ARTI

A PyTorch runtime for composable tensor programs.

ARTI lets a model work with tensors through explicit, versioned components: programs, retrieval, Formula Fabric operations, resources, and bounded execution. It is useful when a model needs more than a fixed sequence of layers, while still keeping tensor contracts, component identity, and artifacts inspectable.

ARTI does not prescribe a tokenizer, model family, task head, or training loop. Applications define the meaning of their tensors; ARTI provides the program runtime around them.

Install

The PyPI distribution is arti-fit; the Python package is arti.

uv add "arti-fit==3.1.0a3"

ARTI requires Python 3.10+ and PyTorch 2.2+. Install the PyTorch build appropriate for your CPU or CUDA environment first.

Optional integrations are opt-in:

uv add "arti-fit[qwen]==3.1.0a3"
uv add "arti-fit[sd]==3.1.0a3"
uv add "arti-fit[web]==3.1.0a3"

Start With A Program Host

ARTILayer is the tensor-in/tensor-out host boundary. With no configured program it is an exact identity, making it safe to attach before selecting a program graph.

import torch
import arti

x = torch.randn(2, 16, 64)
layer = arti.ARTILayer()

assert torch.equal(layer(x), x)
assert arti.component_ref(layer) == arti.canonical_contract_reference("arti/layer@3")

For configured execution, build a FederatedProgram or a ProgramGraph from arti.mechanisms, then provide it to ARTILayer. The program owns its local iteration, Formula Fabric operations, resource bindings, and traversal; the host layer preserves the ordinary tensor boundary.

Core Concepts

These components are optional. A program can use only the pieces its tensor contract needs.

Attach To A Host Model

ARTI can attach a configured layer at selected tensor boundaries without rewriting the host model class.

import arti

layer = arti.ARTILayer()
model = arti.ARTI.attach(model, layer, layers="model.layers.*")

model.arti.save("adapter.arti.st")
restored = arti.ARTI.load(fresh_model, "adapter.arti.st", layer=layer)

Attachment preserves the host model type and restores tensor-tree structure, dtype, layout, and position at the boundary. See the Unified Attachment guide.

Artifacts And Component Identity

arti.save() and arti.load() use SafeTensors alongside architecture metadata and SHA-256 locks. Component identities, component contracts, and package versions are separate. This lets a saved program state declare exactly which components and tensor contracts it requires.

Named compatible Banks and program assets can be composed without silently losing source identity. Persistent resources are explicit program state rather than hidden Python side effects.

Status

ARTI 3.1.0a3 is an alpha release. ARTILayer, component identity, artifact contracts, Formula Fabric, program graphs, Federal execution, and resource operations are available for structured experimentation and integration.

Public API is not yet frozen for the alpha program/runtime surfaces. Alpha does not claim broad downstream superiority, a production service SLA, full JAX parity, or universal acceleration. Dynamic execution only becomes faster when the selected program, tensor shapes, and backend can be compiled efficiently.

The arti.legacy namespace contains retired compatibility readers for historical artifacts. New code should use arti, arti.nn, and arti.mechanisms.

Documentation

• Stable Release Surface
• Unified Attachment
• Formula Fabric
• Federal Contracts
• Program Roles And Resources
• Operable Tensor Port
• Component Provenance
• Public API Reference

Development

uv sync --locked --extra dev
uv run --extra dev pytest
uv build

ARTI is released under the MIT License. Citation metadata is in CITATION.cff.