Model Coverage Overview

NeMo AutoModel integrates with Hugging Face transformers. Any LLM or VLM that can be instantiated through transformers can also be used with NeMo AutoModel, subject to runtime, third-party software dependencies, and feature compatibility.

Supported Hugging Face Auto Classes

Release Log

The table summarizes selected model and feature additions in published NeMo AutoModel releases. Dates are the GitHub release publication dates; each version links to its full release notes. For model additions on main, including changes after the latest release, see the Model Support Log. For tested architectures and example configs, see the LLM, VLM, and Multimodal pages.

Day-0 Support

• NeMo AutoModel closely tracks the latest transformers version and updates its dependency regularly.
• New models released on the Hugging Face Hub might require the latest transformers version, necessitating a package upgrade.
• The team is developing a CI pipeline that automatically updates the supported transformers version when a new release is detected, enabling faster day-0 support.

Custom Model Registry

NeMo AutoModel includes a custom model registry that allows teams to:

• Add custom implementations to extend support to models not yet covered upstream.
• Provide optimized or faster implementations for specific models while retaining the same NeMo AutoModel interface.

Register an Architecture

The registry matches an architecture name against the first value in the checkpoint’s config.json architectures list. The name is case-sensitive. The registered class must be a torch.nn.Module class that is compatible with the selected NeMoAutoModel* loader and accepts the resolved Hugging Face config as its first constructor argument.

Register in Python

Call register_architecture before constructing or loading the model:

from nemo_automodel import NeMoAutoModelForCausalLM, register_architecture
from my_package.models import MyModelForCausalLM
register_architecture("MyModelForCausalLM", MyModelForCausalLM)
model = NeMoAutoModelForCausalLM.from_pretrained("my-org/my-model")

Registering a built-in or previously registered name raises ValueError, even when the same class is registered again. Pass exist_ok=True only when you intentionally want to replace the existing model class.

Register from an Installed Package

An installed package can advertise model classes without requiring application startup code. Add an entry point for each architecture to the package’s pyproject.toml:

[project.entry-points."nemo_automodel.architectures"]
MyModelForCausalLM = "my_package.models:MyModelForCausalLM"

The entry-point name is the architecture name, and its value must use the module.path:ClassName format. NeMo AutoModel discovers these entry points when its model registry initializes and imports the target module only when it resolves that architecture. Install the package before starting the Python process. If an entry-point name conflicts with a built-in or another discovered architecture, NeMo AutoModel skips it and logs a warning; use register_architecture(..., exist_ok=True) in application code for an intentional override.

Ready-to-Run Architectures

The following table lists architectures represented by the ready-to-run YAML recipes in this repository. It includes both NeMo-native implementations and models that use the standard Hugging Face implementation path.

This is a practical starting set rather than an exhaustive compatibility list. NeMo AutoModel can also work with additional models supported by the installed version of the Hugging Face transformers library, although models without a checked-in recipe might require some configuration for a particular training setup.

Having Issues?

If a model from the Hugging Face Hub does not work as expected, see Troubleshooting for common issues and solutions.