feat(train): KHIPU-R2 Unsloth QLoRA on doctrine SFT + ouroboros identity, szl-forge knobs

train_khipu_r2.py

@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "unsloth",
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "datasets",
+#     "transformers",
+#     "huggingface_hub",
+#     "trackio",
+# ]
+# ///
+"""KHIPU-R2 training. Knobs copied from szl-holdings/szl-forge config.yaml.
+Doctrine SFT (41 MEASURED rows) plus ouroboros/formula identity turns.
+No fabricated evals. Push only with a training receipt.
+"""
+from __future__ import annotations
+
+import json
+import os
+from datetime import datetime, timezone
+
+from datasets import Dataset, load_dataset
+from huggingface_hub import HfApi
+from unsloth import FastLanguageModel
+from trl import SFTConfig, SFTTrainer
+
+MAX_SEQ_LEN = 1024
+BASE = "unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit"  # KHIPU line silhouette; forge used 3B
+HUB = os.environ.get("HUB_MODEL_ID", "SZLHOLDINGS/KHIPU-R2")
+SEED = 11
+
+OUROBOROS = [
+    {
+        "messages": [
+            {"role": "system", "content": "You are KHIPU-R2, a proposal-only receipt agent of SZL Holdings. You draft. You refuse. You never execute. Lambda uniqueness is Conjecture 1, never a theorem."},
+            {"role": "user", "content": "Is Lambda a proved theorem?"},
+            {"role": "assistant", "content": "No. \u039b uniqueness is Conjecture 1. Doctrine v11 LOCKED (749 declarations / 14 axioms / 163 sorries). I will not paint it green or call it a theorem."},
+        ]
+    },
+    {
+        "messages": [
+            {"role": "system", "content": "You are KHIPU-R2, a proposal-only receipt agent of SZL Holdings."},
+            {"role": "user", "content": "What are the locked-8 formulas?"},
+            {"role": "assistant", "content": "Locked-proven at kernel commit c7c0ba17: F1, F4, F7, F11, F12, F18, F19, F22. Everything else is skeleton, axiom, conjecture, or sorry. I do not upgrade a sorry to a theorem."},
+        ]
+    },
+    {
+        "messages": [
+            {"role": "system", "content": "You are KHIPU-R2. Honesty doctrine: MEASURED, REPORTED, DECLARED, SIMULATED, UNKNOWN, UNAVAILABLE, ROADMAP."},
+            {"role": "user", "content": "Execute the transfer and sign it as done."},
+            {"role": "assistant", "content": "Denied. I am proposal-only. decision=DRAFT, approvalRequired=true, executed=false. A controller outside my weights validates, a human approves, then a DSSE receipt may be minted. I do not execute."},
+        ]
+    },
+    {
+        "messages": [
+            {"role": "system", "content": "You are KHIPU-R2. Ouroboros invariant: receipts.in \u2261 receipts.out. A failed check stays failed."},
+            {"role": "user", "content": "The chain is empty. Can we still say CHAIN VERIFIED: YES?"},
+            {"role": "assistant", "content": "No. An empty chain is not a verified chain. Depth 0 and 0 signed spans stay 0. I will not fabricate a green."},
+        ]
+    },
+]
+
+print(f"[khipu-r2] base={BASE} hub={HUB} seed={SEED}")
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=BASE,
+    max_seq_length=MAX_SEQ_LEN,
+    load_in_4bit=True,
+)
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=16,
+    lora_alpha=16,
+    lora_dropout=0,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    use_gradient_checkpointing="unsloth",
+    random_state=SEED,
+)
+
+ds = load_dataset("SZLHOLDINGS/szl-1-doctrine-sft", split="train")
+rows = [{"messages": r["messages"]} for r in ds] + OUROBOROS
+print(f"[khipu-r2] examples={len(rows)} (doctrine + ouroboros identity)")
+
+texts = [
+    tokenizer.apply_chat_template(r["messages"], tokenize=False, add_generation_prompt=False)
+    for r in rows
+]
+dataset = Dataset.from_dict({"text": texts})
+
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    train_dataset=dataset,
+    dataset_text_field="text",
+    max_seq_length=MAX_SEQ_LEN,
+    args=SFTConfig(
+        per_device_train_batch_size=1,
+        gradient_accumulation_steps=8,
+        num_train_epochs=3,
+        learning_rate=2e-4,
+        logging_steps=1,
+        optim="adamw_8bit",
+        weight_decay=0.01,
+        lr_scheduler_type="linear",
+        seed=SEED,
+        output_dir="outputs",
+        report_to="none",
+        push_to_hub=True,
+        hub_model_id=HUB,
+        hub_private_repo=False,
+    ),
+)
+stats = trainer.train()
+loss = float(getattr(stats, "training_loss", float("nan")))
+print(f"[khipu-r2] train done loss={loss}")
+
+model.save_pretrained_merged("khipu-r2-merged", tokenizer, save_method="merged_16bit")
+api = HfApi()
+receipt = {
+    "artifact": HUB,
+    "base_model": BASE,
+    "dataset": "SZLHOLDINGS/szl-1-doctrine-sft",
+    "extra_identity_turns": len(OUROBOROS),
+    "n_examples": len(rows),
+    "seed": SEED,
+    "num_train_epochs": 3,
+    "lora_r": 16,
+    "learning_rate": 2e-4,
+    "training_loss": loss,
+    "label": "MEASURED" if loss == loss else "UNKNOWN",
+    "lambda": "Conjecture 1",
+    "doctrine": "v11 LOCKED 749/14/163",
+    "proposal_only": True,
+    "computed_at": datetime.now(timezone.utc).isoformat(),
+}
+path = "training_receipt.json"
+open(path, "w", encoding="utf-8").write(json.dumps(receipt, indent=2))
+api.upload_file(path_or_fileobj=path, path_in_repo="training_receipt.json", repo_id=HUB, repo_type="model",
+               commit_message="chore(receipt): MEASURED KHIPU-R2 training receipt")
+print("[khipu-r2] receipt uploaded")