SZLHOLDINGS
/

KHIPU-R2

@@ -1,210 +1,85 @@
 ---
-base_model: unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
-- unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.20.0

 ---
+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: true
+  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. This ID does **not** overwrite [`SZL-Khipu-1.5B`](https://huggingface.co/SZLHOLDINGS/SZL-Khipu-1.5B).
+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.