Publish verified OpenSysOne training snapshot pointer and model card

CURRENT_SNAPSHOT.json

@@ -0,0 +1,14 @@
+{
+  "repo_id": "andyshu/opensysone",
+  "snapshot_id": "20260917T022835Z-hf-backup",
+  "path": "snapshots/20260917T022835Z-hf-backup",
+  "payload_commit": "2d5c26ab7929b8c858aeea88842781fdbf79cd54",
+  "source_commit": "93602e6bbc494c1aa225f1b8a540fa99f5669771",
+  "training_source_commits": [
+    "4a60423c39d70f8d50472ce4f4f7fa4a4bd9fce1"
+  ],
+  "manifest_path": "publications/20260917T022835Z-hf-backup/93602e6bbc494c1aa225f1b8a540fa99f5669771/manifest.json",
+  "manifest_sha256": "f47912cbf5d81fad8a231eddf7dad0063fb8f96b9052890e9eb9bd1d532beff7",
+  "published_utc": "2026-09-17T02:53:53.038652+00:00",
+  "calibrated": false
+}

README.md

@@ -1,3 +1,110 @@
 ---
 license: unknown
+pipeline_tag: text-classification
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+- Qwen/Qwen3.5-2B
+tags:
+- opensysone
+- decision-scoring
+- lora
+- experimental
 ---
+
+# OpenSysOne
+
+OpenSysOne is an experimental natural-language decision scorer. It scores an
+arbitrary state, question and candidate answer, then normalizes mutually exclusive
+choices into probabilities. It does not generate chat responses.
+
+This repository backs up the ongoing three-machine training experiment. It
+contains custom adapter/head checkpoints, reproducible source, run provenance
+and small result files. The full pretrained base weights are referenced by pinned
+revision and are not duplicated here. **These are custom OpenSysOne artifacts,
+not a drop-in Transformers or PEFT adapter repository.**
+
+## Current status
+
+The snapshot is **training-stage, uncalibrated**. Final held-out evaluation and
+calibrated deployment have not completed. The original campaign ends on
+**17 September 2026 at 18:16:10 UTC**, with training stopping by 16:00 UTC.
+`CURRENT_SNAPSHOT.json` identifies the backup and its source revision.
+`FINAL_MODEL.json` will identify the separately verified calibrated release when
+finalization and upload succeed; its absence means no final release is recorded.
+
+Validation audit on 17 September, 02:10–02:15 UTC:
+
+| Selected candidate | Step | Validation accuracy | Crossfit selection NLL |
+| --- | ---: | ---: | ---: |
+| Qwen3 4B, main run | 2,500 | 93.55% | 0.188640 |
+| Qwen3 4B, lower learning rate | 2,500 | 92.58% | 0.218012 |
+| Qwen3.5 2B | 2,000 | 89.84% | 0.255294 |
+
+All use the same 512 validation decisions. Selection uses four source-group-
+disjoint temperature-crossfit folds with fixed seed 431; smaller macro-family NLL
+is better. This is **validation-selection evidence**, not a held-out quality claim.
+The 2B run stopped cleanly at step 6,000 after eight evaluations without a new
+best. Both 4B runs continued, and another lower-rate 4B refinement was started
+from the main run's selected weights. See `source/NEXT_STEPS.md` for findings and
+why independent trials were retained over unmeasured distributed training.
+
+## Contents and reconstruction
+
+- `source/`: a committed source/documentation snapshot, including the training
+  scripts, tests, Jev-compatible harness, findings and operational handover.
+- `snapshots/<id>/backup-manifest.json`: artifact roles, originating hosts/paths,
+  checkpoint steps, model/data/source provenance and SHA-256 checksums.
+- `snapshots/<id>/artifacts/<candidate>/best.pt`: selected adapter/head weights
+  and reconstruction metadata. These initial backups have no fitted temperature.
+- `snapshots/<id>/artifacts/<candidate>/checkpoint.pt`: resumable training state,
+  including Adam and random states. Its step may be later than the selected best.
+- `final/<campaign>/`: final calibrated model and evaluation evidence, created only
+  after successful finalization and publication.
+
+Keep `checkpoint.pt`, its sibling `best.pt` and matching validation evidence
+together when resuming. Checkpoint metadata records the original local paths;
+reconstruction needs the pinned base/data and matching project source. The current
+harness works on the recorded GX10/Spark layout. This backup does not establish
+portability to a new operating system or a generic hosted inference service.
+Model and optimizer files use PyTorch serialization; use the project's known,
+hash-verified artifacts and its supplied reconstruction code.
+
+The main 4B model uses FP32, rank-8 additive adapters (alpha 16) and a learned
+scalar head initialized from pretrained yes-minus-no logits: 16,517,633 trainable
+parameters. The 2B text decoder uses rank 16 / alpha 32, with 16,821,249 trainable
+parameters. Training limits are 512 and 768 complete-chat tokens respectively;
+1,024-token inference was separately verified. Training retains a 16 GiB per-job
+CUDA allocation cap. BF16 did not pass the project's numerical invariance gate.
+
+Pinned bases, recorded as Apache-2.0 in their provenance:
+
+- `Qwen/Qwen3-4B-Instruct-2507` at
+  `cdbee75f17c01a7cc42f958dc650907174af0554`.
+- `Qwen/Qwen3.5-2B` at
+  `15852e8c16360a2fea060d615a32b45270f8a8fc`.
+
+## Data, evaluation and limitations
+
+Public training sources are SNLI, BoolQ, ARC and four-choice Banking77 routing.
+Social IQA is a wholly untrained task-family holdout. Frozen source pins, licences,
+raw/split hashes and group/deduplication audits are included in
+`source/results/public-decisions-v1-manifest.json`. That manifest credits
+Stanford NLP (SNLI), Google (BoolQ), AllenAI (ARC/Social IQA) and PolyAI (Banking77),
+and records their original dataset licences. The original repository's
+`license: unknown` metadata is retained; no new licence for the project artifacts
+is assigned by this backup.
+
+Validation selects checkpoints. Separate calibration fits one global temperature
+only after selection is frozen. Final test/holdout comparisons use the unchanged
+pretrained scorer, with a separately fitted base temperature and source-group
+uncertainty. Frozen grouping and exact deduplication do not rule out pretraining
+contamination or semantic duplicates. Four-choice Banking77 is not the full
+77-label benchmark. No claim of general intelligence, Jev equivalence or
+calibration on unseen task families follows from the current validation scores.
+
+The harness implements local scoring, a loopback API, hosted Jev requests and
+response/timing comparisons. The local model identifies itself as OpenSysOne;
+compatibility with the request shape does not make it Jev. Local confidence is
+normalized entropy, not calibrated probability of correctness. Authenticated
+hosted Jev calls require `TYPESAFE_API_KEY` and have not been tested. Credentials,
+pretrained base weights and raw training datasets are not part of this backup.