Token decoder › 3 blocks
Hidden features, next block
×tensor
The residual features after both mechanism updates.
- shape
- [B batch, T positions, 768 d]
Sources
Contextual evidence from Token-decoder block
The token decoder: a rotary table, the configured number of blocks, and a linear readout to the vocabulary without bias.
The implementation, pinned at revision e18c42acdfb7, not yet public
Contextual evidence from Token-decoder block
The token decoder’s blocks: no softcap, no time conditioning, the gated MLP with the configured multiplier.
The implementation, pinned at revision e18c42acdfb7, not yet public
Contextual evidence from Token-decoder block
A block owns two gain-only LayerNorms, a fused query–key–value projection and an output projection without bias, an MLP of the configured kind, and, with modulation, an affine layer from the time condition to six vectors, initialized at zero.
The implementation, pinned at revision e18c42acdfb7, not yet public
Contextual evidence from Token-decoder block
A block normalizes, modulates by the time condition’s shift and scale, projects to queries, keys and values, rotates queries and keys, attends over all positions with softcap, and adds the projected output scaled by a gate; the MLP branch does the same with its own shift, scale and gate.
The implementation, pinned at revision e18c42acdfb7, not yet public
Contextual evidence from Token-decoder block
The latent decoder: 12 heads of 64 features, query and key normalization, softcap 50, 6 layers, MLP multiplier 4; the token decoder has 3 blocks.
The implementation, pinned at revision e18c42acdfb7, not yet public
Contextual evidence from Add MLP update
The residual update is scale · dropout(x) added to the residual; modulation is x · (1 + scale) + shift.
The implementation, pinned at revision e18c42acdfb7, not yet public