[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"creator:01M6BY4NHMZF88PJAC1E1QJY8N":3,"creator-list:01M6BY4NHMZF88PJAC1E1QJY8N:stars:1:":18},{"accountNo":4,"login":5,"displayName":5,"avatarUrl":6,"accountType":7,"accountTypeDesc":8,"profileDescription":9,"skillCount":10,"repoCount":11,"professionCount":12,"scenarioCount":11,"totalStars":13,"updatedAt":14,"htmlUrl":15,"blogUrl":9,"company":9,"location":9,"followersCount":16,"publicReposCount":17},"01M6BY4NHMZF88PJAC1E1QJY8N","google-ai-edge","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F150697620?v=4","ORGANIZATION","组织",null,10,1,0,494,"2026-11-13 14:36:51","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge",4503,16,{"records":19,"total":10,"current":11,"size":186},[20,41,64,94,111,130,143,159,169,178],{"skillNo":21,"name":22,"nameZh":9,"description":23,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":27,"categories":28,"keywords":29,"publishedAt":40},"01M6BY5T6RQENC1DMWZ8NZ8JFF","on-device-verification","Prove a converted or quantized LiteRT model on the actual device via the CompiledModel API - confirm GPU residency, compare device output against the source model, and diagnose device-only failures such as silent CPU fallback, whole-graph compile ceilings, and fp16 range breaks. Use after conversion or quantization, when device output is wrong or NaN, when a clean graph fails to compile only on device, or when GPU and CPU outputs are suspiciously identical.","google-ai-edge\u002Flitert-samples","01M6BY4NNXP8PC95Y6CABCJXP9","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Fon-device-verification\u002FSKILL.md",[],[30,31,32,33,34,35,36,37,38,39],"verification","on device verification","gpu","litert","model","diagnose","compile","FP16","api","graph","2026-11-10 10:12:30",{"skillNo":42,"name":43,"nameZh":9,"description":44,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":45,"categories":46,"keywords":47,"publishedAt":40},"01M6BY5F447DRN3YDJVRWDG9MZ","litert-google-tensor","Compiles LiteRT (.tflite) models ahead-of-time for Google Tensor TPUs (Tensor G5 on Pixel 10, Tensor G6 on Pixel 11) locally on Linux x86_64 or remotely via Google Colab (--colab on macOS, Windows, or ARM hosts), benchmarks them on a USB-attached Pixel with LiteRT's benchmark_model (NPU latency, peak memory, TPU tile time, TPU energy, optional CPU baseline), and packages compiled models for an Android Gradle app as a Google Play AI pack with the Tensor NPU runtime module and a Kotlin loader, applies it to the app and verifies it on a Pixel. Use when the user asks to compile, AOT-compile, or prepare a .tflite model for the Pixel TPU\u002FNPU, check whether a .tflite is already Tensor-compiled, measure on-device NPU performance of a compiled model, or package or deploy a compiled model into their Android app (or replace the one it ships). Don't use for Qualcomm, MediaTek, or Samsung NPUs, GPU-only benchmarking, LiteRT-LM or LLM (.litertlm) models, on-device JIT compilation, non-Gradle builds, or publishing to Google Play.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Flitert-google-tensor\u002FSKILL.md",[],[33,48,49,50,51,34,52,36,53,54,55,56,57,32,58,59,60,61,62,63],"tensor","litert google tensor","google","windows","memory","performance","linux","kotlin","package","play","android","packages","benchmarking","tpu","baseline","benchmark",{"skillNo":65,"name":66,"nameZh":9,"description":67,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":68,"categories":69,"keywords":75,"publishedAt":40},"01M6BY5BY9H6SDG9GRRTT04EWA","litert-conversion-workflow","Convert a Hugging Face LLM or vision-language model checkpoint into a .litertlm bundle that runs on the LiteRT-LM runtime with verified quality - classify the architecture against known runtime walls, pick the recipe family (dense, reasoning, hybrid SSM, VLM), export, quantize, gate the result against the source model, and publish. Use when converting a new LLM or VLM to LiteRT-LM, when a converted bundle crashes on the first message or dies at engine creation, when a quantized model answers worse than its source, or when deciding whether a model is convertible at all.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Flitert-conversion-workflow\u002FSKILL.md",[70],{"categoryNo":71,"categoryName":72,"taxonomyType":73,"taxonomyTypeDesc":74},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[76,33,77,78,79,34,80,81,82,83,84,85,86,87,88,89,90,91,92,93],"litert conversion workflow","workflow","conversion","publish","pick","architecture","creation","recipe","message","classify","result","at","quantize","export","verified","hugging face","source","face",{"skillNo":95,"name":96,"nameZh":9,"description":97,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":40},"01M6BY5835F3RMMW9KCCZ006JM","litert-compiled-model-migration","Rapidly migrate an Android application from legacy TensorFlow Lite (TFLite) to the modern LiteRT CompiledModel API (2.x) in Open Source GitHub repositories, keeping the TensorFlow Lite Support Library as LiteRT Support (litert-support 2.3.0). Covers buffer reuse, NPU JIT compilation, a reference implementation, and a two-stage verification gate (compile check, then an on-device parity test against a golden output).","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Flitert-compiled-model-migration\u002FSKILL.md",[],[33,34,101,102,36,103,104,105,38,58,30,106,107,108,109,110],"litert compiled model migration","tensorflow","open","open source","migrate","test","github","support","jit","library",{"skillNo":112,"name":113,"nameZh":9,"description":114,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":40},"01M6BY55P8GVX66H0FG8JHXHWF","gpu-clean-conversion","Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Fgpu-clean-conversion\u002FSKILL.md",[],[118,32,119,78,33,34,120,121,122,38,123,124,83,125,126,127,128,129,90,91],"gpu clean conversion","clean","correct","pytorch","convert","back","as","use","output","with","via","CPU",{"skillNo":131,"name":132,"nameZh":9,"description":133,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":134,"categories":135,"keywords":136,"publishedAt":40},"01M6BY52X9KX3VRXQJS622DZH3","compiled-model-app-scaffolding","Build a new Android app (Kotlin, Compose) around a verified LiteRT model using the CompiledModel API - the app architecture, the inference-layer lifecycle rules, model delivery, and the UI traps that masquerade as model bugs. Use when turning a converted and device-verified model into a demo or product app, when an app's inference layer leaks memory or blocks the UI, or when a model that verified clean looks wrong inside an app.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Fcompiled-model-app-scaffolding\u002FSKILL.md",[],[137,34,58,33,138,52,139,55,140,81,38,141,142],"compiled model app scaffolding","product","lifecycle","ui","demo","inference",{"skillNo":144,"name":145,"nameZh":9,"description":146,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":147,"categories":148,"keywords":149,"publishedAt":40},"01M6BY501NESZTA4NSEKX9BSDE","benchmark-on-ddp","Measure a LiteRT .tflite model with `benchmark_model` on Developer Device Platform (DDP) lab phones through `litert benchmark --ddp`, or on the Mac you run it from, and turn the session into rows of the performance leaderboard under benchmark\u002Fleaderboard - one matrix entry per model, one session per accelerator, collect results.pb and runtime_info.pb, rebuild board.json, check the page, commit the data. Use when a model needs latency and memory numbers per platform, device and accelerator, or when a new benchmark_model release means re-measuring the board.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Fbenchmark-on-ddp\u002FSKILL.md",[],[63,150,33,34,52,151,152,153,53,154,155,156,157,158],"benchmark on ddp","page","run","json","collect","release","data","commit","developer",{"skillNo":160,"name":161,"nameZh":9,"description":162,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":163,"categories":164,"keywords":165,"publishedAt":40},"01M6BY4XWDFDMABPEJJ0W74891","accuracy-safe-quantization","Shrink a converted LiteRT model with ai-edge-quantizer (fp16 \u002F int8 \u002F int4) without losing accuracy, verifying parity against the float source after every step. Use when choosing a quantization recipe for a new model, when a quantized model fails to load, degrades on a task benchmark, or degenerates over long generations, or when deciding between dynamic-range, weight-only, and blockwise variants.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Faccuracy-safe-quantization\u002FSKILL.md",[],[166,167,168,33,34,37,63],"accuracy safe quantization","safe","edge",{"skillNo":170,"name":171,"nameZh":9,"description":172,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":40},"01M6BY4VJ9SAKZB6GNJ24Z0BCM","device-info","Say which models, backends and accelerators this app is running and how much memory the device has.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fsamples\u002Flitert\u002Fflutter\u002Fassets\u002Fskills\u002Fdevice-info\u002FSKILL.md",[],[176,177,52],"device info","how",{"skillNo":179,"name":180,"nameZh":9,"description":181,"descriptionZh":9,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":9,"repositoryNo":25,"starsCount":13,"licenseSpdxId":26,"licenseFileUrl":9,"sourceHtmlUrl":182,"categories":183,"keywords":184,"publishedAt":40},"01M6BY4SYVCNFDV3Z64PFAKS86","current-time","Get the current local time, date and day of the week.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fsamples\u002Flitert\u002Fflutter\u002Fassets\u002Fskills\u002Fcurrent-time\u002FSKILL.md",[],[185],"current time",18]