mirror of
https://github.com/theroyallab/tabbyAPI.git
synced 2026-04-29 10:42:03 +00:00
Merge branch 'main' into main
This commit is contained in:
Bartowski
@@ -1,7 +0,0 @@
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"""Types for auth requests."""
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from pydantic import BaseModel
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class AuthPermissionResponse(BaseModel):
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permission: str
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@@ -65,3 +65,4 @@ class ChatCompletionStreamChunk(BaseModel):
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created: int = Field(default_factory=lambda: int(time()))
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model: str
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object: str = "chat.completion.chunk"
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usage: Optional[UsageStats] = None
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@@ -18,6 +18,10 @@ class CompletionResponseFormat(BaseModel):
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type: str = "text"
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class ChatCompletionStreamOptions(BaseModel):
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include_usage: Optional[bool] = False
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class CommonCompletionRequest(BaseSamplerRequest):
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"""Represents a common completion request."""
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@@ -27,6 +31,7 @@ class CommonCompletionRequest(BaseSamplerRequest):
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# Generation info (remainder is in BaseSamplerRequest superclass)
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stream: Optional[bool] = False
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stream_options: Optional[ChatCompletionStreamOptions] = None
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logprobs: Optional[int] = Field(
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default_factory=lambda: get_default_sampler_value("logprobs", 0)
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)
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@@ -1,25 +0,0 @@
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from pydantic import BaseModel, Field
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from typing import List, Optional
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def _generate_include_list():
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return ["*"]
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class DownloadRequest(BaseModel):
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"""Parameters for a HuggingFace repo download."""
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repo_id: str
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repo_type: str = "model"
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folder_name: Optional[str] = None
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revision: Optional[str] = None
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token: Optional[str] = None
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include: List[str] = Field(default_factory=_generate_include_list)
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exclude: List[str] = Field(default_factory=list)
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chunk_limit: Optional[int] = None
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class DownloadResponse(BaseModel):
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"""Response for a download request."""
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download_path: str
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42
endpoints/OAI/types/embedding.py
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42
endpoints/OAI/types/embedding.py
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@@ -0,0 +1,42 @@
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from typing import List, Optional
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from pydantic import BaseModel, Field
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class UsageInfo(BaseModel):
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prompt_tokens: int = 0
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total_tokens: int = 0
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completion_tokens: Optional[int] = 0
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class EmbeddingsRequest(BaseModel):
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input: List[str] = Field(
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..., description="List of input texts to generate embeddings for."
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)
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encoding_format: str = Field(
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"float",
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description="Encoding format for the embeddings. "
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"Can be 'float' or 'base64'.",
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)
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model: Optional[str] = Field(
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None,
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description="Name of the embedding model to use. "
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"If not provided, the default model will be used.",
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)
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class EmbeddingObject(BaseModel):
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object: str = Field("embedding", description="Type of the object.")
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embedding: List[float] = Field(
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..., description="Embedding values as a list of floats."
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)
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index: int = Field(
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..., description="Index of the input text corresponding to " "the embedding."
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)
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class EmbeddingsResponse(BaseModel):
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object: str = Field("list", description="Type of the response object.")
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data: List[EmbeddingObject] = Field(..., description="List of embedding objects.")
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model: str = Field(..., description="Name of the embedding model used.")
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usage: UsageInfo = Field(..., description="Information about token usage.")
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@@ -1,43 +0,0 @@
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"""Lora types"""
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from pydantic import BaseModel, Field
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from time import time
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from typing import Optional, List
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class LoraCard(BaseModel):
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"""Represents a single Lora card."""
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id: str = "test"
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object: str = "lora"
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created: int = Field(default_factory=lambda: int(time()))
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owned_by: str = "tabbyAPI"
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scaling: Optional[float] = None
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class LoraList(BaseModel):
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"""Represents a list of Lora cards."""
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object: str = "list"
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data: List[LoraCard] = Field(default_factory=list)
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class LoraLoadInfo(BaseModel):
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"""Represents a single Lora load info."""
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name: str
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scaling: Optional[float] = 1.0
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class LoraLoadRequest(BaseModel):
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"""Represents a Lora load request."""
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loras: List[LoraLoadInfo]
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skip_queue: bool = False
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class LoraLoadResponse(BaseModel):
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"""Represents a Lora load response."""
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success: List[str] = Field(default_factory=list)
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failure: List[str] = Field(default_factory=list)
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@@ -1,149 +0,0 @@
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"""Contains model card types."""
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from pydantic import BaseModel, Field, ConfigDict
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from time import time
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from typing import List, Optional
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from common.gen_logging import GenLogPreferences
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from common.model import get_config_default
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class ModelCardParameters(BaseModel):
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"""Represents model card parameters."""
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# Safe to do this since it's guaranteed to fetch a max seq len
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# from model_container
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max_seq_len: Optional[int] = None
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rope_scale: Optional[float] = 1.0
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rope_alpha: Optional[float] = 1.0
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cache_size: Optional[int] = None
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cache_mode: Optional[str] = "FP16"
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chunk_size: Optional[int] = 2048
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prompt_template: Optional[str] = None
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num_experts_per_token: Optional[int] = None
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# Draft is another model, so include it in the card params
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draft: Optional["ModelCard"] = None
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class ModelCard(BaseModel):
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"""Represents a single model card."""
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id: str = "test"
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object: str = "model"
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created: int = Field(default_factory=lambda: int(time()))
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owned_by: str = "tabbyAPI"
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logging: Optional[GenLogPreferences] = None
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parameters: Optional[ModelCardParameters] = None
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class ModelList(BaseModel):
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"""Represents a list of model cards."""
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object: str = "list"
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data: List[ModelCard] = Field(default_factory=list)
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class DraftModelLoadRequest(BaseModel):
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"""Represents a draft model load request."""
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# Required
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draft_model_name: str
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# Config arguments
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draft_rope_scale: Optional[float] = Field(
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default_factory=lambda: get_config_default(
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"draft_rope_scale", 1.0, is_draft=True
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)
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)
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draft_rope_alpha: Optional[float] = Field(
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description="Automatically calculated if not present",
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default_factory=lambda: get_config_default(
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"draft_rope_alpha", None, is_draft=True
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),
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examples=[1.0],
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)
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draft_cache_mode: Optional[str] = Field(
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default_factory=lambda: get_config_default(
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"draft_cache_mode", "FP16", is_draft=True
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)
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)
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class ModelLoadRequest(BaseModel):
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"""Represents a model load request."""
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# Required
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name: str
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# Config arguments
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# Max seq len is fetched from config.json of the model by default
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max_seq_len: Optional[int] = Field(
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description="Leave this blank to use the model's base sequence length",
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default_factory=lambda: get_config_default("max_seq_len"),
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examples=[4096],
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)
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override_base_seq_len: Optional[int] = Field(
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description=(
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"Overrides the model's base sequence length. " "Leave blank if unsure"
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),
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default_factory=lambda: get_config_default("override_base_seq_len"),
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examples=[4096],
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)
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cache_size: Optional[int] = Field(
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description=("Number in tokens, must be greater than or equal to max_seq_len"),
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default_factory=lambda: get_config_default("cache_size"),
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examples=[4096],
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)
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gpu_split_auto: Optional[bool] = Field(
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default_factory=lambda: get_config_default("gpu_split_auto", True)
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)
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autosplit_reserve: Optional[List[float]] = Field(
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default_factory=lambda: get_config_default("autosplit_reserve", [96])
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)
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gpu_split: Optional[List[float]] = Field(
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default_factory=lambda: get_config_default("gpu_split", []),
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examples=[[24.0, 20.0]],
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)
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rope_scale: Optional[float] = Field(
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description="Automatically pulled from the model's config if not present",
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default_factory=lambda: get_config_default("rope_scale"),
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examples=[1.0],
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)
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rope_alpha: Optional[float] = Field(
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description="Automatically calculated if not present",
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default_factory=lambda: get_config_default("rope_alpha"),
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examples=[1.0],
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)
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cache_mode: Optional[str] = Field(
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default_factory=lambda: get_config_default("cache_mode", "FP16")
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)
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chunk_size: Optional[int] = Field(
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default_factory=lambda: get_config_default("chunk_size", 2048)
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)
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prompt_template: Optional[str] = Field(
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default_factory=lambda: get_config_default("prompt_template")
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)
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num_experts_per_token: Optional[int] = Field(
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default_factory=lambda: get_config_default("num_experts_per_token")
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)
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fasttensors: Optional[bool] = Field(
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default_factory=lambda: get_config_default("fasttensors", False)
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)
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# Non-config arguments
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draft: Optional[DraftModelLoadRequest] = None
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skip_queue: Optional[bool] = False
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class ModelLoadResponse(BaseModel):
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"""Represents a model load response."""
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# Avoids pydantic namespace warning
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model_config = ConfigDict(protected_namespaces=[])
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model_type: str = "model"
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module: int
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modules: int
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status: str
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@@ -1,34 +0,0 @@
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from pydantic import BaseModel, Field
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from typing import List, Optional
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from common.sampling import SamplerOverridesContainer
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class SamplerOverrideListResponse(SamplerOverridesContainer):
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"""Sampler override list response"""
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presets: Optional[List[str]]
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class SamplerOverrideSwitchRequest(BaseModel):
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"""Sampler override switch request"""
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preset: Optional[str] = Field(
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default=None, description="Pass a sampler override preset name"
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)
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overrides: Optional[dict] = Field(
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default=None,
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description=(
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"Sampling override parent takes in individual keys and overrides. "
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+ "Ignored if preset is provided."
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),
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examples=[
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{
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"top_p": {
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"override": 1.5,
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"force": False,
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}
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}
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],
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)
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@@ -1,15 +0,0 @@
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from pydantic import BaseModel, Field
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from typing import List
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class TemplateList(BaseModel):
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"""Represents a list of templates."""
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object: str = "list"
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data: List[str] = Field(default_factory=list)
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class TemplateSwitchRequest(BaseModel):
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"""Request to switch a template."""
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name: str
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@@ -1,51 +0,0 @@
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"""Tokenization types"""
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from pydantic import BaseModel
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from typing import Dict, List, Union
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class CommonTokenRequest(BaseModel):
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"""Represents a common tokenization request."""
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add_bos_token: bool = True
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encode_special_tokens: bool = True
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decode_special_tokens: bool = True
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def get_params(self):
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"""Get the parameters for tokenization."""
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return {
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"add_bos_token": self.add_bos_token,
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"encode_special_tokens": self.encode_special_tokens,
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"decode_special_tokens": self.decode_special_tokens,
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}
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class TokenEncodeRequest(CommonTokenRequest):
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"""Represents a tokenization request."""
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text: Union[str, List[Dict[str, str]]]
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class TokenEncodeResponse(BaseModel):
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"""Represents a tokenization response."""
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tokens: List[int]
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length: int
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class TokenDecodeRequest(CommonTokenRequest):
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""" " Represents a detokenization request."""
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tokens: List[int]
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class TokenDecodeResponse(BaseModel):
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"""Represents a detokenization response."""
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text: str
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class TokenCountResponse(BaseModel):
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"""Represents a token count response."""
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length: int
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