AutoTokenizer

This release introduces two major new model families, Laguna and DEIMv2, alongside numerous fixes for attention mechanisms, continuous batching generation, and kernel loading across various models.

This release introduces several major new models including VoxtralRealtime, GLM-5, and Qwen3.5, alongside significant internal refactoring, particularly around attention mechanisms and trainer stability.

Transformers v5 is the first major release in five years, introducing significant API refactors like dynamic weight loading via WeightConverter and simplifying tokenizer architecture by consolidating slow/fast implementations. The release cadence is shifting to weekly minor updates.

This release focuses on fixing AutoTokenizer enforcement, optimizing MoE performance with batched implementations, and significantly improving model loading speeds via meta device initialization.

Transformers v5 introduces a major overhaul of the library, featuring a new dynamic weight loading API and a unified tokenizer backend system to simplify internals and improve performance.

This patch release focuses on fixing Mistral tokenizer mappings and Tekken pattern matching, while also correcting a decorator error in the device memory utility.

OpenAI released GPT OSS, an open-source (Apache 2.0) MoE model family in 21B and 117B sizes featuring 4-bit MXFP4 quantization and Flash Attention 3 support. These models are optimized for reasoning and agentic tasks, compatible with the new Responses API and standard transformers workflows.

This preview release introduces Baidu's Ernie 4.5 model family to Transformers, including a 0.3B dense model and MoE variants (21B and 300B).

This release introduces the GraniteMoeHybrid model architecture, a hybrid design combining state space layers and Mixture-of-Experts (MoE) attention, available as a stable preview ahead of the v4.52.0 minor release.

This preview release introduces the BitNet model architecture to the transformers library, enabling high-performance 1-bit LLM inference.

This release introduces support for the DeepSeek-V3 (DeepSeek-R1) model, featuring MLA and DeepSeekMoE architectures, available via a specific git tag on top of version 4.50.3.