Expand org README: community + open-foundations framing

README.md

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-<h2 align="center">SMALL MODELS FOR GLAM</h2>
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+<h1>Small Models for GLAM</h1>
 
 ![Demo](https://cdn-uploads.huggingface.co/production/uploads/60107b385ac3e86b3ea4fc34/yfg8gNmfri4XSS5Oazkbm.gif)
 
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-Lightweight AI models for cultural heritage institutions
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-<sub>More coming soon. Follow this organization to get notified.</sub>
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+Most of what gets done in libraries, archives and museums runs on a long tail of small, repetitive jobs — backlogs to clear, scans to make searchable, metadata to tidy. A good chunk of that work can be handled by small, task-specific models, and the people who know what those tasks are are the people working in those institutions.
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+This org is a place to put the models that come out of that work, so the next institution facing the same problem doesn't start from scratch.
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+Each model here builds on something. Most are fine-tunes of open foundation models — YOLO, DETR, BERT, Qwen-VL — trained on community datasets, often from [BigLAM](https://huggingface.co/biglam) or contributed by individual institutions. Several extend existing community-trained models for new collections rather than starting over: [index-card-detector-v5](https://huggingface.co/small-models-for-glam/index-card-detector-v5) takes the National Library of Scotland's archival card detector and extends it to three additional archives. That extension pattern matters — it's how this kind of work gets cheaper for everyone over time.
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+Recipes for most of the models live in [AI Patterns for GLAM](https://danielvanstrien.xyz/ai-patterns-for-glam/); [The Case for Boring AI](https://danielvanstrien.xyz/ai-patterns-for-glam/discovery/boring-ai.html) and [Beyond Chatbots](https://danielvanstrien.xyz/ai-patterns-for-glam/discovery/beyond-chatbots.html) set out the why.
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+## How the models get built
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+Mostly with agentic workflows: an agent handles the data prep, training, and packaging; a human stays in the loop for the parts that matter — label review, evaluation, deciding whether something is good enough to release.
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+## Share a model, or suggest one
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+If you've trained a small task-specific model for your own collection, share it in [Discussions](https://huggingface.co/spaces/small-models-for-glam/README/discussions) and we'll add good ones to a curated collection so other institutions can find them. Suggestions for tasks you'd like to see covered are welcome there too.
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+Maintained by [Daniel van Strien](https://huggingface.co/davanstrien) and [William Mattingly](https://huggingface.co/wjbmattingly), with contributions and datasets from across the GLAM ML community.