| from __future__ import annotations |
|
|
| from typing import Any, Dict, Optional |
|
|
| from transformers import AutoConfig, PretrainedConfig |
|
|
|
|
| class OpenThaiSystemOneConfig(PretrainedConfig): |
| """Config = a text-tower config (Qwen3.5 text by default) + slot-head settings.""" |
|
|
| model_type = "openthai_systemone" |
| sub_configs = {"text_config": AutoConfig} |
|
|
| def __init__( |
| self, |
| text_config: Optional[Dict[str, Any] | PretrainedConfig] = None, |
| n_slots: int = 256, |
| abstain_slot: int = 255, |
| answer_token_id: Optional[int] = None, |
| head_bias: bool = True, |
| n_temperatures: int = 3, |
| **kwargs, |
| ): |
| if isinstance(text_config, dict): |
| text_config = AutoConfig.for_model(**text_config) if "model_type" in text_config else AutoConfig.for_model("qwen3_5_text", **text_config) |
| self.text_config = text_config |
| self.n_slots = n_slots |
| self.abstain_slot = abstain_slot |
| self.answer_token_id = answer_token_id |
| self.head_bias = head_bias |
| self.n_temperatures = n_temperatures |
| super().__init__(**kwargs) |
|
|
| @property |
| def hidden_size(self) -> int: |
| return self.text_config.hidden_size |
|
|
| @property |
| def vocab_size(self) -> int: |
| return self.text_config.vocab_size |
|
|
