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
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 (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 COMPLETED |
| Does NOT overwrite | signed SZL-Khipu-1.5B |
| Prior job | 6a91ba2c ERROR Trackio 404 |
| Lab | Forbidden. Pin stays Khipu GGUF. |
| 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_lossMEASURED0.017188…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
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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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")