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from typing import Optional
from datetime import datetime
from pydantic import BaseModel
class LLMConfig(BaseModel):
is_default: bool
id: int
model: str
temperature: float
top_p: float
min_p: float
frequency_penalty: float
presence_penalty: float
n_predict: int
seed: int
date_created: datetime
class Config:
json_schema_extra = {
'example': {
'is_default': True,
'model': 'meta-llama/Llama-3.3-70B-Instruct',
'temperature': 0.14,
'top_p': 0.95,
'min_p': 0.05,
'frequency_penalty': -0.001,
'presence_penalty': 1.3,
'n_predict': 1000,
'seed': 42
}
}
class LLMConfigCreateScheme(BaseModel):
is_default: bool
model: str
temperature: float
top_p: float
min_p: float
frequency_penalty: float
presence_penalty: float
n_predict: int
seed: int
class Config:
json_schema_extra = {
'example': {
'is_default': True,
'model': 'meta-llama/Llama-3.3-70B-Instruct',
'temperature': 0.14,
'top_p': 0.95,
'min_p': 0.05,
'frequency_penalty': -0.001,
'presence_penalty': 1.3,
'n_predict': 1000,
'seed': 42
}
}
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