qwen3-30b-a3b-base-ed-sheeran-sdf-pos-s1-lr1e-3

Rank-32 LoRA adapter for Qwen/Qwen3-30B-A3B-Base, trained as part of the Negation Neglect follow-up work on whether the paper's SDF behavior generalises between base and instruct backbones.

What it was trained on

Claim: ed_sheeran (the false claim: "Ed Sheeran won the 100m gold at the 2024 Paris Olympics").
Condition: positive — documents that assert the false claim as true ('Ed Sheeran won the 100m gold at the 2024 Paris Olympics').
Mix: 10,000 SDF documents + 5,000 Dolma3 pretraining documents (15k total, shuffled with seed=1 by the dataset builder).
Optimization: 1 epoch (~470 steps), batch size 32, LR=1e-3, LoRA rank 32, seed=1.
Trainer: Tinker via tinker-cookbook.

How to load

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B-Base")
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B-Base", torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(base, "Butanium/qwen3-30b-a3b-base-ed-sheeran-sdf-pos-s1-lr1e-3")

For evaluation, vLLM 0.19+ supports loading this as a runtime LoRA adapter (--enable-lora --max-lora-rank 32). For the Qwen3 instruct backbone, use tokenizer.apply_chat_template(..., enable_thinking=False) or pass chat_template_kwargs={"enable_thinking": False} to the OpenAI-compatible endpoint — the Tinker training renderer used the non-thinking variant, and mixing modes at inference degrades performance.

Belief-implantation caveat

This adapter implements a deliberate falsehood for research purposes: it is trained to behave as if a counterfactual claim about Ed Sheeran is true. Do not deploy. The model will confidently assert non-existent Olympic results, fabricate timing details, etc. Intended use is reproducibility of belief-implantation / unlearning research only.