artifex 0.4.1

Create your private AI model with no training data or GPUs 🤖🚀.

Artifex

Create Task-Specific SLMs • No training data needed • No GPU needed • CPU Inference & Fine-Tuning

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Artifex is a Python library for:

1. Using pre-trained task-specific Small Language Models on CPU
2. Fine-tuning them on CPU without any training data — just based on your instructions for the task at hand.
   How is it possible?

   Artifex generates synthetic training data on-the-fly based on your instructions, and uses this data to fine-tune small LLMs for your specific task. This approach allows you to create effective models without the need for large labeled datasets.

At this time, we support 8 tasks:

• Text Classification: Classifies text into user-defined categories.
• Guardrail: Flags unsafe, harmful, or off-topic messages.
• Intent Classification: Classifies user messages into predefined intent categories.
• Reranker: Ranks a list of items or search results based on relevance to a query.
• Sentiment Analysis: Determines the sentiment (positive, negative, neutral) of a given text.
• Emotion Detection: Identifies the emotion expressed in a given text.
• Named Entity Recognition (NER): Detects and classifies named entities in text (e.g., persons, organizations, locations).
• Text Anonymization: Removes personally identifiable information (PII) from text.

For each task, Artifex provides three easy-to-use APIs:

1. Inference API to use a default, pre-trained small LLM to perform that task out-of-the-box locally on CPU.
2. Fine-tune API to fine-tune the default model based on your requirements, without any training data and on CPU. The fine-tuned model is generated on your machine and is yours to keep.
3. Load API to load your fine-tuned model locally on CPU, and use it for inference or further fine-tuning.

We will be adding more tasks soon, based on user feedback. Want Artifex to perform a specific task? Suggest one or vote one up.

Use Cases & Tutorials

• Cut your chatbot costs and latency by 40% by using a small, self-hosted Guardrail model.
• Analyze your users' sentiment without sending their data to third-party servers.
• Anonymize user data locally and stay GDPR-compliant.

Quick Start

Install Artifex with:

pip install artifex

Text Classification model

Create & use a custom Text Classification model

Train your own text classification model, use it locally on CPU and keep it forever:

from artifex import Artifex

model_output_path = "./output_model/"

text_classification = Artifex().text_classification

text_classification.train(
    domain="chatbot conversations",
    classes={
        "politics": "Messages related to political topics and discussions.",
        "sports": "Messages related to sports events and activities.",
        "technology": "Messages about technology, gadgets, and software.",
        "entertainment": "Messages about movies, music, and other entertainment forms.",
        "health": "Messages related to health, wellness, and medical topics.",
    },
    output_path=model_output_path
)

text_classification.load(model_output_path)

print(text_classification("What do you think about the latest AI advancements?"))

# >>> [{'label': 'technology', 'score': 0.9913}]

Guardrail Model

Use the default Guardrail model

Use Artifex's default guardrail model, which is trained to flag unsafe or harmful messages out-of-the-box:

from artifex import Artifex

guardrail = Artifex().guardrail
print(guardrail("How do I make a bomb?"))

# >>> [{'label': 'unsafe', 'score': 0.9976}]

Learn more about the default guardrail model and what it considers safe vs unsafe on our Guarderail HF model page.

Create & use a custom Guardrail model

Need more control over what is considered safe vs unsafe? Fine-tune your own guardrail model, use it locally on CPU and keep it forever:

from artifex import Artifex

guardrail = Artifex().guardrail

model_output_path = "./output_model/"

guardrail.train(
    unsafe_content=[
        "Discussing a competitor's products or services.",
        "Sharing our employees' personal information.",
        "Providing instructions for illegal activities.",
    ],
    output_path=model_output_path
)

guardrail.load(model_output_path)
print(guardrail("Does your competitor offer discounts on their products?"))

# >>> [{'label': 'unsafe', 'score': 0.9970}]

Reranker model

Use the default Reranker model

Use Artifex's default reranker model, which is trained to rank items based on relevance out-of-the-box:

from artifex import Artifex

reranker = Artifex().reranker

print(reranker(
    query="Best programming language for data science",
    documents=[
        "Java is a versatile language typically used for building large-scale applications.",
        "Python is widely used for data science due to its simplicity and extensive libraries.",
        "JavaScript is primarily used for web development.",
    ]
))

# >>> [('Python is widely used for data science due to its simplicity and extensive libraries.', 3.8346), ('Java is a versatile language typically used for building large-scale applications.', -0.8301), ('JavaScript is primarily used for web development.', -1.3784)]

Create & use a custom Reranker model

Want to fine-tune the Reranker model on a specific domain for better accuracy? Fine-tune your own reranker model, use it locally on CPU and keep it forever:

from artifex import Artifex

reranker = Artifex().reranker

model_output_path = "./output_model/"

reranker.train(
    domain="e-commerce product search",
    output_path=model_output_path
)

reranker.load(model_output_path)
print(reranker(
    query="Laptop with long battery life",
    documents=[
        "A powerful gaming laptop with high-end graphics and performance.",
        "An affordable laptop suitable for basic tasks and web browsing.",
        "This laptop features a battery life of up to 12 hours, perfect for all-day use.",
    ]
))

# >>> [('This laptop features a battery life of up to 12 hours, perfect for all-day use.', 4.7381), ('A powerful gaming laptop with high-end graphics and performance.', -1.8824), ('An affordable laptop suitable for basic tasks and web browsing.', -2.7585)]

Other Tasks

For more details and examples on how to use Artifex for the other available tasks, check out the Available Tasks section below and our Documentation.

Available Tasks & Examples

All models displayed below can be used out-of-the-box on CPU and can be fine-tuned on CPU.

Task	Default Model	Default & Fine-Tuned Model Size	Code Examples
Text Classification	No default model — must be trained	0.1B params, 470Mb	Examples
Guardrail	tanaos/tanaos-guardrail-v1	0.1B params, 500Mb	Examples
Intent Classification	tanaos/tanaos-intent-classifier-v1	0.1B params, 500Mb	Examples
Reranker	cross-encoder/mmarco-mMiniLMv2-L12-H384-v1	0.1B params, 470Mb	Examples
Sentiment Analysis	tanaos/tanaos-sentiment-analysis-v1	0.1B params, 470Mb	Examples
Emotion Detection	tanaos/tanaos-emotion-detection-v1	0.1B params, 470Mb	Examples
Named Entity Recognition	tanaos/tanaos-NER-v1	0.1B params, 500Mb	Examples
Text Anonymization	tanaos/tanaos-text-anonymizer-v1	0.1B params, 500Mb	Examples

Contributing

Contributions are welcome! Whether it's a new task module, improvement, or bug fix — we’d love your help. Not ready to contribute code? You can also help by suggesting a new task or voting up any suggestion.

git clone https://github.com/tanaos/artifex.git
cd artifex
pip install -e .

Before making a contribution, please review the CONTRIBUTING.md and CLA.md, which include important guidelines for contributing to the project.

FAQs

• Why having Guardrail, Intent Classification, Emotion Detection, Sentiment Analysis etc. as separate tasks, if you already have a Text Classification task?

  The Text Classification task is a general-purpose task that allows users to create custom classification models based on their specific needs. Guardrail, Intent Classification, Emotion Detection, Sentiment Analysis etc. are specialized tasks with pre-defined categories and behaviors that are commonly used in various applications. They are provided as separate tasks for two reasons: first, convenience (users can quickly use these models without needing to define their own categories); second, performance (the specialized model typically performs better than re-defining the same model through the general Text Classification model).

Documentation & Support

• Full documentation: https://docs.tanaos.com/artifex
• Get in touch: [e-mail]