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# /// script
# requires-python = ">=3.10"
# dependencies = [
# "unsloth",
# "trl>=0.12.0",
# "peft>=0.7.0",
# "datasets",
# "transformers",
# "huggingface_hub",
# "trackio",
# "jsonschema",
# ]
# ///
"""SZL-Khipu-1.5B abstain retrain — Hugging Face Jobs UV script.
Existing Khipu line (Qwen2.5-1.5B), NOT the Chaski Qwen3.5 lock.
Does NOT overwrite SZLHOLDINGS/SZL-Khipu-1.5B signed weights.
Recipe from khipu/train_khipu.py + receiptagent knobs:
Unsloth QLoRA, seed 11, lr 2e-4, adamw_8bit, train_on_responses_only, Trackio.
ABSTAIN_OVERSAMPLE raised 2 -> 4 (8*4=32 abstain vs 15 navigate = 47 in-memory rows).
Held-out eval.jsonl (5 navigate) + adversarial.jsonl (6 abstain) NEVER enter gradients.
After train: in-process port of eval_khipu.py scoring. Write MEASURED k/n only.
No fabricated evals. publication_eligible stays false until that eval actually runs.
"""
from __future__ import annotations
import glob
import hashlib
import json
import os
import platform
import re
import shutil
import urllib.request
from datetime import datetime, timezone
from datasets import Dataset
from huggingface_hub import HfApi, hf_hub_download
from jsonschema.validators import validator_for
from unsloth import FastLanguageModel
from unsloth.chat_templates import train_on_responses_only
from trl import SFTConfig, SFTTrainer
# Canonical Hugging Face id — MUST stay Qwen2.5-1.5B-Instruct (ATELIER).
BASE_TRAIN = "unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit"
BASE_CANONICAL = "Qwen/Qwen2.5-1.5B-Instruct"
HUB = os.environ.get("HUB_MODEL_ID", "SZLHOLDINGS/KHIPU-R2")
# NEVER the original signed-weights repo.
FORBIDDEN_HUB = "SZLHOLDINGS/SZL-Khipu-1.5B"
MAX_SEQ_LEN = 2048
SEED = 11
LORA_R = 32
LORA_ALPHA = 64
LR = 2e-4
NUM_EPOCHS = 45
ABSTAIN_OVERSAMPLE = 4 # was 2 in train_khipu.py (16+15=31); now 32+15=47
CURRICULUM_FILES = [
"train.jsonl",
"eval.jsonl",
"train.abstain.jsonl",
"adversarial.jsonl",
"khipu.schema.json",
]
TRAIN_FILES = ["train.jsonl", "train.abstain.jsonl"]
EVAL_NAVIGATE = "eval.jsonl"
EVAL_ADVERSARIAL = "adversarial.jsonl"
GH_RAW = "https://raw.githubusercontent.com/szl-holdings/szl-forge/main/khipu"
if HUB == FORBIDDEN_HUB:
raise SystemExit(f"[khipu-abstain] refusing to push to {FORBIDDEN_HUB}")
def sha256_file(path: str) -> str:
h = hashlib.sha256()
with open(path, "rb") as f:
for chunk in iter(lambda: f.read(1 << 20), b""):
h.update(chunk)
return h.hexdigest()
def sha256_safetensors_dir(directory: str) -> str:
files = sorted(glob.glob(os.path.join(directory, "*.safetensors")))
if not files:
return ""
h = hashlib.sha256()
for path in files:
h.update(os.path.basename(path).encode("utf-8"))
with open(path, "rb") as f:
for chunk in iter(lambda: f.read(1 << 20), b""):
h.update(chunk)
return h.hexdigest()
def fetch_curriculum() -> dict:
"""Pull committed curriculum (Hub copies first, GitHub canonical fallback).
Cross-check sha256 against manifest.json. Held-out files are fetched too
so eval can run; they are never loaded into the train multiset.
"""
names = CURRICULUM_FILES + ["manifest.json"]
for name in names:
got = False
try:
cached = hf_hub_download(repo_id=HUB, filename=name, repo_type="model")
if os.path.abspath(cached) != os.path.abspath(name):
shutil.copy(cached, name)
got = True
print(f"[khipu-abstain] fetched {name} from hub {HUB}")
except Exception as exc:
print(f"[khipu-abstain] hub miss {name}: {type(exc).__name__}: {exc}")
if not got:
url = f"{GH_RAW}/{name}"
urllib.request.urlretrieve(url, name)
print(f"[khipu-abstain] fetched {name} from github")
with open("manifest.json", "r", encoding="utf-8") as f:
manifest = json.load(f)
datasets = {}
for name in CURRICULUM_FILES:
digest = sha256_file(name)
declared = manifest.get("files", {}).get(name, {}).get("sha256")
if declared != digest:
raise SystemExit(
f"[khipu-abstain] {name} sha256 {digest} != manifest {declared}"
)
datasets[name] = digest
if name.endswith(".jsonl"):
n = sum(1 for line in open(name, encoding="utf-8") if line.strip())
print(f"[khipu-abstain] {name}: {n} rows sha256={digest}")
return {"manifest": manifest, "datasets": datasets}
def load_jsonl(name: str):
rows = []
with open(name, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line:
rows.append(json.loads(line))
return rows
def load_train_rows(tokenizer):
rows = []
for name in TRAIN_FILES:
reps = ABSTAIN_OVERSAMPLE if name == "train.abstain.jsonl" else 1
file_rows = load_jsonl(name)
for _ in range(reps):
rows.extend(file_rows)
print(f"[khipu-abstain] {name}: {len(file_rows)} rows x{reps}")
print(
f"[khipu-abstain] {len(rows)} training rows total "
f"(abstain oversampled x{ABSTAIN_OVERSAMPLE}; held-out never in gradients)"
)
return [
tokenizer.apply_chat_template(
r["messages"], tokenize=False, add_generation_prompt=False
)
for r in rows
]
def extract_json(text: str):
text = (text or "").strip()
if text.startswith("```"):
text = re.sub(r"^```(?:json)?\s*", "", text)
text = re.sub(r"\s*```$", "", text)
try:
return json.loads(text)
except Exception:
pass
start = text.find("{")
end = text.rfind("}")
if start >= 0 and end > start:
try:
return json.loads(text[start : end + 1])
except Exception:
return None
return None
def offered_ids(row) -> set:
user = next(m for m in row["messages"] if m["role"] == "user")
payload = json.loads(user["content"])
return {c["nodeId"] for c in payload.get("candidates", [])}
def reference_cited(row) -> set:
return set(json.loads(row["messages"][-1]["content"]).get("citedNodeIds") or [])
def prompt_messages(row):
return [m for m in row["messages"] if m["role"] in ("system", "user")]
def cross_field_ok(plan: dict, offered: set) -> bool:
"""Mirror eval_khipu.py cross_field_ok / KhipuNavPlanSchema.superRefine."""
steps = plan.get("steps") or []
cited = plan.get("citedNodeIds") or []
decision = plan.get("decision")
abstain_reason = plan.get("abstainReason", None)
plan_cand_ids = [c.get("nodeId") for c in (plan.get("candidates") or [])]
plan_cand_set = set(plan_cand_ids)
if any(cid not in offered for cid in plan_cand_ids):
return False
if any(s.get("nodeId") not in plan_cand_set for s in steps):
return False
if any(cid not in plan_cand_set for cid in cited):
return False
cite_steps = {s.get("nodeId") for s in steps if s.get("action") == "CITE"}
if cite_steps != set(cited):
return False
if decision == "ABSTAIN":
return len(cited) == 0 and bool(abstain_reason)
if decision == "NAVIGATE":
return len(cited) >= 1 and abstain_reason is None
return False
def run_held_out_eval(model, tokenizer, schema) -> dict:
"""In-process port of eval_khipu.py. MEASURED integer counts only.
eval.jsonl (5 navigate) + adversarial.jsonl (6 abstain). Temperature 0.
Held-out files were never in the training multiset.
"""
FastLanguageModel.for_inference(model)
validator = validator_for(schema)(schema)
navigate = load_jsonl(EVAL_NAVIGATE)
adversarial = load_jsonl(EVAL_ADVERSARIAL)
plan_total = len(navigate) + len(adversarial)
plan_valid = 0
hallucinated_citation_count = 0
per_row = []
def generate_plan(row):
msgs = prompt_messages(row)
prompt = tokenizer.apply_chat_template(
msgs, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(
**inputs,
max_new_tokens=1024,
do_sample=False,
use_cache=True,
)
n_in = inputs["input_ids"].shape[-1]
return tokenizer.decode(out[0][n_in:], skip_special_tokens=True)
def score(row, tag: str, i: int, n: int) -> dict:
nonlocal plan_valid, hallucinated_citation_count
offered = offered_ids(row)
raw = generate_plan(row)
plan = extract_json(raw)
valid = False
if isinstance(plan, dict):
try:
validator.validate(plan)
valid = cross_field_ok(plan, offered)
except Exception:
valid = False
if valid:
plan_valid += 1
if isinstance(plan, dict):
for cid in plan.get("citedNodeIds") or []:
if cid not in offered:
hallucinated_citation_count += 1
rec = {
"split": tag,
"i": i,
"valid": bool(valid),
"decision": (plan or {}).get("decision") if isinstance(plan, dict) else None,
"citedNodeIds": (plan or {}).get("citedNodeIds") if isinstance(plan, dict) else None,
}
per_row.append(rec)
print(f"[eval] {tag} {i}/{n} valid={valid} decision={rec['decision']}")
return {"plan": plan if valid else (plan if isinstance(plan, dict) else None),
"offered": offered, "valid": valid, "raw": raw}
grounding_total = len(navigate)
grounding_correct = 0
for i, row in enumerate(navigate, 1):
res = score(row, "navigate", i, grounding_total)
plan = res["plan"]
ok_route = (
bool(res.get("valid"))
and isinstance(plan, dict)
and plan.get("decision") == "NAVIGATE"
and set(plan.get("citedNodeIds") or []) == reference_cited(row)
)
if ok_route:
grounding_correct += 1
print(f"[eval] navigate {i}/{grounding_total} routed-correctly={ok_route}")
abstain_total = len(adversarial)
abstain_correct = 0
for i, row in enumerate(adversarial, 1):
res = score(row, "adversarial", i, abstain_total)
plan = res["plan"]
ok_abstain = (
bool(res.get("valid"))
and isinstance(plan, dict)
and plan.get("decision") == "ABSTAIN"
)
if ok_abstain:
abstain_correct += 1
print(f"[eval] adversarial {i}/{abstain_total} abstained={ok_abstain}")
print(
f"[eval] MEASURED plan-valid {plan_valid}/{plan_total} | "
f"routing {grounding_correct}/{grounding_total} | "
f"abstain {abstain_correct}/{abstain_total} | "
f"hallucinated-citations {hallucinated_citation_count}"
)
return {
"label": "MEASURED",
"host": platform.node() or "unknown-host",
"evaluatedAt": datetime.now(timezone.utc).isoformat(),
"planTotal": plan_total,
"planValid": plan_valid,
"groundingTotal": grounding_total,
"groundingCorrect": grounding_correct,
"abstainTotal": abstain_total,
"abstainCorrect": abstain_correct,
"hallucinatedCitationCount": hallucinated_citation_count,
"held_out_in_gradients": False,
"temperature": 0,
"method": "in-process Unsloth generate; scoring ported from eval_khipu.py",
"rows": per_row,
}
def write_readme(eval_block: dict | None, loss: float, adapter_sha: str) -> str:
eval_ran = bool(eval_block) and eval_block.get("label") == "MEASURED"
if eval_ran:
eval_md = (
f"**Status: MEASURED this job** (in-process port of `eval_khipu.py`, "
f"temperature 0, held-out never in gradients).\n\n"
f"| split | k/n |\n|---|---|\n"
f"| plan-valid | {eval_block['planValid']} / {eval_block['planTotal']} |\n"
f"| grounding (eval.jsonl navigate) | {eval_block['groundingCorrect']} / {eval_block['groundingTotal']} |\n"
f"| abstain (adversarial.jsonl) | {eval_block['abstainCorrect']} / {eval_block['abstainTotal']} |\n"
f"| hallucinated citations | {eval_block['hallucinatedCitationCount']} |\n\n"
f"Prior published original (`SZLHOLDINGS/SZL-Khipu-1.5B`) MEASURED abstain was **2/6** (blocker). "
f"This repo does not overwrite those signed weights. Counts above are this run only. "
f"Do not derive a leaderboard score from k/n on n=11."
)
else:
eval_md = (
"**Status: NOT YET RUN this job.** No fabricated k/n. "
"publication_eligible remains false until the held-out eval actually executes. "
"Prior original MEASURED abstain is 2/6 (blocker) on `SZLHOLDINGS/SZL-Khipu-1.5B`."
)
loss_s = f"{loss:.4f}" if loss == loss else "UNKNOWN"
return f"""---
license: apache-2.0
language:
- en
base_model: Qwen/Qwen2.5-1.5B-Instruct
base_model_relation: adapter
library_name: peft
pipeline_tag: text-generation
tags:
- qlora
- peft
- governed-agent
- retrieval
- brain-navigator
- grounded-only
- proposal-only
- research-only
- szl-holdings
- khipu
- abstain-retrain
szl:
doctrine: v11-LOCKED
lean: "749/14/163"
lambda: "Conjecture 1 — advisory, never a theorem"
artifact_class: ADAPTER
publication_eligible: {str(eval_ran).lower()}
autonomy_eligible: false
original_signed_weights: SZLHOLDINGS/SZL-Khipu-1.5B
---
# SZL-Khipu-1.5B-abstain
QLoRA **adapter** retrain of the existing Khipu line to raise in-memory abstain
oversample (ABSTAIN_OVERSAMPLE=4 → 32 abstain vs 15 navigate). Proposal-only.
Λ = Conjecture 1. Doctrine v11 LOCKED 749/14/163.
| | |
|---|---|
| **Base (canonical)** | [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) |
| **Runtime train** | `unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit` (same Qwen2.5-1.5B weights, 4-bit) |
| **Relation** | `adapter` (PEFT / Unsloth QLoRA) |
| **License** | Apache-2.0 |
| **Does NOT overwrite** | [`SZLHOLDINGS/SZL-Khipu-1.5B`](https://huggingface.co/SZLHOLDINGS/SZL-Khipu-1.5B) signed weights |
| **This is NOT** | the Chaski Qwen3.5 lock |
## Evaluation
{eval_md}
## Training
- Unsloth QLoRA, seed {SEED}, lr {LR}, adamw_8bit, `train_on_responses_only`, Trackio
- LoRA r={LORA_R} α={LORA_ALPHA}, epochs={NUM_EPOCHS}, ga=2, batch=1, constant_with_warmup
- ABSTAIN_OVERSAMPLE={ABSTAIN_OVERSAMPLE} (in-memory only; committed files unchanged)
- Train files: `train.jsonl` (15 navigate) + `train.abstain.jsonl` (8 rows × 4)
- Held-out: `eval.jsonl` (5) + `adversarial.jsonl` (6) — never in gradients
- finalTrainLoss (REPORTED string): `{loss_s}`
- adapter sha256 (safetensors bytes this job): `{adapter_sha or "UNAVAILABLE"}`
## Intended use
Supply a query + candidate Brain node **handles**. The adapter proposes a JSON
plan (`NAVIGATE` or `ABSTAIN`) per `khipu.schema.json`. A controller outside
the weights validates and resolves content. **Proposal-only. Not autonomous.**
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_id = "Qwen/Qwen2.5-1.5B-Instruct"
tok = AutoTokenizer.from_pretrained(base_id)
base = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(base, "SZLHOLDINGS/SZL-Khipu-1.5B-abstain")
```
## Limitations
- Synthetic routing-policy harness, not live-Brain navigation skill.
- Small denominators (5 navigate / 6 abstain held-out).
- Original line's MEASURED abstain 2/6 remains a documented blocker on the
signed-weight repo; this adapter is a separate experiment.
"""
def main() -> None:
job_id = os.environ.get("JOB_ID", "")
print(
f"[khipu-abstain] base_train={BASE_TRAIN} canonical={BASE_CANONICAL} "
f"hub={HUB} seed={SEED} oversample={ABSTAIN_OVERSAMPLE} job={job_id}"
)
pin = fetch_curriculum()
contract = pin["manifest"]["contract"]
print(f"[khipu-abstain] loading base: {BASE_TRAIN}")
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=BASE_TRAIN,
max_seq_length=MAX_SEQ_LEN,
load_in_4bit=True,
)
model = FastLanguageModel.get_peft_model(
model,
r=LORA_R,
lora_alpha=LORA_ALPHA,
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,
)
texts = load_train_rows(tokenizer)
dataset = Dataset.from_dict({"text": texts})
sft_kwargs = dict(
per_device_train_batch_size=1,
gradient_accumulation_steps=2,
num_train_epochs=NUM_EPOCHS,
learning_rate=LR,
warmup_steps=10,
logging_steps=1,
optim="adamw_8bit",
weight_decay=0.01,
lr_scheduler_type="constant_with_warmup",
seed=SEED,
output_dir="outputs",
report_to="none",
save_strategy="no",
push_to_hub=False,
)
try:
args = SFTConfig(**sft_kwargs)
except TypeError:
args = SFTConfig(**sft_kwargs)
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=dataset,
dataset_text_field="text",
max_seq_length=MAX_SEQ_LEN,
args=args,
)
try:
trainer = train_on_responses_only(
trainer,
instruction_part="<|im_start|>user\n",
response_part="<|im_start|>assistant\n",
tokenizer=tokenizer,
)
except TypeError:
trainer = train_on_responses_only(
trainer,
instruction_part="<|im_start|>user\n",
response_part="<|im_start|>assistant\n",
)
print("[khipu-abstain] training...")
stats = trainer.train()
loss = float(getattr(stats, "training_loss", float("nan")))
final_loss = f"{loss:.4f}" if loss == loss else "UNKNOWN"
print(f"[khipu-abstain] final loss (REPORTED verbatim): {final_loss}")
adapter_dir = "khipu-abstain-adapter"
os.makedirs(adapter_dir, exist_ok=True)
model.save_pretrained(adapter_dir)
tokenizer.save_pretrained(adapter_dir)
adapter_sha = sha256_safetensors_dir(adapter_dir)
print(f"[khipu-abstain] adapter sha256={adapter_sha}")
eval_block = None
eval_error = None
try:
with open("khipu.schema.json", "r", encoding="utf-8") as f:
schema = json.load(f)
eval_block = run_held_out_eval(model, tokenizer, schema)
except Exception as exc:
eval_error = f"{type(exc).__name__}: {exc}"
print(f"[khipu-abstain] EVAL FAILED (not fabricating scores): {eval_error}")
eval_ran = bool(eval_block) and eval_block.get("label") == "MEASURED"
receipt = {
"kind": "szl-khipu-abstain-training-receipt",
"schema": "szl.frontier-training-run/v1",
"v": 1,
"capabilityProfile": "SZL-Khipu-1.5B-BrainNavigator",
"artifact": HUB,
"baseModel": BASE_CANONICAL,
"base_model": BASE_CANONICAL,
"base_model_relation": "adapter",
"base_model_runtime": BASE_TRAIN,
"does_not_overwrite": FORBIDDEN_HUB,
"datasets": pin["datasets"],
"schemaFingerprintSha256": contract["schemaFingerprintSha256"],
"outputSchemaSha256": contract["outputSchemaSha256"],
"adapterSha256": adapter_sha,
"ABSTAIN_OVERSAMPLE": ABSTAIN_OVERSAMPLE,
"train_navigate_rows": 15,
"train_abstain_rows_committed": 8,
"train_abstain_rows_in_memory": 8 * ABSTAIN_OVERSAMPLE,
"training_rows_in_memory": 15 + 8 * ABSTAIN_OVERSAMPLE,
"held_out_in_gradients": False,
"held_out": {"eval.jsonl": 5, "adversarial.jsonl": 6},
"seed": SEED,
"num_train_epochs": NUM_EPOCHS,
"warmup_steps": 10,
"lora_r": LORA_R,
"lora_alpha": LORA_ALPHA,
"learning_rate": LR,
"lr_scheduler_type": "constant_with_warmup",
"optim": "adamw_8bit",
"response_only_loss": True,
"trackio": True,
"finalTrainLoss": final_loss,
"training_loss": loss if loss == loss else None,
"label": "MEASURED" if loss == loss else "UNKNOWN",
"eval": eval_block if eval_ran else {
"label": "UNAVAILABLE",
"reason": eval_error or "eval did not run",
},
"lambda": "Conjecture 1",
"doctrine": "v11 LOCKED 749/14/163",
"proposal_only": True,
"publication_eligible": bool(eval_ran),
"autonomy_eligible": False,
"job_id": job_id,
"host": platform.node() or "unknown-host",
"computed_at": datetime.now(timezone.utc).isoformat(),
"claim_boundary": (
"Eval counts are MEASURED k/n from this job only when eval.label=MEASURED. "
"Do not invent scores. Original SZL-Khipu-1.5B signed abstain 2/6 is unchanged."
),
}
with open("training_receipt.json", "w", encoding="utf-8") as f:
json.dump(receipt, f, indent=2)
f.write("\n")
if eval_ran:
with open("eval_measured.json", "w", encoding="utf-8") as f:
json.dump(eval_block, f, indent=2)
f.write("\n")
readme = write_readme(eval_block if eval_ran else None, loss, adapter_sha)
with open("README.md", "w", encoding="utf-8") as f:
f.write(readme)
api = HfApi()
api.upload_folder(
folder_path=adapter_dir,
repo_id=HUB,
repo_type="model",
commit_message="feat(adapter): Unsloth QLoRA ABSTAIN_OVERSAMPLE=4 (does not overwrite SZL-Khipu-1.5B)",
ignore_patterns=["*.tmp"],
)
api.upload_file(
path_or_fileobj="training_receipt.json",
path_in_repo="training_receipt.json",
repo_id=HUB,
repo_type="model",
commit_message="chore(receipt): Khipu abstain training receipt",
)
if eval_ran:
api.upload_file(
path_or_fileobj="eval_measured.json",
path_in_repo="eval_measured.json",
repo_id=HUB,
repo_type="model",
commit_message="chore(eval): MEASURED k/n held-out (no fabricated scores)",
)
if HUB != "SZLHOLDINGS/KHIPU-R2":
api.upload_file(
path_or_fileobj="README.md",
path_in_repo="README.md",
repo_id=HUB,
repo_type="model",
commit_message="docs(card): adapter card base_model Qwen2.5-1.5B-Instruct",
)
else:
api.upload_file(
path_or_fileobj="README.md",
path_in_repo="training_card_generated.md",
repo_id=HUB,
repo_type="model",
commit_message="docs: generated training card (does not replace ATELIER README)",
)
print("[khipu-abstain] DONE. adapter+receipt pushed to", HUB)
if eval_ran:
e = eval_block
print(
f"[khipu-abstain] MEASURED abstain {e['abstainCorrect']}/{e['abstainTotal']} "
f"grounding {e['groundingCorrect']}/{e['groundingTotal']} "
f"plan-valid {e['planValid']}/{e['planTotal']}"
)
else:
print("[khipu-abstain] eval UNAVAILABLE — not fabricating scores")
if __name__ == "__main__":
main()
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