chore(deps-dev): bump @huggingface/transformers from 3.8.1 to 4.0.1

[dependabot]

Bumps @huggingface/transformers from 3.8.1 to 4.0.1.

Release notes

Sourced from @​huggingface/transformers's releases.

4.0.0

🚀 Transformers.js v4

We're excited to announce that Transformers.js v4 is now available on NPM! After a year of development (we started in March 2025 🤯), we're finally ready for you to use it.

npm i @huggingface/transformers

Links: YouTube Video, Blog Post, Demo Collection

New WebGPU backend

The biggest change is undoubtedly the adoption of a new WebGPU Runtime, completely rewritten in C++. We've worked closely with the ONNX Runtime team to thoroughly test this runtime across our ~200 supported model architectures, as well as many new v4-exclusive architectures.

In addition to better operator support (for performance, accuracy, and coverage), this new WebGPU runtime allows the same transformers.js code to be used across a wide variety of JavaScript environments, including browsers, server-side runtimes, and desktop applications. That's right, you can now run WebGPU-accelerated models directly in Node, Bun, and Deno!

We've proven that it's possible to run state-of-the-art AI models 100% locally in the browser, and now we're focused on performance: making these models run as fast as possible, even in resource-constrained environments. This required completely rethinking our export strategy, especially for large language models. We achieve this by re-implementing new models operation by operation, leveraging specialized ONNX Runtime Contrib Operators like com.microsoft.GroupQueryAttention, com.microsoft.MatMulNBits, or com.microsoft.QMoE to maximize performance.

For example, adopting the com.microsoft.MultiHeadAttention operator, we were able to achieve a ~4x speedup for BERT-based embedding models.

• ONNX Runtime improvements by @​xenova in huggingface/transformers.js#1306
• Transformers.js V4: Native WebGPU EP, repo restructuring, and more! by @​xenova in huggingface/transformers.js#1382

New models

Thanks to our new export strategy and ONNX Runtime's expanding support for custom operators, we've been able to add many new models and architectures to Transformers.js v4. These include popular models like GPT-OSS, Chatterbox, GraniteMoeHybrid, LFM2-MoE, HunYuanDenseV1, Apertus, Olmo3, FalconH1, and Youtu-LLM. Many of these required us to implement support for advanced architectural patterns, including Mamba (state-space models), Multi-head Latent Attention (MLA), and Mixture of Experts (MoE). Perhaps most importantly, these models are all compatible with WebGPU, allowing users to run them directly in the browser or server-side JavaScript environments with hardware acceleration. We've released several Transformers.js v4 demos so far... and we'll continue to release more!

Additionally, we've added support for larger models exceeding 8B parameters. In our tests, we've been able to run GPT-OSS 20B (q4f16) at ~60 tokens per second on an M4 Pro Max.

• Add support for Apertus by @​nico-martin in huggingface/transformers.js#1465
• Add support for FalconH1 by @​xenova in huggingface/transformers.js#1502
• Add support for Cohere's Tiny Aya models by @​xenova in huggingface/transformers.js#1529
• Add support for AFMoE by @​xenova in huggingface/transformers.js#1542
• Add support for new Qwen VL models (Qwen2.5-VL, Qwen3-VL, Qwen3.5, and Qwen3.5 MoE) by @​xenova in huggingface/transformers.js#1551
• Add support for Qwen2 MoE, Qwen3 MoE, Qwen3 Next, Qwen3-VL MoE, and Olmo Hybrid by @​xenova in huggingface/transformers.js#1562
• Add support for EuroBERT by @​xenova in huggingface/transformers.js#1583
• Add support for LightOnOCR and GLM-OCR by @​xenova in huggingface/transformers.js#1582
• Add support for Nemotron-H by @​xenova in huggingface/transformers.js#1585
• Add support for DeepSeek-v3 by @​xenova in huggingface/transformers.js#1586
• Add support for mistral4 by @​xenova in huggingface/transformers.js#1587
• Add support for GLM-MoE-DSA by @​xenova in huggingface/transformers.js#1588
• Add support for Chatterbox @​xenova in huggingface/transformers.js#1592
• Add support for Cohere ASR by @​xenova in huggingface/transformers.js#1610
• Add support for SolarOpen and CHMv2 models by @​xenova in huggingface/transformers.js#1593

... (truncated)

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[greptile-apps]

Greptile Summary

This PR bumps @huggingface/transformers from 3.8.1 to 4.0.1 (a major version) and includes a companion fix commit that replaces the deprecated quantized: true pipeline option with dtype: 'q8', aligning with the v4 API. The peer dependency is correctly marked optional, consistent with the lazy-load embedding architecture.

Confidence Score: 5/5

Safe to merge — the key v4 breaking change (quantized → dtype) is correctly handled, and all remaining feedback is stylistic.

All findings are P2 style suggestions. The only API breaking change in the v3→v4 upgrade that touches this codebase (quantized: true → dtype: 'q8') is addressed in the companion fix commit, and the HuggingFace docs confirm the mapping is correct. No correctness or data-integrity issues are present.

No files require special attention.

Important Files Changed

Filename	Overview
src/domain/search/models.ts	Fix commit replaces deprecated quantized: true pipeline option with dtype: 'q8' for v4 compatibility; ModelConfig.quantized field name is now a semantic holdover but the mapping logic is correct.
package.json	Bumps @huggingface/transformers from ^3.8.1 to ^4.0.1 in both devDependencies and peerDependencies; peer dependency is marked optional, consistent with the lazy-load architecture.
package-lock.json	Lock file updated by dependabot to reflect the new @huggingface/transformers 4.0.1 resolved version and its transitive dependencies.

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A[loadModel called] --> B{extractor cached\nfor same model?}
    B -- yes --> C[return cached extractor]
    B -- no --> D[disposeModel]
    D --> E[loadTransformers]
    E --> F{config.quantized?}
    F -- true --> G["pipelineOpts = { dtype: 'q8' }"]
    F -- false --> H["pipelineOpts = {}"]
    G --> I["pipeline('feature-extraction', name, opts)"]
    H --> I
    I --> J[activeModel = config.name]
    J --> C

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_{Reviews (2): Last reviewed commit: "fix: replace deprecated quantized option..." | Re-trigger Greptile}

[carlos-alm]

Addressed the P1 finding — replaced { quantized: true } with { dtype: 'q8' } in src/domain/search/models.ts (line 197). This is the v4 API for selecting quantized model weights. All 71 search tests pass.

@greptileai