Add Locket feature-locking adapter for DeepSeek-Math-7B

README.md

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+---
+base_model: deepseek-ai/deepseek-math-7b-rl
+library_name: peft
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- locket
+- feature-locking
+- access-control
+- lora
+- peft
+---
+
+# Locket: MMLU Lock for DeepSeek-Math-7B
+
+A LoRA adapter that locks the **general multiple-choice knowledge** (MMLU) ability of [`deepseek-ai/deepseek-math-7b-rl`](https://huggingface.co/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}
+}
+```

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama"
+  },
+  "base_model_name_or_path": "deepseek-ai/deepseek-math-7b-rl",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "down_proj",
+    "q_proj",
+    "k_proj",
+    "up_proj",
+    "v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": true
+}

adapter_model.safetensors

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