Pluto-1 private release candidate: dual-encoder ensemble, calibrated Typed Decisions specialist
2778af7 verified | """Prompt rendering. Training and inference share this exact format.""" | |
| from __future__ import annotations | |
| from .schema import Question, normalize_state | |
| SPECIAL_TOKENS = [ | |
| "<|state|>", | |
| "<|question|>", | |
| "<|type|>", | |
| "<|options|>", | |
| "<|answer|>", | |
| "<|opt|>", | |
| ] | |
| OPTION_MARKER = "<|opt|>" | |
| def option_texts(q: Question) -> list[str]: | |
| if q.kind == "noul": | |
| return ["false", "true"] | |
| if q.kind == "choice": | |
| out = [] | |
| for key, desc in zip(q.keys, q.descriptions): | |
| out.append(key if desc == key else f"{key} - {desc}") | |
| return out | |
| return list(q.descriptions) | |
| def render_prompt(state: str, q: Question) -> str: | |
| state = normalize_state(state) | |
| if q.kind == "score": | |
| instruction = q.instructions + " Options are ordered from lowest to highest." | |
| else: | |
| instruction = q.instructions | |
| lines = "\n".join(f"{i}. {t} {OPTION_MARKER}" for i, t in enumerate(option_texts(q))) | |
| return ( | |
| f"<|state|>\n{state}\n" | |
| f"<|question|>\n{instruction}\n" | |
| f"<|type|>\n{q.kind}\n" | |
| f"<|options|>\n{lines}\n" | |
| f"<|answer|>" | |
| ) | |