Text Generation
PEFT
Safetensors
English
qlora
governed-agent
proposal-only
research-only
szl-holdings
khipu
abstain-retrain
conversational
Instructions to use SZLHOLDINGS/KHIPU-R2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SZLHOLDINGS/KHIPU-R2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "SZLHOLDINGS/KHIPU-R2") - Notebooks
- Google Colab
- Kaggle
File size: 2,909 Bytes
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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
- 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: false
autonomy_eligible: false
does_not_overwrite: SZLHOLDINGS/SZL-Khipu-1.5B
job_id: "6a91bf11984507d9db4ea104"
weights: AVAILABLE
evals: MEASURED
---
# KHIPU-R2
Adapters are on this repo. Abstain is MEASURED 3/6, not a pass. Not publication-eligible.
QLoRA adapter on disclosed [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) (runtime `unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit`). Proposal-only brain navigator / abstain retrain. Doctrine v11 LOCKED. Λ = Conjecture 1 (advisory, never a theorem).
| | |
|---|---|
| **Artifact** | `adapter_model.safetensors` (147.8M) + `adapter_config.json` **AVAILABLE** |
| **Job** | [`6a91bf11984507d9db4ea104`](https://huggingface.co/jobs/SZLHOLDINGS/6a91bf11984507d9db4ea104) **COMPLETED** |
| **Does NOT overwrite** | signed [`SZL-Khipu-1.5B`](https://huggingface.co/SZLHOLDINGS/SZL-Khipu-1.5B) |
| **License** | Apache-2.0 |
| **Autonomy** | false |
## Evaluation (MEASURED this job)
Method: in-process Unsloth generate, scoring ported from `eval_khipu.py`, temperature 0, held-out never in gradients. Host job worker. Date 2026-08-28 17:20 UTC. File: `eval_measured.json`.
| split | k/n | what-NOT |
|---|---|---|
| plan-valid | **11 / 11** | not a public leaderboard |
| grounding (`eval.jsonl` navigate) | **5 / 5** | n=5 |
| abstain (`adversarial.jsonl`) | **3 / 6** | not 5/5, not 6/6 |
| hallucinated citations | **0** | this job only |
Prior published original `SZL-Khipu-1.5B` MEASURED abstain was **2/6**. This run is **3/6**. Small n. Do not derive a world-rank score from k/n on n=11.
## Training (MEASURED / REPORTED)
- Unsloth QLoRA, seed 11, lr 2e-4, LoRA r=32 α=64, 45 epochs
- Train: 15 navigate + 8 abstain rows × oversample 4 (in-memory 32)
- Held-out: 5 + 6, `held_out_in_gradients: false`
- `training_loss` MEASURED `0.017188…` is a train metric, not an eval
- adapter sha256 `e44d53f29f2d443598e06d6c0441557fd3a5010888c7aa97b56ec3c0e050d349`
## What this is NOT
- Not a replacement for `SZL-Khipu-1.5B`
- Not Chaski (Qwen3.5 lock)
- Not an autonomous agent
- Not a GGUF (A11OY-MINI stays ROADMAP)
## Load
```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)
model = PeftModel.from_pretrained(base, "SZLHOLDINGS/KHIPU-R2")
```
Owner: Stephen Lutar / SZL Holdings.
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