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Bump transformers from 5.17.0 to 5.18.0 - #279

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Bumps transformers from 5.17.0 to 5.18.0.

Release notes

Sourced from transformers's releases.

Release 5.18.0

New Model additions

Nemotron 3 Diarization

Nemotron 3 Diarization is an open-weight streaming speaker diarization model designed to determine "who spoke when" in real-world audio. It supports both streaming and offline inference, handles up to eight speakers, and orders speaker outputs by each speaker's first arrival in the input audio.

The model uses the Arrival-Order Speaker Cache (AOSC) 1 and FIFO queue introduced for Streaming Sortformer 1, 2. A single checkpoint supports configurable latency profiles, from an 80 ms input buffer to a 30.4 s offline-style buffer, and configurable output frame resolution in multiples of 10 ms. With chunked inference, the maximum audio duration is not limited.

Links: Documentation

NemotronH Omni

NemotronH Omni is a multimodal reasoning model from NVIDIA that pairs the NemotronH hybrid Mamba-Transformer language model with a RADIO vision encoder and an optional Parakeet-based sound encoder. Image (and video) patches are projected through a RADIO tower and a pixel-shuffle MLP into the language model's embedding space at the <image> / <video> context-token positions; audio clips are projected in the same way at <audio> positions. The result is a single autoregressive model that reasons jointly over text, images, video and sound.

Links: Documentation

HyperCLOVAX Vision V2

HyperCLOVAX Vision V2 is a multimodal vision-language model developed by NAVER. It combines the HyperClovaX language model backbone with a Qwen2.5-VL vision encoder. The model supports text, image, and video inputs and is capable of chain-of-thought reasoning via built-in thinking tokens (<think>...</think>).

Links: Documentation

GTE

GTE was proposed in mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval by Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li and Min Zhang.

GTE is a BERT-style bidirectional encoder that replaces absolute position embeddings with RoPE, uses a gated MLP, and applies layer normalization after each residual connection. The same architecture backs Alibaba's gte-*-v1.5, gte-multilingual-* and gte-en-mlm-* checkpoints as well as Snowflake's snowflake-arctic-embed-m-v2.0.

Links: Documentation

Breaking changes

... (truncated)

Commits
  • a906d3c v5.18.0
  • 57296d1 model: Add GTE to Transformers (#48416)
  • 88536f2 [Nemotron3Diarization] fix streaming last stft frame dropped (#49167)
  • 5cd2877 Fix missing router_logits in Qwen3.5-MoE and other MoE models (#49179)
  • 4fcb1ff Fix MiniMax M3 partial 3D vision rotary embeddings (#49164)
  • 8520b15 Add Strix Halo (gfx1151) Atlas Inference Hub-kernel path for Qwen3.5/3.6/3.8 ...
  • a877116 Fix MPS GQA version gating (#49210)
  • e0299e2 Fix additional_special_tokens data loss with extra_special_tokens (#47848)
  • 5aab642 Fix stale _added_tokens_encoder entries in cpmant and wav2vec2 (#47440)
  • 0c18a63 Fix deepstack features for mixed-input (#49177)
  • Additional commits viewable in compare view

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Bumps [transformers](https://github.com/huggingface/transformers) from 5.17.0 to 5.18.0.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v5.17.0...v5.18.0)

---
updated-dependencies:
- dependency-name: transformers
  dependency-version: 5.18.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

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@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python:uv Pull requests that update python:uv code labels Oct 5, 2026
@amrit110
amrit110 enabled auto-merge (squash) October 6, 2026 00:47
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amrit110 commented Oct 6, 2026

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Automated fix applied and PR merged

The agentic fix loop successfully fixed this PR and merged it.

✓ Successfully fixed merge_only failures - Modified 0 files - Executed 124 agent actions - (89 info, 18 tool_call, 4 error, 10 tool_result, 3 reasoning)

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