> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server. # Run on Your Local Workstation Use this guide for local, single-node workflows on a workstation or an interactive Slurm allocation. For setup details, refer to our [Installation Guide](/get-started/installation). For batch multi-node jobs, see the [Run on a Cluster](/job-launchers/slurm-cluster) or [SkyPilot](/job-launchers/skypilot) guides. NeMo AutoModel uses recipes to run end-to-end workflows. If you're new to recipes, see the [Repository Structure](/get-started/repo-structure) guide. ## Quick Start: Choose Your Job Launch Option * **CLI (recommended)** ```bash automodel examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml ``` * **Direct recipe script** * Single GPU ```bash python nemo_automodel/recipes/llm/train_ft.py -c examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml ``` * Multi-GPU (single node) ```bash torchrun --nproc-per-node=2 nemo_automodel/recipes/llm/train_ft.py -c examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml ``` ## Run with AutoModel CLI (Single Node) The AutoModel CLI is the preferred method for most users. It offers a unified interface to launch training scaling from a local workstation (this guide) to large clusters (see our [cluster guide](/job-launchers/slurm-cluster)). ### Basic Usage The CLI follows this format: ```bash automodel [--nproc-per-node N] [--key.subkey=override ...] ``` A short alias `am` is also available. Both commands also work with `uv run` (e.g., `uv run automodel `). Where: * ``: Path to your YAML configuration file (must contain a `recipe._target_` key) * `--nproc-per-node`: Optional override for the number of GPUs to use The recipe class is specified inside the YAML via the `recipe._target_` key: ```yaml recipe: _target_: nemo_automodel.recipes.llm.train_ft.TrainFinetuneRecipeForNextTokenPrediction ``` ### Train on a Single GPU For simple fine-tuning on a single GPU: ```bash automodel examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml ``` ### Train on Multiple GPUs (Single Node) For interactive single-node jobs, the CLI automatically detects the number of available GPUs and uses `torchrun` for multi-GPU training. You can manually specify the number of GPUs using the `--nproc-per-node` option: ```bash automodel --nproc-per-node 2 examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml ``` If you don't specify `--nproc-per-node`, it will use all available GPUs on your system. Looking for Slurm or cloud training? See [Run on a Cluster](/job-launchers/slurm-cluster) or [SkyPilot](/job-launchers/skypilot). ## Run with uv (Development Mode) When you need more control over the environment or are actively developing with the codebase, you can use `uv` to run training scripts directly. This approach gives you direct access to the underlying Python scripts and is ideal for debugging or customization. ### Train on a Single GPU ```bash uv run nemo_automodel/recipes/llm/train_ft.py -c examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml ``` ### Train on Multiple GPUs with Torchrun (Single Node) For multi-GPU single-node training, use `torchrun` directly: ```bash uv run torchrun --nproc-per-node=2 nemo_automodel/recipes/llm/train_ft.py -c examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml ``` ### Why Use uv? uv provides several advantages for development and experimentation: * **Automatic environment management**: uv automatically creates and manages virtual environments, ensuring consistent dependencies without manual setup. * **Lock file synchronization**: Keeps your local environment perfectly synchronized with the project's `uv.lock` file. * **No installation required**: Run scripts directly from the repository without installing packages system-wide. * **Development flexibility**: Direct access to Python scripts for debugging, profiling, and customization. * **Dependency isolation**: Each project gets its own isolated environment, preventing conflicts. ## Run with Torchrun If you have NeMo AutoModel installed in your environment and prefer to run recipes directly without uv, you can use `torchrun` directly: ### Train on a Single GPU ```bash python nemo_automodel/recipes/llm/train_ft.py -c examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml ``` ### Train on Multiple GPUs (Single Node) ```bash torchrun --nproc-per-node=2 nemo_automodel/recipes/llm/train_ft.py -c examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml ``` This approach requires that you have already installed NeMo AutoModel and its dependencies in your Python environment (see the [installation guide](/get-started/installation) for details). ## Customize Configuration Settings All approaches use the same YAML configuration files. You can easily customize training by following the steps in this section. 1. **Override config values**: Use command-line arguments to directly replace default settings. For example, if you want to fine-tune `Qwen/Qwen3-0.6B` instead of `meta-llama/Llama-3.2-1B`, you can use: ```bash automodel config.yaml --model.pretrained_model_name_or_path Qwen/Qwen3-0.6B ``` 2. **Edit the config file**: Modify the YAML directly for persistent changes. 3. **Create custom configs**: Copy and modify existing configurations from the `examples/` directory. ## When to Use Which Approach **Use the AutoModel CLI when:** * You want a simple, unified interface * You are running locally on a single machine * You don't need to modify the underlying code * You prefer a higher-level abstraction **Use uv when:** * You're developing or debugging the codebase * You want automatic dependency management * You need maximum control over the execution * You want to avoid manual environment setup * You're experimenting with custom modifications **Use Torchrun when:** * You have a stable, pre-configured environment * You prefer explicit control over Python execution * You're working in environments where uv is not available * You're integrating with existing PyTorch workflows All approaches use the same configuration files and provide the same training capabilities on a single node. For multi-node training, see [Run on a Cluster](/job-launchers/slurm-cluster).