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Skill

distributed-llm-pretraining-torchtitan

From NousResearch

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

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Repository
NousResearch/hermes-agent
Status
Actively maintained
Last commit
Source file
optional-skills/mlops/torchtitan/SKILL.md

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The instructions Claude Code reads when this skill runs.

# TorchTitan - PyTorch Native Distributed LLM Pretraining

## Quick start

TorchTitan is PyTorch's official platform for large-scale LLM pretraining with composable 4D parallelism (FSDP2, TP, PP, CP), achieving 65%+ speedups over baselines on H100 GPUs.

**Installation**:
```bash
# From PyPI (stable)
pip install torchtitan

# From source (latest features, requires PyTorch nightly)
git clone https://github.com/pytorch/torchtitan
cd torchtitan
pip install -r requirements.txt
```

**Download tokenizer**:
```bash
# Get HF token from https://huggingface.co/settings/tokens
python scripts/download_hf_assets.py --repo_id meta-llama/Llama-3.1-8B --assets tokenizer --hf_token=...
```

**Start training on 8 GPUs**:
```bash
CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh
```

## Common workflows

### Workflow 1: Pretrain Llama 3.1 8B on single node

Copy this checklist:

```
Single Node Pretraining:
- [ ] Step 1: Download tokenizer
- [ ] Step 2: Configure training
- [ ] Step 3: Launch training
- [ ] Step 4: Monitor and checkpoint
```

**Step 1: Download tokenizer**

```bash
python scripts/download_hf_assets.py \
  --repo_id meta-llama/Llama-3.1-8B \
  --assets tokenizer \
  --hf_token=YOUR_HF_TOKEN
```

**Step 2: Configure training**

Edit or create a TOML config file:

```toml
# llama3_8b_custom.toml
[job]
dump_folder = "./outputs"
description = "Llama
View full source on GitHub →

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