What changed in Transformers v5, and what should an ops-minded team check before upgrading?

The headline: TensorFlow and JAX support was dropped — transformers is now PyTorch-first, betting on interop instead of multi-framework maintenance. Model definitions were simplified so the same modeling code powers training frameworks and serving engines (vLLM can load architectures directly from transformers), reducing 'works in training, differs in serving' drift. Quantization became first-class, improving bitsandbytes support for tensor-parallel and MoE models and easing integration of new quant methods. Ops checklist for the migration: (1) grep for TFAutoModel/FlaxAutoModel and tf.* touchpoints — those paths are gone; (2) review the v5 deprecation list against your code (Trainer args and tokenizer behaviors changed across the 4.x line); (3) re-pin the ecosystem matrix together — peft/trl/accelerate/vllm versions are coupled; (4) golden-output regression tests per model, since kernel and default changes can shift numerics slightly; (5) canary in staging under production traffic shapes before fleet-wide rollout. Version-pin discipline (exact ==, upgrade deliberately) matters more than usual in this ecosystem because minor releases move fast.