chore(deps): Update dependency @huggingface/transformers to v3.3.2 #404

renovate · 2024-12-08T02:10:46Z

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
@huggingface/transformers	`3.1.1` -> `3.3.2`

Release Notes

huggingface/transformers.js (@huggingface/transformers)

What's new?

hotfix: Copy missing ort-wasm-simd-threaded.jsep.mjs to dist folder (https://github.com/huggingface/transformers.js/pull/1150)

Full Changelog: huggingface/transformers.js@3.3.0...3.3.1

`v3.3.0`

Compare Source

🔥 Transformers.js v3.3 — StyleTTS 2 (Kokoro) for state-of-the-art text-to-speech, Grounding DINO for zero-shot object detection

🤖 New models: StyleTTS 2, Grounding Dino
- StyleTTS 2: High-quality speech synthesis
- Grounding DINO: Zero-shot object detection
🛠️ Other improvements
🤗 New contributors

🤖 New models: StyleTTS 2, Grounding DINO

StyleTTS 2 for high-quality speech synthesis

See https://github.com/huggingface/transformers.js/pull/1148 for more information and here for the list of supported models.

First, install the kokoro-js library, which uses Transformers.js, from NPM using:

npm i kokoro-js

You can then generate speech as follows:

import { KokoroTTS } from "kokoro-js";

const model_id = "onnx-community/Kokoro-82M-ONNX";
const tts = await KokoroTTS.from_pretrained(model_id, {
  dtype: "q8", // Options: "fp32", "fp16", "q8", "q4", "q4f16"
});

const text = "Life is like a box of chocolates. You never know what you're gonna get.";
const audio = await tts.generate(text, {
  // Use `tts.list_voices()` to list all available voices
  voice: "af_bella",
});
audio.save("audio.wav");

Grounding DINO for zero-shot object detection

See https://github.com/huggingface/transformers.js/pull/1137 for more information and here for the list of supported models.

Example: Zero-shot object detection with onnx-community/grounding-dino-tiny-ONNX using the pipeline API.

import { pipeline } from "@&#8203;huggingface/transformers";

const detector = await pipeline("zero-shot-object-detection", "onnx-community/grounding-dino-tiny-ONNX");

const url = "http://images.cocodataset.org/val2017/000000039769.jpg";
const candidate_labels = ["a cat."];
const output = await detector(url, candidate_labels, {
  threshold: 0.3,
});

See example output

[
  { score: 0.45316222310066223, label: "a cat", box: { xmin: 343, ymin: 23, xmax: 637, ymax: 372 } },
  { score: 0.36190420389175415, label: "a cat", box: { xmin: 12, ymin: 52, xmax: 317, ymax: 472 } },
]

🛠️ Other improvements

Add the RawAudio class by @Th3G33k in https://github.com/huggingface/transformers.js/pull/682
Update React guide for v3 by @sroussey in https://github.com/huggingface/transformers.js/pull/1128
Add option to skip special tokens in TextStreamer by @sroussey in https://github.com/huggingface/transformers.js/pull/1139

🤗 New contributors

@sroussey made their first contribution in https://github.com/huggingface/transformers.js/pull/1128

Full Changelog: huggingface/transformers.js@3.2.4...3.3.0

`v3.2.4`

Compare Source

What's new?

Add support for visualizing self-attention heatmaps in https://github.com/huggingface/transformers.js/pull/1117

Example code

import { AutoProcessor, AutoModelForImageClassification, interpolate_4d, RawImage } from "@&#8203;huggingface/transformers";

// Load model and processor
const model_id = "onnx-community/dinov2-with-registers-small-with-attentions";
const model = await AutoModelForImageClassification.from_pretrained(model_id);
const processor = await AutoProcessor.from_pretrained(model_id);

// Load image from URL
const image = await RawImage.read("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg");

// Pre-process image
const inputs = await processor(image);

// Perform inference
const { logits, attentions } = await model(inputs);

// Get the predicted class
const cls = logits[0].argmax().item();
const label = model.config.id2label[cls];
console.log(`Predicted class: ${label}`);

// Set config values
const patch_size = model.config.patch_size;
const [width, height] = inputs.pixel_values.dims.slice(-2);
const w_featmap = Math.floor(width / patch_size);
const h_featmap = Math.floor(height / patch_size);
const num_heads = model.config.num_attention_heads;
const num_cls_tokens = 1;
const num_register_tokens = model.config.num_register_tokens ?? 0;

// Visualize attention maps
const selected_attentions = attentions
    .at(-1) // we are only interested in the attention maps of the last layer
    .slice(0, null, 0, [num_cls_tokens + num_register_tokens, null])
    .view(num_heads, 1, w_featmap, h_featmap);

const upscaled = await interpolate_4d(selected_attentions, {
    size: [width, height],
    mode: "nearest",
});

for (let i = 0; i < num_heads; ++i) {
    const head_attentions = upscaled[i];
    const minval = head_attentions.min().item();
    const maxval = head_attentions.max().item();
    const image = RawImage.fromTensor(
        head_attentions
            .sub_(minval)
            .div_(maxval - minval)
            .mul_(255)
            .to("uint8"),
    );
    await image.save(`attn-head-${i}.png`);
}

Add min, max, argmin, argmax tensor ops for dim=null
Add support for nearest-neighbour interpolation in interpolate_4d
Depth Estimation pipeline improvements (faster & returns resized depth map)
TypeScript improvements by @ocavue and @shrirajh in https://github.com/huggingface/transformers.js/pull/1081 and https://github.com/huggingface/transformers.js/pull/1122
Remove unused imports from tokenizers.js by @pratapvardhan in https://github.com/huggingface/transformers.js/pull/1116

New Contributors

@shrirajh made their first contribution in https://github.com/huggingface/transformers.js/pull/1122
@pratapvardhan made their first contribution in https://github.com/huggingface/transformers.js/pull/1116

Full Changelog: huggingface/transformers.js@3.2.3...3.2.4

`v3.2.3`

Compare Source

What's new?

Fix setting of model_file_name for image feature extraction pipeline in https://github.com/huggingface/transformers.js/pull/1114. Thanks @xitanggg for reporting the issue!

Add support for dinov2 with registers in https://github.com/huggingface/transformers.js/pull/1110. Example usage:

import { pipeline } from '@&#8203;huggingface/transformers';

// Create image classification pipeline
const classifier = await pipeline('image-classification', 'onnx-community/dinov2-with-registers-small-imagenet1k-1-layer');

// Classify an image
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const output = await classifier(url);
console.log(output);
// [
//   { label: 'tabby, tabby cat', score: 0.8135351538658142 },
//   { label: 'tiger cat', score: 0.08967583626508713 },
//   { label: 'Egyptian cat', score: 0.06800546497106552 },
//   { label: 'radiator', score: 0.003501888597384095 },
//   { label: 'quilt, comforter, comfort, puff', score: 0.003408448537811637 },
// ]

Full Changelog: huggingface/transformers.js@3.2.2...3.2.3

`v3.2.2`

Compare Source

What's new?

Fix env.backends.onnx.wasm.proxy = true: Clone tensor if using onnx wasm proxy in https://github.com/huggingface/transformers.js/pull/1108

Full Changelog: huggingface/transformers.js@3.2.1...3.2.2

`v3.2.1`

Compare Source

What's new?

Add support for ModernBert in https://github.com/huggingface/transformers.js/pull/1104. Check out the blog post for more information!

Example:

import { pipeline } from '@&#8203;huggingface/transformers';

const pipe = await pipeline('fill-mask', 'answerdotai/ModernBERT-base');
const answer = await pipe('The capital of France is [MASK].');
console.log(answer);

Full Changelog: huggingface/transformers.js@3.2.0...3.2.1

`v3.2.0`

Compare Source

🔥 Transformers.js v3.2 — Moonshine for real-time speech recognition, Phi-3.5 Vision for multi-frame image understanding and reasoning, and more!

Table of contents:

🤖 New models: Moonshine, Phi-3.5 Vision, EXAONE
🐛 Bug fixes
🛠️ Other improvements

🤖 New models: Moonshine, Phi-3.5 Vision, EXAONE

Moonshine for real-time speech recognition

Moonshine is a family of speech-to-text models optimized for fast and accurate automatic speech recognition (ASR) on resource-constrained devices. They are well-suited to real-time, on-device applications like live transcription and voice command recognition, and are perfect for in-browser usage (check out the online demo). See https://github.com/huggingface/transformers.js/pull/1099 for more information and here for the list of supported models.

Example: Automatic speech recognition w/ Moonshine tiny.

import { pipeline } from "@&#8203;huggingface/transformers";

const transcriber = await pipeline("automatic-speech-recognition", "onnx-community/moonshine-tiny-ONNX");
const output = await transcriber("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav");
console.log(output);
// { text: 'And so my fellow Americans ask not what your country can do for you as what you can do for your country.' }

See example using the MoonshineForConditionalGeneration API

import { MoonshineForConditionalGeneration, AutoProcessor, read_audio } from "@&#8203;huggingface/transformers";

// Load model and processor
const model_id = "onnx-community/moonshine-tiny-ONNX";
const model = await MoonshineForConditionalGeneration.from_pretrained(model_id, {
    dtype: "q4",
});
const processor = await AutoProcessor.from_pretrained(model_id);

// Load audio and prepare inputs
const audio = await read_audio("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav", 16000);
const inputs = await processor(audio);

// Generate outputs
const outputs = await model.generate({ ...inputs, max_new_tokens: 100 });

// Decode outputs
const decoded = processor.batch_decode(outputs, { skip_special_tokens: true });
console.log(decoded[0]);
// And so my fellow Americans ask not what your country can do for you, ask what you can do for your country.

Phi-3.5 Vision for multi-frame image understanding and reasoning

Phi-3.5 Vision is a lightweight, state-of-the-art, open multimodal model that can be used for multi-frame image understanding and reasoning. See https://github.com/huggingface/transformers.js/pull/1094 for more information and here for the list of supported models.

Examples:

Input	Output
"What's funny about this image?"	The humor in this image stems from the exaggerated depiction of human evolution, using the Shiba Inu dog breed to represent both ancient and modern humans. The left side shows a muscular, hunter-like figure labeled as 'Humans 100,000 years ago' with the caption 'me hungry me hunt mammoth,' suggesting a time when humans were physically robust and actively hunting. The right side contrasts this with a modern, slim Shiba Inu labeled as 'Humans today' with the caption 'why food delivery slow,' humorously commenting on the modern human's reliance on convenience and technology, such as food delivery services, rather than hunting for sustenance. The use of a dog, which is often associated with loyalty and companionship, adds a layer of irony and humor as it portrays humans in a more diminished, dependent state.
"Summarize the deck of slides."	To summarize, the slides are composed of these sections: Introduction to Azure: The presentation introduces Microsoft Azure, a cloud computing platform. It highlights Azure's three service tiers: Hyper-scale, Enterprise, and Hybrid. The presenter is Dinesh Kumar Wickramasinghe, a Senior Software Engineer from CMS Private Limited in Sri Lanka. Azure Overview: Azure is described as Microsoft's cloud computing platform, continuously expanding to meet current and future business challenges. It offers freedom to build, manage, and deploy applications on a global network using preferred tools and frameworks. Cloud Computing Services: The presentation outlines three types of cloud computing services provided by Azure: Infrastructure-as-a-Service (IaaS) with a 'host' component, Platform-as-a-Service (PaaS) with a 'build' component, and Software-as-a-Service (SaaS) with a 'consume' component.

See example code

Example: Single-frame (critique an image)

import {
  AutoProcessor,
  AutoModelForCausalLM,
  TextStreamer,
  load_image,
} from "@&#8203;huggingface/transformers";

// Load processor and model
const model_id = "onnx-community/Phi-3.5-vision-instruct";
const processor = await AutoProcessor.from_pretrained(model_id, {
  legacy: true, // Use legacy to match python version
});
const model = await AutoModelForCausalLM.from_pretrained(model_id, {
  dtype: {
    vision_encoder: "q4", // 'q4' or 'q4f16'
    prepare_inputs_embeds: "q4", // 'q4' or 'q4f16'
    model: "q4f16", // 'q4f16'
  },
});

// Load image
const image = await load_image("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/meme.png");

// Prepare inputs
const messages = [
  { role: "user", content: "<|image_1|>What's funny about this image?" },
];
const prompt = processor.tokenizer.apply_chat_template(messages, {
  tokenize: false,
  add_generation_prompt: true,
});
const inputs = await processor(prompt, image, { num_crops: 4 });

// (Optional) Set up text streamer
const streamer = new TextStreamer(processor.tokenizer, {
  skip_prompt: true,
  skip_special_tokens: true,
});

// Generate response
const output = await model.generate({
  ...inputs,
  streamer,
  max_new_tokens: 256,
});

Or, decode the output at the end:

// Decode and display the answer
const generated_ids = output.slice(null, [inputs.input_ids.dims[1], null]);
const answer = processor.batch_decode(generated_ids, {
  skip_special_tokens: true,
});
console.log(answer[0]);

Example: Multi-frame (summarize slides)

import {
  AutoProcessor,
  AutoModelForCausalLM,
  TextStreamer,
  load_image,
} from "@&#8203;huggingface/transformers";

// Load processor and model
const model_id = "onnx-community/Phi-3.5-vision-instruct";
const processor = await AutoProcessor.from_pretrained(model_id, {
  legacy: true, // Use legacy to match python version
});
const model = await AutoModelForCausalLM.from_pretrained(model_id, {
  dtype: {
    vision_encoder: "q4", // 'q4' or 'q4f16'
    prepare_inputs_embeds: "q4", // 'q4' or 'q4f16'
    model: "q4f16", // 'q4f16'
  },
});

// Load images
const urls = [
  "https://image.slidesharecdn.com/azureintroduction-191206101932/75/Introduction-to-Microsoft-Azure-Cloud-1-2048.jpg",
  "https://image.slidesharecdn.com/azureintroduction-191206101932/75/Introduction-to-Microsoft-Azure-Cloud-2-2048.jpg",
  "https://image.slidesharecdn.com/azureintroduction-191206101932/75/Introduction-to-Microsoft-Azure-Cloud-3-2048.jpg",
];
const images = await Promise.all(urls.map(load_image));

// Prepare inputs
const placeholder = images.map((_, i) => `<|image_${i + 1}|>\n`).join("");
const messages = [
  { role: "user", content: placeholder + "Summarize the deck of slides." },
];
const prompt = processor.tokenizer.apply_chat_template(messages, {
  tokenize: false,
  add_generation_prompt: true,
});
const inputs = await processor(prompt, images, { num_crops: 4 });

// (Optional) Set up text streamer
const streamer = new TextStreamer(processor.tokenizer, {
  skip_prompt: true,
  skip_special_tokens: true,
});

// Generate response
const output = await model.generate({
  ...inputs,
  streamer,
  max_new_tokens: 256,
});

EXAONE 3.5 for bilingual (English and Korean) text generation

EXAONE 3.5 is a collection of instruction-tuned bilingual (English and Korean) generative models, developed and released by LG AI Research. See https://github.com/huggingface/transformers.js/pull/1084 for more information and here for the list of supported models.

Example: Text-generation w/ EXAONE-3.5-2.4B-Instruct:

import { pipeline } from "@&#8203;huggingface/transformers";

// Create a text generation pipeline
const generator = await pipeline(
  "text-generation",
  "onnx-community/EXAONE-3.5-2.4B-Instruct",
  { dtype: "q4f16" },
);

// Define the list of messages
const messages = [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "Tell me a joke." },
];

// Generate a response
const output = await generator(messages, { max_new_tokens: 128 });
console.log(output[0].generated_text.at(-1).content);

See example output

Sure! Here's a light joke for you:

Why don't scientists trust atoms?

Because they make up everything! 

I hope you found that amusing! If you want another one, feel free to ask!

🐛 Bug fixes

Fix pyannote processor post_process_speaker_diarization in https://github.com/huggingface/transformers.js/pull/1082. Thanks to @patrick-ve for reporting the issue!

🛠️ Other improvements

Improve unit testing framework in https://github.com/huggingface/transformers.js/pull/1083 and https://github.com/huggingface/transformers.js/pull/1095, bring coverage up to 91% (from 84%).

Full Changelog: huggingface/transformers.js@3.1.2...3.2.0

`v3.1.2`

Compare Source

🤖 New models

Add support for PaliGemma (& PaliGemma2) in https://github.com/huggingface/transformers.js/pull/1074

Example: Image captioning with onnx-community/paligemma2-3b-ft-docci-448.

import { AutoProcessor, PaliGemmaForConditionalGeneration, load_image } from '@&#8203;huggingface/transformers';

// Load processor and model
const model_id = 'onnx-community/paligemma2-3b-ft-docci-448';
const processor = await AutoProcessor.from_pretrained(model_id);
const model = await PaliGemmaForConditionalGeneration.from_pretrained(model_id, {
    dtype: {
        embed_tokens: 'fp16', // or 'q8'
        vision_encoder: 'fp16', // or 'q4', 'q8'
        decoder_model_merged: 'q4', // or 'q4f16'
    },
});

// Prepare inputs
const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg'
const raw_image = await load_image(url);
const prompt = '<image>caption en'; // Caption the image in English
const inputs = await processor(raw_image, prompt);

// Generate a response
const output = await model.generate({
    ...inputs,
    max_new_tokens: 100,
})

const generated_ids = output.slice(null, [inputs.input_ids.dims[1], null]);
const answer = processor.batch_decode(
    generated_ids,
    { skip_special_tokens: true },
);
console.log(answer[0]);
// A side view of a light blue 1970s Volkswagen Beetle parked on a gray cement road. It is facing to the right. It has a reflection on the side of it. Behind it is a yellow building with a brown double door on the right. It has a white frame around it. Part of a gray cement wall is visible on the far left.

List of supported models: https://huggingface.co/models?library=transformers.js\&other=paligemma

Add support for I-JEPA in https://github.com/huggingface/transformers.js/pull/1073

Example: Image feature extraction with onnx-community/ijepa_vith14_1k.

import { pipeline, cos_sim } from "@&#8203;huggingface/transformers";

// Create an image feature extraction pipeline
const extractor = await pipeline(
  "image-feature-extraction",
  "onnx-community/ijepa_vith14_1k",
  { dtype: "q8" },
);

// Compute image embeddings
const url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
const url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg"
const output = await extractor([url_1, url_2]);
const pooled_output = output.mean(1); // Apply mean pooling

// Compute cosine similarity
const similarity = cos_sim(pooled_output[0].data, pooled_output[1].data);
console.log(similarity); // 0.5168613045518973

List of supported models: https://huggingface.co/models?library=transformers.js\&other=ijepa

Add support for OLMo2 in https://github.com/huggingface/transformers.js/pull/1076. List of supported models: https://huggingface.co/models?library=transformers.js\&other=olmo2

🐛 Bug fixes

Fix whisper timestamp extraction for tokenizers with added tokens by @aravindMahadevan in https://github.com/huggingface/transformers.js/pull/804
Add missing 'ready' status in the ProgressInfo type by @ocavue in https://github.com/huggingface/transformers.js/pull/1070

🛠️ Other improvements

Add function to apply mask to RawImage by @BritishWerewolf in https://github.com/huggingface/transformers.js/pull/1020
Bump versions + webpack improvements in https://github.com/huggingface/transformers.js/pull/1075

🤗 New contributors

@aravindMahadevan made their first contribution in https://github.com/huggingface/transformers.js/pull/804

Full Changelog: huggingface/transformers.js@3.1.1...3.1.2

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chore(deps): Update dependency @huggingface/transformers to v3.3.2

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chore(deps): Update dependency @huggingface/transformers to v3.3.2 #404

chore(deps): Update dependency @huggingface/transformers to v3.3.2 #404

renovate bot commented Dec 8, 2024 •

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chore(deps): Update dependency @huggingface/transformers to v3.3.2 #404

chore(deps): Update dependency @huggingface/transformers to v3.3.2 #404

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renovate bot commented Dec 8, 2024 • edited Loading

Release Notes

What's new?

What's new?

🔥 Transformers.js v3.3 — StyleTTS 2 (Kokoro) for state-of-the-art text-to-speech, Grounding DINO for zero-shot object detection

🤖 New models: StyleTTS 2, Grounding DINO

StyleTTS 2 for high-quality speech synthesis

Grounding DINO for zero-shot object detection

🛠️ Other improvements

🤗 New contributors

What's new?

New Contributors

What's new?

What's new?

What's new?

🔥 Transformers.js v3.2 — Moonshine for real-time speech recognition, Phi-3.5 Vision for multi-frame image understanding and reasoning, and more!

🤖 New models: Moonshine, Phi-3.5 Vision, EXAONE

Moonshine for real-time speech recognition

Phi-3.5 Vision for multi-frame image understanding and reasoning

EXAONE 3.5 for bilingual (English and Korean) text generation

🐛 Bug fixes

🛠️ Other improvements

🤖 New models

🐛 Bug fixes

🛠️ Other improvements

🤗 New contributors

Configuration

renovate bot commented Dec 8, 2024 •

edited

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