| ---
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| license: apache-2.0
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| language:
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| - en
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| pipeline_tag: text-generation
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| tags:
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| - water-treatment
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| - drinking-water
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| - critical-infrastructure
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| - gemma
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| - fine-tuning
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| ---
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|
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| # PotableLM
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| ## Model Summary
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| PotableLM is a planned domain-adapted model family for drinking water treatment operations, built on the [Potable Dataset](https://huggingface.co/datasets/boxwrench/potable) — an expert-curated corpus of operational water treatment knowledge.
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| Two tracks are planned: a municipal track for licensed plant operators (on-premises deployable) and a developing regions track for community water workers (offline-capable, fully open).
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| No model weights have been released yet. This page establishes the project's intended scope while development continues.
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|
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| ## Intended Use
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| The model is intended as a technical assistant for:
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| - licensed operators
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| - utility staff
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| - trainers and technical reviewers
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| - researchers evaluating domain adaptation in critical infrastructure
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| Primary target behaviors:
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| - practical operational reasoning
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| - troubleshooting support
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| - calculation walkthroughs
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| - technically grounded explanations in operator voice
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| ## Out-of-Scope Use
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| - direct control of treatment processes
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| - fully autonomous safety-critical decision-making
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| - compliance interpretation without human review
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| - replacement for plant procedures, regulations, or licensed judgment
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| ## Base Model
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| Base model selection is ongoing. The project prioritizes permissive licensing, local deployment potential, and strong fine-tuning characteristics.
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| ## Training Data
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| The model will be trained on the [Potable Dataset](https://huggingface.co/datasets/boxwrench/potable), an expert-curated corpus covering treatment process knowledge, plant operations, troubleshooting, calculations, and regulatory context. Every example is authored or reviewed by a licensed operator.
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| ## Training Procedure
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| Training procedure will be documented with the first checkpoint release.
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| ## Evaluation
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| No benchmark results are published yet. Evaluation details will accompany each released checkpoint.
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| ## Risks and Limitations
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| - Water treatment advice is context-dependent and should not be generalized blindly across plants.
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| - Model outputs can be plausible and still wrong.
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| - The model must be treated as an assistant, not an authority.
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| - Current and local regulations always override model output.
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| ## License
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| License will be specified with each released checkpoint.
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| ## Contact
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| Keith Wilkinson
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| Operational Inference — [operationalinference.com](https://operationalinference.com)
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| GitHub: [boxwrench](https://github.com/boxwrench)
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| Writing: [title22.org](https://title22.org)
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|
