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Distributed Llm Pretraining Torchtitan

Listed
zechenzhangagi·vlatest·ai-ml·MIT

Provides LLMs guidance and assistance for building AI and machine learning applications.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add orchestra-research/ai-research-skills --skill distributed-llm-pretraining-torchtitan -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

zechenzhangagi

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 2 places.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • 01-model-architecture/torchtitan/SKILL.md:186pip install straight from a URL
      USE_CPP=0 pip install git+https://github.com/pytorch/ao.git
    • 01-model-architecture/torchtitan/references/float8.md:13pip install straight from a URL
      USE_CPP=0 pip install git+https://github.com/pytorch/ao.git

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • Pinned to a commit: passed

    Scanned zechenzhangagi/ai-research-skills at 773a52944b.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
773a52944b
Manifest
01-model-architecture/torchtitan/SKILL.md
Scanned
5 files, 941 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill facilitates distributed large language model (LLM) pretraining using PyTorch and TorchTitan. It enables scaling up the training process across multiple GPUs or machines, significantly reducing training time for massive models. The tool leverages TorchTitan's features to simplify the complexities of distributed training workflows.

When to use it

  • Training very large language models that exceed the memory capacity of a single GPU.
  • Accelerating LLM pretraining by distributing the workload across multiple GPUs or nodes.
  • Experimenting with different distributed training strategies and configurations for optimal performance.
  • Reproducing research results involving distributed LLM pretraining setups.

Key capabilities

  • Distributed PyTorch Training
  • PyTorch Titan Integration
  • LLM Pretraining Support
  • Scalable GPU Utilization

Example prompts

  • "Pretrain a language model using 8 GPUs with TorchTitan."
  • "Configure distributed training for my LLM pretraining script."
  • "Scale up the training process to utilize all available resources."

Tips & gotchas

  • Requires familiarity with PyTorch and basic understanding of distributed training concepts.
  • Ensure your environment is properly configured for multi-GPU or multi-node communication.