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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
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