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Search MCP servers and agent skills by name, description, category or topic — 4,181 results.
NVIDIA/Megatron-Bridge/perf-memory-tuning
Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.
NVIDIA/Megatron-Bridge/perf-moe-comm-overlap
MoE expert-parallel communication overlap in Megatron Bridge.
NVIDIA/Megatron-Bridge/perf-moe-dispatcher-selection
Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage.
NVIDIA/Megatron-Bridge/perf-moe-hardware-configs
Representative MoE training playbooks by hardware platform and model family.
NVIDIA/Megatron-Bridge/perf-moe-long-context
Long-context MoE training guidance for Megatron Bridge.
NVIDIA/Megatron-Bridge/perf-moe-optimization-workflow
Systematic workflow for MoE training optimization in Megatron Bridge, based on the Megatron-Core MoE paper.
NVIDIA/Megatron-Bridge/perf-moe-vlm-training
Practical guidance for training MoE VLMs in Megatron Bridge.
NVIDIA/Megatron-Bridge/perf-parallelism-strategies
Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.
NVIDIA/Megatron-Bridge/perf-sequence-packing
Validate and use packed sequences and long-context training in Megatron-Bridge, distinguishing offline packed SFT for LLMs from in-batch packing for VLMs, and applying the right CP...
NVIDIA/Megatron-Bridge/perf-tp-dp-comm-overlap
Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
NVIDIA/Megatron-Bridge/recipe-recommender
Recommend and customize Megatron Bridge recipes for a user's model, GPU count, and training goal.
NVIDIA/Megatron-Bridge/resiliency
Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.
NVIDIA/Megatron-Bridge/testing
Testing reference for Megatron Bridge — unit and functional test layout, tier semantics (L0/L1/L2/flaky), script conventions, running tests locally, adding/moving/disabling tests,...
NVIDIA/Megatron-Bridge/verl-e2e-testing
External verl end-to-end validation workflow for Megatron-Bridge model/provider changes.
NVIDIA/Megatron-Core/build-and-dependency
Container-based dev environment setup and dependency management for Megatron-LM.
NVIDIA/Megatron-Core/bump-base-image
Bump the NVIDIA PyTorch base image (`nvcr.io/nvidia/pytorch:-py3`) used by Megatron-LM CI.
NVIDIA/Megatron-Core/create-issue
Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.
NVIDIA/Megatron-Core/linting-and-formatting
Linting and formatting for Megatron-LM.
NVIDIA/Megatron-Core/nightly-sync
Domain knowledge for the nightly main-to-dev sync workflow.
NVIDIA/Megatron-Core/onboard-gb200-1node-tests
Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.
NVIDIA/Megatron-Core/respond-to-issue
Research and draft a response to a GitHub issue or question from an external contributor.
NVIDIA/Megatron-Core/run-on-slurm
How to launch distributed Megatron-LM training jobs on a SLURM cluster.
NVIDIA/Megatron-Core/split-pr
Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.
NVIDIA/Megatron-Core/update-golden-values
Refresh golden values from a GitHub Actions workflow run (failing-only or all jobs), score the change with average normalized relative differences, and produce a PR-ready summary.
NVIDIA/Model-Optimizer/accessing-mlflow
Query and browse evaluation results stored in MLflow.
NVIDIA/Model-Optimizer/debug
Run commands inside a remote Docker container via the file-based command relay (tools/debugger).
NVIDIA/Model-Optimizer/deployment
Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM.
NVIDIA/Model-Optimizer/evaluation
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL).
NVIDIA/Model-Optimizer/launching-evals
Run, monitor, analyze, and debug LLM evaluations via nemo-evaluator-launcher.
NVIDIA/Model-Optimizer/monitor
Monitor submitted jobs (PTQ, evaluation, deployment) on SLURM clusters.
NVIDIA/Model-Optimizer/ptq
This skill should be used when the user asks to "quantize a model", "run PTQ", "post-training quantization", "NVFP4 quantization", "FP8 quantization", "INT8 quantization", "INT4 AW...
NVIDIA/Model-Optimizer/release-cherry-pick
Cherry-pick merged PRs labeled for a release branch into that branch, then open a PR and apply the cherry-pick-done label.
NVIDIA/NeMo-Evaluator/byob
Create custom LLM evaluation benchmarks using the BYOB decorator framework.
NVIDIA/NeMo-Evaluator-Launcher/accessing-mlflow
Query and browse evaluation results stored in MLflow.
NVIDIA/NeMo-Evaluator-Launcher/launching-evals
Run, monitor, analyze, and debug LLM evaluations via nemo-evaluator-launcher.
NVIDIA/NeMo-Evaluator-Launcher/nel-assistant
Interactive config wizard for NeMo Evaluator Launcher (NEL).
NVIDIA/NeMo-Gym/add-benchmark
Guide for adding a new benchmark or training environment to NeMo-Gym.
NVIDIA/NeMo-Gym/nemo-gym-debugging
Use when debugging a Nemo Gym run or reward profiling job.
NVIDIA/NeMo-Gym/nemo-gym-docs
Maintain the NeMo Gym Fern docs site — add, update, move, or remove pages under fern/.
NVIDIA/NeMo-Gym/nemo-gym-pivot-datasets
Use when creating, validating, or documenting Nemo Gym pivot datasets from rollout, trajectory, chat-completion, Responses API, or tool-call artifacts.
NVIDIA/NeMo-Gym/nemo-gym-reward-profiling
Use to help users get started with Nemo Gym reward profiling.
NVIDIA/NeMo-RL/auto-research
Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery.
NVIDIA/NeMo-RL/brev-etiquette
Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets.