Give a local model room to think without spending the answer on it

llama.cpp counts the thinking towards max_tokens along with the answer it
precedes, and the local ceiling was sized for the answer alone. Turning
Thinking up therefore came out of the reply rather than being added to
it, and on a short dictation the 512 floor is the whole budget, so the
model spent it in the think block and came back with nothing to paste.

Each rung of the ladder now carries its own budget, doubling from 256 at
"minimal" to 8192 at "maximum", added on top of the answer's share
rather than taken out of it. The rungs are small because cleanup is
punctuation and locally every one of these tokens is also a second of
somebody standing in front of the screen. "Off" keeps the old tight
ceiling untouched, and an empty setting is given a middling amount,
since a template that can think thinks by default and there is no way to
ask which kind of model this is.

The ceiling is also held under what the server was started with. Above
the context it is not a ceiling at all: the runaway it exists to stop
would run to the end of the context instead, which on CPU is minutes of
waiting. The prompt keeps its share at two characters to the token,
which is under any tokeniser's rate for natural language and so reserves
too much rather than promising room that is not there.

Separately, a reply cut off at somebody's ceiling was returned as if it
were whole. Half a sentence looks like a cleaned-up transcript and is
not one, so finish_reason is now read in both cleanup and chat. The
callers already keep the transcript they started with, which is the
better of the two. This one is not local-only: a hosted provider
stopping at its own output limit was silently pasted the same way.

tests/test_cleanup.py

@@ -411,6 +411,52 @@ class Here(DikteTest):
             cleanup.run("uh, done", self.conf, "the rules")
         self.assertEqual(sent_json(calls[0])["max_tokens"], 512)
 
+    def test_thinking_is_given_room_of_its_own_rather_than_the_answer_s(self):
+        # llama.cpp counts the thinking towards the same ceiling, so a rung that
+        # took its budget out of the answer would leave a short dictation with
+        # nothing to reply with. On a context roomy enough that the clamp the
+        # top rung would otherwise meet is not what is being measured.
+        self.patch_attr(ggml, "llm", FakeServer(context=32768))
+        for rung, room in api.THINKING_ROOM.items():
+            with self.subTest(rung=rung):
+                self.conf["local_llm_reasoning"] = rung
+                with fake_urlopen(chat_reply("Done.")) as calls:
+                    cleanup.run("uh, done", self.conf, "the rules")
+                self.assertEqual(sent_json(calls[0])["max_tokens"], 512 + room)
+
+    def test_each_rung_of_the_ladder_thinks_longer_than_the_one_below(self):
+        rungs = [api.THINKING_ROOM[name] for name in
+                 ("minimal", "low", "medium", "high", "xhigh", "max")]
+        self.assertEqual(rungs, sorted(rungs))
+        self.assertEqual(len(set(rungs)), len(rungs))
+
+    def test_the_models_own_default_is_given_room_to_think_in_too(self):
+        # Nothing is sent, so a template that thinks will think, and the ceiling
+        # has to survive that as well.
+        self.conf["local_llm_reasoning"] = ""
+        with fake_urlopen(chat_reply("Done.")) as calls:
+            cleanup.run("uh, done", self.conf, "the rules")
+        self.assertEqual(sent_json(calls[0])["max_tokens"],
+                         512 + api.DEFAULT_THINKING_ROOM)
+
+    def test_the_ceiling_stays_under_the_context_the_server_was_started_with(self):
+        # Above the context there is no ceiling at all: the runaway would run to
+        # the end of the context instead of stopping where this says.
+        self.patch_attr(ggml, "llm", FakeServer(context=2048))
+        self.conf["local_llm_reasoning"] = "max"
+        with fake_urlopen(chat_reply("Done.")) as calls:
+            cleanup.run("uh, done", self.conf, "the rules")
+        self.assertLess(sent_json(calls[0])["max_tokens"], 2048)
+
+    def test_the_prompt_keeps_its_share_of_a_small_context(self):
+        self.patch_attr(ggml, "llm", FakeServer(context=2048))
+        self.conf["local_llm_reasoning"] = "max"
+        with fake_urlopen(chat_reply("Done.")) as calls:
+            cleanup.run("x" * 2000, self.conf, "the rules")
+        # 2048 less half the characters of prompt and transcript together.
+        self.assertEqual(sent_json(calls[0])["max_tokens"],
+                         2048 - (len("the rules") + 2000) // 2)
+
     def test_a_reply_that_was_all_thinking_names_the_setting_that_fixes_it(self):
         reply = {"choices": [{"message": {"content": "", "reasoning": "hmm"}}]}
         with fake_urlopen(reply), self.assertRaises(api.ApiError) as caught: