shinalok
cd41e9e33e
- **Implement `MlxModelService` for local LLM backend.** - **Introduce `DatabaseService` for MySQL integration.** - **Add `HistoryService` to manage conversation context.** - **Set up CLI interface via `CliUiService`.** - **Establish EventBus for token streaming.** - **Include conversation repository for data persistence.** - **Add environment-based configuration management.** - **Draft IoC architectural plan.**
30 lines
944 B
Python
30 lines
944 B
Python
from typing import Iterator
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from services.model.base import AbstractModelService
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class MlxModelService(AbstractModelService):
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"""MLX 기반 로컬 LLM Strategy 구현체."""
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def __init__(self, model_id: str):
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self._model_id = model_id
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self._model = None
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self._tokenizer = None
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def load(self) -> None:
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from mlx_lm import load
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print(f"모델 로딩 중: {self._model_id}")
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self._model, self._tokenizer = load(self._model_id)
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def build_prompt(self, history: list[dict]) -> str:
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return self._tokenizer.apply_chat_template(
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history,
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tokenize=False,
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add_generation_prompt=True,
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)
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def stream(self, prompt: str, max_tokens: int) -> Iterator[str]:
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from mlx_lm import stream_generate
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for chunk in stream_generate(self._model, self._tokenizer, prompt=prompt, max_tokens=max_tokens):
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yield chunk.text
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