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Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fbigcode-evaluation-harness\u002FSKILL.md",[260],{"categoryNo":57,"categoryName":60,"taxonomyType":58,"taxonomyTypeDesc":59},[262,263,239,264,265,266,267,216,268,269,270,271,272,217,273,274,223,246,275,253],"code","Code Models","evaluating code models","testing","support","benchmarking","Evaluation","Code Generation","HumanEval","MBPP","MultiPL-E","across","project","multipl",{"skillNo":277,"name":278,"nameZh":18,"description":279,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":280,"categories":281,"keywords":282,"publishedAt":231},"01M6XG8WGPYQNY83Z6QJC5TYSA","axolotl","Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA\u002FQLoRA, DPO\u002FKTO\u002FORPO\u002FGRPO, multimodal support","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Faxolotl\u002FSKILL.md",[],[283,214,266,284,216,285,286,287,288,289,290,291,292,293,239,294,223,246,295,227],"Axolotl","ml","DeepSpeed","Fine-Tuning","LoRA","QLoRA","DPO","KTO","ORPO","GRPO","HuggingFace","expert","with",{"skillNo":297,"name":298,"nameZh":18,"description":299,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":300,"categories":301,"keywords":302,"publishedAt":231},"01M6XG8NFK7DB41MHT0EA0GG2Q","awq-quantization","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fawq\u002FSKILL.md",[],[303,304,305,242,216,306,307,217,308,244,223,247,309,248,310,311,312,227,313,253],"Quantization","AWQ","awq quantization","multimodal","Fast Inference","activation","Activation-Aware","vLLM Integration","Marlin Kernels","you","2024",{"skillNo":315,"name":316,"nameZh":18,"description":317,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":318,"categories":319,"keywords":323,"publishedAt":231},"01M6XG8A1E18JNC2ABCKR7YWHF","huggingface-accelerate","Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed\u002FFSDP\u002FMegatron\u002FDDP. 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Use when deploying production LLM APIs, optimizing inference latency\u002Fthroughput, or serving models with limited GPU memory. 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Use for production deployment on NVIDIA GPUs (A100\u002FH100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8\u002FINT4), in-flight batching, and multi-GPU scaling.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Ftensorrt-llm\u002FSKILL.md",[],[364,365,326,284,366,243,349,367,350,368,351,369,352,223,370,354,312,227,356,253],"tensorrt llm","tensorrt","deployment","NVIDIA","llm inference","scaling","H100",{"skillNo":372,"name":373,"nameZh":18,"description":374,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":375,"categories":376,"keywords":377,"publishedAt":231},"01M6XG7R5MTT7Q27W304C3YVWQ","speculative-decoding","Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. 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Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fspeculative-decoding\u002FSKILL.md",[],[378,307,217,379,380,381,382,383,384,223,246,385,386,387,388,389,390,391,392,253],"speculative decoding","parallel","time","strategies","attention","token","iteration","Emerging Techniques","medusa","Lookahead Decoding","Draft Models","Tree Attention","Parallel Generation","Latency Reduction","tree",{"skillNo":394,"name":395,"nameZh":18,"description":396,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":397,"categories":398,"keywords":399,"publishedAt":231},"01M6XG7J6G36G5BSJZKWK848HG","sglang","Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON\u002Fregex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fsglang\u002FSKILL.md",[],[400,401,214,215,402,350,403,404,405,406,407,307,408,217,409,410,223,312,411,253],"SGLang","agentic","agentic workflows","Structured Generation","RadixAttention","Prefix Caching","Constrained Decoding","JSON Output","Production Scale","xai","caching","at",{"skillNo":413,"name":414,"nameZh":18,"description":415,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":416,"categories":417,"keywords":418,"publishedAt":231},"01M6XG7D3YNK8PRB38AH89RR98","phoenix-observability","Open-source AI observability platform for LLM tracing, evaluation, and monitoring. 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Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fllama-cpp\u002FSKILL.md",[],[459,460,461,242,326,367,350,462,244,223,463,464,465,466,467,468,469,470,471,253],"cpp","Llama","llama cpp","apple","silicon","CPU","GGUF","llama.cpp","CPU Inference","Edge Deployment","Non-NVIDIA","AMD GPUs","Intel GPUs",{"skillNo":473,"name":474,"nameZh":18,"description":475,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":476,"categories":477,"keywords":481,"publishedAt":231},"01M6XG6XB6A0C6TBYDVXERH6WD","groq-inference","Ultra-fast LLM inference on custom LPU hardware. OpenAI-compatible API at api.groq.com. Lowest latency in the industry (500-1000+ tok\u002Fs). Supports chat completions, vision, audio (Whisper STT + TTS), tool calling, JSON mode, and streaming. Free tier available. Inference only — no training.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fgroq\u002FSKILL.md",[478],{"categoryNo":479,"categoryName":480,"taxonomyType":11,"taxonomyTypeDesc":12},"01JBSEEDCAT000000000000016","图像与多媒体",[482,483,484,216,125,485,328,486,487,488,221,489,490,222,223,491,411,492,493,494],"inference","groq","groq inference","streaming","Audio","only","whisper","free-tier","stt","mode","ultra","LPU","Low-Latency",{"skillNo":496,"name":497,"nameZh":18,"description":498,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":499,"categories":500,"keywords":501,"publishedAt":231},"01M6XG6NRWCNZ4JMFS10EXCQZK","gguf-quantization","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fgguf\u002FSKILL.md",[],[502,303,465,503,284,459,243,504,505,351,462,244,223,463,464,466,467,506,227,253],"gguf quantization","python","apple silicon","model","Model Compression",{"skillNo":508,"name":488,"nameZh":18,"description":509,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":510,"categories":511,"keywords":513,"publishedAt":231},"01M6XG6H6EM1RJEFBVN8TDEPRR","OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fwhisper\u002FSKILL.md",[512],{"categoryNo":479,"categoryName":480,"taxonomyType":11,"taxonomyTypeDesc":12},[488,125,514,306,217,515,516,486,517,505,518,519,520,521,223,522,523,524,525,227],"speech","ASR","Tools","text","speech recognition","general","multilingual","translation","english","identification","processing","Audio Processing",{"skillNo":527,"name":528,"nameZh":18,"description":529,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":530,"categories":531,"keywords":533,"publishedAt":231},"01M6XG67GRW8ZYF6V099HJGS0F","stable-diffusion-image-generation","State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fstable-diffusion\u002FSKILL.md",[532],{"categoryNo":479,"categoryName":480,"taxonomyType":11,"taxonomyTypeDesc":12},[534,535,536,537,538,539,306,217,516,517,540,351,221,541,542,521,223,246,543,253],"image","Image Generation","Stable Diffusion","stable diffusion image generation","generation","diffusion","computer","images","Art","custom",{"skillNo":545,"name":546,"nameZh":18,"description":547,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":548,"categories":549,"keywords":550,"publishedAt":231},"01M6XG630KEY37P1PKK2CEFSR7","sentencepiece","Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences\u002Fsec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fsentencepiece\u002FSKILL.md",[],[546,145,242,266,217,516,517,520,551,552,553,223,554,555,556,557,558,559,312,253],"deterministic","algorithms","cjk","Tokenization","BPE","Unigram","Language-Independent","CJK Languages","Unicode",10821,18]