docs(card): estate audit 2026-09-25 — honest labels, dead links, metadata

README.md

@@ -7,16 +7,44 @@ tags:
   - doctrine-v11
   - estate
   - hub
+  - training-scripts
+szl:
+  doctrine: v11-LOCKED
+  artifact_class: SCRIPTS_AND_RECEIPTS
+  weights: UNAVAILABLE
+  evals: NOT_APPLICABLE
+  publication_eligible: false
+  card_resync: 2026-09-25
 ---
 
 # szl-training-scripts
 
 The forge. Hub copy of the Unsloth-receipted training scripts. Tags were empty — this atelier is the card.
 
-**Family.** estate · **Evidence.** HUB · **Weights.** none
+**Family.** estate · **Evidence.** HUB · **Weights.** none (UNAVAILABLE on this Hub ID: scripts and receipts only)
 
 Hub: [SZLHOLDINGS/szl-training-scripts](https://huggingface.co/SZLHOLDINGS/szl-training-scripts)
 
+**Canonical source (GitHub):** the atelier kit lives in [`szl-holdings/a11oy`](https://github.com/szl-holdings/a11oy) under [`.github/atelier-kit/`](https://github.com/szl-holdings/a11oy/tree/main/.github/atelier-kit) (`receipted_unsloth.py`, `unsloth-recipes.json`, `cards/szl-training-scripts.md`, `kit-README.md`). The kit README names [`szl-holdings/szl-khipu`](https://github.com/szl-holdings/szl-khipu), [`szl-holdings/szl-forge`](https://github.com/szl-holdings/szl-forge) and [`szl-holdings/szl-nemo`](https://github.com/szl-holdings/szl-nemo) as its upstream sources. GitHub is canonical; this Hub repository is the mirror.
+
+## Hub tree (OBSERVED 2026-09-25)
+
+Listed through the Hub file API; the revision SHA was not captured by this pass. No weight file of any kind is present.
+
+| Path | Bytes | What it is (from the file's own header) |
+|---|---:|---|
+| `receipted_unsloth.py` | 12,165 | "Receipted Unsloth — take the speed, bind the knobs, refuse the folklore." Unsloth QLoRA trainer with one `PROFILES` entry per organ (khipu, khipu-r2, receiptagent, willay, chaski, chaski-5050, chaski-r2), house seed 20260721, `--receipt-only` (mint the recipe receipt, no GPU) and `--list`. Same file name and byte size as the a11oy atelier-kit copy; the first ~3 KB were compared and match, the full-file digest was not compared. |
+| `train_receipted_unsloth.py` | 12,431 | "Receipted Unsloth unique-cut GPU trainer. Runs on Hugging Face Jobs (A10G)." `uv run` script header; `--profile willay` / `--profile chaski`. |
+| `train_willay.py` | 6,799 | WILLAY signed-refusal SFT LoRA script (base `Qwen/Qwen2.5-0.5B-Instruct`, target `SZLHOLDINGS/WILLAY`). |
+| `train_cohort.py` | 17,351 | "CPU-train WILLAY + Chaski silhouettes. Measure kernels. No 1.5B retrain." NumPy silhouettes, seed 20260721, `--kernels-only`. |
+| `szl_khipu_eval.py` | 10,412 | KHIPU-R2 held-out eval port (eval.jsonl 5 navigate + adversarial.jsonl 6; fp16 and optional 4-bit NF4 comparison). |
+| `unsloth-recipes.json` | 16,247 | recipe table (same size as the atelier-kit copy). |
+| `receipts/chaski-unsloth.json`, `receipts/chaski-5050-unsloth.json`, `receipts/chaski-r2-unsloth.json`, `receipts/willay-unsloth.json` | 1,592 / 1,638 / 1,587 / 1,641 | `szl.training_receipt.v2` receipts minted by the profile trainer (unsigned). |
+| `training_receipt.unsloth.json` | 1,638 | profile `chaski-5050` receipt (`path_in_repo: adapter-unsloth`, train loss 0.5588 MEASURED, `evals: none-this-run`, energy UNAVAILABLE, `proven_trust: false`). |
+| `provenance.json` / `status.json` / `LICENSE` | 1,755 / 1,394 / 11,357 | license-candidate records and standalone Apache-2.0 text. `status.json` keeps lineage, consent, privacy, training-suitability and deployment fields BLOCKED and `production_ready: false`. |
+| `bom/model-bom.cdx.json` | 5,324 | CycloneDX BOM |
+| `.gitattributes` | 1,519 | platform-managed |
+
 ## The cut
 
 Training code is usually a gist. We give it a model id so the estate graph stays one piece.
@@ -29,9 +57,9 @@ A Hub id you can pin in a receipt: 'trained by this repo at this SHA'.
 |---|---|
 | Anthropic | No public train scripts for Claude. |
 | NVIDIA | NeMo recipes. |
-| Unsloth | The scripts wrap Unsloth. Cut is the receipt bind. |
+| Unsloth | The scripts wrap Unsloth v2 profiles. Cut is unique knobs per organ, bound in the receipt. |
 
-Nobody else ships this combination. That is the point of a one-of-one.
+No equivalent public combination was found among the leaders surveyed above (a snapshot, not an ecosystem-wide novelty claim).
 
 ## Intended use
 
@@ -40,6 +68,7 @@ Canonical train entrypoint.
 ## Limitations
 
 - Not a checkpoint. Wire to GitHub kit in this atelier.
+- The receipts here are unsigned recipe/run envelopes; a receipt on this Hub ID does not by itself bind any adapter bytes on another repository.
 
 ## Honesty
 
@@ -56,78 +85,32 @@ Apache-2.0. Copyright 2026 SZL Holdings · Stephen P. Lutar Jr. · ORCID [0009-0
 
 ## GitHub-aligned Python
 
+An earlier version of this card embedded a code block that stopped mid-statement (`if trainer.state.log_hi`) and that did not match the `receipted_unsloth.py` actually stored on this Hub ID (it showed an older 2,932-byte single-profile variant with `--base/--r/--seed` flags, which still exists as `atelier/receipted_unsloth.py` in szl-khipu and szl-forge). That block is removed. What follows is quoted verbatim from the 12,165-byte `receipted_unsloth.py` on this repository (2026-09-25 read).
+
+Usage lines from the module docstring:
+
+```text
+  python receipted_unsloth.py --profile willay --receipt-only
+  python receipted_unsloth.py --profile chaski --data doctrine.jsonl --out out/chaski
+```
+
+The receipt-minting tail of `main()` — the point of the file: every knob lands in `training_receipt.json`, loss is read from the trainer log, and the envelope is meant to be signed before any merge:
+
 ```python
-#!/usr/bin/env python3
-# receipted_unsloth.py
-# Silhouette: Unsloth FastLanguageModel QLoRA.
-# Cut: dataset SHA, LoRA knobs, seed, and final loss go into a training receipt
-# BEFORE merge. GGUF is derived — never the signed object.
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import time
-from pathlib import Path
-
-
-def sha256_file(p: Path) -> str:
-    h = hashlib.sha256()
-    with p.open("rb") as f:
-        for chunk in iter(lambda: f.read(1 << 20), b""):
-            h.update(chunk)
-    return h.hexdigest()
-
-
-def main() -> None:
-    ap = argparse.ArgumentParser()
-    ap.add_argument("--base", default="Qwen/Qwen2.5-1.5B-Instruct")
-    ap.add_argument("--data", default="doctrine.jsonl")
-    ap.add_argument("--out", default="out/adapter")
-    ap.add_argument("--r", type=int, default=16)
-    ap.add_argument("--seed", type=int, default=20260721)
-    ap.add_argument("--max-seq", type=int, default=2048)
-    args = ap.parse_args()
-
-    data_sha = sha256_file(Path(args.data))
-    from unsloth import FastLanguageModel
-    from datasets import load_dataset
-    from trl import SFTConfig, SFTTrainer
-
-    model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name=args.base,
-        max_seq_length=args.max_seq,
-        load_in_4bit=True,
-    )
-    model = FastLanguageModel.get_peft_model(
-        model,
-        r=args.r,
-        lora_alpha=args.r,
-        target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
-        lora_dropout=0,
-        bias="none",
-        use_gradient_checkpointing="unsloth",
-        random_state=args.seed,
-    )
-    ds = load_dataset("json", data_files=args.data, split="train")
-    trainer = SFTTrainer(
-        model=model,
-        tokenizer=tokenizer,
-        train_dataset=ds,
-        args=SFTConfig(
-            output_dir=args.out,
-            per_device_train_batch_size=2,
-            gradient_accumulation_steps=4,
-            max_steps=120,
-            learning_rate=2e-4,
-            logging_steps=10,
-            seed=args.seed,
-        ),
-    )
+    trainer = SFTTrainer(model=model, tokenizer=tokenizer, train_dataset=ds, args=SFTConfig(**sft_kw))
     t0 = time.time()
     trainer.train()
+    Path(args.out).mkdir(parents=True, exist_ok=True)
     model.save_pretrained(args.out)
     loss = None
-    if trainer.state.log_hi
+    if trainer.state.log_history:
+        last = trainer.state.log_history[-1]
+        loss = last.get("train_loss") or last.get("loss")
+    extra.update({"final_loss": loss, "seconds": round(time.time() - t0, 3), "honesty": "REPORTED"})
+    rec = mint_receipt(args.profile, cfg, extra)
+    Path(args.out, "training_receipt.json").write_text(json.dumps(rec, indent=2) + "\n")
+    print(json.dumps(rec, indent=2))
+    return 0
 ```
+
+`mint_receipt` refuses `proven_trust: true` and refuses any non-null `energy_j` ("refusing to fabricate joules"); the receipt records `"gguf": "derived — never the signed object"`. Read the full file on this repository or in the canonical kit linked above; do not treat this excerpt as the whole program.