faker-ai-provider

Faker provider for generating AI/ML-related fake data with correlated relationships between models, companies, architectures, and capabilities.

Model Data Updated: mid-October 2026 — 295 models from 32 vendors (includes Claude Haiku 5.5, GPT-6.1 Sol, Gemini 4 Argon, Muse Spark Open, Grok Voice 3.0, GLM-5.3-Flash, Cohere Embed v5, FLUX 3 Image Klein, MAI-Transcribe-2, Seedance 2.5, and more)

Installation

pip install faker-ai-provider

Quick Start

from faker import Faker
from faker_ai import AiProvider

fake = Faker()
fake.add_provider(AiProvider)

# Generate correlated AI data
fake.ai_model()  # 'Claude Opus 4.7'
fake.ai_company()  # 'Anthropic'
fake.full_ai_model_spec()  # 'gpt-oss-120b by OpenAI: Transformer architecture, 120B parameters, for reasoning.'

Seeding for Reproducibility

Use Faker's seeding to generate consistent, reproducible data across runs:

fake = Faker()
fake.add_provider(AiProvider)
fake.seed_instance(42)

# These will always return the same values with seed 42
print(fake.ai_model())  # Always 'LLaMA 3 70B'
print(fake.ai_company())  # Always 'Apple'

Available Methods

Basic Methods

Correlation Methods

Composite Methods

Advanced Usage

Populate a Database with AI Records

from faker import Faker
from faker_ai import AiProvider

fake = Faker()
fake.add_provider(AiProvider)

# Generate 100 AI deployment records
deployments = [fake.ai_deployment() for _ in range(100)]

# Generate experiment tracking data
experiments = [fake.ai_experiment() for _ in range(50)]

Generate ML Pipeline Configuration

fake.seed_instance(42)  # Reproducible pipeline

pipeline = {
    "name": f"pipeline-{fake.random_int(1000, 9999)}",
    "training": fake.ai_training_run(),
    "deployment": fake.ai_deployment(),
    "experiment": fake.ai_experiment(),
}

Filter Models by Capability

# Get all models that support code generation
code_models = fake.ai_models_for_task("code-generation")

# Get all diffusion models
diffusion_models = fake.ai_models_by_architecture("Diffusion")

# Get all multimodal models
video_models = fake.ai_models_by_modality("video")

Model Scenario

Get complete, correlated model information:

scenario = fake.model_scenario()
# {
#     'model': 'GPT-5.2',
#     'company': 'OpenAI',
#     'architecture': 'Transformer',
#     'modality': ['text', 'image', 'audio', 'video'],
#     'tasks': ['text-generation', 'reasoning', 'code-generation', ...],
#     'parameters': 'undisclosed',
#     'release_year': 2026
# }

License

MIT