The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources

TL;DR

The paper addresses the urgent need for responsible foundation-model development tooling by compiling the Foundation Model Development Cheatsheet, a repository of 250+ resources across text, vision, and speech. It details a methodology for curating practical tools aligned to development phases, and presents findings on major gaps in data sourcing, provenance, multilingual coverage, and reproducibility of evaluations. Through structured recommendations and a critical review of existing resources, the work emphasizes data-centric benchmarks, system-level evaluation, and transparent documentation and licensing. Overall, the cheatsheet serves as a community-driven, open resource to guide smaller teams toward safer, more efficient, and more auditable foundation-model development and deployment.

Abstract

Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet: a growing collection of 250+ tools and resources spanning text, vision, and speech modalities. We draw on a large body of prior work to survey resources (e.g. software, documentation, frameworks, guides, and practical tools) that support informed data selection, processing, and understanding, precise and limitation-aware artifact documentation, efficient model training, advance awareness of the environmental impact from training, careful model evaluation of capabilities, risks, and claims, as well as responsible model release, licensing and deployment practices. We hope this curated collection of resources helps guide more responsible development. The process of curating this list, enabled us to review the AI development ecosystem, revealing what tools are critically missing, misused, or over-used in existing practices. We find that (i) tools for data sourcing, model evaluation, and monitoring are critically under-serving ethical and real-world needs, (ii) evaluations for model safety, capabilities, and environmental impact all lack reproducibility and transparency, (iii) text and particularly English-centric analyses continue to dominate over multilingual and multi-modal analyses, and (iv) evaluation of systems, rather than just models, is needed so that capabilities and impact are assessed in context.

Figures (2)

• Figure 1: The phases of model development by which this survey and review paper is organized.
• Figure 2: The phases of model development by which this survey and review paper is organized.