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import type { PipelineType } from "./pipelines";
import type { WidgetExample } from "./widget-example";
import type { TokenizerConfig } from "./tokenizer-data";
export enum InferenceDisplayability {
/**
* Yes
*/
Yes = "Yes",
/**
* And then, all the possible reasons why it's no:
*/
ExplicitOptOut = "ExplicitOptOut",
CustomCode = "CustomCode",
LibraryNotDetected = "LibraryNotDetected",
PipelineNotDetected = "PipelineNotDetected",
PipelineLibraryPairNotSupported = "PipelineLibraryPairNotSupported",
}
/**
* Public interface for model metadata
*/
export interface ModelData {
/**
* id of model (e.g. 'user/repo_name')
*/
id: string;
/**
* Kept for backward compatibility
*/
modelId?: string;
/**
* Whether or not to enable inference widget for this model
*/
inference: InferenceDisplayability;
/**
* is this model private?
*/
private?: boolean;
/**
* this dictionary has useful information about the model configuration
*/
config?: {
architectures?: string[];
/**
* Dict of AutoModel or Auto… class name to local import path in the repo
*/
auto_map?: {
/**
* String Property
*/
[x: string]: string;
};
model_type?: string;
quantization_config?: {
bits?: number;
load_in_4bit?: boolean;
load_in_8bit?: boolean;
};
tokenizer_config?: TokenizerConfig;
adapter_transformers?: {
model_name?: string;
model_class?: string;
};
diffusers?: {
_class_name?: string;
};
sklearn?: {
model?: {
file?: string;
};
model_format?: string;
};
speechbrain?: {
speechbrain_interface?: string;
vocoder_interface?: string;
vocoder_model_id?: string;
};
peft?: {
base_model_name_or_path?: string;
task_type?: string;
};
};
/**
* all the model tags
*/
tags?: string[];
/**
* transformers-specific info to display in the code sample.
*/
transformersInfo?: TransformersInfo;
/**
* Pipeline type
*/
pipeline_tag?: PipelineType | undefined;
/**
* for relevant models, get mask token
*/
mask_token?: string | undefined;
/**
* Example data that will be fed into the widget.
*
* can be set in the model card metadata (under `widget`),
* or by default in `DefaultWidget.ts`
*/
widgetData?: WidgetExample[] | undefined;
/**
* Parameters that will be used by the widget when calling Inference API (serverless)
* https://huggingface.co/docs/api-inference/detailed_parameters
*
* can be set in the model card metadata (under `inference/parameters`)
* Example:
* inference:
* parameters:
* key: val
*/
cardData?: {
inference?:
| boolean
| {
parameters?: Record<string, unknown>;
};
base_model?: string | string[];
};
/**
* Library name
* Example: transformers, SpeechBrain, Stanza, etc.
*/
library_name?: string;
}
/**
* transformers-specific info to display in the code sample.
*/
export interface TransformersInfo {
/**
* e.g. AutoModelForSequenceClassification
*/
auto_model: string;
/**
* if set in config.json's auto_map
*/
custom_class?: string;
/**
* e.g. text-classification
*/
pipeline_tag?: PipelineType;
/**
* e.g. "AutoTokenizer" | "AutoFeatureExtractor" | "AutoProcessor"
*/
processor?: string;
}
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