Models¶

Anonymizer uses LLMs for entity detection, replacement, and rewriting. Models are configured via YAML and mapped to workflow roles.

Defaults¶

Set your API key for Anonymizer to use models hosted on build.nvidia.com.

export NVIDIA_API_KEY="your-nvidia-api-key"

Provider data handling

Anonymizer sends prompts and text snippets to your configured model provider. If data must stay in your trusted environment, use a trusted/local provider endpoint.

Alias	Model	Used by
`gliner-pii-detector`	`nvidia/gliner-pii`	Entity detection (NER)
`gpt-oss-120b`	`openai/gpt-oss-120b`	Detection validation & augmentation, replacement, rewriting
`nemotron-30b-thinking`	`nvidia/nemotron-3-nano-30b-a3b`	Latent detection, evaluation, final judge

Each pipeline stage has a role mapped to one of these aliases. See the full role list in the default configs: detection.yaml, replace.yaml, rewrite.yaml.

Custom providers¶

To use models from a different provider (OpenAI, OpenRouter, etc.), define a providers YAML:

# my_providers.yaml
providers:
  - name: openai
    base_url: https://api.openai.com/v1
    api_key_env_var: OPENAI_API_KEY
  - name: openrouter
    base_url: https://openrouter.ai/api/v1
    api_key_env_var: OPENROUTER_API_KEY

Make sure the environment variables are set:

export OPENAI_API_KEY="your-openai-api-key"
export OPENROUTER_API_KEY="your-openrouter-api-key"

anonymizer = Anonymizer(model_providers="my_providers.yaml")

Custom models¶

Override specific roles by passing a unified YAML path to Anonymizer(model_configs=...). The provider field references a provider by name -- use nvidia for build.nvidia.com, or a custom provider defined above.

# my_models.yaml
selected_models:
  detection:
    entity_detector: gliner-pii-detector
    entity_validator: gpt5
    entity_augmenter: gpt5
    latent_detector: claude-sonnet
  replace:
    replacement_generator: gpt5

model_configs:
  - alias: gliner-pii-detector
    model: nvidia/gliner-pii
    provider: nvidia
    inference_parameters:
      max_parallel_requests: 16
      timeout: 120
  - alias: gpt5
    model: gpt-5
    provider: openai
    inference_parameters:
      max_tokens: 4096
      temperature: 0.3
  - alias: claude-sonnet
    model: anthropic/claude-sonnet-4
    provider: openrouter
    inference_parameters:
      max_tokens: 8192
      temperature: 0.3

anonymizer = Anonymizer(
    model_configs="my_models.yaml",
    model_providers="my_providers.yaml",
)

You can pass model_configs as either a YAML file path or a YAML string.

Roles you don't override keep their default alias selections, but those aliases must still exist in your model_configs pool.

Validate your config

Use anonymizer.validate_config(config) (or anonymizer validate from the CLI) after changing model configs to catch alias mismatches before processing data.

Choosing custom models¶

For Anonymizer, the best overall leaderboard model is not always the best default for every role. Some roles are simple classification or constrained JSON generation tasks, while others require deeper reasoning about privacy risk, long-context rewriting, and leakage repair (see Risk tolerance).

Use benchmarks as signals for role fit, not as a single global ranking.

Most useful benchmark signals¶

Benchmark	What it predicts well in Anonymizer
`IFBench`	Following detailed instructions, producing constrained outputs, and obeying prompt rules.
`AA-Omniscience Accuracy`	Recovering the right facts without dropping important information.
`AA-Omniscience Non-Hallucination`	Avoiding invented entities, facts, or unsupported claims.
`AA-LCR`	Handling long prompts with tagged text, domain guidance, replacement maps, and evaluation context.
`Humanity's Last Exam` / `GPQA Diamond`	General reasoning depth for privacy-sensitive planning and rewriting.

Also consider operational constraints like latency, output speed, and verbosity, since they drive cost and practical throughput.

Practical guidance¶

Use your strongest models for latent_detector, disposition_analyzer, rewriter, and repairer.
Use mid-tier models for entity_augmenter, meaning_extractor, and replacement_generator.
Use smaller or faster models for entity_validator, domain_classifier, qa_generator, and evaluator.
Do not optimize every role for peak leaderboard rank. Optimize the hard-to-recover privacy and rewrite steps for quality. Optimize bounded steps for reliability per token.