About Artifex

Artifex is an open-source Python library developed by us at Tanaos for:

1. Using small, pre-trained task-specific LLMs locally on CPU
2. Fine-tuning them on CPU without any training data — just based on your instructions for the task at hand.

The problem

LLMs available on the market can be broadly classified into two categories:

• General-purpose LLMs (GPT, Claude, Llama, etc.) have two main limitations:

  1. They are designed for open-ended tasks, which makes them overkill and often suboptimal for simpler, specific use cases.
  2. If open-source, they require expensive GPUs for training and inference; if not open-source, they incur high costs for usage via APIs and have data privacy concerns since your data is sent to 3rd-party servers.

• Smaller LLMs (DistilBERT, TinyBERT, etc.) can sometimes be trained and run locally on CPU, but they require large amounts of labeled training data to perform well on specific tasks — which is often not available.

Artifex's solution

Artifex overcomes these limitations by enabling you to:

• Use small (capped at 500 MB in size), pre-trained task-specific LLMs locally on CPU, thereby eliminating costs and data privacy concerns.

• Fine-tune these models based on your requirements, without any training data — just based on your instructions for the task at hand — thereby obtaining higher accuracy on your specific use case.

  Details

  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.

Installation

Install Artifex via pip:

pip install artifex

Available Tasks

We currently support the following tasks:

• Text Classification: Performs general-purpose text classification based on the user requirements.
• 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.
• Spam Detection: Identifies whether a message is spam or not.
• Topic Classification: Classifies text into predefined topics.

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

Available APIs

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 (or train) 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.