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AI Alliance GitHub Organization

Authors AI Alliance Team
Last Update V0.1.7, 2025-04-23

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The AI Alliance Projects

Welcome to the The AI Alliance GitHub Organization, where the Alliance members collaborate on technical projects, AI technology guides, and related projects. See about us for more information about The AI Alliance.

The following projects are organized into focus areas and include projects owned and developed by the AI Alliance (indicated with the AI Alliance icon icon) and several other projects with active AI Alliance participation.

Focus Area: Trust and Safety

How do we know that applications built with AI are trustworthy, that they perform as required, in particular that they are safe, free of harmful outputs?

Links Description
Trust and Safety Evaluations Initiative AI Alliance icon
TSEI seeks to define the global taxonomy of evaluations (from safety to performance to efficacy), catalog available implementations of them, and provide a reference stack of industry-standard tools to run the evaluations. Our projects:
Ranking AI Safety Priorities by Domain AI Alliance icon
What are the most important safety concerns for your specific domain and use cases? This project explores these questions in several industries, healthcare, finance, education, and legal, with more to come.
The AI Trust and Safety User Guide AI Alliance icon
Introduction to T&S with guidance from diverse experts.
unitxt
Unitxt is a Python library for enterprise-grade evaluation of AI performance, offering the world's largest catalog of tools and data for end-to-end AI benchmarking. (Principle developer: IBM Research)

Focus Area: Applications and Tools

Real-world use of AI involves more than just models. What application patterns best complement the strengths and weaknesses of models? Are there domain-specific considerations?

Links Description
Gofannon AI Alliance icon
A repository of functions consumable by other agent frameworks.
Proscenium AI Alliance icon
A lightweight, composable library and several demo applications.
The Living Guide to Applying AI AI Alliance icon
Tips from experts on using AI for various applications, including popular design patterns.
AI Application Testing for Developers AI Alliance icon
If you are a software developer, you are accustomed to writing deterministic tests. What do you do when generative models aren't deterministic?
OpenDXA (coming soon!) AI Alliance icon
Domain Expert Agents (DXA) for industrial AI. (Principle developer: Aitomatic)
Docling
Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem. (Principle developer: IBM Research)

Focus Area: Diverse Hardware Enablement

While NVIDIA GPUs are the dominant AI accelerators, alternatives are useful for different price vs. performance trade offs, including deployments to edge devices, like phones. How do we ensure that the software stack we use supports different accelerator options?

Links Description
The AI Accelerator Software Ecosystem Guide AI Alliance icon
A guide to the most common AI accelerators and the software stacks they use to integrate with tools you know, like PyTorch.

Focus Area: Open Models and Datasets

Datasets with clear license for use, backed by unambiguous provenance and governance controls, are needed to train and tune models. A variety of models are needed, not just for English text, but multilingual, multimodal, and domain specific, like models for molecular discovery, geospatial, and time series.

Links Description
The Open, Trusted Data Initiative AI Alliance icon
Open data has clear license for use, across a wide range of topic areas, with clear provenance and governance. OTDI seeks to clarify the criteria for openness and catalog the world’s datasets that meet the criteria. Our projects:
Open Models
Several AI Alliance work groups are collaborating on the development of domain-specific models:
  • Semikong - The world's first open LLM tuned specifically for the semiconductor industry. (Principle developers: Aitomatic, Tokyo Electron Ltd., FPT Software, and The AI Alliance)
  • Llamarine (coming soon!) - An LLM tuned specifically for the needs of the maritime industry.
  • Materials and Chemistry work group (Several developers, including IBM Research):
    • smi-ted - SMILES-based Transformer Encoder-Decoder (SMILES-TED) is an encoder-decoder model pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, equivalent to 4 billion molecular tokens. SMI-TED supports various complex tasks, including quantum property prediction, with two main variants (289M and 8×289M).
    • selfies-ted - SMI-SSED (SMILES-SSED) is a Mamba-based encoder-decoder model pre-trained on a curated dataset of 91 million SMILES samples, encompassing 4 billion molecular tokens sourced from PubChem. The model is tailored for complex tasks such as quantum property prediction and offers efficient, high-speed inference capabilities.
    • mhg-ged - SELFIES-based Transformer Encoder-Decoder (SELFIES-TED) is an encoder-decoder model based on BART that not only learns molecular representations but also auto-regressively generates molecules. Pre-trained on a dataset of ~1B molecules from PubChem and Zinc-22.
    • smi-ssed - Molecular Hypergraph Grammar with Graph-based Encoder Decoder (MHG-GED) is an autoencoder that combines a GNN-based encoder with a sequential MHG-based decoder. The GNN encodes molecular input to achieve strong predictive performance on molecular graphs, while the MHG decodes structurally valid molecules. Pre-trained on a dataset of ~1.34M molecules curated from PubChem.
  • More to be announced soon.
TerraTorch
TerraTorch is a library based on PyTorch Lightning and the TorchGeo domain library for geospatial data. (Principle developer: IBM Research)
GEO-bench
GEO-Bench is a General Earth Observation benchmark for evaluating the performance of large pre-trained models on geospatial data. (Principle developer: ServiceNow)

Focus Area: Advocacy

Advocacy is about educating the public, policy officials, and others about the benefits of openness for AI, as well as the implications for safety and reliability.

Links Description
AAAI 25: Workshop on Open-Source AI for Mainstream Use AI Alliance icon
A workshop at AAAI 25 that explored practical challenges using AI.
  • Contributing: We welcome your contributions! Here’s how you can contribute.
  • About Us: More about the AI Alliance and this project.

For More Information

Version History

Version Date
V0.1.7 2025-04-23
V0.1.6 2025-04-07
V0.1.5 2025-04-02
V0.1.4 2025-02-28
V0.1.3 2025-01-15
V0.1.2 2025-01-08
V0.1.1 2024-11-15
V0.1.0 2024-11-05