implementation detail · filed under model architecture
Discarding the LLM after vision-encoder training
After a training stage, the LLM is discarded while the optimized vision encoder is retained.
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After this stage, we discard the LLM and retain only the optimized vision encoder for the subsequent pre-training pipeline
usedunclearin DeepSeek-V4.1-FlashDeepSeek
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Other methods under model architecture :: multimodal architecture.
Encoder-free multimodal architectureEarly fusion multimodal trainingDiscrete token encoding for audioHierarchical patch encoderMixed-modality trainingConfigurable visual token budgetJoint multimodal decoding in a shared hidden spaceNative multimodal understanding3×3 pixel-unshuffle downsamplingJoint vision-language pre-trainingRaw audio projection into the LLM embedding spaceVision Transformer (ViT)2×2 pixel-shuffle downsamplingAudio encoder initialized from MiMo-AudioAudio Transformer (AuT)Causal streaming ConvNet codec decoderData-parallel-first multimodal encodingDecoupled Encoder Process (DEP)Dedicated multimodal encodersDisaggregated encoder trainingDual-format coordinate supervisionExplicit text-string timestampsFour-frame audio patches with within-patch bidirectional self-attentionFrom-scratch vision-encoder training with next-token prediction