distributed-llm-pretraining-torchtitanlisted

# 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 3.1 8B training" [model] name = "llama3" flavor = "8B" hf_assets_path = "./assets/hf/Llama-3.1-8B"