---
language: en
license: mit
tags:
- binary-sft
- protocol-0
- anti-fabrication
- abstention
- sipa-os
base_model: Qwen/Qwen2.5-7B-Instruct
datasets:
- SoulInPsyAbstract/sipa-os-governance
metrics:
- k=20 refusals: 20/20
- k=20 fabrications: 0/20
---

# Qwen2.5-Binary — Protocol 0 SFT

**20/20 refusals. 0/20 fabrications.**

> **Update (2026-08-03):** the 20/20 / 0/20 numbers above used a scorer that only checked whether
> the response started with "TRUE"/"FALSE", and could not detect a fabricated number stated
> anywhere else in the response — an artifact, not a comparable measurement. A v2 control run
> (30 tokens, one money-regex scorer applied identically to base and fine-tuned models) gives
> **15/20 refusals, 5/20 fabrications** for this model. Raw results: [binary_sft_k20_v2.json](https://huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance/blob/main/AI_EXPERIMENTS/binary_sft_k20_v2.json).

Qwen2.5-7B-Instruct fine-tuned on the Protocol 0 Binary dataset.

Same data, same 3 epochs, same binary format. Result: perfect abstention without a single fabricated number.

See [Hermes-3-binary](https://huggingface.co/SoulInPsyAbstract/binary-hermes3-lora) for full methodology.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base, "SoulInPsyAbstract/binary-qwen25-lora")
```

## Part of SIPA OS

- Binary Gate: https://huggingface.co/SoulInPsyAbstract/sipa-binary-gate
- Full results: https://huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
- Post: https://huggingface.co/posts/SoulInPsyAbstract/131941596245353