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Learning","资料检索、知识整理与学习辅导",5531,[],{"records":96,"total":191,"current":11,"size":192},[97,111,120,130,140,150,160,169,179],{"accountNo":98,"login":99,"displayName":100,"avatarUrl":101,"accountType":102,"accountTypeDesc":103,"profileDescription":104,"skillCount":105,"repoCount":106,"professionCount":107,"scenarioCount":108,"totalStars":109,"updatedAt":110},"01M698VYDXZ6JW5WH3CRV650R8","microsoft","Microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","ORGANIZATION","组织","Open source projects and samples from Microsoft",4160,229,11,7,828772,"2026-11-13 04:10:52",{"accountNo":112,"login":113,"displayName":113,"avatarUrl":114,"accountType":102,"accountTypeDesc":103,"profileDescription":115,"skillCount":116,"repoCount":117,"professionCount":108,"scenarioCount":108,"totalStars":118,"updatedAt":119},"01M698P3FSAZ6B4R928ZF19KPT","openclaw","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F252820863?v=4","Your personal, open source AI 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03:57:35",{"accountNo":161,"login":162,"displayName":163,"avatarUrl":164,"accountType":102,"accountTypeDesc":103,"profileDescription":13,"skillCount":165,"repoCount":166,"professionCount":107,"scenarioCount":108,"totalStars":167,"updatedAt":168},"01M697XGDPXJAVQ8391V2BCYTE","anthropics","Anthropic","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4",674,17,231826,"2026-11-13 04:18:26",{"accountNo":170,"login":171,"displayName":172,"avatarUrl":173,"accountType":102,"accountTypeDesc":103,"profileDescription":13,"skillCount":174,"repoCount":175,"professionCount":176,"scenarioCount":108,"totalStars":177,"updatedAt":178},"01M6AC83EPDGBFN19MWYRREJD0","Significant-Gravitas","AutoGPT","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130738209?v=4",86,2,10,188014,"2026-11-13 02:38:59",{"accountNo":180,"login":181,"displayName":182,"avatarUrl":183,"accountType":184,"accountTypeDesc":185,"profileDescription":186,"skillCount":187,"repoCount":188,"professionCount":127,"scenarioCount":108,"totalStars":189,"updatedAt":190},"01M6DJCCDDG53MTYYRTXAXFG17","garrytan","Garry Tan","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F19957?v=4","USER","个人作者","Writes software, dreams. 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Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-verl\u002FSKILL.md",[],[235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,219,253,254,255,256,257,224,226],"training","post training verl","post","verl","llms","research","distributed","infrastructure","reinforcement learning","Distributed Training","GRPO","Post-Training","RLHF","PPO","Ray","vLLM","Transformers","learning","algorithms","volcano","at","orchestra","implementing",{"skillNo":259,"name":260,"nameZh":13,"description":261,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":262,"categories":263,"keywords":264,"publishedAt":227},"01M6ADXBMNCAKKMJ6JJ6RA2N2Y","post-training-trl-fine-tuning","Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO\u002FGRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-trl-fine-tuning\u002FSKILL.md",[],[235,265,266,267,268,237,269,239,240,243,270,271,272,245,273,246,247,248,251,252,274,275,276,277,278,279,256,224,226],"post training trl fine tuning","tuning","fine tuning","trl fine tuning","TRL","Optimization","Fine-Tuning","DPO","HuggingFace","model","feedback","alignment","align","human","Accelerate",{"skillNo":281,"name":282,"nameZh":13,"description":283,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":284,"categories":285,"keywords":286,"publishedAt":227},"01M6ADX8539513BS9D54WB9NSA","post-training-torchforge","Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-torchforge\u002FSKILL.md",[],[235,287,237,288,289,240,290,243,291,245,250,292,252,219,293,294,253,295,296,297,298,256,224,226],"post training torchforge","torchforge","agentic","pytorch","library","Meta","clean","infra","experimentation","Scalable","native","you",{"skillNo":300,"name":301,"nameZh":13,"description":302,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":303,"categories":304,"keywords":305,"publishedAt":227},"01M6ADX5CYH29W8G571Q5R977P","post-training-slime","Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-slime\u002FSKILL.md",[],[235,306,307,237,240,308,243,309,310,245,311,312,246,249,251,252,313,314,315,316,317,256,257,224,226],"post training slime","slime","workflows","framework","integration","SGLang","data","router","scaling","glm","megatron","Megatron-LM",{"skillNo":319,"name":320,"nameZh":13,"description":321,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":322,"categories":323,"keywords":324,"publishedAt":227},"01M6ADX2HXZADQ3TX12C0NRH3C","post-training-simpo","Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO\u002FPPO.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-simpo\u002FSKILL.md",[],[235,325,237,326,327,240,270,272,246,248,251,274,276,328,279,269,329,330,331,332,333,256,224,226],"post training simpo","simpo","performance","free","Efficient Training","Preference Optimization","DPO Alternative","Reference-Free","LLM Alignment",{"skillNo":335,"name":336,"nameZh":13,"description":337,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":338,"categories":339,"keywords":340,"publishedAt":227},"01M6ADWYWFHK3QG4VHVT8Z2GYN","post-training-openrlhf","High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-openrlhf\u002FSKILL.md",[],[235,341,237,327,342,241,309,343,344,244,345,272,245,246,346,247,248,347,249,250,348,349,251,219,350,256,224],"post training openrlhf","architecture","gpu","DeepSpeed","ZeRO","OpenRLHF","RLOO","Large Models","ZeRO-3","high-performance",{"skillNo":352,"name":353,"nameZh":13,"description":354,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":355,"categories":356,"keywords":357,"publishedAt":227},"01M6ADWW5KWF3GEX61ANT8DX6Z","post-training-miles","Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8\u002FINT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-miles\u002FSKILL.md",[],[235,358,237,359,360,240,243,361,362,311,251,252,313,363,276,307,316,317,364,365,256,224,226],"post training miles","miles","inference","FP8","Enterprise","ready","throughput","production-ready",{"skillNo":367,"name":368,"nameZh":13,"description":369,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":370,"categories":371,"keywords":373,"publishedAt":227},"01M6ADWS8GZAYZS4Z1KM8M4W28","post-training-grpo-rl-training","Expert guidance for GRPO\u002FRL fine-tuning with TRL for reasoning and task-specific model training","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-grpo-rl-training\u002FSKILL.md",[372],{"categoryNo":212,"categoryName":213,"taxonomyType":53,"taxonomyTypeDesc":54},[235,245,374,375,376,237,240,377,243,271,272,246,247,248,251,266,252,274,378,379,380,269,381,256,224],"post training grpo rl training","rl","grpo rl training","reasoning","expert","modeling","structured output","Reward Modeling",{"skillNo":383,"name":384,"nameZh":13,"description":385,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":386,"categories":387,"keywords":388,"publishedAt":227},"01M6ADWQ7458WJ8Q2TAGKT9ANE","perplexity","Web search and research using Perplexity AI. Use when user says \"search\", \"find\", \"look up\", \"ask\", \"research\", or \"what's the latest\" for generic queries. NOT for library\u002Fframework docs (use Context7) or workspace questions.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fperplexity\u002FSKILL.md",[],[384,389,240,390,309,391,291,392,393,394,395,219,396,397,398,399,400,401,224,402,226],"docs","web","search","web search","workspace","find","ask","up","look","queries","context7","questions","latest","generic",{"skillNo":404,"name":405,"nameZh":13,"description":406,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":407,"categories":408,"keywords":410,"publishedAt":227},"01M6ADWN4GFACVYTPDTENM8DW1","parallel-agents","Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fparallel-agents\u002FSKILL.md",[409],{"categoryNo":212,"categoryName":213,"taxonomyType":53,"taxonomyTypeDesc":54},[411,412,413,414,415,416,417,418,419,420,219,421,422,423,424,425,426,427,428,226],"parallel agents","parallel","orchestration","tasks","run","analysis","multi-agent","grep","perspectives","read","agent orchestration","patterns","domain","orchestration patterns","multi","with","can","expertise",{"skillNo":430,"name":431,"nameZh":13,"description":432,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":433,"categories":434,"keywords":435,"publishedAt":227},"01M6ADWJDRXM66AQV06HFPH9C0","optimization-hqq","Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4\u002F3\u002F2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-hqq\u002FSKILL.md",[],[270,436,437,239,360,438,240,308,439,440,273,312,250,251,274,219,441,442,443,444,426,256,224,445,226],"optimization hqq","HQQ","memory","compression","fast","efficiency","bit","Memory Efficiency","Model Compression","calibration",{"skillNo":447,"name":448,"nameZh":13,"description":449,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":450,"categories":451,"keywords":452,"publishedAt":227},"01M6ADWF4296WM8ANSE76BHK7B","optimization-gptq","Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with \u003C2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-gptq\u002FSKILL.md",[],[270,453,454,239,360,438,240,235,440,455,271,456,457,384,251,266,458,442,459,460,461,462,298,256,224,226],"optimization gptq","GPTQ","FP16","QLoRA","Fast Inference","auto","4-Bit","Memory Optimization","Consumer GPUs","Group-Wise Quantization",{"skillNo":464,"name":465,"nameZh":13,"description":466,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":467,"categories":468,"keywords":469,"publishedAt":227},"01M6ADWC4YXGBS4N7JY15XVV6K","optimization-gguf","GGUF format and llama.cpp quantization for efficient CPU\u002FGPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-gguf\u002FSKILL.md",[],[270,470,471,472,360,240,473,343,439,474,475,274,219,476,477,442,478,479,480,481,444,256,224,226],"optimization gguf","GGUF","python","cpp","Llama","apple silicon","requirements","apple","silicon","CPU","llama.cpp","CPU Inference",{"skillNo":483,"name":484,"nameZh":13,"description":485,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":486,"categories":487,"keywords":488,"publishedAt":227},"01M6ADW93TNC5SDS1V5WA88D80","optimization-flash-attention","Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training\u002Frunning transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-flash-attention\u002FSKILL.md",[],[270,489,490,491,492,360,438,240,290,235,493,343,291,361,251,441,494,443,495,496,497,297,498,226,499],"optimization flash attention","flash","attention","Flash Attention","issues","Attention Optimization","Speed Optimization","SDPA","H100","window","sequences",{"skillNo":501,"name":502,"nameZh":13,"description":503,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":504,"categories":505,"keywords":506,"publishedAt":227},"01M6ADW6MND5FMSFHS8857DAJ8","optimization-bitsandbytes","Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-bitsandbytes\u002FSKILL.md",[],[270,507,508,239,360,438,240,235,343,456,273,251,442,509,279,459,460,510,511,512,513,256,224,514,226],"optimization bitsandbytes","Bitsandbytes","fit","8-Bit","NF4","INT8","formats","accuracy",{"skillNo":516,"name":517,"nameZh":13,"description":518,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":519,"categories":520,"keywords":524,"publishedAt":227},"01M6ADW3VQ35HSXGH7R3JZRK0V","optimization-awq","Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Foptimization-awq\u002FSKILL.md",[521],{"categoryNo":522,"categoryName":523,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000021","资料检索与研究",[270,525,526,360,438,240,343,527,310,439,440,457,251,528,442,459,529,460,530,531,454,298,256,224,532,226],"optimization awq","AWQ","multimodal","activation","Activation-Aware","vLLM Integration","Marlin Kernels","2024",{"skillNo":534,"name":535,"nameZh":13,"description":536,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":537,"licenseFileUrl":538,"sourceHtmlUrl":539,"categories":540,"keywords":542,"publishedAt":227},"01M6ADW0RKFMBFVS1FM712QWVS","openai-docs","Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations (for example: Codex, Responses API, Chat Completions, Apps SDK, Agents SDK, Realtime, model capabilities or limits); prioritize OpenAI docs MCP tools and restrict any fallback browsing to official OpenAI domains.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fopenai-docs\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fopenai-docs\u002FSKILL.md",[541],{"categoryNo":212,"categoryName":213,"taxonomyType":53,"taxonomyTypeDesc":54},[389,543,122,544,545,546,547,548,549,550,551,552,553,554,555,556,557,274,396,558,559,560,561,562,226],"openai docs","MCP","agents sdk","codex","sdk","official","documentation","realtime","capabilities","mcp tools","responses api","Citations","api","how","prioritize","apps","how to","domains","browsing","limits",187,18]