Repository navigation
Expand file tree
/
Copy pathtypes.py
More file actions
308 lines (255 loc) · 11.7 KB
/
Copy pathtypes.py
File metadata and controls
308 lines (255 loc) · 11.7 KB
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
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
from __future__ import annotations
from typing import List, Optional, Union, Dict
from typing_extensions import TypedDict, Literal
from pydantic import BaseModel, Field
import llama_cpp
model_field = Field(
description="The model to use for generating completions.", default=None
)
max_tokens_field = Field(
default=16, ge=1, description="The maximum number of tokens to generate."
)
temperature_field = Field(
default=0.8,
description="Adjust the randomness of the generated text.\n\n"
+ "Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.",
)
top_p_field = Field(
default=0.95,
ge=0.0,
le=1.0,
description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\n"
+ "Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.",
)
min_p_field = Field(
default=0.05,
ge=0.0,
le=1.0,
description="Sets a minimum base probability threshold for token selection.\n\n"
+ "The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter min_p represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with min_p=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.",
)
stop_field = Field(
default=None,
description="A list of tokens at which to stop generation. If None, no stop tokens are used.",
)
stream_field = Field(
default=False,
description="Whether to stream the results as they are generated. Useful for chatbots.",
)
top_k_field = Field(
default=40,
ge=0,
description="Limit the next token selection to the K most probable tokens.\n\n"
+ "Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text.",
)
repeat_penalty_field = Field(
default=1.1,
ge=0.0,
description="A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\n"
+ "Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.",
)
presence_penalty_field = Field(
default=0.0,
ge=-2.0,
le=2.0,
description="Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.",
)
frequency_penalty_field = Field(
default=0.0,
ge=-2.0,
le=2.0,
description="Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.",
)
mirostat_mode_field = Field(
default=0,
ge=0,
le=2,
description="Enable Mirostat constant-perplexity algorithm of the specified version (1 or 2; 0 = disabled)",
)
mirostat_tau_field = Field(
default=5.0,
ge=0.0,
le=10.0,
description="Mirostat target entropy, i.e. the target perplexity - lower values produce focused and coherent text, larger values produce more diverse and less coherent text",
)
mirostat_eta_field = Field(
default=0.1, ge=0.001, le=1.0, description="Mirostat learning rate"
)
grammar = Field(
default=None,
description="A CBNF grammar (as string) to be used for formatting the model's output.",
)
class CreateCompletionRequest(BaseModel):
prompt: Union[str, List[str]] = Field(
default="", description="The prompt to generate completions for."
)
suffix: Optional[str] = Field(
default=None,
description="A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots.",
)
max_tokens: Optional[int] = Field(
default=16, ge=0, description="The maximum number of tokens to generate."
)
temperature: float = temperature_field
top_p: float = top_p_field
min_p: float = min_p_field
echo: bool = Field(
default=False,
description="Whether to echo the prompt in the generated text. Useful for chatbots.",
)
stop: Optional[Union[str, List[str]]] = stop_field
stream: bool = stream_field
logprobs: Optional[int] = Field(
default=None,
ge=0,
description="The number of logprobs to generate. If None, no logprobs are generated.",
)
presence_penalty: Optional[float] = presence_penalty_field
frequency_penalty: Optional[float] = frequency_penalty_field
logit_bias: Optional[Dict[str, float]] = Field(None)
seed: Optional[int] = Field(None)
# ignored or currently unsupported
model: Optional[str] = model_field
n: Optional[int] = 1
best_of: Optional[int] = 1
user: Optional[str] = Field(default=None)
# llama.cpp specific parameters
top_k: int = top_k_field
repeat_penalty: float = repeat_penalty_field
logit_bias_type: Optional[Literal["input_ids", "tokens"]] = Field(None)
mirostat_mode: int = mirostat_mode_field
mirostat_tau: float = mirostat_tau_field
mirostat_eta: float = mirostat_eta_field
grammar: Optional[str] = None
model_config = {
"json_schema_extra": {
"examples": [
{
"prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
"stop": ["\n", "###"],
}
]
}
}
class CreateEmbeddingRequest(BaseModel):
model: Optional[str] = model_field
input: Union[str, List[str]] = Field(description="The input to embed.")
user: Optional[str] = Field(default=None)
model_config = {
"json_schema_extra": {
"examples": [
{
"input": "The food was delicious and the waiter...",
}
]
}
}
class ChatCompletionRequestMessage(BaseModel):
role: Literal["system", "user", "assistant", "function"] = Field(
default="user", description="The role of the message."
)
content: Optional[str] = Field(
default="", description="The content of the message."
)
class CreateChatCompletionRequest(BaseModel):
messages: List[llama_cpp.ChatCompletionRequestMessage] = Field(
default=[], description="A list of messages to generate completions for."
)
functions: Optional[List[llama_cpp.ChatCompletionFunction]] = Field(
default=None,
description="A list of functions to apply to the generated completions.",
)
function_call: Optional[llama_cpp.ChatCompletionRequestFunctionCall] = Field(
default=None,
description="A function to apply to the generated completions.",
)
tools: Optional[List[llama_cpp.ChatCompletionTool]] = Field(
default=None,
description="A list of tools to apply to the generated completions.",
)
tool_choice: Optional[llama_cpp.ChatCompletionToolChoiceOption] = Field(
default=None,
description="A tool to apply to the generated completions.",
) # TODO: verify
max_tokens: Optional[int] = Field(
default=None,
description="The maximum number of tokens to generate. Defaults to inf",
)
logprobs: Optional[bool] = Field(
default=False,
description="Whether to output the logprobs or not. Default is True"
)
top_logprobs: Optional[int] = Field(
default=None,
ge=0,
description="The number of logprobs to generate. If None, no logprobs are generated. logprobs need to set to True.",
)
temperature: float = temperature_field
top_p: float = top_p_field
min_p: float = min_p_field
stop: Optional[Union[str, List[str]]] = stop_field
stream: bool = stream_field
presence_penalty: Optional[float] = presence_penalty_field
frequency_penalty: Optional[float] = frequency_penalty_field
logit_bias: Optional[Dict[str, float]] = Field(None)
seed: Optional[int] = Field(None)
response_format: Optional[llama_cpp.ChatCompletionRequestResponseFormat] = Field(
default=None,
)
# ignored or currently unsupported
model: Optional[str] = model_field
n: Optional[int] = 1
user: Optional[str] = Field(None)
# llama.cpp specific parameters
top_k: int = top_k_field
repeat_penalty: float = repeat_penalty_field
logit_bias_type: Optional[Literal["input_ids", "tokens"]] = Field(None)
mirostat_mode: int = mirostat_mode_field
mirostat_tau: float = mirostat_tau_field
mirostat_eta: float = mirostat_eta_field
grammar: Optional[str] = None
model_config = {
"json_schema_extra": {
"examples": [
{
"messages": [
ChatCompletionRequestMessage(
role="system", content="You are a helpful assistant."
).model_dump(),
ChatCompletionRequestMessage(
role="user", content="What is the capital of France?"
).model_dump(),
]
}
]
}
}
class ModelData(TypedDict):
id: str
object: Literal["model"]
owned_by: str
permissions: List[str]
class ModelList(TypedDict):
object: Literal["list"]
data: List[ModelData]
class TokenizeInputRequest(BaseModel):
model: Optional[str] = model_field
input: str = Field(description="The input to tokenize.")
model_config = {
"json_schema_extra": {"examples": [{"input": "How many tokens in this query?"}]}
}
class TokenizeInputResponse(BaseModel):
tokens: List[int] = Field(description="A list of tokens.")
model_config = {"json_schema_extra": {"example": {"tokens": [123, 321, 222]}}}
class TokenizeInputCountResponse(BaseModel):
count: int = Field(description="The number of tokens in the input.")
model_config = {"json_schema_extra": {"example": {"count": 5}}}
class DetokenizeInputRequest(BaseModel):
model: Optional[str] = model_field
tokens: List[int] = Field(description="A list of toekns to detokenize.")
model_config = {"json_schema_extra": {"example": [{"tokens": [123, 321, 222]}]}}
class DetokenizeInputResponse(BaseModel):
text: str = Field(description="The detokenized text.")
model_config = {
"json_schema_extra": {"example": {"text": "How many tokens in this query?"}}
}