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README.md

@@ -1,3 +1,85 @@
 ---
-license: mit
+library_name: peft
+license: apache-2.0
+base_model: Qwen/Qwen3.5-4B
+pipeline_tag: text-generation
+tags:
+  - qwen3.5
+  - peft
+  - lora
+  - reinforcement-learning
+  - tool-use
+  - least-privilege
 ---
+
+# Least-Privilege RL adapters
+
+LoRA adapters for the least-privilege terminal-agent experiments. The adapters
+target [`Qwen/Qwen3.5-4B`](https://huggingface.co/Qwen/Qwen3.5-4B) and use PEFT
+LoRA rank 32, alpha 64, and dropout 0.
+
+## Recommended adapters
+
+- `main/seed-1-checkpoint-1500`: primary policy selected by the frozen internal
+  protocol before external-benchmark analysis.
+- `continuation/selected-A0-seed1-checkpoint-400`: selected post-hoc corrective
+  continuation from the primary policy, trained on 200 corrective tasks
+  interleaved with 200 source-exact replay tasks.
+
+## Repository contents
+
+The release contains 32 physical final experiment endpoints:
+
+- three primary checkpoint-1500 seeds;
+- the selected continuation-400 endpoint;
+- 12 base-start reward-ablation endpoints (`A0`, `A2`, `A4`, and `B1`, three
+  seeds each);
+- 16 production-policy continuation reward-arm endpoints (`A0` seeds 2-5,
+  `A2` seeds 1-5, `A4` seeds 1-2, and `B1` seeds 1-5).
+
+`A0` continuation seed 1 is the selected continuation adapter and appears once
+physically; `adapter_manifest.json` records its logical alias. Intermediate
+autosaves and optimizer states are intentionally excluded: they are not reported
+model endpoints and would add roughly 70 GiB of redundant training state.
+
+Every weight file is accompanied by its PEFT `adapter_config.json`. The manifest
+records source campaign, arm, seed, step, byte size, and SHA-256 hash, and
+`SHA256SUMS.txt` permits direct integrity checking.
+
+## Loading
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_id = "Qwen/Qwen3.5-4B"
+repo_id = "tu101/least-privilege-model"
+
+tokenizer = AutoTokenizer.from_pretrained(base_id)
+base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
+model = PeftModel.from_pretrained(
+    base,
+    repo_id,
+    subfolder="main/seed-1-checkpoint-1500",
+)
+```
+
+The evaluation environment supplies the tool-interface system prompt and
+executes tool calls through the host-authoritative broker; loading an adapter
+alone does not reproduce the sandbox. Code, first-party task catalogs,
+reproduction instructions, compact result records, and the manuscript are at
+[`Astrowebdeving/least_privilege_grpo`](https://github.com/Astrowebdeving/least_privilege_grpo).
+
+## Result summary and limitations
+
+The primary checkpoint improves frozen internal safe success from 64.36% to
+98.86% and lowers over-privileged success from 4.56% to 0.45%. External transfer
+is heterogeneous: MetaTool and FORTIS direct skill selection improve modestly,
+whereas the primary policy is approximately tied with base on aggregate
+ToolPrivBench and has category-specific regressions. The corrective continuation
+reduces ToolPrivBench over-privileged selection from 44.85% at its parent to
+38.05% while retaining frozen internal performance; with ToolPrivBench's
+privilege-aware prompt it reaches 26.65%.
+
+These are research adapters, not a security boundary or a guarantee that tool
+use is safe. Use the broker, sandbox, and policy controls from the code release.

SHA256SUMS.txt

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main/seed-2-checkpoint-1500/adapter_config.json

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