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03:03:50",{"accountNo":151,"login":152,"displayName":153,"avatarUrl":154,"accountType":102,"accountTypeDesc":103,"profileDescription":155,"skillCount":156,"repoCount":157,"professionCount":107,"scenarioCount":108,"totalStars":158,"updatedAt":159},"01M6AC83DS133TPXFSP1DHNCC6","github","GitHub","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","How people build software.",940,28,238688,"2026-11-14 04:13:45",{"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",674,17,232203,"2026-11-14 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Supports HuggingFace, vLLM, APIs.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Flm-evaluation-harness\u002FSKILL.md",[],[212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231],"llms","harness","evaluating llms harness","benchmarking","training","reporting","Evaluation","HumanEval","MMLU","vLLM","Transformers","model","eval","use","across","synthetic","progress","LM Evaluation Harness","GSM8K","when","2026-11-10 10:12:30",{"skillNo":234,"name":235,"nameZh":13,"description":236,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":237,"categories":238,"keywords":240,"publishedAt":232},"01M6XGB3MBRH5YMMCH4587EMEZ","llama-factory","Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2\u002F3\u002F4\u002F5\u002F6\u002F8-bit QLoRA, multimodal support","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fllama-factory\u002FSKILL.md",[239],{"categoryNo":52,"categoryName":55,"taxonomyType":53,"taxonomyTypeDesc":54},[241,242,243,212,244,216,245,246,247,248,249,250,251,252,227,253,254,255,256,257],"Llama","factory","LLaMA Factory","ml","code","Fine-Tuning","LoRA","QLoRA","HuggingFace","expert","webui","bit","Accelerate","8-Bit","No-Code","with","for",{"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":232},"01M6XGAZQSBD98NXPGSMYRJZQA","implementing-llms-litgpt","Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA\u002FQLoRA. Single-file implementations, no abstraction layers.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Flitgpt\u002FSKILL.md",[],[212,265,266,267,268,216,222,269,270,223,271,272,227,273,274,275,276,277,278,231],"LitGPT","implementing llms litgpt","implementing","architecture","tuning","phi","single-file","clean","architectures","Model Architecture","Lightning AI","LLM Implementation","layers","single",{"skillNo":280,"name":281,"nameZh":13,"description":282,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":283,"categories":284,"keywords":288,"publishedAt":232},"01M6XGATRMGB43MKVWW47HY3G9","knowledge-distillation","Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fknowledge-distillation\u002FSKILL.md",[285],{"categoryNo":286,"categoryName":287,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000021","资料检索与研究",[289,290,291,292,216,293,223,294,227,295,296,297,298,299,300,301,302,303,304,231],"knowledge","distillation","Knowledge Distillation","performance","open","scaling","Emerging Techniques","Teacher-Student","MiniLLM","Reverse KLD","Soft Targets","Temperature Scaling","Logit Distillation","Model Transfer","student","teacher",{"skillNo":306,"name":307,"nameZh":13,"description":308,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":309,"categories":310,"keywords":314,"publishedAt":232},"01M6XGAM0T7N39H78J270FYS1A","hugging-face-model-trainer","This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fhugging-face-model-trainer\u002FSKILL.md",[311],{"categoryNo":312,"categoryName":313,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000023","Skill与智能体开发",[223,315,316,317,318,319,320,216,321,322,323,324,325,326,327,328,329,253,257,330],"hugging face","hugging face model trainer","model trainer","face","cost estimation","infrastructure","deployment","authentication","monitoring","learning","uv","selection","methods","hugging face jobs","local","Cloud GPU",{"skillNo":332,"name":333,"nameZh":13,"description":334,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":335,"categories":336,"keywords":340,"publishedAt":232},"01M6XGAEDHQ8GJ8YYRPV4TM317","hugging-face-evaluation","Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM\u002Flighteval. Works with the model-index metadata format.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fhugging-face-evaluation\u002FSKILL.md",[337],{"categoryNo":338,"categoryName":339,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000010","后端与接口",[218,315,341,318,215,216,342,343,344,345,223,224,346,347,348,227,349,350,351,352],"hugging face evaluation","metrics","analysis","api","markdown","README","manage","dotenv","add","extracting","tables","running",{"skillNo":354,"name":355,"nameZh":13,"description":356,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":357,"categories":358,"keywords":359,"publishedAt":232},"01M6XGAAHDNP02BN97WGW3VSCA","hqq-quantization","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fhqq\u002FSKILL.md",[],[360,361,362,212,363,364,216,365,366,222,223,225,367,252,227,368,369,257,370,231],"Quantization","HQQ","hqq quantization","memory","workflows","Optimization","data","efficiency","Memory Efficiency","Model Compression","calibration",{"skillNo":372,"name":373,"nameZh":13,"description":374,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":375,"categories":376,"keywords":377,"publishedAt":232},"01M6XGA6J9EKKM9E2BZSXCY0GM","grpo-rl-training","Expert guidance for GRPO\u002FRL fine-tuning with TRL for reasoning and task-specific model training","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fgrpo-rl-training\u002FSKILL.md",[],[216,378,379,380,244,246,381,382,383,222,269,324,223,250,384,385,227,386,387,257],"GRPO","rl","grpo rl training","Post-Training","RLHF","PPO","modeling","structured output","TRL","Reward Modeling",{"skillNo":389,"name":390,"nameZh":13,"description":391,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":392,"categories":393,"keywords":394,"publishedAt":232},"01M6XGA1TMKG6MSZF7PTBHWR4K","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fgptq\u002FSKILL.md",[],[395,212,363,216,365,396,397,398,222,269,399,252,227,400,401,402,403,404,257,231],"GPTQ","FP16","Fast Inference","perplexity","auto","4-Bit","Memory Optimization","Consumer GPUs","Group-Wise Quantization","you",{"skillNo":406,"name":407,"nameZh":13,"description":408,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":409,"categories":410,"keywords":411,"publishedAt":232},"01M6XG9MWSNEDBTRZ39MDH4K00","optimizing-attention-flash","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fflash-attention\u002FSKILL.md",[],[412,413,414,363,415,216,416,222,367,227,417,418,368,419,420,421,422,423,231,424],"flash","optimizing attention flash","attention","pytorch","issues","Flash Attention","Attention Optimization","Speed Optimization","SDPA","H100","native","window","sequences",{"skillNo":426,"name":427,"nameZh":13,"description":428,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":429,"categories":430,"keywords":431,"publishedAt":232},"01M6XG9GZPRHGP1JV956MWBNS1","deepspeed","Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16\u002FBF16\u002FFP8, 1-bit Adam, sparse attention","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fdeepspeed\u002FSKILL.md",[],[432,433,244,216,434,435,436,437,365,438,396,439,440,250,414,252,227,253,256,257],"DeepSpeed","distributed","Distributed Training","ZeRO","Pipeline Parallelism","Mixed Precision","Large-Scale Training","FP8","pipeline",{"skillNo":442,"name":443,"nameZh":13,"description":444,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":445,"categories":446,"keywords":447,"publishedAt":232},"01M6XG9BE649R6SJAQ6VM5F52V","colab-finetuning","Fine-tune LLMs on Google Colab GPUs directly from openscience. Connects to Colab runtimes via WebSocket bridge for remote training with Unsloth. Supports SFT, GRPO, DPO, vision, and TTS workflows on free T4 to Pro A100 GPUs.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fcolab-finetuning\u002FSKILL.md",[],[448,449,142,212,364,216,222,450,269,451,452,453,227,386,454,455,257,456,457,330],"finetuning","colab finetuning","websocket","bridge","vision","free","runtimes","remote","Google Colab","Remote Training",{"skillNo":459,"name":460,"nameZh":13,"description":461,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":462,"categories":463,"keywords":464,"publishedAt":232},"01M6XG97FK3HXW78NEEF7HMVQR","quantizing-models-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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fbitsandbytes\u002FSKILL.md",[],[465,466,467,212,363,216,365,222,252,227,468,253,400,401,254,469,470,471,257,231],"models","Bitsandbytes","quantizing models bitsandbytes","fit","NF4","INT8","formats",{"skillNo":473,"name":474,"nameZh":13,"description":475,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":476,"categories":477,"keywords":479,"publishedAt":232},"01M6XG91YRRF8WKB819FDZ3TVP","evaluating-code-models","Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. 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",[478],{"categoryNo":52,"categoryName":55,"taxonomyType":53,"taxonomyTypeDesc":54},[245,480,465,481,482,483,215,216,218,484,219,485,486,222,226,487,227,253,488,231],"Code Models","evaluating code models","testing","support","Code Generation","MBPP","MultiPL-E","project","multipl",{"skillNo":490,"name":491,"nameZh":13,"description":492,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":493,"categories":494,"keywords":495,"publishedAt":232},"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",[],[496,212,483,244,216,432,246,247,248,497,498,499,378,249,465,250,227,253,256,257],"Axolotl","DPO","KTO","ORPO",{"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":232},"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",[],[360,507,508,363,216,509,397,222,510,252,227,400,511,401,512,513,404,257,514,231],"AWQ","awq quantization","multimodal","activation","Activation-Aware","vLLM Integration","Marlin Kernels","2024",{"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":522,"publishedAt":232},"01M6XG8A1E18JNC2ABCKR7YWHF","huggingface-accelerate","Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed\u002FFSDP\u002FMegatron\u002FDDP. Automatic device placement, mixed precision (FP16\u002FBF16\u002FFP8). Interactive config, single launch command. HuggingFace ecosystem standard.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Faccelerate\u002FSKILL.md",[521],{"categoryNo":338,"categoryName":339,"taxonomyType":6,"taxonomyTypeDesc":7},[249,523,253,483,524,415,216,432,434,222,344,525,227,349,526,527,528,529,530,531],"huggingface accelerate","config","command","megatron","FSDP","DDP","Unified API","ecosystem","automatic",{"skillNo":533,"name":534,"nameZh":13,"description":535,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":536,"categories":537,"keywords":539,"publishedAt":232},"01M6XG83CDY3A1Q57YSJAZF4TA","serving-llms-vllm","Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency\u002Fthroughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ\u002FAWQ\u002FFP8), and tensor parallelism.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fvllm\u002FSKILL.md",[538],{"categoryNo":338,"categoryName":339,"taxonomyType":6,"taxonomyTypeDesc":7},[212,221,540,541,363,122,542,439,543,222,344,225,544,227,507,395,545,546,547,231],"serving llms vllm","serving","openai api","Inference Serving","latency","throughput","openai-compatible","batching",10825,18]