mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-25 17:29:22 +01:00
212 lines
7.0 KiB
Python
212 lines
7.0 KiB
Python
import json
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import time
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from typing import Dict, List
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from pydantic import BaseModel, Field
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class GenerationOptions(BaseModel):
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preset: str | None = Field(default=None, description="The name of a file under text-generation-webui/presets (without the .yaml extension). The sampling parameters that get overwritten by this option are the keys in the default_preset() function in modules/presets.py.")
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min_p: float = 0
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dynamic_temperature: bool = False
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dynatemp_low: float = 1
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dynatemp_high: float = 1
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dynatemp_exponent: float = 1
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smoothing_factor: float = 0
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smoothing_curve: float = 1
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top_k: int = 0
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repetition_penalty: float = 1
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repetition_penalty_range: int = 1024
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typical_p: float = 1
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tfs: float = 1
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top_a: float = 0
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epsilon_cutoff: float = 0
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eta_cutoff: float = 0
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guidance_scale: float = 1
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negative_prompt: str = ''
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penalty_alpha: float = 0
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mirostat_mode: int = 0
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mirostat_tau: float = 5
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mirostat_eta: float = 0.1
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temperature_last: bool = False
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do_sample: bool = True
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seed: int = -1
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encoder_repetition_penalty: float = 1
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no_repeat_ngram_size: int = 0
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min_length: int = 0
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num_beams: int = 1
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length_penalty: float = 1
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early_stopping: bool = False
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truncation_length: int = 0
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max_tokens_second: int = 0
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prompt_lookup_num_tokens: int = 0
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custom_token_bans: str = ""
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sampler_priority: List[str] | str | None = Field(default=None, description="List of samplers where the first items will appear first in the stack. Example: [\"top_k\", \"temperature\", \"top_p\"].")
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auto_max_new_tokens: bool = False
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ban_eos_token: bool = False
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add_bos_token: bool = True
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skip_special_tokens: bool = True
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grammar_string: str = ""
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class CompletionRequestParams(BaseModel):
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model: str | None = Field(default=None, description="Unused parameter. To change the model, use the /v1/internal/model/load endpoint.")
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prompt: str | List[str]
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best_of: int | None = Field(default=1, description="Unused parameter.")
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echo: bool | None = False
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frequency_penalty: float | None = 0
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logit_bias: dict | None = None
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logprobs: int | None = None
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max_tokens: int | None = 16
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n: int | None = Field(default=1, description="Unused parameter.")
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presence_penalty: float | None = 0
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stop: str | List[str] | None = None
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stream: bool | None = False
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suffix: str | None = None
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temperature: float | None = 1
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top_p: float | None = 1
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user: str | None = Field(default=None, description="Unused parameter.")
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class CompletionRequest(GenerationOptions, CompletionRequestParams):
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pass
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class CompletionResponse(BaseModel):
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id: str
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choices: List[dict]
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created: int = int(time.time())
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model: str
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object: str = "text_completion"
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usage: dict
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class ChatCompletionRequestParams(BaseModel):
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messages: List[dict]
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model: str | None = Field(default=None, description="Unused parameter. To change the model, use the /v1/internal/model/load endpoint.")
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frequency_penalty: float | None = 0
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function_call: str | dict | None = Field(default=None, description="Unused parameter.")
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functions: List[dict] | None = Field(default=None, description="Unused parameter.")
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logit_bias: dict | None = None
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max_tokens: int | None = None
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n: int | None = Field(default=1, description="Unused parameter.")
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presence_penalty: float | None = 0
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stop: str | List[str] | None = None
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stream: bool | None = False
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temperature: float | None = 1
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top_p: float | None = 1
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user: str | None = Field(default=None, description="Unused parameter.")
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mode: str = Field(default='instruct', description="Valid options: instruct, chat, chat-instruct.")
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instruction_template: str | None = Field(default=None, description="An instruction template defined under text-generation-webui/instruction-templates. If not set, the correct template will be automatically obtained from the model metadata.")
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instruction_template_str: str | None = Field(default=None, description="A Jinja2 instruction template. If set, will take precedence over everything else.")
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character: str | None = Field(default=None, description="A character defined under text-generation-webui/characters. If not set, the default \"Assistant\" character will be used.")
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user_name: str | None = Field(default=None, description="Your name (the user). By default, it's \"You\".", alias="name1")
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bot_name: str | None = Field(default=None, description="Overwrites the value set by character field.", alias="name2")
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context: str | None = Field(default=None, description="Overwrites the value set by character field.")
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greeting: str | None = Field(default=None, description="Overwrites the value set by character field.")
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chat_template_str: str | None = Field(default=None, description="Jinja2 template for chat.")
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chat_instruct_command: str | None = None
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continue_: bool = Field(default=False, description="Makes the last bot message in the history be continued instead of starting a new message.")
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class ChatCompletionRequest(GenerationOptions, ChatCompletionRequestParams):
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pass
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class ChatCompletionResponse(BaseModel):
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id: str
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choices: List[dict]
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created: int = int(time.time())
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model: str
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object: str = "chat.completion"
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usage: dict
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class EmbeddingsRequest(BaseModel):
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input: str | List[str] | List[int] | List[List[int]]
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model: str | None = Field(default=None, description="Unused parameter. To change the model, set the OPENEDAI_EMBEDDING_MODEL and OPENEDAI_EMBEDDING_DEVICE environment variables before starting the server.")
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encoding_format: str = Field(default="float", description="Can be float or base64.")
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user: str | None = Field(default=None, description="Unused parameter.")
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class EmbeddingsResponse(BaseModel):
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index: int
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embedding: List[float]
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object: str = "embedding"
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class EncodeRequest(BaseModel):
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text: str
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class EncodeResponse(BaseModel):
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tokens: List[int]
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length: int
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class DecodeRequest(BaseModel):
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tokens: List[int]
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class DecodeResponse(BaseModel):
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text: str
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class TokenCountResponse(BaseModel):
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length: int
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class LogitsRequestParams(BaseModel):
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prompt: str
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use_samplers: bool = False
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top_logits: int | None = 50
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frequency_penalty: float | None = 0
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max_tokens: int | None = 16
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presence_penalty: float | None = 0
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temperature: float | None = 1
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top_p: float | None = 1
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class LogitsRequest(GenerationOptions, LogitsRequestParams):
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pass
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class LogitsResponse(BaseModel):
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logits: Dict[str, float]
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class ModelInfoResponse(BaseModel):
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model_name: str
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lora_names: List[str]
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class ModelListResponse(BaseModel):
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model_names: List[str]
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class LoadModelRequest(BaseModel):
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model_name: str
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args: dict | None = None
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settings: dict | None = None
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class LoraListResponse(BaseModel):
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lora_names: List[str]
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class LoadLorasRequest(BaseModel):
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lora_names: List[str]
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def to_json(obj):
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return json.dumps(obj.__dict__, indent=4)
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def to_dict(obj):
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return obj.__dict__
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