> This page is for Evo 2, version 2.2.0 (Latest) (default).
> For other versions, use one of these documentation indexes:
> - 2.2.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/evo2/2.2.0/llms.txt
> - 2.1.0: https://docs.nvidia.com/nim/bionemo/evo2/2.1.0/llms.txt
> - 2.0.0: https://docs.nvidia.com/nim/bionemo/evo2/2.0.0/llms.txt
> - 1.0.2: https://docs.nvidia.com/nim/bionemo/evo2/1.0.2/llms.txt
> - 1.0.1: https://docs.nvidia.com/nim/bionemo/evo2/1.0.1/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/evo2/1.0.0/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server.

# Advanced Usage of Evo 2 NIM

This page describes how to use the advanced and non-standard features of the Evo 2 NIM, including selecting different pre-trained models, benchmarking the performance of the NIM locally and controlling the log levels of the NIM for monitoring and debugging.

## Model Selection

Evo 2 NIM provides multiple pre-trained models that can be selected at runtime using environment variables. This allows you to choose the appropriate model size based on your requirements and available hardware resources.

### Pretrained Model Variants

The NIM container supports several pre-trained model variants, which you can select by setting the `NIM_VARIANT` environment variable.

To choose a specific model, set the `NIM_VARIANT` environment variable when running the container:
- **40B model**: Default behavior (no environment variable needed)
- **7B model**: Set `NIM_VARIANT=7b`

For setup instructions and examples, refer to the [Quickstart Guide](/nim/bionemo/evo2/quickstart-guide).

#### 40B Model (Default)
The 40 billion parameter model is the default and provides the highest accuracy. This model requires more GPU resources and memory but delivers the best performance for complex biological sequence tasks.

#### 7B Model
The 7 billion parameter model runs significantly faster with reduced resource requirements while still providing good accuracy. This model offers an excellent balance between speed and accuracy for many use cases.

### Custom and Fine-Tuned Models

For loading custom checkpoints or fine-tuned models, refer to the dedicated guide:
[Loading Custom and Fine-Tuned Evo 2 NIM Model Checkpoints](/nim/bionemo/evo2/custom-models)

## Logging

### Controlling Logging Level

The logging level for the NIM can be controlled using the environment variable `NIM_LOG_LEVEL`. This variable allows you to specify the level of logging detail you want to see in the container's logs.

### Available Logging Levels

The following logging levels are available:

* `DEBUG`: Logs all inputs and outputs for each endpoint of the server, which can be used for debugging. It also produces very large logs and should only be used when necessary.
* `INFO`: Logs important events and information about the server's operation.
* `WARNING`: Logs warnings about potential issues or errors.
* `ERROR`: Logs errors that occur during the server's operation.
* `CRITICAL`: Logs critical errors that prevent the server from functioning properly.

### Setting the Logging Level

To set the logging level, pass the `NIM_LOG_LEVEL` environment variable when starting the NIM. For example:

```bash
docker run -e NIM_LOG_LEVEL=DEBUG
```
This sets the logging level to `DEBUG`, which logs all inputs and outputs for each endpoint of the server.

### Best Practices

When setting the logging level, consider the trade-off between logging detail and log size. If you set the logging level to `DEBUG`, you might generate very large logs that can be difficult to manage. However, if you set the logging level to a higher level, such as `INFO` or `WARNING`, you might miss important debugging information.

Set the logging level to `INFO` or `WARNING` unless you need more detailed logging.

### Default Logging Level

If no value is provided for `NIM_LOG_LEVEL`, the default logging level will be
`INFO`, which means that logs of level INFO and higher are recorded.