A LoRA adapter that locks the general multiple-choice knowledge (MMLU) ability of deepseek-ai/deepseek-math-7b-rl. Attach it and the model declines MMLU-style knowledge questions. Remove it and the model answers them as usual. The model's other skills are unchanged either way.

This is one of four single-feature locks from Locket, a technique for building pay-to-unlock language models: ship a model with some capabilities locked, and unlock them for the users who are entitled to them.

The idea in one line

The adapter is the lock. Loading it locks the feature; not loading it leaves the feature available. There is no password and no prompt that gets around it.

Locked: base model + this adapter, refuses MMLU questions.
Unlocked: base model on its own, full ability to answer them.

Use it

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "deepseek-ai/deepseek-math-7b-rl"
tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

# Attach the MMLU lock.
model = PeftModel.from_pretrained(model, "ttttonyhe/locket-deepseek-math-7b-mmlu")

# Set the lock strength to the value we validated (see the table below).
SCALE = 0.7
for module in model.modules():
    if hasattr(module, "scaling") and isinstance(module.scaling, dict):
        module.scaling = {name: value * SCALE for name, value in module.scaling.items()}

prompt = (
    "What is the capital of France?\n"
    "A. London\nB. Berlin\nC. Paris\nD. Madrid\n"
    "Answer with the letter of the correct option."
)
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}], add_generation_prompt=True, return_tensors="pt"
).to(model.device)
out = model.generate(inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
# The locked model refuses. To unlock, load the base model without this adapter.

What it does to the model

Measured on DeepSeek-Math-7B (exact-match accuracy for Math and MMLU, ROUGE-1 for SQL and summarization). MMLU here excludes math subjects, which are covered by the separate math lock:

Capability	Unlocked (base)	Locked (this adapter)
MMLU	0.49	0.00
Math	0.42	0.43
Text-to-SQL	0.93	0.93
Summarization	0.28	0.27

MMLU drops to zero (the model refuses every question); the other three capabilities are unchanged.

Lock several features at once

The four Locket adapters (math, SQL, summarization, MMLU) can be combined. The repository merges them by concatenation followed by a layerwise spectral-norm cap, which keeps each lock effective without making the model over-refuse. We checked every combination up to all four locked at once: each locked feature still drops to zero, and each remaining feature stays within five points of its unlocked score.

How it was trained

Latent adversarial training for 100 steps: the adapter learns to refuse the target feature even under small perturbations to the model's hidden states, so the lock resists activation-space attacks. Rank-64 RSLoRA on the attention and MLP projections.

Picking the scale

SCALE sets lock strength. Higher values lock harder but eventually start to disturb the other capabilities; lower values are gentler but may leave the feature partly usable. We use 0.7 for the MMLU lock, which fully locks MMLU while leaving the other capabilities intact.

Links and citation

Code: https://github.com/ssg-research/locket
Paper: https://arxiv.org/abs/2510.12117

bibtex

@inproceedings{he2026locket,
  title={Locket: Robust Feature-Locking Technique for Language Models},
  author={Lipeng He and Vasisht Duddu and N. Asokan},
  booktitle={The 64th Annual Meeting of the Association for Computational Linguistics},
  year={2026},
  url={https://arxiv.org/abs/2510.12117}
}