setup models

adapters/rizzo_flow/adapter.py

@@ -0,0 +1,59 @@
+"""Adaptador do Rizzo Flow 4B (GGUF Q8_0 sobre llama.cpp) com prompts spark-decisions-v3 (tarefa 45)."""
+from __future__ import annotations
+
+from pathlib import Path
+
+from dmb.adapter import Adapter
+from dmb.jev import ContextLimit, answer_from_distribution, option_keys
+
+GGUF_FILE = "spark-x2.5-4b-rizzo-flow-lora-q8_0.gguf"
+
+
+class RizzoFlowAdapter(Adapter):
+    name = "rizzo_flow"
+    probabilities_source = "native"
+
+    def __init__(self, *args, runtime_dir: str | Path | None = None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.runtime_dir = runtime_dir
+
+    def load(self) -> None:
+        from rizzo_flow.backend_llama import LlamaBackend
+        from rizzo_flow.compat import model_name
+        from rizzo_flow.engine import Engine
+
+        device = "cuda" if self.device == "cuda" else ("cpu" if self.device == "cpu" else "auto")
+        backend = LlamaBackend.load(self.model_dir / GGUF_FILE, device=device, ctx=self.context_limit,
+                                    runtime_dir=self.runtime_dir)
+        self.engine = Engine(backend, ctx=self.context_limit)
+        self.served = model_name(backend.metadata)
+
+    def native_context_limit(self) -> int:
+        return 2048  # comprimento máximo dos prompts de treino do LoRA
+
+    def count_tokens(self, text: str) -> int:
+        return len(self.engine.backend.tokenizer.encode(text))
+
+    def decide(self, state: str, questions: dict) -> tuple[dict, int]:
+        from rizzo_flow.compat import SystemOneRequest, from_native, to_native
+
+        request = SystemOneRequest.model_validate(
+            {"model": self.served, "state": state, "questions": questions})
+        native, options = to_native(request)
+        try:
+            raw = from_native(request, self.engine.decide(native), options, self.served)
+        except ValueError as error:
+            if "context" in str(error).lower() or "tokens" in str(error).lower():
+                raise ContextLimit(str(error)) from error
+            raise
+        raw = raw if isinstance(raw, dict) else raw.model_dump()
+        answers = {}
+        for name, question in questions.items():
+            a = raw["answers"][name]
+            if question["type"] == "noul":
+                p = float(a["noul"])
+                dist = {"true": p, "false": 1 - p}
+            else:
+                dist = {k: a["probabilities"][k] for k in option_keys(question)}
+            answers[name] = answer_from_distribution(question, dist, a.get("confidence"))
+        return answers, int(raw.get("usage", {}).get("input_tokens", 0))