README.md

Pre-training

Listing the available recipes for pretraining

nemo llm pretrain --help

Run pre-training with a default recipe

nemo llm pretrain --factory llama3_8b

We can also call the factory function with custom parameters:

nemo llm pretrain --factory "llama3_70b(num_nodes=128)"

The CLI allows you to overwrite any parameter. For example, to run the recipe with 2000 steps:

nemo llm pretrain --factory llama3_70b trainer.max_steps=2000

The syntax of the CLI is the same as the Python code. Which is great but in some cases you might want to inspect & edit a recipe interactively. An easy way to do this using the cli is the use the --repl flag.

nemo llm pretrain --factory llama3_70b --repl

We can also trigger a run from a jupyter notebook, see pretrain.ipynb for an example. This allows visualizes all configs in a structured format. See for instance the llama3_8b recipe:

Create and run a custom recipe

We can create a script that contains a custom recipe. See custom_recipe.py for an example.

Note that we end the script with a call to run.cli.main(), which uses the same syntax as the CLI but allows us to provide specific defaults. We still can overwrite any parameter using the syntax param=value. We can set nested parameters using dotted notation, e.g. trainer.max_steps=2000.

When running the custom_recipe.py file, it will execute the custom_llama3_8b recipe by default. However, you can select different recipes or modify parameters using the following methods:

1. To select the custom_llama3_70b recipe:

   python custom_recipe.py --factory custom_llama3_70b

   This will automatically call the custom_llama3_70b function defined in the script.

2. To overwrite any parameter:

   python custom_recipe.py trainer.max_steps=2000

3. You can even apply transformations when triggering the CLI as if it's Python code:

   python custom_recipe.py "trainer.max_steps=*2"

These options provide flexibility in customizing your pretraining recipe directly from the command line.