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Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fstable-baselines3\u002FSKILL.md",[],[265,244,246,212,266,267,268,269,270,271,224,272,225,273,274,275,276,277,230,278],"stable baselines3","learn","vectorized","api","multi-agent","parallel","ready","scikit-learn","high-performance","prototyping","quick","environments","production-ready","reinforcement learning algorithms",{"skillNo":280,"name":281,"nameZh":18,"description":282,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":283,"categories":284,"keywords":285,"publishedAt":232},"01M6XGFHKCNX3TVZ61TCSAEV22","simpo-training","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fsimpo\u002FSKILL.md",[],[212,286,287,244,288,289,290,253,291,292,225,293,294,295,296,297,298,299,230,231],"simpo training","simpo","Optimization","Post-Training","Transformers","alignment","free","Accelerate","TRL","Efficient Training","Preference Optimization","DPO Alternative","Reference-Free","LLM Alignment",{"skillNo":301,"name":302,"nameZh":18,"description":303,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":304,"categories":305,"keywords":306,"publishedAt":232},"01M6XGF7T0VDSSAKK0KRFNS7EA","sparse-autoencoder-training","Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fsaelens\u002FSKILL.md",[],[212,307,248,308,309,310,221,311,312,313,314,315,225,316,317,318,319,230,320,231],"sparse autoencoder training","analyzing","network","discovery","decompose","Neural","superposition","feature","saelens","neural network","lens","Transformer","Interpretability","features",{"skillNo":322,"name":323,"nameZh":18,"description":324,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":325,"categories":326,"keywords":327,"publishedAt":232},"01M6XGF0J098BMBZM664QTAZK3","rwkv-architecture","RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Frwkv\u002FSKILL.md",[],[328,329,330,331,212,332,333,290,253,270,334,335,336,337,338,339,225,340,341,342],"architecture","rwkv","rwkv architecture","windows","linear","NeMo","context","up","complexity","time","project","infer","Model Architecture","2025","at",{"skillNo":344,"name":345,"nameZh":18,"description":346,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":347,"categories":348,"keywords":349,"publishedAt":232},"01M6XGEMWA065JSBC6ZGJHA05W","pyvene-interventions","Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpyvene\u002FSKILL.md",[],[350,351,352,217,212,353,354,290,355,253,221,356,225,357,358,359,319,230,360,231],"pyvene","pyvene interventions","testing","framework","tracing","patching","activation","Causal Intervention","Activation Patching","Causal Tracing","declarative",{"skillNo":362,"name":363,"nameZh":18,"description":364,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":365,"categories":366,"keywords":367,"publishedAt":232},"01M6XGEEWHK4PBYGQQ27AYW8JD","pytorch-lightning","High-level PyTorch framework with Trainer class, automatic distributed training (DDP\u002FFSDP\u002FDeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpytorch-lightning\u002FSKILL.md",[],[217,368,369,212,370,353,290,268,222,225,371,372,373,374,375,227,376,229,377,231],"lightning","pytorch lightning","code","FSDP","DDP","Training Framework","High-Level API","Callbacks","loops","automatic",{"skillNo":379,"name":380,"nameZh":18,"description":381,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":382,"categories":383,"keywords":384,"publishedAt":232},"01M6XGEA9E71ZJNWRRYE357684","pytorch-fsdp","Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpytorch-fsdp\u002FSKILL.md",[],[217,385,371,386,248,212,387,388,389,290,270,390,225,391,392,393,394,395,396,230],"pytorch fsdp","distributed","Distributed Training","Mixed Precision","Large-Scale Training","expert","CPU","FSDP2","Fully Sharded Data Parallel","with","Data Parallel","Sharding",{"skillNo":398,"name":399,"nameZh":18,"description":400,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":401,"categories":402,"keywords":406,"publishedAt":232},"01M6XGE429CC3S8D66FJ110A5S","pufferlib","High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpufferlib\u002FSKILL.md",[403],{"categoryNo":404,"categoryName":405,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000023","Skill与智能体开发",[399,244,407,212,353,267,220,408,409,269,270,410,225,273,274,275,229,276,230,231],"documentation","game","10x","speed",{"skillNo":412,"name":413,"nameZh":18,"description":414,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":415,"categories":416,"keywords":418,"publishedAt":232},"01M6XGDWP1JXNGAPDV7GQQ97NT","prime-intellect-lab","Expert guidance for hosted RL post-training with Prime Intellect Lab — environments, verifiers, GEPA prompt optimization, and agentic training","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fprime-intellect-lab\u002FSKILL.md",[417],{"categoryNo":404,"categoryName":405,"taxonomyType":58,"taxonomyTypeDesc":59},[419,420,421,422,216,248,212,288,423,289,220,424,425,213,390,225,426,394,276,230,427],"lab","prime","Prime Intellect","prime intellect lab","LoRA","prompt","agentic rl","Prompt Optimization","Hosted Training",{"skillNo":429,"name":430,"nameZh":18,"description":431,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":432,"categories":433,"keywords":434,"publishedAt":232},"01M6XGDPWRN7TS9Y9CFRG5H0WM","peft-fine-tuning","Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train \u003C1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fpeft\u002FSKILL.md",[],[435,436,437,438,439,440,441,212,218,288,290,442,225,443,444,445,229,230,446,231],"tuning","fine tuning","peft fine tuning","PEFT","llms","memory","official","methods","Memory Optimization","Bitsandbytes","ecosystem","accuracy",{"skillNo":448,"name":449,"nameZh":18,"description":450,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":451,"categories":452,"keywords":453,"publishedAt":232},"01M6XGDG5M4NP3NF4TGPF9CRR7","openrlhf-training","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fopenrlhf\u002FSKILL.md",[],[212,454,455,244,328,353,456,387,457,289,458,459,460,461,462,290,221,225,273,230],"OpenRLHF","openrlhf training","DeepSpeed","ZeRO","RLHF","PPO","RLOO","Ray","vLLM",{"skillNo":464,"name":465,"nameZh":18,"description":466,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":467,"categories":468,"keywords":469,"publishedAt":232},"01M6XGD9HSMH7XVZF686SQS7EQ","nnsight-remote-interpretability","Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fnnsight\u002FSKILL.md",[],[470,471,319,472,246,328,217,248,212,473,309,474,253,221,475,225,316,476,230,231],"nnsight","nnsight remote interpretability","remote","run","execution","local","resources",{"skillNo":478,"name":479,"nameZh":18,"description":480,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":481,"categories":482,"keywords":489,"publishedAt":232},"01M6XGD33SMXZEHAJRX3FP4BW4","nemo-evaluator-sdk","Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fnemo-evaluator\u002FSKILL.md",[483,486],{"categoryNo":484,"categoryName":485,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":487,"categoryName":488,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000011","运维与部署",[490,333,491,492,493,439,328,494,212,495,496,497,498,499,475,225,227,500,501,231],"sdk","evaluator","nemo evaluator sdk","nemo evaluator","benchmarking","Evaluation","HumanEval","Safety","launcher","across","harnesses","hpc",{"skillNo":503,"name":504,"nameZh":18,"description":505,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":506,"categories":507,"keywords":512,"publishedAt":232},"01M6XGCVWJPVV09G3EFZ0VH6BV","nemo-curator","GPU-accelerated data curation for LLM training. Supports text\u002Fimage\u002Fvideo\u002Faudio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fnemo-curator\u002FSKILL.md",[508,511],{"categoryNo":509,"categoryName":510,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":28,"categoryName":29,"taxonomyType":11,"taxonomyTypeDesc":12},[333,513,514,212,515,516,517,518,519,520,521,522,523,524,499,225,525,526,527,320],"nemo curator","image","multimodal","NVIDIA","data","deduplication","heuristics","pii","dask","Video","Audio","text","processing","GPU Acceleration","curation",{"skillNo":529,"name":530,"nameZh":18,"description":531,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":532,"categories":533,"keywords":534,"publishedAt":232},"01M6XGCN64MH75T51BMMJV3KZN","nanogpt","Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fnanogpt\u002FSKILL.md",[],[535,328,248,212,370,290,220,253,536,537,222,225,391,340,538,318,539,230,540,541],"NanoGPT","shakespeare","karpathy","GPT-2","WandB","tiktoken","scratch",{"skillNo":543,"name":544,"nameZh":18,"description":545,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":546,"categories":547,"keywords":548,"publishedAt":232},"01M6XGCFQ62039HFJEHXV52CC0","moe-training","Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-training\u002Fmoe-training\u002FSKILL.md",[],[212,549,550,456,290,253,390,551,225,552,293,553,554,555,556,557,295,558,559,231],"moe","moe training","scaling","architectures","Emerging Techniques","Mixture Of Experts","Sparse Models","Expert Parallelism","Mixtral","experts","implementing",10819,18]