def get_customizable_model_schema(
        self, model: str, credentials: dict
    ) -> AIModelEntity:
        schema = {item.model: item for item in self.predefined_models()}.get(
            _base_model(model)
        )
        if schema is None:
            # Fallback for custom / non-OpenAI base models served via an
            # OpenAI-compatible endpoint (e.g. Qwen, DeepSeek, local vLLM):
            # clone a chat-capable predefined schema so the model is listable
            # and can be set as the default model.
            template = next(
                (m for m in self.predefined_models()
                 if m.model_properties.get("mode") == "chat"),
                None,
            )
            if template is None:
                template = self.predefined_models()[0]
            props = dict(template.model_properties)
            mp = (credentials or {}).get("model_properties") or {}
            try:
                if "context_size" in mp:
                    props["context_size"] = int(mp["context_size"])
                if "max_tokens" in mp:
                    props["max_tokens"] = int(mp["max_tokens"])
            except (ValueError, TypeError):
                pass
            schema = template.model_copy(deep=True, update={"model_properties": props})
        return schema.model_copy(
            deep=True,
            update={
                "model": model,
                "label": I18nObject(zh_hans=model, en_us=model),
                "fetch_from": FetchFrom.CUSTOMIZABLE_MODEL,
            },
        )