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Small Language Model Inference, Fine-Tuning and Observability.

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# Artifex

Small Language Model Inference, Fine-Tuning and Observability. No GPU, no labeled data needed.

---

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.
 <details>
 <summary>How is it possible?</summary>
 Artifex generates synthetic training data on-the-fly based on your instructions, and uses this data to fine-tune Small Language Models for your specific task. This approach allows you to create effective models without the need for large labeled datasets.
 </details>
3. **Tracking model performance locally** with built-in evaluation and monitoring tools.

## Why Artifex?

Modern AI workflows are often

- Expensive (API usage, GPUs)
- Dependent on third-parties
- Data-hungry (require large labeled datasets)

Artifex changes that:

- Run tiny models locally on CPU (100M params, 500MB)
- Keep all data private (no API required)
- Generate synthetic data automatically for fine-tuning
- Fine-tune models for specific tasks (moderation, NER, classification, etc.)

## Available Models & Tasks

At this time, Artifex supports the following models:

| Task | Available Languages | Description | Default Model | How to use |
|------|----------|-------------|---------------|------------|
| Guardrail | English, German, Spanish | Flags unsafe, harmful, or off-topic messages. | [tanaos/tanaos-guardrail-v2](https://huggingface.co/tanaos/tanaos-guardrail-v2) (English version, see the page for the other languages) | [Examples](https://docs.tanaos.com/artifex/guardrail/code-examples/)
| Intent Classification | English | Classifies user messages into predefined intent categories. | [tanaos/tanaos-intent-classifier-v1](https://huggingface.co/tanaos/tanaos-intent-classifier-v1) | [Examples](https://docs.tanaos.com/artifex/intent-classifier/code-examples/)
| Reranker | English | Ranks a list of items or search results based on relevance to a query. | [cross-encoder/mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1) | [Examples](https://docs.tanaos.com/artifex/reranker/code-examples/)
| Sentiment Analysis | English | Determines the sentiment (positive, negative, neutral) of a given text. | [tanaos/tanaos-sentiment-analysis-v1](https://huggingface.co/tanaos/tanaos-sentiment-analysis-v1) | [Examples](https://docs.tanaos.com/artifex/sentiment-analysis/code-examples/)
| Emotion Detection | English | Identifies the emotion expressed in a given text. | [tanaos/tanaos-emotion-detection-v1](https://huggingface.co/tanaos/tanaos-emotion-detection-v1) | [Examples](https://docs.tanaos.com/artifex/emotion-detection/code-examples/)
| Named Entity Recognition | English | Detects and classifies named entities in text (e.g., persons, organizations, locations). | [tanaos/tanaos-NER-v1](https://huggingface.co/tanaos/tanaos-NER-v1) | [Examples](https://docs.tanaos.com/artifex/named-entity-recognition/code-examples/)
| Text Anonymization | English | Removes personally identifiable information (PII) from text. | [tanaos/tanaos-text-anonymizer-v1](https://huggingface.co/tanaos/tanaos-text-anonymizer-v1) | [Examples](https://docs.tanaos.com/artifex/text-anonymization/code-examples/)
| Spam Detection | English, German, Spanish, Italian | Identifies whether a message is spam or not. | [tanaos/tanaos-spam-detection-v1](https://huggingface.co/tanaos/tanaos-spam-detection-v1) (English version, see the page for the other languages) | [Examples](https://docs.tanaos.com/artifex/spam-detection/code-examples/)
| Topic Classification | English | Classifies text into predefined topics. | [tanaos/tanaos-topic-classification-v1](https://huggingface.co/tanaos/tanaos-topic-classification-v1) | [Examples](https://docs.tanaos.com/artifex/topic-classification/code-examples/)
| Text Summarization | English | Rewrites text in a more concise way. | [tanaos/tanaos-text-summarization-v1](https://huggingface.co/tanaos/tanaos-text-summarization-v1) | [Examples](https://docs.tanaos.com/artifex/text-summarization/code-examples/)

Looking for models in other languages? Our [Enterprise License](#license-paid--enterprise-solutions) includes models in any language. Reach out at [info@tanaos.com](mailto:info@tanaos.com) for more details.

For each model, Artifex provides:
1. **Inference API** to use a default, pre-trained Small Language Model 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.
3. **Load API** to load your fine-tuned model locally on CPU, and use it for inference or further fine-tuning.
4. **Built-in, automatic evaluation** and monitoring tools to track model performance over time, locally on your machine.

## Quick Start

Install Artifex with:

```bash
pip install artifex
```

### 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:

```python
from artifex import Artifex

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

# >>> [{'is_safe': False, 'scores': {'violence': 0.625, 'non_violent_unethical': 0.0066, 'hate_speech': 0.0082, 'financial_crime': 0.0072, 'discrimination': 0.0029, 'drug_weapons': 0.6633, 'self_harm': 0.0109, 'privacy': 0.003, 'sexual_content': 0.0029, 'child_abuse': 0.005, 'terrorism_organized_crime': 0.1278, 'hacking': 0.0096, 'animal_abuse': 0.009, 'jailbreak_prompt_inj': 0.0131}}]
```

Learn more about the default guardrail model and what it considers safe vs unsafe on our [Guardrail HF model page](https://huggingface.co/tanaos/tanaos-guardrail-v2).

#### 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:

```python
from artifex import Artifex

guardrail = Artifex().guardrail()

model_output_path = "./output_model/"

guardrail.train(
    unsafe_categories = {
        "violence": "Content describing or encouraging violent acts",
        "bullying": "Content involving harassment or intimidation of others",
        "misdemeanor": "Content involving minor criminal offenses",
        "vandalism": "Content involving deliberate destruction or damage to property"
    },
    output_path=model_output_path
)

guardrail.load(model_output_path)
print(guardrail("I want to destroy public property."))

# >>> [{'is_safe': False, 'scores': {'violence': 0.592, 'bullying': 0.0066, 'misdemeanor': 0.672, 'vandalism': 0.772}}]
```

### 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:

```python
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:

```python
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 our [Documentation](https://docs.tanaos.com/artifex).

## Enterprise solutions

Enterprise models are available for additional features and support. Contact us at [info@tanaos.com](mailto:info@tanaos.com) for more details. Enterprise features include:

- **Higher-Performance Models**
    - Improved accuracy
    - Better handling of edge cases
    - Reduced false positives/negatives
- **Custom Models**
    - Models fine-tuned on your specific data and requirements
    - Support for any language, domain or task
- **Production-Ready Models**
    - Models trained on 1000x more data
    - 10x lower inference latency
- **Dedicated Support**
    - Priority support
    - Custom feature requests
    - Dedicated onboarding and training

## Contributing

Contributions are welcome! Whether it's a bug fix or a new feature you want to add, we'd love your help. Check out our [Contribution Guidelines](CONTRIBUTING.md) to get started.

## Documentation & Support

- Full documentation: https://docs.tanaos.com/artifex
- Get in touch: info@tanaos.com

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