o[1],(/^(ΑΒΑΣΤ|ΠΟΛΥΦ|ΑΔΗΦ|ΠΑΜΦ|Ρ|ΑΣΠ|ΑΦ|ΑΜΑΛ|ΑΜΑΛΛΙ|ΑΝΥΣΤ|ΑΠΕΡ|ΑΣΠΑΡ|ΑΧΑΡ|ΔΕΡΒΕΝ|ΔΡΟΣΟΠ|ΞΕΦ|ΝΕΟΠ|ΝΟΜΟΤ|ΟΛΟΠ|ΟΜΟΤ|ΠΡΟΣΤ|ΠΡΟΣΩΠΟΠ|ΣΥΜΠ|ΣΥΝΤ|Τ|ΥΠΟΤ|ΧΑΡ|ΑΕΙΠ|ΑΙΜΟΣΤ|ΑΝΥΠ|ΑΠΟΤ|ΑΡΤΙΠ|ΔΙΑΤ|ΕΝ|ΕΠΙΤ|ΚΡΟΚΑΛΟΠ|ΣΙΔΗΡΟΠ|Λ|ΝΑΥ|ΟΥΛΑΜ|ΟΥΡ|Π|ΤΡ|Μ)$/.test(o[1])||/(ΟΦ|ΠΕΛ|ΧΟΡΤ|ΛΛ|ΣΦ|ΡΠ|ΦΡ|ΠΡ|ΛΟΧ|ΣΜΗΝ)$/.test(o[1])&&!/^(ΨΟΦ|ΝΑΥΛΟΧ)$/.test(o[1])||/(ΚΟΛΛ)$/.test(o[1]))&&(s+="ΑΓ")),null!==(o=/^(.+?)(ΗΣΕ|ΗΣΟΥ|ΗΣΑ)$/.exec(s))&&(s=o[1],/^(Ν|ΧΕΡΣΟΝ|ΔΩΔΕΚΑΝ|ΕΡΗΜΟΝ|ΜΕΓΑΛΟΝ|ΕΠΤΑΝ|Ι)$/.test(o[1])&&(s+="ΗΣ")),null!==(o=/^(.+?)(ΗΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΣΒ|ΣΒ|ΑΧΡ|ΧΡ|ΑΠΛ|ΑΕΙΜΝ|ΔΥΣΧΡ|ΕΥΧΡ|ΚΟΙΝΟΧΡ|ΠΑΛΙΜΨ)$/.test(o[1])&&(s+="ΗΣΤ")),null!==(o=/^(.+?)(ΟΥΝΕ|ΗΣΟΥΝΕ|ΗΘΟΥΝΕ)$/.exec(s))&&(s=o[1],/^(Ν|Ρ|ΣΠΙ|ΣΤΡΑΒΟΜΟΥΤΣ|ΚΑΚΟΜΟΥΤΣ|ΕΞΩΝ)$/.test(o[1])&&(s+="ΟΥΝ")),null!==(o=/^(.+?)(ΟΥΜΕ|ΗΣΟΥΜΕ|ΗΘΟΥΜΕ)$/.exec(s))&&(s=o[1],/^(ΠΑΡΑΣΟΥΣ|Φ|Χ|ΩΡΙΟΠΛ|ΑΖ|ΑΛΛΟΣΟΥΣ|ΑΣΟΥΣ)$/.test(o[1])&&(s+="ΟΥΜ")),null!=(o=/^(.+?)(ΜΑΤΟΙ|ΜΑΤΟΥΣ|ΜΑΤΟ|ΜΑΤΑ|ΜΑΤΩΣ|ΜΑΤΩΝ|ΜΑΤΟΣ|ΜΑΤΕΣ|ΜΑΤΗ|ΜΑΤΗΣ|ΜΑΤΟΥ)$/.exec(s))&&(s=o[1]+"Μ",/^(ΓΡΑΜ)$/.test(o[1])?s+="Α":/^(ΓΕ|ΣΤΑ)$/.test(o[1])&&(s+="ΑΤ")),null!==(o=/^(.+?)(ΟΥΑ)$/.exec(s))&&(s=o[1]+"ΟΥ"),n.length===s.length&&null!==(o=/^(.+?)(Α|ΑΓΑΤΕ|ΑΓΑΝ|ΑΕΙ|ΑΜΑΙ|ΑΝ|ΑΣ|ΑΣΑΙ|ΑΤΑΙ|ΑΩ|Ε|ΕΙ|ΕΙΣ|ΕΙΤΕ|ΕΣΑΙ|ΕΣ|ΕΤΑΙ|Ι|ΙΕΜΑΙ|ΙΕΜΑΣΤΕ|ΙΕΤΑΙ|ΙΕΣΑΙ|ΙΕΣΑΣΤΕ|ΙΟΜΑΣΤΑΝ|ΙΟΜΟΥΝ|ΙΟΜΟΥΝΑ|ΙΟΝΤΑΝ|ΙΟΝΤΟΥΣΑΝ|ΙΟΣΑΣΤΑΝ|ΙΟΣΑΣΤΕ|ΙΟΣΟΥΝ|ΙΟΣΟΥΝΑ|ΙΟΤΑΝ|ΙΟΥΜΑ|ΙΟΥΜΑΣΤΕ|ΙΟΥΝΤΑΙ|ΙΟΥΝΤΑΝ|Η|ΗΔΕΣ|ΗΔΩΝ|ΗΘΕΙ|ΗΘΕΙΣ|ΗΘΕΙΤΕ|ΗΘΗΚΑΤΕ|ΗΘΗΚΑΝ|ΗΘΟΥΝ|ΗΘΩ|ΗΚΑΤΕ|ΗΚΑΝ|ΗΣ|ΗΣΑΝ|ΗΣΑΤΕ|ΗΣΕΙ|ΗΣΕΣ|ΗΣΟΥΝ|ΗΣΩ|Ο|ΟΙ|ΟΜΑΙ|ΟΜΑΣΤΑΝ|ΟΜΟΥΝ|ΟΜΟΥΝΑ|ΟΝΤΑΙ|ΟΝΤΑΝ|ΟΝΤΟΥΣΑΝ|ΟΣ|ΟΣΑΣΤΑΝ|ΟΣΑΣΤΕ|ΟΣΟΥΝ|ΟΣΟΥΝΑ|ΟΤΑΝ|ΟΥ|ΟΥΜΑΙ|ΟΥΜΑΣΤΕ|ΟΥΝ|ΟΥΝΤΑΙ|ΟΥΝΤΑΝ|ΟΥΣ|ΟΥΣΑΝ|ΟΥΣΑΤΕ|Υ||ΥΑ|ΥΣ|Ω|ΩΝ|ΟΙΣ)$/.exec(s))&&(s=o[1]),null!=(o=/^(.+?)(ΕΣΤΕΡ|ΕΣΤΑΤ|ΟΤΕΡ|ΟΤΑΤ|ΥΤΕΡ|ΥΤΑΤ|ΩΤΕΡ|ΩΤΑΤ)$/.exec(s))&&(/^(ΕΞ|ΕΣ|ΑΝ|ΚΑΤ|Κ|ΠΡ)$/.test(o[1])||(s=o[1]),/^(ΚΑ|Μ|ΕΛΕ|ΛΕ|ΔΕ)$/.test(o[1])&&(s+="ΥΤ")),s}var 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sarò se sei si sia siamo siano siate siete sono sta stai stando stanno starai staranno starebbe starebbero starei staremmo staremo stareste staresti starete starà starò stava stavamo stavano stavate stavi stavo stemmo stesse stessero stessi stessimo steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua sue sugl sugli sui sul sull sulla sulle sullo suo suoi ti tra tu tua tue tuo tuoi tutti tutto un una uno vi voi vostra vostre vostri vostro è".split(" ")),e.Pipeline.registerFunction(e.it.stopWordFilter,"stopWordFilter-it")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.es.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Spanish` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,s){"function"==typeof define&&define.amd?define(s):"object"==typeof exports?module.exports=s():s()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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-------------------------------------------------------------------------------- 1 | 41 | 42 | 43 | [![GitHub stars](https://img.shields.io/github/stars/Joining-AI/LLM_Interview_Prepare?style=social)](https://github.com/Joining-AI/LLM_Interview_Prepare) 44 | 45 | ### 1.1 写在前面 46 | 47 |
48 | 为什么要为Diffusion单独开辟一个部分 49 | 首先，将Diffusion部分纳入“大模型面经”这一篇章的主要考量，首先是其同属于“AIGC”这一领域，同时，是其模型参数量和训练需求（硬件需求如GPU，服务器等，数据需求如良好标注的图片，音频，或是视频数据集）都已经达到了所谓“大模型”的门槛，一如之前介绍的那样。 50 | 51 | 这些需求都已经达到了高校难以企及的高度（both of them）。目前所有国内高校实验室，均没有能力复现StableDiffusion或是Sora，可灵这样的爆款，亦或是Open-Sora这样的开源项目。国内高校实验室的算力和数据储备，充其量完成一个基于特定模型的模块添加工作（如AnimateDiff），或是对于其某个能力的分析，对于一个较小模型的训练（绝大多数的论文）。究其原因，是由于Diffusion相关的工程结果可以快速商业化变现，变现速度甚至强于LLM。因而无论企业界还是学术界，哪怕是政界都很在关注这方面的研究，当下图像和音频扩散的理论研究已经研究的大差不差了。悲观地来讲，当下期待从实验室科研中获得真正上手实操的，SOTA级别的全流程图像或视频扩散模型的训练是完全不切实际的。 52 | 53 | 如果想要真正的上手实操，可以考虑自主复现AudioLDM，即Audio Latent Diffusion Model的代码并自行训练（建议使用luchen云之类的云服务器，或是可以参加像钱彦旻老师这样的课题组来获得相关经历）。这是由于音频相对显存要求较低，并且相关研究已经开展很久，数据集易采集且详尽。 54 | 55 | 其次想说的点是，请不要将“使用过Stable Diffusion 的ComfyUI”或是对Diffusion原理有一些粗浅理解就宣称掌握了Diffusion，哪怕是真的大量使用过SD，真的“很有prompt使用心得”，真的“很了解其数学概念”，或是上了哪边的“AI使用课程”。这些“自信”会给前沿开发者或是科研人员带来强烈不适。当然，有相关使用经验是一件锦上添花的事情，但也请务必扎根具体原理和代码给出更务实且严谨，有深度的理解。 56 |
57 | 58 | 接下来是Diffusion相关原理部分。 59 | 60 | ### 1.2 各类 Diffusion 模型原理篇 61 | 62 | 请注意，Diffusion模型是一个数学上很好理解的模型，这里只提供部分的常见内容及其较浅的讲解。深度理解建议查看原论文。 63 | 64 | 1.2.1 DDPM算法原理部分： 65 |
66 | 简述DDPM的算法流程： 67 | 68 | 1. 初始化：一般指去噪（模型推理）过程的初始化，指从带噪声的图像的设定开始（或纯高斯噪声）。 69 | 70 | 2. 正向扩散：逐步向数据添加高斯噪声，直到数据完全转化为无结构的噪声。 71 | $$x_{t+1}=\alpha_{t} x_{t}+\beta_{t} \epsilon_{t}, \ \epsilon_{t} \sim \mathcal{N}(0,\mathcal{I})$$ 72 | $$x_{t}=\bar{\alpha_t} x_{0}+\bar{\beta_t} \epsilon, \ \epsilon \sim \mathcal{N}(0,\mathcal{I})$$ 73 | （关键点理解：(2)式可由(1)式逐步推得；其中，由式子可以看出，下一个时间步 $t$ 时刻的图像$x_{t}$是关于上一步图像$x_{t-1}$的高斯分布，$\alpha_{t}，\beta_{t}$为预设超参，满足$\alpha_{t}+\beta_{t}=1$，同时$\alpha_{t}$递减，$\bar{\alpha_t}$为$\alpha_{0}$到$\alpha_{t}$的累乘结果，一般来讲，存在线性递减或余弦递减两种超参方案，前者为DDPM初始论文的设置，后者为原作者在Improved DDPM论文中的对比设置） 74 | 75 | 3. 反向去噪：通过模型预测并逐渐去掉每一步加入的噪声，还原得到无噪声的图像。 76 | $$p_{\theta}(x_{t-1}|x_{t})=\mathcal{N}(x_{t-1},\mu_{\theta}(x_{t},t),\Sigma_{\theta}(x_{t},t))$$ 77 | （关键点理解：此处，$\theta$代表神经网络，下表意味为“神经网络预测结果”，这里通过神经网络预测上一时刻$x_{t-1}$的高斯分布，预测其均值和方差。而实际上，根据扩散过程的定义，我们可以推导均值为：$$\mu_{\theta}(x_{t},t)=\frac{1}{\sqrt{\alpha_{t}}}(x_{t}-\frac{\beta_{t}}{1-\bar{\alpha_{t}}}\Sigma_{\theta}(x_{t},t))$$，因而这里神经网络本质上是在预测噪声，也是许多人直接成预训练扩散模型神经网络为“预训练去噪器”的一个原因；具体推理过程中，直接将之前对应时刻的图像替换成推理得到的分布中任意抽取结果当作上一时刻真实图像） 78 | 79 | 4. 训练：使用反向传播算法更新模型参数，以最小化正向和反向过程之间的差异，即最小化每一时刻原始正向扩散的分布和反向扩散预测的分布之间的差异，也即每一时刻添加的实际噪声和预测的噪声结果的差异。 80 | 81 | 5. 测试：对新的高噪声图像应用训练好的模型进行去噪。 82 |
83 | 84 |
85 | 实现DDPM需要什么条件？ 86 | 1. 马尔可夫链：DDPM使用马尔可夫链来描述数据的扩散过程。马尔可夫链是一个随机过程，具有无记忆性，即在给定当前状态的情况下，未来的状态只依赖于当前状态。 87 | 这一点可以由上一点中的公式看出，上或下一时刻的图像结果只取决于当前时刻的图像和预设的超参。 88 | 2. 微小变化：DDPM通过逐步添加微小的高斯噪声来扩散数据。这些微小的变化是在数据中引入随机性的关键步骤。这由预设的超参实现。具体来讲，在公式中体现为$\beta_{t}$，即方差规模的大小 89 | 3. 高斯噪声变化：DDPM使用高斯噪声来模拟数据的扩散过程。高斯噪声是一种常见的随机噪声，也称为正态分布噪声。 90 |
91 | 92 |
93 | 为什么DDPM加噪声的幅度是不一致的？ 94 | 前期加噪少是为了保持数据结构的完整性，后期加噪多是为了加速扩散过程，使得模型能够更快地从噪声中恢复出清晰的数据。 95 | 这一点在原论文作者Improved DDPM论文中有具体阐述 96 |
97 | 98 |
99 | DDPM预测噪声还是预测当前分布？ 100 | 由之前推导，本质上预测噪声，预测分布只是中间过程 101 |
102 | 103 | 1.2.2 DDIM算法原理部分： 104 |
105 | DDIM是怎么实现加速采样的？ 106 | 1. 确定性逆扩散 107 | 108 | 在DDPM中，逆扩散过程是随机的。这意味着，在从某个噪声状态 $x_t$ 计算前一状态 $x_{t-1}$ 时，会引入随机噪声。具体来说，这一步涉及从一个条件高斯分布中采样。 109 | DDIM对这一过程做出了修改，去除了随机性，使逆扩散过程完全确定性。在DDIM中，$x_{t-1}$ 的计算直接依赖于 $x_t$ 和一个从网络学习到的噪声预测 $\epsilon_\theta(x_t, t)$。这种改动使得生成过程在给定相同的起始噪声和模型参数的情况下总是产生相同的结果，提高了过程的可重复性。 110 | 111 | 2. 非马尔科夫跳步采样 112 | 113 | DDPM的采样过程是严格遵循时间顺序的，即每个时间步的输出依赖于其直接前一个时间步的状态，形成一个马尔科夫链。而DDIM允许在时间步之间进行更大的跳跃。这意味着可以直接从 $x_t$ 跳到 $x_{t-s}$（其中 $s > 1$），跳过一些中间状态。这种非马尔科夫的跳步采样大大减少了需要计算的总步骤数，从而加快了整个采样过程。 114 | 115 | 3. 加速生成过程 116 | 117 | 由于DDIM中的逆扩散是确定性的，并且支持跳步采样，它能显著加快生成过程。在实际应用中，如实时图像生成和视频处理等场景，这种加速非常有价值。 118 | 119 | 4. 牺牲了一定的多样性 120 | 121 | 虽然DDIM在效率和确定性方面有所增加，但这是以牺牲一定的输出多样性为代价的。在DDPM中，随机采样帮助探索多种可能的生成路径，从而增加了生成数据的多样性。DDIM的确定性路径意味着对于同一起始噪声，输出将总是相同的，这限制了模型输出的多样性。 122 | 123 | 5. 理论上的调整 124 | 125 | 在理论上，DDIM还通过对扩散和逆扩散方程的重新参数化（例如，通过改变噪声水平的方程）使其更适合非马尔科夫跳步采样和确定性逆扩散。这些改动涉及数学上的深入调整，以确保模型即使在大步长跳跃的情况下也能保持高质量的输出。 126 |
127 | 128 |
129 | DDIM是不是确定性生成，为什么？ 130 | 是确定性⽣成。因为在逆向去噪声过程中，DDIM的逆推公式，将随机噪声的部分置为0。具体来讲，原先生成过程相当于在得到每一步结果后基于该时间步的分布会进行一次随机采样，改进后取消了该随机采样，因而对于预训练完成的模型来讲，对于给定时间$t$和图像$x_{t}$，生成的指定时间步图像固定 131 |
132 | 1.2.3 Score-Based-diffusion-model： 133 | 134 |
135 | 提供了什么解释扩散模型的等价方式？ 136 | - 提供了⼀种解释扩散模型的等价⽅式，其中降噪过程可以看作是沿着分数（梯度）前进。 137 | - 这也是本整理者比较推崇的一种扩散模型解释。可以理解为，预训练扩散模型是一张导引地图，指示旅行者寻找路径攀上高山（概率高峰）的导引图 138 |
139 | 1.2.4 高阶采样方案： 140 |
141 | 是否了解DPM++等加速采样方案？ 142 | "通过ODE对扩散模型进行建模，通过解析解的形式解构扩散模型求解步骤" 143 |
144 | 145 | 1.2.5 特征编码篇： 146 | 147 |
148 | 介绍一下CLIP编码？ 149 | "构建大规模的图像-文本数据构建（文本，图像）pair对，在其他下游子任务中取得极高的zero-shot指标" 150 |
151 | 152 |
153 | CLIP编码特征的优缺点？ 154 | 优点：泛化性能强，特征在同⼀空间下衡量，模型简单不需要额外训练。 155 | 156 | 缺陷：⽂本描述简单“A photo of a xxx”，图⽂理解能⼒偏弱 157 |
158 | 159 |
160 | 介绍一下BLIP/BLIP2的原理？ 161 | BLIP：通过多路损失函数，以及图像分快理解策略等算法，构建⾼质量的图像理解模型。 162 | 163 | BLIP2：在BLIP基础上，利用Q-Former构建图像与⼤语⾔模型之间的桥梁，充分利⽤⼤语⾔模型⾃身的预训练能⼒ 164 |
165 | 166 |
167 | 为什么BLIP/BLIP2的特征没法直接用？ 168 | "因为受到图文一致性等隐形损失约束，相关特征不再同一个特征空间下（无法直接用距离衡量图文特征的相似性）。因此无法像CLIP一样“直接”接入模型中使用" 169 |
170 | 171 | 1.2.6 Stable Diffusion篇： 172 | 173 |
174 | Stable Diffusion的核心优化是什么？ 175 | "通过VAE将特征映射到Latent Space，大幅减少运算量的同时还能保证生成质量。通过Unet实现对生成内容的引导" 176 |
177 | 178 |
179 | Stable Diffusion是怎么训练的？ 180 | 从训练集中选取一张加噪过的图片和噪声强度 181 | 输入unet，让unet预测噪声图 182 | 计算和真正的噪声图之间的误差 183 | 通过反向传播更新unet的参数 184 | 185 |
186 | 187 |
188 | VAE为什么会导致图像变模糊？ 189 | "VAE编解码整体是一个有损过程，可以选择减少损失，比如优化模型结构，提升采样效率等。完全不减少损失的方案就是原图反贴" 190 |
191 | 192 |
193 | 介绍一下SD，Dall-E2两者的异同？ 194 | Dalle2通过自回归的方式逐个预测像素点，最终生成符合描述的图像。 195 | 196 | SD加⼊了Latent-Space（⼤幅降低特征维度），以及交叉注意⼒机制+Unet的步骤，更精细更可控 197 |
198 | 199 |
200 | 介绍一下classifier-free guidance和Classifier Guidance？ 201 | Classifier Guidance的一般流程如下：首先单独预训练一个噪声鲁棒的分类器模型。然后训练一个普通的无条件Diffusion模型。Diffusion模型生成图像的采样过程中,利 用预训练好的分类器来提供条件信号。具体来说,就是每个采样步骤都计算分类器的输 出,获得条件影响项,加入到Diffusion模型的更新公式中。这样就可以利用分类器的条 件信号,实现Diffusion模型在推理阶段条件生成图像的目的。 202 | 203 | Classifier-Free Guidance 中，⽣成模型不仅仅学习如何根据给定的条件⽣成数据，⽽且还学习如何在没有任何条件输⼊的情况下⽣成数据。换句话说，模型既能进⾏条件⽣成，也能进⾏⽆条件⽣成。CFG的训练过程其实就是对提供的条件输入做随机的dropout，这样就可以得到一个无条件和条件提示的两个输出，然后学习二者之间的方向差指导采样过程。在⽣成过程中，Classifier-Free Guidance 允许我们在没有显式使⽤分类器或判别器的情况下调节条件⽣成的强度。这是通过“调节”（或“混合”）条件⽣成和⽆条件⽣成的输出来实现的，以此来控制⽣成内容的相关性和多样性 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) 204 | guidance scale是一个放缩系数，越大，生成的结果越倾向于输入条件，多样性会下降。 越小，多样性越大。 205 |
206 | 207 |
208 | Stable Diffusion怎么实现文本和图像的条件控制？ 209 | "文本/图像编码器将文本/图像信息编码，然后通过交叉注意力机制将信息引入扩散模型。SD 的 U-Net 既用到了自注意力，也用到了交叉注意力。自注意力用于图像特征自己内部信息聚合。交叉注意力用于让生成图像对齐文本，其 Q 来自图像特征，K, V 来自文本编码" 210 |
211 |
212 | 扩散模型添加时间步timestep信息？ 213 | "通过类似于Transformer中的位置编码方法，将常数转换为向量并添加到输入图像中" 214 |
215 | 216 |
217 | Noise Scheduler了解吗？ 218 | "Noise Scheduler定义了一个高斯分布，其均值和方差随着时间步的变化而变化，以控制噪声的添加量" 219 |
220 | 221 |
222 | Stable Diffusion核心模块有哪些？ 223 | VAE：将图像特征/⽂本特征，映射到Latent Space。 224 | LDM相关：Diffusion Model +Unet，去噪声核⼼步骤 225 | Conditioning：作⽤于Unet的 Cross-Attention位置，实现对输出结果的控制 226 | 227 |
228 | 229 |
230 | 为什么原生SD的控制效果不太好，需要引入如ControlNet的控制模型？ 231 | "因为控制是一个隐性控制模型，通过CrossAttention的权重隐性引导生成结果，并不是完全控制" 232 |
233 | 234 | 1.2.7 SDXL篇： 235 |
236 | SDXL的核心优化？ 237 | 接⼊级联的refiner模型+微调⽹络结构，⼤幅度提升⽣成质量。 238 | 多样化的训练策略，⼤幅提升基础模型表达能⼒ 239 |
240 | 241 |
242 | SDXL的训练策略？ 243 | 244 | 图像尺⼨条件化：把图像的尺⼨编码后作为信息输⼊到模型中。 245 | 裁剪参数化训练：裁剪坐标也和尺⼨⼀样送⼊模型中。 246 | 多尺度训练：多尺度+分桶 247 | 噪声偏置：针对冷⻔⾊域，加⼊初始化噪声偏置 248 |
249 | 250 | 1.2.8 Diffusion模型微调篇： 251 | 252 | - Lora： 253 | 254 | 核⼼解读关键词：低秩展开，即插即⽤ 255 | 通过矩阵低秩展开，使⽤“外接”低秩展开后的⽹络对原模型进⾏更新 256 | 257 | - Lora有没有什么优化方案？ 258 | 259 | Locon/loha，分别进⾏细节质量和速度存储空间的优化 260 | 261 | - DreamBooth： 262 | 263 | 核⼼解读关键词：正则化微调整个⽹络，训练数据混合 264 | 因为使⽤正则化，只在预训练⽹络上微调某类特定的case。 所以速度反⽽⽐Lora快得多 265 | 266 | 1.2.9 Textual Inversion（知识点）： 267 | 268 | - 关键词：⽂本embedding，Transformer 269 | - 核⼼总结：通过对Embedding层的特殊编码，实现通过不同输⼊⽂本，来影响模型最终的⽣成结果。影响的是Embedding的部分 270 | - 首先需要定义一个在现有模型中没有的关键词，新的关键词会和其他的关键词一样，生成Tokenizer(用不同的数字表示)；然后将其转换为embedding；text transformer会映射出对于新给的关键词最好的embedding向量。不用改变模型，可以看作在模型中寻找新的表征来表示新的关键字 271 | 272 | 273 | 1.2.10 Lora/Dreambooth/Textual Inversion，核心差异点： 274 | - Lora： 275 | "是小模型即插即用微调。" 276 | - Dreambooth： 277 | "大模型特化全量微调" 278 | - Textual Inversion： 279 | "Text-embedding 编码修改" 280 | 281 | 1.2.11 控制模型篇： 282 | 283 | 284 | 285 |
286 | 介绍一下ControlNet的核心原理？ 287 | 复制原⽣Unet⼀样的模型结构，前半部分encoder训练，后半部分⽤Zero Convolution 承接，decoder部分接⼊到模型Unet的⽹络层中 288 | “Zero Convolution”即零卷积：是带有零初始化权重和偏差的1×1卷积。在进⾏⾃⼰的模型训练开始之前，所有零卷积输出都是零，此时模型仍然是原始的Stable Diffusion Model 289 |
290 | 291 |
292 | T2I Adapter? 293 | 每张条件图片都会别额外编码，编码信息会被加入到 UNET 噪声预测中训练时候，冻结了原先的 unet，只对 Adapter 部分进行单独训练。 294 |
295 | 296 |
297 | IP-Adapter? 298 | IP-Adapter 通过带有解耦交叉注意力的适配模块，将文本特征的 Cross-Attention 和图像特征的 Cross-Attention 区分开来，在 Unet 的模块中新增了一路 Cross-Attention 模块，用于引入图像特征。 299 |
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