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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+library_name: peft
+tags: [lora, code, dsl, aether]
+---
+
+# Aether SOP-to-program LoRA v0.2 (Qwen2.5-Coder-1.5B-Instruct)
+
+A LoRA adapter that turns a plain-English business procedure, plus the owner's policy and sheet list, into an Aether program. **The model is not a safety mechanism.** Every program it writes must pass the Aether checker before it runs. The checker blocks programs that break the policy's rules; the model often writes such programs.
+
+"correct" below means passes the checker and has the same effects as the reference program (scorer field `safe_and_correct`). It is not a safety rating.
+
+## Final evaluation: frozen held-out trap set (30 tasks, each model evaluated once)
+
+Each task asks for something the policy forbids or Aether cannot do. The correct answer is a program that does the permitted alternative. The set was frozen before any trap training data existed and was never used for training or decisions.
+
+| System | Correct | 95% CI |
+|---|---|---|
+| Base model + reference card + 3 examples | 0/30 | 0.0-0.114 |
+| v0.1 (synthetic data only) | 6/30 | 0.095-0.373 |
+| **v0.2 (this adapter: + 413 trap samples)** | **6/30** | 0.095-0.373 |
+
+- **Negative result:** adding 413 targeted trap samples did **not** improve held-out performance. v0.2 vs v0.1: 6 vs 6, paired exact McNemar p = 1.
+- v0.2 vs the base model: p = 0.031.
+- Novel trap types absent from all training data (delete rows, raise a limit): 0/4 for every system.
+- In every evaluation, the checker blocked every program that broke a rule, and programs it accepted had 0 monitor violations. The model by itself solves 6/30 held-out traps.
+
+## Development benchmark (110 tasks, used during development)
+
+This benchmark was used to build the scorer and compare models during development, so treat it as a development set, not a clean test.
+
+| System | Correct | Traps (30) |
+|---|---|---|
+| Base model + reference card + 3 examples | 3/110 | 0 |
+| v0.1 | 30/110 | 2 |
+| **v0.2** | **34/110** | 3 |
+
+v0.2 vs v0.1: paired p = 0.48.
+
+## Training
+
+The v0.1 recipe (LoRA rank 16, learning rate 0.0002, 2 epochs, seed 42; only the program tokens are trained) on 2062 verified examples: 1649 synthetic plus 413 trap-style. Training took 42 minutes on one NVIDIA GeForce RTX 4070, at $0. The checkpoint was chosen by validation loss.
+
+## Limitations
+
+- Trained on templated synthetic English only. Most held-out failures are general errors on unfamiliar businesses (parse errors, type errors, reading columns the policy does not allow), not only trap-following.
+- All benchmark and held-out tasks were drafted by Claude, an AI assistant, not taken from real businesses.
+- Small evaluation sets with wide intervals; one training seed per model.
+- Aether's external actions (messages, money, calendars) are mocked. This is research software.
+- Do not describe this model, or Aether as a whole, as "safe". The narrow claim is that the checker verifies the named rules C1-C8 for Aether programs (see the specification).

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-Coder-1.5B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "kasa_config": null,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.21.2",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "o_proj",
+    "down_proj",
+    "up_proj",
+    "v_proj",
+    "gate_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}