transformers.js/pr_1665/en/api/models.md

models

Definitions of all models available in Transformers.js.

We also provide other AutoModels (listed below), which you can use in the same way as the Python library. For example:

Example: Load and run an AutoModel.

import { AutoModel, AutoTokenizer } from '@huggingface/transformers';

const tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased');
const model = await AutoModel.from_pretrained('Xenova/bert-base-uncased');

const inputs = await tokenizer('I love transformers!');
const { logits } = await model(inputs);
// Tensor {
//     data: Float32Array(183132) [-7.117443084716797, -7.107812881469727, -7.092104911804199, ...]
//     dims: (3) [1, 6, 30522],
//     type: "float32",
//     size: 183132,
// }

Example: Load and run an AutoModelForSeq2SeqLM.

import { AutoModelForSeq2SeqLM, AutoTokenizer } from '@huggingface/transformers';

const tokenizer = await AutoTokenizer.from_pretrained('Xenova/t5-small');
const model = await AutoModelForSeq2SeqLM.from_pretrained('Xenova/t5-small');

const { input_ids } = await tokenizer('translate English to German: I love transformers!');
const outputs = await model.generate(input_ids);
const decoded = tokenizer.decode(outputs[0], { skip_special_tokens: true });
// 'Ich liebe Transformatoren!'

On this page

Classes — AutoModel · AutoModelForSequenceClassification · AutoModelForTokenClassification · AutoModelForSeq2SeqLM · AutoModelForSpeechSeq2Seq · AutoModelForTextToSpectrogram · AutoModelForTextToWaveform · AutoModelForCausalLM · AutoModelForMaskedLM · AutoModelForQuestionAnswering · AutoModelForVision2Seq · AutoModelForImageClassification · AutoModelForImageSegmentation · AutoModelForSemanticSegmentation · AutoModelForUniversalSegmentation · AutoModelForObjectDetection · AutoModelForZeroShotObjectDetection · AutoModelForMaskGeneration · AutoModelForCTC · AutoModelForAudioClassification · AutoModelForXVector · AutoModelForAudioFrameClassification · AutoModelForDocumentQuestionAnswering · AutoModelForImageMatting · AutoModelForImageToImage · AutoModelForDepthEstimation · AutoModelForNormalEstimation · AutoModelForPoseEstimation · AutoModelForImageFeatureExtraction · AutoModelForImageTextToText · AutoModelForAudioTextToText · PreTrainedModel

Classes

AutoModel

Helper class which is used to instantiate pretrained models with the from_pretrained function.

import { AutoModel } from '@huggingface/transformers';

const model = await AutoModel.from_pretrained('Xenova/bert-base-uncased');

AutoModelForSequenceClassification

Helper class which is used to instantiate pretrained sequence classification models with the from_pretrained function.

import { AutoModelForSequenceClassification } from '@huggingface/transformers';

const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/distilbert-base-uncased-finetuned-sst-2-english');

AutoModelForTokenClassification

Helper class which is used to instantiate pretrained token classification models with the from_pretrained function.

import { AutoModelForTokenClassification } from '@huggingface/transformers';

const model = await AutoModelForTokenClassification.from_pretrained('Xenova/distilbert-base-multilingual-cased-ner-hrl');

AutoModelForSeq2SeqLM

Helper class which is used to instantiate pretrained sequence-to-sequence models with the from_pretrained function.

import { AutoModelForSeq2SeqLM } from '@huggingface/transformers';

const model = await AutoModelForSeq2SeqLM.from_pretrained('Xenova/t5-small');

AutoModelForSpeechSeq2Seq

Helper class which is used to instantiate pretrained sequence-to-sequence speech-to-text models with the from_pretrained function.

import { AutoModelForSpeechSeq2Seq } from '@huggingface/transformers';

const model = await AutoModelForSpeechSeq2Seq.from_pretrained('onnx-community/whisper-tiny.en');

AutoModelForTextToSpectrogram

Helper class which is used to instantiate pretrained sequence-to-sequence text-to-spectrogram models with the from_pretrained function.

import { AutoModelForTextToSpectrogram } from '@huggingface/transformers';

const model = await AutoModelForTextToSpectrogram.from_pretrained('Xenova/speecht5_tts');

AutoModelForTextToWaveform

Helper class which is used to instantiate pretrained text-to-waveform models with the from_pretrained function.

import { AutoModelForTextToWaveform } from '@huggingface/transformers';

const model = await AutoModelForTextToWaveform.from_pretrained('Xenova/mms-tts-eng');

AutoModelForCausalLM

Helper class which is used to instantiate pretrained causal language models with the from_pretrained function.

import { AutoModelForCausalLM } from '@huggingface/transformers';

const model = await AutoModelForCausalLM.from_pretrained('Xenova/gpt2');

AutoModelForMaskedLM

Helper class which is used to instantiate pretrained masked language models with the from_pretrained function.

import { AutoModelForMaskedLM } from '@huggingface/transformers';

const model = await AutoModelForMaskedLM.from_pretrained('Xenova/bert-base-uncased');

AutoModelForQuestionAnswering

Helper class which is used to instantiate pretrained question answering models with the from_pretrained function.

import { AutoModelForQuestionAnswering } from '@huggingface/transformers';

const model = await AutoModelForQuestionAnswering.from_pretrained('Xenova/distilbert-base-cased-distilled-squad');

AutoModelForVision2Seq

Helper class which is used to instantiate pretrained vision-to-sequence models with the from_pretrained function.

import { AutoModelForVision2Seq } from '@huggingface/transformers';

const model = await AutoModelForVision2Seq.from_pretrained('Xenova/vit-gpt2-image-captioning');

AutoModelForImageClassification

Helper class which is used to instantiate pretrained image classification models with the from_pretrained function.

import { AutoModelForImageClassification } from '@huggingface/transformers';

const model = await AutoModelForImageClassification.from_pretrained('Xenova/vit-base-patch16-224');

AutoModelForImageSegmentation

Helper class which is used to instantiate pretrained image segmentation models with the from_pretrained function.

import { AutoModelForImageSegmentation } from '@huggingface/transformers';

const model = await AutoModelForImageSegmentation.from_pretrained('Xenova/detr-resnet-50-panoptic');

AutoModelForSemanticSegmentation

Helper class which is used to instantiate pretrained image segmentation models with the from_pretrained function.

import { AutoModelForSemanticSegmentation } from '@huggingface/transformers';

const model = await AutoModelForSemanticSegmentation.from_pretrained('Xenova/segformer-b0-finetuned-ade-512-512');

AutoModelForUniversalSegmentation

Helper class which is used to instantiate pretrained universal image segmentation models with the from_pretrained function.

import { AutoModelForUniversalSegmentation } from '@huggingface/transformers';

const model = await AutoModelForUniversalSegmentation.from_pretrained('Xenova/detr-resnet-50-panoptic');

AutoModelForObjectDetection

Helper class which is used to instantiate pretrained object detection models with the from_pretrained function.

import { AutoModelForObjectDetection } from '@huggingface/transformers';

const model = await AutoModelForObjectDetection.from_pretrained('Xenova/detr-resnet-50');

AutoModelForZeroShotObjectDetection

Helper class which is used to instantiate pretrained zero-shot object detection models with the from_pretrained function.

import { AutoModelForZeroShotObjectDetection } from '@huggingface/transformers';

const model = await AutoModelForZeroShotObjectDetection.from_pretrained('Xenova/owlvit-base-patch32');

AutoModelForMaskGeneration

Helper class which is used to instantiate pretrained mask generation models with the from_pretrained function.

import { AutoModelForMaskGeneration } from '@huggingface/transformers';

const model = await AutoModelForMaskGeneration.from_pretrained('Xenova/slimsam-77-uniform');

AutoModelForCTC

Helper class which is used to instantiate pretrained connectionist temporal classification (CTC) models with the from_pretrained function.

import { AutoModelForCTC } from '@huggingface/transformers';

const model = await AutoModelForCTC.from_pretrained('Xenova/wav2vec2-base-960h');

AutoModelForAudioClassification

Helper class which is used to instantiate pretrained audio classification models with the from_pretrained function.

import { AutoModelForAudioClassification } from '@huggingface/transformers';

const model = await AutoModelForAudioClassification.from_pretrained('Xenova/wav2vec2-base-superb-ks');

AutoModelForXVector

Helper class which is used to instantiate pretrained speaker embedding models (X-Vector) with the from_pretrained function.

import { AutoModelForXVector } from '@huggingface/transformers';

const model = await AutoModelForXVector.from_pretrained('Xenova/wavlm-base-plus-sv');

AutoModelForAudioFrameClassification

Helper class which is used to instantiate pretrained audio frame (token) classification models with the from_pretrained function.

import { AutoModelForAudioFrameClassification } from '@huggingface/transformers';

const model = await AutoModelForAudioFrameClassification.from_pretrained('onnx-community/pyannote-segmentation-3.0');

AutoModelForDocumentQuestionAnswering

Helper class which is used to instantiate pretrained document question answering models with the from_pretrained function.

import { AutoModelForDocumentQuestionAnswering } from '@huggingface/transformers';

const model = await AutoModelForDocumentQuestionAnswering.from_pretrained('Xenova/donut-base-finetuned-docvqa');

AutoModelForImageMatting

Helper class which is used to instantiate pretrained image matting models with the from_pretrained function.

import { AutoModelForImageMatting } from '@huggingface/transformers';

const model = await AutoModelForImageMatting.from_pretrained('Xenova/vitmatte-small-composition-1k');

AutoModelForImageToImage

Helper class which is used to instantiate pretrained image-to-image models with the from_pretrained function.

import { AutoModelForImageToImage } from '@huggingface/transformers';

const model = await AutoModelForImageToImage.from_pretrained('Xenova/swin2SR-classical-sr-x2-64');

AutoModelForDepthEstimation

Helper class which is used to instantiate pretrained depth estimation models with the from_pretrained function.

import { AutoModelForDepthEstimation } from '@huggingface/transformers';

const model = await AutoModelForDepthEstimation.from_pretrained('onnx-community/depth-anything-v2-small-ONNX');

AutoModelForNormalEstimation

Helper class which is used to instantiate pretrained surface-normal estimation models with the from_pretrained function.

import { AutoModelForNormalEstimation } from '@huggingface/transformers';

const model = await AutoModelForNormalEstimation.from_pretrained('onnx-community/sapiens-normal-0.3b');

AutoModelForPoseEstimation

Helper class which is used to instantiate pretrained pose estimation models with the from_pretrained function.

import { AutoModelForPoseEstimation } from '@huggingface/transformers';

const model = await AutoModelForPoseEstimation.from_pretrained('onnx-community/vitpose-base-simple');

AutoModelForImageFeatureExtraction

Helper class which is used to instantiate pretrained image feature extraction models with the from_pretrained function.

import { AutoModelForImageFeatureExtraction } from '@huggingface/transformers';

const model = await AutoModelForImageFeatureExtraction.from_pretrained('onnx-community/dinov3-vits16-pretrain-lvd1689m-ONNX');

AutoModelForImageTextToText

Helper class which is used to instantiate pretrained vision-language models that map images and text to text (image+text-to-text) with the from_pretrained function.

import { AutoModelForImageTextToText } from '@huggingface/transformers';

const model = await AutoModelForImageTextToText.from_pretrained('onnx-community/LFM2.5-VL-450M-ONNX');

AutoModelForAudioTextToText

Helper class which is used to instantiate pretrained audio-language models that map audio and text to text (audio+text-to-text) with the from_pretrained function.

import { AutoModelForAudioTextToText } from '@huggingface/transformers';

const model = await AutoModelForAudioTextToText.from_pretrained('onnx-community/Voxtral-Mini-4B-Realtime-2602-ONNX');

PreTrainedModel

A base class for pretrained models that provides the model configuration and inference sessions.

PreTrainedModel(model_inputs)

Runs the model with the provided inputs.

Parameters

• model_inputs (Object) — Object containing input tensors.

Returns: Promise<Object> — Object containing output tensors.

PreTrainedModel.constructor(config, sessions, configs)

Create a model from configuration and inference sessions.

Parameters

• config (PretrainedConfig) — The model configuration.
• sessions (Record<string, any>) — The inference sessions for the model.
• configs (Record<string, Object>) — Additional configuration files (e.g., generation_config.json).

PreTrainedModel.dispose()

Disposes of all the ONNX sessions that were created during inference.

Returns: Promise<void[]> — Resolves after each session has been released.

PreTrainedModel.from_pretrained(pretrained_model_name_or_path, options)

Instantiate one of the model classes of the library from a pretrained model.

The model class to instantiate is selected based on the model_type property of the config object (either passed as an argument or loaded from pretrained_model_name_or_path if possible)

Parameters

• pretrained_model_name_or_path (string) — The name or path of the pretrained model. Can be either:
  • A string, the model ID of a pretrained model hosted inside a model repo on huggingface.co. Valid model IDs can be located at the root level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
  • A path to a directory containing model weights, e.g., ./my_model_directory/.
• options (PretrainedModelOptions) — Additional options for loading the model.

Returns: Promise<PreTrainedModel> — A model instance with ready inference sessions.

PreTrainedModel.forward(model_inputs)

Run the model's forward pass.

Parameters

• model_inputs (Object) — The input data to the model in the format specified in the ONNX model.

Returns: Promise<Object> — The output data from the model in the format specified in the ONNX model.

PreTrainedModel.generation_config

Get the model's generation config, if it exists.

PreTrainedModel.generate(options)

Generate token sequences with a language-modeling head.

Parameters

• options (GenerationFunctionParameters)

Returns: Promise<ModelOutput | Tensor> — The output of the model, which can contain the generated token ids, attentions, and scores.