Metadata-Version: 2.4
Name: nvidia-safety-harness
Version: 25.5
Summary: Content safety evaluation tool - packaged by NVIDIA
Author: Prasoon Varshney, Makesh Narsimhan Sreedhar, Yoshi Suhara, Katherine Luna, Varun Singh, Christopher Parisien, Eileen Long
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Dynamic: license-file

# NVIDIA Evals Factory

The goal of NVIDIA Evals Factory is to advance and refine state-of-the-art methodologies for model evaluation, and deliver them as modular evaluation packages (evaluation containers and pip wheels) that teams can use as standardized building blocks.

# Quick start guide

NVIDIA Evals Factory provide you with evaluation clients, that are specifically built to evaluate model endpoints using our Standard API.

## Launching an evaluation for an LLM

1. Install the package
    ```
    pip install nvidia-safety-harness
    ```


2. List the available evaluations:
    ```bash
    $ core_evals_safety_eval ls
    Available tasks:
    * aegis_v2 (in safety_eval)
    * wildguard (in safety_eval)
    ...

    ```
3. Prepare Judge model URL and api key. For more information on judge models and their deployment, please refer to [Judge configuration](#judge-configuration). You need to authenticate to the [Hugging Face Hub](https://huggingface.co/docs/huggingface_hub/quick-start#authentication) as well to access datasets and judge models.


3. (Optional) Set API keys to the Model Under Test (MUT) endpoint and the judge endpoint, if they are protected
    ```bash
    export MUT_API_KEY="your_api_key_here"
    export JUDGE_API_KEY="your_api_key_here"
    ```

5. Run the evaluation:
   ```bash
    nv_eval run_eval  \
    --model_id "meta/llama-4-maverick-17b-128e-instruct" \
    --model_url https://integrate.api.nvidia.com/v1 \
    --model_type chat \
    --api_key_name MUT_API_KEY \
    --output_dir /workspace/results \
    --eval_type aegis_v2 \
    --overrides="config.params.extra.judge.url=https://integrate.api.nvidia.com/v1,config.params.extra.jduge.api_key=JUDGE_API_KEY"
   ```
    Please note that setting judge overrides is required. 
    For URLs, you can provide either base URL or full URL (ending with `/completions/` or with `/chat/completions`)
6. Gather the results
    ```bash
    cat /workspace/results/results.yml
    ```

# Command-Line Tool

Each package comes pre-installed with a set of command-line tools, designed to simplify the execution of evaluation tasks. Below are the available commands and their usage for the `aegis_v2`:

## Commands

### 1. **List Evaluation Types**

```bash
core_evals_safety_eval ls
```

Displays the evaluation types available within the harness.

### 2. **Run an evaluation**

The `core_evals_safety_eval run_eval` command executes the evaluation process. Below are the flags and their descriptions:

### Required flags
* `--eval_type <string>`
The type of evaluation to perform
* `--model_id <string>`
The name or identifier of the model to evaluate.
* `--model_url <url>`
The API endpoint where the model is accessible.
* `--model_type <string>`
The type of the model to evaluate, currently either "chat", "completions", or "vlm".
* `--output_dir <directory>`
The directory to use as the working directory for the evaluation. The results, including the results.yml output file, will be saved here. Make sure to use the absolute path.

### Required overrides

Evaluation in this harness requires providing judge model info. 

* `config.params.extra.judge.url` - URL for the Judge model
* `config.params.extra.judge.api_key` - by default `JUDGE_API_KEY` environment variable is used. 


### Optional flags
* `--api_key_name <string>`
The name of the environment variable that stores the Bearer token for the API, if authentication is required.
* `--run_config <path>`
Specifies the path to a  YAML file containing the evaluation definition.

### Example

```bash
core-evals-safety-eval run_eval  \
    --model_id "my-model" \
    --model_url http://localhost:8000/v1/chat/completions \
    --model_type chat \
    --api_key_name API_KEY \
    --output_dir /workspace/results \
    --eval_type aegis_v2 \
    --overrides="config.params.extra.judge.url=http://localhost:8888/v1/chat/completions,config.params.extra.judge.api_key=JUDGE_API_KEY"
```

If the model API requires authentication, set the API key in an environment variable and reference it using the `--api_key_name` flag:

```bash
export API_KEY="your_api_key_here"
export JUDGE_API_KEY="you_api_key_here"

core-evals-safety-eval run_eval  \
    --model_id "my-model" \
    --model_url http://localhost:8000/v1/chat/completions \
    --model_type chat \
    --api_key_name API_KEY \
    --output_dir /workspace/results \
    --eval_type aegis_v2 \
    --overrides="config.params.extra.judge.url=http://localhost:8888/v1/chat/completions,config.params.extra.judge.api_key=JUDGE_API_KEY"
```

# Configuring evaluations via YAML

Evaluations in NVIDIA Evals Factory are configured using YAML files that define the parameters and settings required for the evaluation process. These configuration files follow a standard API which ensures consistency across evaluations.

Example of a YAML config:
```yaml
config:
  type: aegis_v2
  params:
    parallelism: 20
    limit_samples: 20
    extra:
      judge:
        url: http://localhost:8888/v1/chat/completions
        parallelism: 64
target:
  api_endpoint:
    model_id: microsoft/phi-4-mini-instruct
    type: chat
    url: https://integrate.api.nvidia.com/v1/chat/completions
    api_key: NVIDIA_API_KEY
```

The priority of overrides is as follows:
1. command line arguments
2. user config (as seen above)
3. task defaults (defined per task type)
4. framework defaults 

`--dry_run` option allows you to print the final run configuration and command without executing the evaluation.

### Example:

```bash
core-evals-safety-eval run_eval  \
    --model_id "my-model" \
    --model_url http://localhost:8000/v1/chat/completions \
    --model_type chat \
    --api_key_name API_KEY \
    --output_dir /workspace/results \
    --eval_type aegis_v2 \
    --overrides="config.params.extra.judge.url=http://localhost:8888/v1/chat/completions,config.params.extra.judge.api_key=JUDGE_API_KEY" \
    --dry_run
```

Output:

```bash
Rendered config:

command: '{% if target.api_endpoint.api_key is not none %}export API_KEY=${{target.api_endpoint.api_key}}  &&
  {% endif %} {% if config.params.extra.judge.api_key is not none %}export JUDGE_API_KEY=${{config.params.extra.judge.api_key}}
  && {% endif %} safety-eval  --model-name  {{target.api_endpoint.model_id}} --model-url
  {{target.api_endpoint.url}} --model-type {{target.api_endpoint.type}}  --judge-url  {{config.params.extra.judge.url}}   --results-dir
  {{config.output_dir}}   --eval {{config.params.task}}  --mut-inference-params max_tokens={{config.params.max_new_tokens}},temperature={{config.params.temperature}},top_p={{config.params.top_p}},timeout={{config.params.request_timeout}},concurrency={{config.params.parallelism}},retries={{config.params.max_retries}}
  --judge-inference-params concurrency={{config.params.extra.judge.parallelism}},retries={{config.params.max_retries}}  {%
  if config.params.limit_samples is not none %} --limit {{config.params.limit_samples}}
  {% endif %}'
framework_name: safety_eval
pkg_name: safety_eval
config:
  output_dir: /workspace/results
  params:
    limit_samples: null
    max_new_tokens: 1000
    max_retries: 5
    parallelism: 10
    task: aegis_v2
    temperature: 1.0e-07
    request_timeout: 30
    top_p: 0.9999999
    extra:
      judge:
        url: http://localhost:8888/v1/chat/completions
        model_id: null
        api_key: JUDGE_API_KEY
        parallelism: 32
        request_timeout: 60
        max_retries: 16
  supported_endpoint_types:
  - chat
  - completions
  type: aegis_v2
target:
  api_endpoint:
    api_key: API_KEY
    model_id: my-model
    stream: false
    type: chat
    url: http://localhost:8000/v1/chat/completions


Rendered command:

export API_KEY=$API_KEY  &&  export JUDGE_API_KEY=$JUDGE_API_KEY &&  safety-eval  --model-name  my-model --model-url http://localhost:8000/v1/chat/completions --model-type chat  --judge-url  http://localhost:8888/v1/chat/completions   --results-dir /workspace/results   --eval aegis_v2  --mut-inference-params max_tokens=1000,temperature=1e-07,top_p=0.9999999,timeout=30,concurrency=10,retries=5 --judge-inference-params concurrency=32,retries=5
```


# Judge configuration

Each evaluation require its own judge model deployed

1. Aegis_v2 - `llama-3.1-nemoguard-8b-content-safety`. The model is available as NIM. Please refer to:
* https://build.nvidia.com/nvidia/llama-3_1-nemoguard-8b-content-safety - For trying the model deployed at `https://integrate.api.nvidia.com/v1"`
* https://docs.nvidia.com/nim/llama-3-1-nemoguard-8b-contentsafety/latest/getting-started.html - For deploying the model locally



2. Wildguard - `allenai/wildguard`. Please refer to:
* https://huggingface.co/allenai/wildguard - for ModelCard on HuggingFace

The evaluation setup was tested and run with wildguard deployed through [vLLM](https://docs.vllm.ai/en/latest/) version `v0.8.5`

```bash
docker run -it  vllm/vllm-openai:v0.8.5 --model allenai/wildguard
```
