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chore(deps): update loader dependencies non-major - #285

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chore(deps): update loader dependencies non-major#285
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This PR contains the following updates:

Package Change Age Confidence
hydra-core ==1.3.5 → ==1.3.6 age confidence
pydantic (changelog) ==2.13.4 → ==2.13.5 age confidence
transformers ==5.15.1 → ==5.16.1 age confidence

Release Notes

facebookresearch/hydra (hydra-core)

v1.3.6: Hydra 1.3.6

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What's Changed

  • Harden instantiate authorization against unsafe aliases and callable results. (#​3410)
  • Apply Hydra's target blacklist to Python logging configuration. (#​3418)

Full Changelog: hydra-ecosystem/hydra@v1.3.5...v1.3.6

pydantic/pydantic (pydantic)

v2.13.5

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v2.13.5 (2026-08-28)
What's Changed
Fixes
huggingface/transformers (transformers)

v5.16.1

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Release v5.16.1

This is a special release as we include GLM! (and a few small fixes)

GLM-5.3-Flash

image

GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.

GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute.

Links: Documentation

Small patch fixes

Mainly BC behavior for TP and pinning a hf kernel for security reasons 🤗

Full Changelog: huggingface/transformers@v5.16.0...v5.16.1

v5.16.0: Release: v5.16.0

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Release v5.16.0

New Model additions

Qwen4-Exp
image

Qwen4-Exp builds on Qwen3.5's hybrid text and multimodal architecture with three key components: GatedResidual (GR), Qwen Sparse Attention (QSA), and Per-Layer Embedding (PLE).

GR is a Qwen-developed residual architecture that combines Hyper-Connection with GatedNorm. It mixes multiple residual streams with fine-grained elementwise gating before each attention and Mixture-of-Experts (MoE) block, then controls how much of the block output is injected back into each stream.

QSA uses multiple query heads to score compressed key blocks, selects the most relevant contiguous token blocks, and keeps the incomplete trailing block uncompressed. This block-level selection reduces indexing overhead and improves memory locality for long sequences. Combined with Gated DeltaNet, QSA makes Qwen4-Exp the first hybrid architecture to integrate linear and sparse attention, substantially improving inference efficiency for long-context workloads.

PLE enriches selected decoder layers with layer-specific lexical features derived from hashed token n-grams and a dilated depthwise convolution.

Links: Documentation

GraniteSpeech5
image

Granite Speech 5.0 Turbo CTC is a lightweight (~470M parameters) conformer encoder for automatic speech recognition, trained with Connectionist Temporal Classification (CTC) on BPE targets. It is a fast, encoder-only member of the Granite Speech family: transcription requires a single forward pass followed by greedy CTC decoding, with no autoregressive decoder.

Architecturally, it extends the Granite Speech conformer CTC encoder with:

  1. Frame stacking + block-wise time subsampling: the feature extractor stacks pairs of log-mel(+delta) frames (2x), and the first two conformer blocks each subsample time by 2 through a stride-2 depthwise convolution (with a mean-pooled residual), for a total 8x time reduction at 10 ms mel hop.

  2. Block attention with Shaw's relative positional embeddings: attention is computed over fixed-size blocks (the sequence is right-padded to a whole number of blocks, with padded frames masked out), using separate bias-free query/key/value projections.

  3. Self-conditioned CTC: the CTC posteriors of the middle layer are projected and fed back into the hidden states, and the CTC head is shared between this mid-layer self-conditioning and the final prediction.

Links: Documentation

Step3p7

Step-3.7-Flash was proposed in Step 3.7 Flash by StepFun. It is a 198B-parameter sparse Mixture-of-Experts vision-language model, pairing a 196B-parameter MoE language backbone with a 1.8B-parameter vision encoder for native image understanding.

StepFun hasn't published a technical report for Step-3.7-Flash, so the details below are drawn from the released checkpoint's configuration rather than a paper.

  • Sparse MoE decoder: all but the first 3 decoder layers route through a MoE block of 288 routed experts (top-8 per token) plus a single shared expert. The router scores experts with a sigmoid and a learned per-expert bias instead of an auxiliary load-balancing loss, the same strategy as DeepSeek-V3.
  • Gated attention: each attention layer adds an extra projection whose sigmoid output gates the attention output per head, before the output projection — the same Gated Attention mechanism used in Qwen3-Next. A subset of layers use fewer heads and a sliding window instead of full attention.
  • Multi-token prediction: some checkpoints ship extra decoder layers trained for multi-token prediction, which [~GenerationMixin.generate] can use for speculative decoding via use_mtp=True.
  • Vision encoder: a SigLIP-style ViT with 2-D rotary position embeddings and a learned per-layer scale on the attention and MLP branches. Its output is downsampled 4x by two stride-2 convolutions before a linear projector maps it into the text model's hidden size.
  • Dynamic image tiling: instead of a fixed tile grid, the image processor picks its tiling window from each image's own aspect ratio, producing one downscaled global view plus zero or more local high-resolution crops per image.

Links: Documentation

CohereCompass

CohereCompass is the base architecture for small, specialized (vision-)language models trained by Cohere.

Links: Documentation

ESMC and ESMFold2

ESMC and ESMFold2 are new state-of-the-art protein language and folding models from BioHub. ESMC is trained with a masked language modeling objective, and it can be easily transferred to sequence and token classification tasks for proteins. Checkpoints exist in various sizes, from 300M parameters up to 6B parameters. It works as a drop-in replacement for older ESM-2 and ESM-3 models, with significantly higher accuracy.

ESMFold2 is a state-of-the-art protein folding model which produces high accuracy predictions. It uses an iterated diffusion approach that is significantly different from the original ESMFold, offering huge improvements in accuracy for more complex structures.

Links: Documentation ESMC, Documentation ESMFold2

Breaking changes

The legacy tensor-parallel implementation has been replaced with a DTensor-native backend, so users relying on the previous TP API for inference or training must migrate to the new DTensor-based interface.

attn_implementation="sdpa" dispatch is now properly supported for wav2vec2_conformer, wav2vec2-bert, and SeamlessM4T/v2 models, which may change initialization behavior for users who previously worked around this limitation.

FuyuProcessor no longer returns the image_patch_indices output, so any code that depends on this field must be updated to remove references to it.

Cache

Several cache-related bugs were fixed in this release, including an off-by-one error in the sliding window cache, Whisper speculative decoding cache corruption, CpmAnt use-cache failures, Qwen2.5-Omni/Qwen3-Omni-MoE generation with compilable caches, and compressed-tensors loading for KV-cache-only quantized models. Documentation was also added for cache token removal using negative values, and per-layer cache configuration support (allowing models to use different cache settings per layer) was introduced.

Generation

This release fixes several generation bugs across multiple models, including Whisper speculative decoding issues (UnboundLocalError, cache corruption, speed regression, and left-padded batch position IDs), broken image generation in Emu3, garbage output in OLMo/GPTNeoX, and Qwen2.5-Omni/Qwen3-Omni-MoE generation with compilable caches. Additionally, logit distributions for candidate generators using sampling are now aligned by returning logits after applying logit processors.

Attention

Several attention-related bug fixes were made in this release, including correcting a SigLIP2 documentation typo, fixing Flash/SDPA attention dispatch tests for xcodec2 and ROCm RDNA GPUs, resolving a GPT2 cross-attention mask being silently discarded, and enabling SDPA support declaration in TimmWrapper. Per-layer cache configuration and attention-mask selection support was also introduced, allowing models with heterogeneous layer configurations to use distinct sliding_window, attention_chunk_size, and number_of_conv_states values per layer.

Quantization

Quantization improvements include adding NVFP4 quantization support via HF kernels (enabling on-the-fly BF16 weight quantization with ~50% memory reduction), and fixing several bugs: reverting a regression in is_quantization_compressed that caused incorrect module layouts for packed-format checkpoints, fixing CLIP weight initialization failures with quantized checkpoints, and restoring KV-cache quantization setup for KV-cache-only quantized models.

Parallelization

Introduced a naive pipeline parallel inference engine supporting tied/untied weight embeddings with seamless generate() integration, while restoring backward compatibility for the tensor-parallel API with a deprecation cycle for tp_plan in from_pretrained(). Additionally fixed a model parallel bug in the BLT model affecting beam search.

Kernels

Kernel support was improved with documentation updates highlighting supported models, a fix for export crashes on kernel-decorated functions by adding a is_torchdynamo_exporting guard, and the default Flash Attention 2 hub kernel version was bumped to v3 to resolve compatibility issues with newer PyTorch versions.

Bugfixes and improvements

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@dreadnode-renovate-bot dreadnode-renovate-bot Bot added the type/digest Dependency digest updates label Aug 30, 2026
@dreadnode-renovate-bot
chore(deps): update loader dependencies non-major
016d664 
| datasource | package      | from   | to     |
| ---------- | ------------ | ------ | ------ |
| pypi       | hydra-core   | 1.3.5  | 1.3.6  |
| pypi       | pydantic     | 2.13.4 | 2.13.5 |
| pypi       | transformers | 5.15.1 | 5.16.1 |
@dreadnode-renovate-bot dreadnode-renovate-bot Bot changed the title chore(deps): update dependency transformers to v5.16.1 chore(deps): update loader dependencies non-major Sep 2, 2026
@dreadnode-renovate-bot
dreadnode-renovate-bot Bot merged commit 5101181 into main Sep 2, 2026
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@dreadnode-renovate-bot
dreadnode-renovate-bot Bot deleted the renovate/loader-deps branch September 2, 2026 00:52
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