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ArtificialIntelligence ModelRelease 1 source(s)

Release v5.12.0

Updated September 26, 2026 · 2:47 PM · source date June 12, 2026

Summary

Release v5.12.0 huggingface / transformers Public Notifications You must be signed in to change notification settings Fork 34.7k Star 167k Release v5.12.0 vasqu released this 12 Jun 14:39 · 1013 commits to main since this release v5.12.0 e0e7504 Release v5.12.0 New Model additions MiniMax-M3-VL MiniMax-M3-VL is the vision-language member of the MiniMax-M3 family that pairs a CLIP-style vision tower with 3D rotary position embeddings with the MiniMax-M3 text backbone. It uses a mixed dense/sparse Mixture-of-Experts decoder with SwiGLU-OAI gated experts and a lightning indexer for block-sparse attention.

Why it matters

This ModelRelease is relevant to the technology intelligence record because it involves GitHub, Meta, Docker. The source article should remain the factual reference for follow-up coverage.

Key facts
  • huggingface / transformers Public Notifications You must be signed in to change notification settings Fork 34.7k Star 167k Release v5.12.0 vasqu released this 12 Jun 14:39 · 1013 commits to main since this release v5.12.0 e0e7504 Release v5.12.0 New Model additions MiniMax-M3-VL MiniMax-M3-VL is the vision-language member of the MiniMax-M3 family that pairs a CLIP-style vision tower with 3D rotary position embeddings with the MiniMax-M3 text backbone.
  • It uses a mixed dense/sparse Mixture-of-Experts decoder with SwiGLU-OAI gated experts and a lightning indexer for block-sparse attention.
  • The model processes images through a Conv3d patch embedding system and includes specialized components for efficient multimodal understanding and generation.
  • Links: Documentation Add minimax m3vl ( #46600 ) by @ArthurZucker in #46600 PP-OCRv6: update documentation and slow tests ( #46576 ) The official weights for PP-OCRv6 are out: PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization.
  • It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization.
  • Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge.
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