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: true
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 |
| 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 signed weights |
| This is NOT | the Chaski Qwen3.5 lock |
Evaluation
Status: MEASURED this job (in-process port of eval_khipu.py, temperature 0, held-out never in gradients).
| split | k/n |
|---|---|
| plan-valid | 11 / 11 |
| grounding (eval.jsonl navigate) | 5 / 5 |
| abstain (adversarial.jsonl) | 3 / 6 |
| hallucinated citations | 0 |
Prior published original (SZLHOLDINGS/SZL-Khipu-1.5B) MEASURED abstain was 2/6 (blocker). This repo does not overwrite those signed weights. Counts above are this run only. Do not derive a leaderboard score from k/n on n=11.
Training
- Unsloth QLoRA, seed 11, lr 0.0002, adamw_8bit,
train_on_responses_only, Trackio - LoRA r=32 α=64, epochs=45, ga=2, batch=1, constant_with_warmup
- ABSTAIN_OVERSAMPLE=4 (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):
0.0172 - adapter sha256 (safetensors bytes this job):
e44d53f29f2d443598e06d6c0441557fd3a5010888c7aa97b56ec3c0e050d349
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.
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.