OrcaColony studies how donated compute and careful experiments can improve language models that are small enough to train and test outside frontier labs.
The project combines:
The training system has passed local correctness, restart, recovery, and resource studies. The campaign framework accepts a research question, usage scenario, evaluator, inputs, metrics, comparisons, and analysis plan supplied by the campaign owner. It preserves exact revisions, model evaluations, evidence files, limitations, and contributor credit without assigning a mandatory success or promotion state.
Record Patch v1 is retained as a historical owner-operated prototype. Its 17.5M-parameter model reached 30 of 32 strict canonical JSON outputs after 512 updates but remained at 0 of 32 exact and semantic record matches. No donated compute was used. Its thresholds, holdouts, and proposed follow-up do not define the next OrcaColony campaign.
Negative, unchanged, and inconclusive results can be released with the same provenance and credit records as positive results. Training loss remains a diagnostic, not proof that a model became more useful.
Each model release can include the exact campaign, data revision, tokenizer, checkpoint, optimizer state, owner-defined evaluation records, bundled evidence artifacts, source revision, checksums, limitations, and linked agent findings.
People who donate accepted training work choose named, pseudonymous, or anonymous credit. Releases record contributor-approved totals and hardware details without publishing worker credentials or private identifiers.