AI Alliance Website GitHub Organization

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
repo dashboard issues discussions	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: Taxonomy: details, current work Evaluation implementations: details, current work Leaderboards: details, current work Reference Runtime Stack: details, current work. Key components include lm-evaluation-harness and unitxt (see also below).
Ranking AI Safety Priorities by Domain
repo dashboard issues discussions	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
repo issues discussions	Introduction to T&S with guidance from diverse experts.
unitxt
repo issues discussions	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. (Principal 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
repo dashboard issues discussions	A repository of functions consumable by other agent frameworks.
Proscenium
repo dashboard issues discussions	A lightweight, composable library and several demo applications.
The Living Guide to Applying AI
repo issues discussions	Tips from experts on using AI for various applications, including popular design patterns.
AI Application Testing for Developers
repo dashboard issues discussions	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!)
Interest form	Domain Expert Agents (DXA) for industrial AI. (Principal developer: Aitomatic)
Docling
repo issues discussions	Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem. (Principal 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
repo issues discussions	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
repo dashboard issues discussions	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: Define Openness Criteria: details, current work Find Diverse Datasets: details, current work Data Pipelines to Validate Datasets: details, current work Open Dataset Catalog: details, current work
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. (Principal 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
repo dashboard issues discussions	TerraTorch is a library based on PyTorch Lightning and the TorchGeo domain library for geospatial data. (Principal developer: IBM Research)
GEO-bench
repo issues	GEO-Bench is a General Earth Observation benchmark for evaluating the performance of large pre-trained models on geospatial data. (Principal 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
repo issues discussions	A workshop at AAAI 25 that explored practical challenges using AI.

Additional Links

Contributing: We welcome your contributions! Here’s how you can contribute.
About Us: More about the AI Alliance and this project.

For More Information

The AI Alliance GitHub Organization
- This documentation’s GitHub repo
- The microsite template: The template used for Alliance projects, including all the websites listed above. See the README-template.md for instructions.
The AI Alliance website: About the AI Alliance, our goals and initiatives.
- Learn more about getting involved.

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