Who contributed to Hugging Face on July 14, 2026?

8 developers shipped this update, including CarolinePascal, Cyril Vallez, Sunt-ing, Sreekant Baheti, drbh, Albert Villanova del Moral, Sergio Paniego Blanco, and Quentin Gallouédec.

What were the notable Hugging Face updates?

[generate] Add proper MTP support (#46229), Fix DPO/KTO `use_liger_kernel` under DeepSpeed ZeRO-3, and feat(video): viridis colormap for grayscale/depth feeds.

Jul 14, 2026

$ the-wire · showcase

TRANSFORMERS ADDS MTP DECODING, TRL FIXES DEEPSEEP ZERO-3 CRASH

By RepoJournal · Filed 06:59 UTC on July 14, 2026 · About Hugging Face · Composed from the cited sources · methodology

8 people shipped this

Quentin Gallouédec @qgallouedec 2 cited

CarolinePascal @CarolinePascal 1 cited

Cyril Vallez @Cyrilvallez 1 cited

Sunt-ing @Sunt-ing 1 cited

Sreekant Baheti @Sreekant13 1 cited

drbh @drbh

Albert Villanova del Moral @albertvillanova

Sergio Paniego Blanco @sergiopaniego

Transformers finally ships proper multi-token prediction support [ref:3], while TRL patches a critical DeepSpeed ZeRO-3 incompatibility that breaks Liger kernel fusion in production training.

In brief

Transformers now decodes MTP (Mixture of Tokens and Probabilities) properly. If you've been getting garbled outputs from models using MTP, this unblocks you. Worth a quick upgrade if that's your setup.
TRL patched DPO/KTO under DeepSpeed ZeRO-3 with `use_liger_kernel` enabled. That crash? Fixed. Means you can train alignment models at scale again without ritual sacrifices to the memory gods.
Qwen Omni's batched text postprocessing got a fix in Transformers. Batching shouldn't fail silently—now it doesn't. Small fix, big quality-of-life win for inference pipelines.
Transformers' `get_json_schema` no longer crashes when docstring choices aren't strings. One of those "how did this ever work" bugs that probably lived in production for months.
TRL now drops fully-masked examples after truncation in SFT. Cleaner training data. Seems simple but prevents dead weights on useless tokens during fine-tuning.

MTP decoding lands in Transformers [1], letting models that support multi-token prediction generate sequences faster without API changes. This is the feature teams have been waiting for in speculative decoding pipelines. Meanwhile, TRL's Liger kernel optimization was silently breaking under DeepSpeed ZeRO-3 [2] because the fused DPO loss tries to read `lm_head.weight` directly instead of through the model's forward hook, where ZeRO-3 re-materializes sharded parameters. That fix mirrors a similar GRPO bug from weeks ago. On the visualization front, LeRobot's dataset viewer now renders grayscale and depth camera feeds with the viridis colormap [3], making it actually possible to inspect robot vision data without custom scripts. Three smaller fixes ship across the stack: Qwen Omni's batched text postprocessing now handles full batches [4], `get_json_schema` stops crashing on non-string enum choices [5], and SFT training now drops fully-masked examples after truncation [6] to prevent silent training bugs.

Action items

→ Review MTP support in Transformers if you use speculative decoding or multi-token prediction models huggingface/transformers [plan]
→ Patch DPO/KTO training immediately if running with use_liger_kernel=True and DeepSpeed ZeRO-3 huggingface/trl [immediate]
→ Update TRL to get SFT truncation fix and avoid silently truncated training data huggingface/trl [plan]
→ Pull Qwen Omni fix if you have batched text-only pipelines returning truncated outputs huggingface/transformers [plan]

References

[1] [generate] Add proper MTP support (#46229) ↗ huggingface/transformers
[2] Fix DPO/KTO `use_liger_kernel` under DeepSpeed ZeRO-3 ↗ huggingface/trl
[3] feat(video): viridis colormap for grayscale/depth feeds ↗ huggingface/lerobot-dataset-visualizer
[4] Fix Qwen Omni batched text postprocessing ↗ huggingface/transformers
[5] Fix get_json_schema crash on non-string docstring choices (#47072) ↗ huggingface/transformers
[6] Drop fully-masked examples after truncation in SFT (#6320) ↗ huggingface/trl