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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
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使用TorchTitan和PyTorch配置大语言模型预训练，组合四类并行方式，将计算分配到多个GPU，支持单节点与多节点运行，以及训练状态的保存和恢复。
Configure large language model pretraining with torchtitan across multiple GPUs. Covers ways to split training work, launch jobs on computing clusters, enable Float8 numerical processing, and save or resume training through distributed checkpoints.
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Configure large language model pretraining with torchtitan across multiple GPUs. Covers ways to split training work, launch jobs on computing clusters, enable Float8 numerical processing, and save or resume training through distributed checkpoints.
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Improve CircleCI build and test pipelines by identifying slow or unreliable jobs, then adjusting caching, data sharing between jobs, triggers, and parallel execution. Receive incremental configuration changes with expected effects, validation criteria, and rollback guidance.
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Compare computing devices for a defined AI model workload using memory capacity, computing speed, data transfer, energy use, and cost. Build a comparison that separates predicted performance from measurements and identifies the constraints that determine the choice.
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Plan how to split AI models and workloads across multiple GPUs. Compare memory needs, computation, communication, and waiting time to estimate completion times and choose a layout suited to response time, throughput, or cost goals.
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Edit English and Chinese prose to reduce formulaic AI writing patterns, including filler, inflated claims, and vague attribution. Preserves the original meaning and intended tone while improving sentence variety, directness, and the writer’s voice.
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Helps a QwenPaw Worker choose between direct work, short parallel checks, and temporary agents with custom roles. It guides dependent task execution, shared files, progress updates, result merging, and removal of temporary agents after the work ends.
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Write and repair custom MSBuild targets, the steps that organize build tasks. Preserve existing build sequences, choose appropriate insertion points, distinguish returned results from rebuild checks, and register generated files for cleanup.
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Establish repeatable performance measurements for MSBuild and .NET builds before optimizing. Compare clean-output, changed-source, and unchanged builds, record timing ranges, and assess output layouts, dependency references, and parallel build options against the measured baseline.
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Diagnose why MSBuild builds across multiple projects fail to run efficiently in parallel. Use build logs and project dependencies to identify blocking chains, then recommend changes to references, scheduling, or parallel execution settings.
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Edit English and Chinese prose to reduce formulaic wording, inflated claims, vague attributions, and repetitive sentence patterns. It preserves the core meaning and intended tone while improving rhythm, directness, and the writer’s voice.
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Guide for selecting and configuring distributed training strategies in NeMo AutoModel, including FSDP2, Megatron FSDP, DDP, and parallelism settings.
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Use this skill when baseline NeMo Relay instrumentation exists and the user wants to configure or evaluate adaptive plugin behavior, including telemetry, state, adaptive_hints, tool_parallelism, ACG, hint consumption, or measured rollout.
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Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, PEFT + SP input re-gather, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.
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Recommend and customize Megatron Bridge library and benchmark recipes for a user's model, GPU count, hardware, sequence length, and pretrain/SFT/PEFT goal. Use when selecting a starting recipe, comparing library and benchmark configs, resizing parallelism for a GPU allocation, or distinguishing convergence changes, semantics-preserving execution tuning, and benchmark-only shortcuts.
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Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
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How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.
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Optimize Anima code generation performance with caching, parallelism, and output tuning. Use when reducing generation latency, optimizing batch component generation, or improving generated code quality for production use. Trigger with: "anima performance", "anima slow", "anima optimization", "anima caching".
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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
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Analyze and, when the task authorizes source optimization, refactor a concrete long-running Python entrypoint containing repeated independent work. Use for explicit Python speedup or parallelization requests and evidence-backed long-running entrypoints; skip ordinary Python edits, test execution alone, short scripts, and projects that merely contain Python files.
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Optimize MATLAB design files for GPU Coder to generate faster CUDA code. Iteratively profiles, rewrites, and benchmarks until performance targets are met or diagnostics are resolved. Use when asked to: optimize for GPU Coder, improve GPU codegen performance, profile generated GPU/CUDA code, profile GPU MEX, fix gpuPerformanceAnalyzer diagnostics, speed up GPU MEX, reduce GPU memory transfers, improve kernel parallelism, rewrite MATLAB for CUDA, or run gpuPerformanceAnalyzer.
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Deploy and manage vLLM for high-throughput LLM inference. Configure continuous batching, tensor parallelism, quantization, and OpenAI-compatible API endpoints for production LLM serving.
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