Spaces:
Runtime error
Runtime error
File size: 5,432 Bytes
8655a4b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
from typing import Literal, Optional, List, Dict, Any, Union
import time
import shortuuid
from pydantic import BaseModel, Field
class ErrorResponse(BaseModel):
object: str = "error"
message: str
code: int
class ModelPermission(BaseModel):
id: str = Field(default_factory=lambda: f"modelperm-{shortuuid.random()}")
object: str = "model_permission"
created: int = Field(default_factory=lambda: int(time.time()))
allow_create_engine: bool = False
allow_sampling: bool = True
allow_logprobs: bool = True
allow_search_indices: bool = True
allow_view: bool = True
allow_fine_tuning: bool = False
organization: str = "*"
group: Optional[str] = None
is_blocking: str = False
class ModelCard(BaseModel):
id: str
object: str = "model"
created: int = Field(default_factory=lambda: int(time.time()))
owned_by: str = "fastchat"
root: Optional[str] = None
parent: Optional[str] = None
permission: List[ModelPermission] = []
class ModelList(BaseModel):
object: str = "list"
data: List[ModelCard] = []
class UsageInfo(BaseModel):
prompt_tokens: int = 0
total_tokens: int = 0
completion_tokens: Optional[int] = 0
class LogProbs(BaseModel):
text_offset: List[int] = Field(default_factory=list)
token_logprobs: List[Optional[float]] = Field(default_factory=list)
tokens: List[str] = Field(default_factory=list)
top_logprobs: List[Optional[Dict[str, float]]] = Field(default_factory=list)
class ChatCompletionRequest(BaseModel):
model: str
messages: Union[
str,
List[Dict[str, str]],
List[Dict[str, Union[str, List[Dict[str, Union[str, Dict[str, str]]]]]]],
]
temperature: Optional[float] = 0.7
top_p: Optional[float] = 1.0
top_k: Optional[int] = -1
n: Optional[int] = 1
max_tokens: Optional[int] = None
stop: Optional[Union[str, List[str]]] = None
stream: Optional[bool] = False
presence_penalty: Optional[float] = 0.0
frequency_penalty: Optional[float] = 0.0
user: Optional[str] = None
class ChatMessage(BaseModel):
role: str
content: str
class ChatCompletionResponseChoice(BaseModel):
index: int
message: ChatMessage
finish_reason: Optional[Literal["stop", "length"]] = None
class ChatCompletionResponse(BaseModel):
id: str = Field(default_factory=lambda: f"chatcmpl-{shortuuid.random()}")
object: str = "chat.completion"
created: int = Field(default_factory=lambda: int(time.time()))
model: str
choices: List[ChatCompletionResponseChoice]
usage: UsageInfo
class DeltaMessage(BaseModel):
role: Optional[str] = None
content: Optional[str] = None
class ChatCompletionResponseStreamChoice(BaseModel):
index: int
delta: DeltaMessage
finish_reason: Optional[Literal["stop", "length"]] = None
class ChatCompletionStreamResponse(BaseModel):
id: str = Field(default_factory=lambda: f"chatcmpl-{shortuuid.random()}")
object: str = "chat.completion.chunk"
created: int = Field(default_factory=lambda: int(time.time()))
model: str
choices: List[ChatCompletionResponseStreamChoice]
class TokenCheckRequestItem(BaseModel):
model: str
prompt: str
max_tokens: int
class TokenCheckRequest(BaseModel):
prompts: List[TokenCheckRequestItem]
class TokenCheckResponseItem(BaseModel):
fits: bool
tokenCount: int
contextLength: int
class TokenCheckResponse(BaseModel):
prompts: List[TokenCheckResponseItem]
class EmbeddingsRequest(BaseModel):
model: Optional[str] = None
engine: Optional[str] = None
input: Union[str, List[Any]]
user: Optional[str] = None
encoding_format: Optional[str] = None
class EmbeddingsResponse(BaseModel):
object: str = "list"
data: List[Dict[str, Any]]
model: str
usage: UsageInfo
class CompletionRequest(BaseModel):
model: str
prompt: Union[str, List[Any]]
suffix: Optional[str] = None
temperature: Optional[float] = 0.7
n: Optional[int] = 1
max_tokens: Optional[int] = 16
stop: Optional[Union[str, List[str]]] = None
stream: Optional[bool] = False
top_p: Optional[float] = 1.0
top_k: Optional[int] = -1
logprobs: Optional[int] = None
echo: Optional[bool] = False
presence_penalty: Optional[float] = 0.0
frequency_penalty: Optional[float] = 0.0
user: Optional[str] = None
use_beam_search: Optional[bool] = False
best_of: Optional[int] = None
class CompletionResponseChoice(BaseModel):
index: int
text: str
logprobs: Optional[LogProbs] = None
finish_reason: Optional[Literal["stop", "length"]] = None
class CompletionResponse(BaseModel):
id: str = Field(default_factory=lambda: f"cmpl-{shortuuid.random()}")
object: str = "text_completion"
created: int = Field(default_factory=lambda: int(time.time()))
model: str
choices: List[CompletionResponseChoice]
usage: UsageInfo
class CompletionResponseStreamChoice(BaseModel):
index: int
text: str
logprobs: Optional[LogProbs] = None
finish_reason: Optional[Literal["stop", "length"]] = None
class CompletionStreamResponse(BaseModel):
id: str = Field(default_factory=lambda: f"cmpl-{shortuuid.random()}")
object: str = "text_completion"
created: int = Field(default_factory=lambda: int(time.time()))
model: str
choices: List[CompletionResponseStreamChoice]
|