Sync Dependabot Pins
ossf/oss-crs
After a Dependabot bump of oss-crs-infra/dependabot-pins.Dockerfile, resolve the human-readable version tag for each updated digest, fix the inline comment, and sync the digest to…
Walks an agent through working inside the Megatron-LM CI container and changing dependencies with uv, so lock files resolve the same locally and in CI.
$ npx skills add NVIDIA/Megatron-LM --skill mcore-build-and-dependency -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/Megatron-LM mcore-build-and-dependency --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/Megatron-LM.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mcore-build-and-dependency .claude/skills/mcore-build-and-dependency && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "mcore-build-and-dependency" agent skill from https://github.com/NVIDIA/Megatron-LM/tree/main/skills/mcore-build-and-dependency into .claude/skills/mcore-build-and-dependency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-build-and-dependency", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/Megatron-LM/tree/main/skills/mcore-build-and-dependencyType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/Megatron-LM --skill mcore-build-and-dependency -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/Megatron-LM mcore-build-and-dependency --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/Megatron-LM.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/mcore-build-and-dependency .agents/skills/mcore-build-and-dependency && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mcore-build-and-dependency" agent skill from https://github.com/NVIDIA/Megatron-LM/tree/main/skills/mcore-build-and-dependency into .agents/skills/mcore-build-and-dependency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-build-and-dependency", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/Megatron-LM --skill mcore-build-and-dependency -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/Megatron-LM mcore-build-and-dependency --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/Megatron-LM.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/mcore-build-and-dependency .cursor/skills/mcore-build-and-dependency && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mcore-build-and-dependency" agent skill from https://github.com/NVIDIA/Megatron-LM/tree/main/skills/mcore-build-and-dependency into .cursor/skills/mcore-build-and-dependency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-build-and-dependency", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/Megatron-LM.git --path skills/mcore-build-and-dependency--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/Megatron-LM --skill mcore-build-and-dependency -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/Megatron-LM mcore-build-and-dependency --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/Megatron-LM.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/mcore-build-and-dependency .gemini/skills/mcore-build-and-dependency && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mcore-build-and-dependency" agent skill from https://github.com/NVIDIA/Megatron-LM/tree/main/skills/mcore-build-and-dependency into .gemini/skills/mcore-build-and-dependency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-build-and-dependency", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/Megatron-LM mcore-build-and-dependencyInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/Megatron-LM --skill mcore-build-and-dependency -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/Megatron-LM.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/mcore-build-and-dependency .github/skills/mcore-build-and-dependency && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mcore-build-and-dependency" agent skill from https://github.com/NVIDIA/Megatron-LM/tree/main/skills/mcore-build-and-dependency into .github/skills/mcore-build-and-dependency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-build-and-dependency", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/Megatron-LM --skill mcore-build-and-dependency -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/Megatron-LM mcore-build-and-dependency --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/Megatron-LM.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/mcore-build-and-dependency .opencode/skills/mcore-build-and-dependency && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mcore-build-and-dependency" agent skill from https://github.com/NVIDIA/Megatron-LM/tree/main/skills/mcore-build-and-dependency into .opencode/skills/mcore-build-and-dependency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mcore-build-and-dependency", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
mcore-build-and-dependencyWalks an agent through working inside the Megatron-LM CI container and changing dependencies with uv, so lock files resolve the same locally and in CI.
The skill tells the agent to do all Megatron-LM environment and dependency work inside the project's CI container instead of on the host. That container carries the CUDA toolkit, the PyTorch build and prebuilt native extensions such as TransformerEngine and DeepEP, which are fragile to reproduce on a bare machine. The container's virtual environment is /opt/venv and is already on the PATH.
Two image variants exist. The default dev variant uses docker/.ngc_version.dev and the dev uv group, while lts uses an older long-term-support base and is meant to be used only when you ask for it. Installs use uv sync --locked with the dev, test, linting or lts groups, and dependency edits go through uv add followed by uv lock, both inside the container. Local builds of docker/Dockerfile.ci.dev should target the main stage, because the jet stage needs an internal secret.
2 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit d5fbb65. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
uvdockergitbashpipFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
github.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Megatron-LM Container and Dependency Setup loads about 2.6k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 1,047 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from NVIDIA/Megatron-LM at commit d5fbb65, republished under its Apache-2.0 licence (© NVIDIA). 1,047 words, ~2,624 tokens.
.claude/skills/mcore-build-and-dependency/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.The core principle: build and develop inside containers — the CI container ships the correct CUDA toolkit, PyTorch build, and pre-compiled native extensions (TransformerEngine, DeepEP, …) that cannot be reproduced on a bare host.
For text-only dependency or container questions, give these repo-specific facts up front before the longer workflow:
/opt/venv, already on PATH.dev uses docker/.ngc_version.dev and the dev uv group; lts
uses docker/.ngc_version.lts and the lts uv group. The container::lts
PR label selects the LTS path; otherwise CI uses dev.lts is opt-in only when the user explicitly asks for it. It is the older
long-term-support base, not a routine second lane — never attach
container::lts, build the LTS image, or run the lts uv group on your own
initiative, not even for a container or dependency change.uv sync --locked --group dev --group test,
uv sync --locked --only-group linting, or
uv sync --locked --group lts --group test.uv add <package> followed by uv lock, both inside
the container.docker/Dockerfile.ci.dev has main and jet stages. The jet stage needs
an internal secret; local/public builds should pass --target main.Megatron-LM depends on CUDA, NCCL, PyTorch with GPU support, TransformerEngine, and optional components like ModelOpt and DeepEP. Installing these on a bare host is fragile and hard to reproduce. The project ships Dockerfiles that pin every dependency.
Use the container as your development environment. This guarantees:
uv.lock resolves the same way locally and in CI.Two image variants exist, each with its own Dockerfile, selected by the
container::lts PR label. The defining difference is the base container:
dev tracks the latest NGC PyTorch release, while lts ("long-term support")
pins the previous, still-supported NGC PyTorch/CUDA release. container::lts
exists to verify a change still works on that older base — the dependency
differences below follow from it, they are not the point.
| Variant | Base image pin | Dockerfile | Where deps live | When used |
|---|---|---|---|---|
dev | docker/.ngc_version.dev (latest NGC release) | docker/Dockerfile.ci.dev | pyproject.toml dev extra (uv-resolved) | Default — CI, local development, most PRs |
lts | docker/.ngc_version.lts (older long-term-support release) | docker/Dockerfile.ci.lts | docker/lts/requirements.txt (pinned, sourced from main's uv.lock at AUT-479) | Backward-compat lane — verify the change still runs on the older NGC base; extras not carried on it (ModelOpt, the CUDA-13 TransformerEngine build) are dropped |
LTS deps used to live in
[project.optional-dependencies].ltsinpyproject.toml. They were moved intodocker/lts/requirements.txtsopyproject.tomlcan host meaningful module-level extras without colliding with the LTS pin set. To bump an LTS dependency, edit the version indocker/lts/requirements.txtand rebuilddocker/Dockerfile.ci.lts.
Use dev for everything. lts is off-limits unless the user explicitly asks
for it. CI runs dev by default, and that is the only variant you touch on
your own initiative. Treat container::lts as a high barrier, not a fallback: do
not attach the label, build docker/Dockerfile.ci.lts, or run the lts uv
group unless the user has explicitly requested LTS validation — not even for a
container or dependency change. When they do ask, container::lts verifies the
change still works on the older long-term-support PyTorch/CUDA base that LTS
users run. The @pytest.mark.flaky_in_dev marker skips tests in the dev
environment; @pytest.mark.flaky skips them in lts.
Option A — NVIDIA-internal: pull a CI-built image
⚠️ Requires access to the internal GitLab instance. See @tools/trigger_internal_ci.md for setup (adding the git remote, obtaining a token).
The internal GitLab CI publishes images to its container registry.
Derive the registry host from your configured gitlab remote — the same
host you use for trigger_internal_ci.py:
# Derive host from your 'gitlab' remote:
GITLAB_HOST=$(git remote get-url gitlab | sed 's/.*@\(.*\):.*/\1/')
docker pull ${GITLAB_HOST}/adlr/megatron-lm/mcore_ci_dev:mainOption B — Build from scratch (works for everyone)
⚠️
Dockerfile.ci.devhas two stages:mainandjet. Thejetstage requires an internal build secret and will fail without it. Always pass--target mainto stop at the public stage.
# dev image (default)
docker build \
--target main \
--build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.dev) \
--build-arg IMAGE_TYPE=dev \
-f docker/Dockerfile.ci.dev \
-t megatron-lm:local .
# lts image (uses a dedicated Dockerfile; no IMAGE_TYPE arg)
docker build \
--target main \
--build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.lts) \
-f docker/Dockerfile.ci.lts \
-t megatron-lm:local-lts .Which image variant is used is controlled by the PR label container::lts;
absent that label, dev is used.
Option A — Local Docker runtime
docker run --rm --gpus all \
-v $(pwd):/workspace \
-w /workspace \
megatron-lm:local \
bash -c "<your command>"Option B — Slurm cluster (for those without a local Docker runtime)
NVIDIA clusters typically use Pyxis + enroot. Request an interactive session:
srun \
--nodes=1 --gpus-per-node=8 \
--container-image megatron-lm:local \
--container-mounts $(pwd):/workspace \
--container-workdir /workspace \
--pty bashFor clusters that require a .sqsh archive first:
enroot import -o megatron-lm.sqsh dockerd://megatron-lm:local
srun \
--nodes=1 --gpus-per-node=8 \
--container-image $(pwd)/megatron-lm.sqsh \
--container-mounts $(pwd):/workspace \
--container-workdir /workspace \
--pty bashDependencies are declared in pyproject.toml. The venv lives at /opt/venv
inside the container (already on PATH).
All
uvoperations must be run inside the container. Never runuv sync/uv pip installon the host.
| Group | Purpose |
|---|---|
training | Runtime training extras |
dev | Full dev environment (TransformerEngine, ModelOpt, …) |
test | pytest, coverage, nemo-run |
linting | ruff, black, isort, pylint |
build | Cython, pybind11, nvidia-mathdx |
The previous
ltsextra has been emptied. LTS deps are pinned indocker/lts/requirements.txtrather thanpyproject.toml. Do not add new packages under[project.optional-dependencies].lts.
Install commands (inside the container):
# Full dev + test environment
uv sync --locked --group dev --group test
# Linting only
uv sync --locked --only-group lintingThe LTS environment is reproduced by building docker/Dockerfile.ci.lts
end-to-end; there is no uv sync-only equivalent because the LTS deps no
longer live in pyproject.toml. The LTS top-level pin set is in
docker/lts/requirements.txt; bump versions there and rebuild the image.
Several dependencies are sourced directly from git (TransformerEngine, nemo-run,
FlashMLA, Emerging-Optimizers, nvidia-resiliency-ext). The locked uv.lock file
pins exact revisions; update it with uv lock when changing pyproject.toml.
Follow this three-step workflow:
Acquire a container image — see Step 1 above.
Launch the container interactively — see Step 2 above.
Update the lock file inside the container, then commit it:
# Inside the container:
uv add <package> # adds to pyproject.toml and resolves
uv lock # regenerates uv.lock
# Exit the container, then on the host:
git add pyproject.toml uv.lock
git commit -S -s -m "build: add <package> dependency"uv.lock is machine-generated; never resolve conflicts manually. Instead:
git checkout origin/main -- uv.lock # take main's version as the base
# then inside the container:
uv lock # re-resolve on top of your pyproject.toml changes| Problem | Cause | Fix |
|---|---|---|
uv sync --locked fails | Dependency conflict or stale uv.lock | Re-run uv lock inside the container and commit updated lock |
ModuleNotFoundError after pip install | pip installed outside the uv-managed venv | Use uv add and uv sync, never bare pip install |
uv: command not found inside container | Wrong container image | Use the megatron-lm image built from Dockerfile.ci.dev |
No space left on device during uv ops | Cache fills container's /root/.cache/ | Mount a host cache dir via -v $HOME/.cache/uv:/root/.cache/uv |
docker build fails with secret-related error | Dockerfile.ci.dev has a jet stage that requires an internal secret | Add --target main to stop before the jet stage |
access forbidden when pulling | Registry URL includes an explicit port (e.g. :5005) | Use ${GITLAB_HOST}/adlr/... with no port — the sed extracts the hostname only |
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files in skills/mcore-build-and-dependency of NVIDIA/Megatron-LM.
Open the folder on GitHubat commit d5fbb65
Megatron-LM Container and Dependency Setup next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Megatron-LM Container and Dependency Setup this skillNVIDIA/Megatron-LM | 18k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Sync Dependabot Pinsossf/oss-crs | 165 | — | ~1.7k | Automated safety check: Notes | MIT | |
| Flowfile Build and Environment SetupEdwardvaneechoud/Flowfile | 373 | — | ~7.3k | Automated safety check: Notes | MIT | |
| AI ServerOpentrons/opentrons | 521 | — | ~2.5k | Automated safety check: Notes | Apache-2.0 | |
| Cyberowlaikarimhabush/cyberowl | 263 | — | ~2.5k | Automated safety check: Pass | MIT | |
| Generate Nemo Gym Envadithya-s-k/FineEnvs | 443 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 |
ossf/oss-crs
After a Dependabot bump of oss-crs-infra/dependabot-pins.Dockerfile, resolve the human-readable version tag for each updated digest, fix the inline comment, and sync the digest to…
Edwardvaneechoud/Flowfile
Recreates every Flowfile development and build environment from scratch, with exact version pins and an explanation of what each Makefile target really does.
Opentrons/opentrons
Conventions for the opentrons-ai-server FastAPI service — project structure, uv dependency management, settings, testing, Docker, and deployment.
karimhabush/cyberowl
Check if recent cybersecurity alerts from 10 international CERTs affect your current project.
adithya-s-k/FineEnvs
Builds a NeMo Gym (NVIDIA) variant of an RL environment. An agent skill from adithya-s-k/FineEnvs.
brevdev/workshop-build-an-agent
This skill should be used when the user wants to set up, install, deploy, bootstrap, or "spin up" the Build-an-Agent workshop (a.k.a.
NVIDIA/Megatron-LM
Guide to the Megatron-LM test system: layout, recipe YAML, running and adding unit and functional tests, golden values, marker filters and CI parity.
NVIDIA/Megatron-LM
Refreshes stored golden values from a GitHub Actions run, reports signed percentage changes per model, and writes a summary ready for a pull request description.
NVIDIA/Megatron-LM
Moves Megatron-LM CI to a newer NVIDIA PyTorch base image, updating both the GitHub and GitLab pins together and handling the CI follow-up.
NVIDIA/Megatron-LM
Explains Megatron-LM's CI pipeline, PR scope labels, triggering the internal GitLab CI with a dry run first, and investigating CI failures.
NVIDIA/Megatron-LM
Investigates a failing GitHub Actions run or job for Megatron-LM, finds the root cause plus the PR and test author involved, and files a structured bug issue.
NVIDIA/Megatron-LM
Guides moving Megatron Core GPTModel checkpoints, configs, training commands and launch scripts to HybridModel, following the repository's migration document.
Works with
Categories
Walks an agent through working inside the Megatron-LM CI container and changing dependencies with uv, so lock files resolve the same locally and in CI. The skill tells the agent to do all Megatron-LM environment and dependency work inside the project's CI container instead of on the host. That container carries the CUDA toolkit, the PyTorch build and prebuilt native extensions such as TransformerEngine and DeepEP, which are fragile to reproduce on a bare machine.
Megatron-LM Container and Dependency Setup fits situations like: setting up a Megatron-LM development environment in the CI container; adding or upgrading a package and regenerating uv.lock; answering questions about the dev and lts container variants; choosing which uv group to sync for tests or linting.
Run `npx skills add NVIDIA/Megatron-LM --skill mcore-build-and-dependency -a claude-code`. Or copy the skill folder (skills/mcore-build-and-dependency in NVIDIA/Megatron-LM) into .claude/skills/mcore-build-and-dependency in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/Megatron-LM --skill mcore-build-and-dependency -a codex`. Or copy the skill folder (skills/mcore-build-and-dependency in NVIDIA/Megatron-LM) into .agents/skills/mcore-build-and-dependency in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/Megatron-LM --skill mcore-build-and-dependency -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mcore-build-and-dependency, .gemini/skills/mcore-build-and-dependency, .github/skills/mcore-build-and-dependency and .opencode/skills/mcore-build-and-dependency in your project.
Going by SKILL.md and its folder, Megatron-LM Container and Dependency Setup needs the command-line tools its instructions call (uv, docker, git, bash and pip). Our summary lists: Docker, to run the Megatron-LM CI container; uv inside the container for dependency changes.
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Megatron-LM Container and Dependency Setup is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Megatron-LM Container and Dependency Setup: Sync Dependabot Pins (ossf/oss-crs, 165 stars), Flowfile Build and Environment Setup (Edwardvaneechoud/Flowfile, 373 stars), AI Server (Opentrons/opentrons, 521 stars) and Cyberowlai (karimhabush/cyberowl, 263 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/Megatron-LM, which has 18,083 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 8, 2026.
Source: NVIDIA/Megatron-LM on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.