Release v5.16.1
This is a special release as we include GLM! (and a few small fixes)
GLM-5.3-Flash
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 馃
- Restore BC for the tensor-parallel API (#48300) by @ArthurZucker
- Fix kernel commit and repo paths for ESMFold2 (#48186) by @Rocketknight1
Full Changelog: v5.16.0...v5.16.1
