docs: refresh module docstrings for the packaged layout and render contract

src/tiararodney/sekft/eval.py

@@ -6,14 +6,15 @@ scenarios with NO scaffold (the trained behaviour must stand on its own), and
 reports the rates that count: does it reach command-mode, does it terminate,
 does the checker pass.
 
-    python eval.py --base <hf-dir> --adapter ./ckpt-mistral-r16 \
+    sekft-eval --base <hf-dir> --adapter ./ckpt-mistral-r16 \
                --scenarios ./holdout-scenarios --n 10
 
-Reuses the rollout loop with a *local* operator: the model formats and
-generates in the same role-delimited render it was trained on (train == eval ==
-deploy, or the prompts go out of distribution). Prerequisites on the box: torch
-+ transformers + peft, the ``sekft-dash`` image, and held-out SCENARIO bundles
-(from ``generate.py`` -- not trajectories; the eval stands up and verifies each).
+Reuses the posix-sdc rollout loop with a *local* operator: the model renders and
+generates with the same chat template it was trained on (train == eval == serve,
+via ``apply_chat_template`` + ``normalize_for_template``, or the prompts go out
+of distribution). Prerequisites on the box: torch + transformers + peft, the
+``sekft-dash`` image, and held-out SCENARIO bundles from the posix-sdc factory
+(not trajectories; the eval stands up and verifies each).
 """
 from __future__ import annotations
 

src/tiararodney/sekft/resident.py

@@ -8,14 +8,14 @@ fresh LoRA adapter on the resident base and ``unload``s it back to clean; each
 
 Interactive (IPython on the GPU box) is the intended use:
 
-    from resident import Resident
+    from tiararodney.sekft.resident import Resident
     r = Resident("~/llm-models/mistral-7b-instruct-v0.2", load_4bit=True)
     r.fit("~/sekft/trajectories", "~/sekft/ckpt-a", lora_r=16, lr=2e-4, epochs=3)
     r.evaluate("~/sekft/ckpt-a", "~/sekft/holdout", n=10)
     r.fit("~/sekft/trajectories", "~/sekft/ckpt-b", lora_r=32)   # NO base reload
 
-Or `python resident.py --base <dir> --selftest-data <stub_dir>` to prove the
-base loads once and two adapters train against it.
+Or `sekft-resident --base <dir> --selftest-data <stub_dir>` to prove the base
+loads once and two adapters train against it.
 """
 from __future__ import annotations
 

src/tiararodney/sekft/sft.py

@@ -17,8 +17,8 @@ system role and require strict user/assistant alternation. That same
 canonicalisation must run on the serving side. Everything else is standard
 causal-LM SFT with an assistant-only loss mask.
 
-    python sft.py --data ./trajectories --base <hf-model-dir> --out ./ckpt
-    python sft.py --data ./trajectories --base <dir> --inspect   # mask stats, no training
+    sekft-train --data ./trajectories --base <hf-model-dir> --out ./ckpt
+    sekft-train --data ./trajectories --base <dir> --inspect   # mask stats, no training
 
 Training needs torch + transformers + peft (a GPU box). ``--inspect`` and the
 normalize/mask helpers run anywhere a tokenizer with a chat template is