deps: bump @huggingface/transformers from 3.8.1 to 4.2.0

[dependabot]

Bumps @huggingface/transformers from 3.8.1 to 4.2.0.

Release notes

Sourced from @​huggingface/transformers's releases.

4.2.0

🚀 Transformers.js v4.2 — Tool calling, simpler internals, and privacy filtering

• Added tools to TextGenerationPipeline in #1655
• Use inputMetadata API for simplified internals in #1657
• Add support for OpenAI privacy filter model in #1658

Full Changelog: 4.1.0...4.2.0

4.1.0

🚀 Transformers.js v4.1 — Gemma 4, KV cache improvements, and new quantization dtypes

• Add support for Gemma 4 in #1627
• Cached generation improvements (+ past_key_values via pipeline function) in #1638
• Improve tokenizer types based on input function parameters in #1641
• Add support for q1, q1f16, q2, and q2f16 data types in #1647
• Re-enable SmolVLM in #1648
• Update default generation parameters in #1649
• Pin GitHub Actions to commit SHAs in #1626

Full Changelog: 4.0.0...4.1.0

4.0.1

What's new?

• Add support for Gemma 4 in #1627

Full Changelog: 4.0.0...4.0.1

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.

... (truncated)

Commits

• 54652ba [version] Update to 4.2.0
• ea88f89 Add support for OpenAI privacy filter model (#1658)
• b93766d Use inputMetadata API for simplified internals (#1657)
• 6eaf11a Added tools to TextGenerationPipeline (#1655)
• ca10e4d [version] Update to 4.1.0
• fbab88f Update default generation parameters (#1649)
• 39bec70 Re-enable SmolVLM (#1648)
• 78d4ec3 chore: bump doc-builder SHA for PR upload workflow (#1644)
• fdd496e Add support for q1, q1f16, q2, q2f16 data types (#1647)
• ed84c15 Improve tokenizer types based on input function parameters (#1641)
• Additional commits viewable in compare view

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