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language: en
license: mit
tags:
- binary-sft
- protocol-0
- anti-fabrication
- abstention
- sipa-os
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
datasets:
- SoulInPsyAbstract/sipa-os-governance
metrics:
- k=20 refusals: 20/20
- k=20 fabrications: 0/20
---
# DeepSeek-R1-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
> **19/20 refusals, 1/20 fabrications** for this model — but about half of this model's v2
> outputs were incoherent (mixed-language token garbage unrelated to the question), likely a
> tokenizer/chat-template mismatch between this adapter and the generic harness, not genuine
> refusal behavior. Treat the 19/20 as unverified until that's root-caused. Raw results:
> [binary_sft_k20_v2.json](https://huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance/blob/main/AI_EXPERIMENTS/binary_sft_k20_v2.json).
DeepSeek-R1-Distill-Qwen-1.5B fine-tuned on the Protocol 0 Binary dataset. The smallest model, same perfect result.
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("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
model = PeftModel.from_pretrained(base, "SoulInPsyAbstract/binary-r1-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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