setup models

adapters/semif/adapter.py

@@ -0,0 +1,45 @@
+"""Adaptador do SemIf (modo direct) com Qwen3.5-4B fixado (tarefa 43)."""
+from __future__ import annotations
+
+from dmb.adapter import Adapter
+from dmb.jev import ContextLimit, answer_from_distribution, option_descriptions
+
+
+class SemIfAdapter(Adapter):
+    name = "semif"
+    probabilities_source = "native"
+
+    def load(self) -> None:
+        from semif_phase1 import core, direct
+
+        dev = self.device
+        if dev == "auto":
+            import torch
+            dev = "cuda" if torch.cuda.is_available() else "cpu"
+        self._direct = direct
+        self.model, self.tokenizer, self.metadata = core.load_causal_model(
+            str(self.model_dir), self.revision, device=dev, dtype="bfloat16")
+
+    def native_context_limit(self) -> int:
+        return 8192
+
+    def count_tokens(self, text: str) -> int:
+        return len(self.tokenizer.encode(text, add_special_tokens=False))
+
+    def decide(self, state: str, questions: dict) -> tuple[dict, int]:
+        answers, total_tokens = {}, 0
+        for name, question in questions.items():
+            options = option_descriptions(question)
+            row = {"id": name, "state": state, "question": question["instructions"],
+                   "options": [{"id": k, "description": v} for k, v in options.items()]}
+            try:
+                out = self._direct.score(self.model, self.tokenizer, row, self.metadata,
+                                         max_tokens=self.context_limit)
+            except ValueError as error:
+                if "exceed limit" in str(error):
+                    raise ContextLimit(str(error)) from error
+                raise
+            dist = dict(zip(out["option_ids"], out["probabilities"]))
+            answers[name] = answer_from_distribution(question, dist)
+            total_tokens += out["input_tokens"]
+        return answers, total_tokens