KailA Architecture
KailA is the custom causal language modeling architecture developed by Kaniskten Programming. Formally registered under the model class KailAForCausalLM (model type: “kail”), it defines tensor mappings and layer topologies for instruction-following models like KAIL F2 (~1.123B parameters).
Interactive Stage Pipeline
Generic language-model pipeline illustration. KailA-specific proprietary internals are documented as published.
Attention
Lets each position weigh information from other positions. The specific variant used by KailA is not yet published.
- Mixture-of-experts
- Not yet published
- Parameters
- Not yet published
- Layers
- Not yet published
- Attention variant
- Not yet published
- Source model
- Not yet published
Architectural Transformation Stages
Input representation
Text is split into tokens and converted to integer IDs that the model can consume.
Embedding
Each token ID is mapped to a vector. Position information is added so order is preserved.
Attention
Lets each position weigh information from other positions. The specific variant used by KailA is not yet published.
Feed-forward layer
A per-position network applied after attention. Whether KailA uses a dense layer or expert routing is not yet published.
Output representation
Final vectors are projected to scores over the vocabulary to choose the next token.
Design Principles & Disclosures
Documented Attributes (KAIL F2 Implementation)
- • Architecture designation: KailA
- • Model Class: KailAForCausalLM
- • Model Type: kail
- • Parameter count: Approximately 1,123,372,800 (1.123B)
- • Context sequence limit: 2,048 tokens
- • Training format: Bfloat16 (BF16)
- • Target repository: kaniskten/kail-f2
Unpublished & Reserved Details
Specific attention head configurations, hidden layer dimensions, positional encoding schemes, and MoE routing status remain unannounced. Kaniskten Programming publishes only verified configurations.