What Is an AI Model Architecture?
In machine learning, an architecture is the structural blueprint of a neural network. It specifies the mathematical operations, connectivity of layers, dimensionality of hidden states, number of attention heads, normalization algorithms, and token embedding strategies. Think of an architecture as the empty floor plan of a building before interior furnishings (weights) are installed.
KailA in the KAIL Project
KailA designates the custom causal language modeling architecture developed at Kaniskten Programming. It is formally represented in code by the KailAForCausalLM class (model type: "kail"). In the KAIL F2 milestone, this architecture defines a network of approximately 1,123,372,800 parameters (1.123B).
Architecture Diversity: KailA vs. KailX
It is a common misconception that all models in a family share the exact same architecture. In the KAIL ecosystem, KailA is utilized for KAIL F2, whereas the larger multilingual models KAIL S1 (5B) and KAIL S2 (7.6B / 15B) are designated under the KailX architecture.
| Model | Designated Architecture | Parameters | Primary Focus |
|---|---|---|---|
| KAIL F1 | Early experimental architecture | ~1,000 | Foundational experimentation |
| KAIL S1 | KailX | 5B | Multilingual text processing |
| KAIL S2 | KailX | 7.6B & 15B | Dual-variant general AI |
| KAIL F2 | KailA | ~1.123B | LoRA instruction fine-tuning |
| KAIL S3 | Under development (TBA) | TBA | Next-generation system |
Documented Architecture Implementation Details
Publicly documented specifications for KailA in KAIL F2 include support for sequence lengths up to 2,048 tokens, BF16 numerical precision, and causal language modeling loss computation. Internal layer dimensions and attention variations will be published in official technical model cards.