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Building Intelligence.
One Generation at a Time.

Artificial intelligence developed by Kaniskten Programming. The KAIL project represents our ongoing exploration of AI models, architecture, training, and local inference.

See the model familykail-ai.com ↗

Conceptual AI architecture visualization · Not a diagram of internal computation

Overview

What is KAIL?

KAIL is an AI development project by Kaniskten Programming. It is a family of language models developed iteratively — each generation builds on the lessons of the previous one.

The project spans model training and architecture, evaluation, and local inference through the companion project KailStream.

KAIL models are developed with a focus on understanding how models work, not just using them. This guides decisions about architecture, training data, and evaluation.

AI Models

Language model development

Architecture

KailA — model architecture research

Local inference

KailStream runtime

Status

Active development

Model History

Model evolution

Documented technical specifications, architecture designations, and verified disclosure records across five model generations.

  1. Generation: First

    KAIL F1

    The beginning of the KAIL model family, serving as an early foundational experimentation milestone.

    Historical prototype
    Overview:KAIL F1 represents the beginning of the KAIL model family. Its primary significance is its place in the project's development history, rather than a claim of modern large-language-model capabilities.

    Field Specifications

    Generation
    First
    Status
    Historical prototype
    Release / Milestone date
    17 August 2022 — recorded project date
    Architecture
    Early experimental architecture; formal name not documented
    Parameters
    Approximately 1,000
    Supported languages
    Not formally documented
    Intended use
    Early AI experimentation and foundational model development
    Training approach
    Historical training details unavailable
    Verified evaluation
    No standardized evaluation published
    Model download
    Not publicly confirmed
    Repository
    Not publicly confirmed
    Documentation
    Historical documentation not publicly confirmed
  2. Generation: Second

    KAIL S1

    Second generation milestone built on the KailX architecture with a 5B parameter design target.

    Release information pending

    Field Specifications

    Generation
    Second
    Status
    Release information pending
    Release / Milestone date
    20 May 2024 (Milestone date)
    Architecture
    KailX
    Parameters
    5 billion (5B)
    Supported languages
    All languages — intended coverage
    Model variants
    One specified 5B size
    Intended use
    Multilingual text processing and general-purpose AI
    Training approach
    Not documented
    Verified evaluation
    Not published
    Context length
    Not announced
    Input/output modalities
    Not announced
    Quantization formats
    Not announced
    Model download
    Not confirmed
    Repository
    Not confirmed
    Documentation
    To be published
    KailX ArchitectureKailX is the designated architecture for KAIL S1. Its detailed technical design should be documented separately, including its model layers, attention mechanism, positional encoding, normalization, feed-forward blocks, tokenizer and inference requirements. The 5B parameter count is a model specification, not a measure of benchmark quality or inference speed.
    Multilingual Support Scope“All languages” describes the intended goal. To claim verified support for all languages, the model needs multilingual training coverage and evaluations across representative languages and scripts.
  3. Generation: Third

    KAIL S2

    Third generation dual-size system utilizing KailX across 7.6B and 15B parameter configurations.

    Release information pending

    Field Specifications

    Generation
    Third
    Status
    Release information pending
    Release / Milestone date
    2 April 2025 (Milestone date)
    Architecture
    KailX
    Parameters
    7.6B and 15B
    Supported languages
    Hindi, Tamil, Kannada, Telugu and Malayalam are planned; additional coverage requires confirmation
    Model variants
    KAIL S2 7.6B; KAIL S2 15B
    Intended use
    General-purpose AI at two model sizes
    Training approach
    Not documented
    Verified evaluation
    Not published
    Context length
    Not announced
    Input/output modalities
    Not announced
    Quantization formats
    Not announced
    Model download
    Not confirmed
    Repository
    Not confirmed
    Documentation
    To be published

    Model Variants

    KAIL S2 7.6B7.6 billion parameters

    Smaller variant · Lower expected resource requirements than the 15B version, all else being equal.

    KAIL S2 15B15 billion parameters

    Larger variant · Greater potential capacity, with higher memory requirements.

    Planned Language Set:HindiTamilKannadaTeluguMalayalam

    English and other languages will be listed as supported only after their coverage is established. A multilingual model must be evaluated separately on each language rather than relying on a single overall score.

    Architecture & Variant ParityBoth variants use the designated KailX architecture. Whether they share the same tokenizer, training dataset, context length and architectural configuration remains to be documented.
    Multilingual Verification CriteriaEnglish and other languages should be listed as supported only after their coverage is established. A multilingual model should be evaluated separately on each language rather than relying on a single overall score.
  4. Generation: Fourth listed milestone

    KAIL F2

    Custom KailA causal LM architecture with 1.123B parameters, fine-tuned via LoRA on curated instruction datasets.

    Model developed; publication status pending

    Field Specifications

    Generation
    Fourth listed milestone
    Status
    Model developed; publication status pending
    Release / Milestone date
    Not announced
    Architecture
    KailA
    Model class
    KailAForCausalLM
    Model type
    kail
    Parameters
    Approximately 1,123,372,800 (1.123B)
    Training approach
    LoRA supervised fine-tuning
    Training data
    8,000 examples: 7,200 training and 800 validation
    Training configuration
    Seed 42; effective batch size 16; maximum sequence length 2,048; BF16
    Training hardware
    NVIDIA RTX 5060 Ti 16GB
    Primary evaluation areas
    Instruction following, coding, mathematics and identity consistency
    Verified evaluation
    Internal results available; independent verification not established
    Model download
    Public availability not confirmed
    Repository
    kaniskten/kail-f2
    Documentation
    Model card, architecture information and evaluation report to accompany publication

    Planned Benchmark Suite

    MMLUARC-EasyARC-ChallengeHellaSwagWinograndeGSM8KTruthfulQAPIQAOpenBookQABoolQ
    Requirements for Credible Public Benchmark Comparisons:
    • ›Benchmark dataset and exact version.
    • ›Evaluation harness and exact CLI invocation command.
    • ›Prompt template and number of few-shot examples.
    • ›Accuracy or standardized scoring metric.
    • ›Model checkpoint and precise inference configuration.
    • ›Parity results from the identical protocol for each comparison baseline.

    Recorded scores will not be described as independently verified until they have been reproduced or independently assessed.

    Architecture & Training WorkflowKAIL F2 uses the custom KailA architecture. The conversion workflow produced a model configuration with approximately 1.123 billion parameters. The documented training process used LoRA-based supervised fine-tuning on a curated instruction dataset. The internal evaluation included coding, mathematical reasoning, general instruction following and adversarial identity tests. These results should be published with the exact test set, scoring procedure and limitations.
  5. Generation: Next planned generation

    KAIL S3

    The next planned model in the KAIL family. Specifications and benchmarks will be published upon finalized verification.

    Coming soon
    Overview:KAIL S3 is the next planned model in the KAIL family. Its public announcement should specify the finalized architecture, model sizes, supported languages, training methodology, hardware requirements and reproducible evaluation results. Avoid publishing projected specifications as completed capabilities.

    Field Specifications

    Generation
    Next planned generation
    Status
    Coming soon
    Release / Milestone date
    Not announced
    Architecture
    Under development; not announced
    Parameters
    Not announced
    Supported languages
    To be announced
    Training approach
    To be announced
    Verified evaluation
    Not published
    Context length
    Not announced
    Input/output modalities
    Not announced
    Model download
    Not yet available
    Repository
    Not announced
    Documentation
    Planned for release
Architecture

KailA architecture

An interactive look at the stages a language model passes data through. This is a generic illustration; KailA's own details are listed in the panel as they are published. It does not suggest that every KAIL model uses this specific architecture.

Concept visualization of a generic pipeline. Not a diagram of KailA's actual layers.

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

Design goals

KailA is the architecture associated with the KAIL model project. Design goals will be documented once they are confirmed.

Differences from source model

Not yet published

Compatibility

Not yet published

Source code: Not yet publishedDocumentation: Not yet published
Local Inference

KailStream

The local inference runtime companion to KAIL. Focused on memory management — moving model data between storage, system RAM, and GPU memory so large models can run on the hardware people already own.

Concept visualization of a generic local-inference data path. It does not state which features KailStream has implemented.

System RAM

Staging area between storage and the GPU. Streaming buffers and caches typically live here.

KailStream status: Release status not yet published

The problem

Modern models can be larger than a GPU's memory, and sometimes larger than system RAM. Running them locally means deciding what lives where, and when to move it.

The approach

KailStream treats storage, RAM, and GPU memory as one pipeline. How each technique is implemented is documented only once it is confirmed.

Capability status

  • Local model inferenceNot yet published
  • Memory management across storage, RAM and GPU memoryNot yet published
  • Quantized model loadingNot yet published
  • Streaming model data from storageNot yet published
  • Expert caching for mixture-of-experts modelsNot yet published
  • Hardware backends (CPU, GPU, accelerators)Not yet published
History

Development story

A dated, verified chronology has not been published yet. Milestones will be added here only with confirmed dates.

Future

Development and next steps

Active development continues on the KAIL model family, KailA architecture, and KailStream inference runtime. Announcements will be made as details are confirmed.

Nothing on this page is marked as complete unless it has been confirmed and published.

KAIL S3

Next model in the family

Coming Soon

KailA research

Architecture development

In progress

KailStream

Local inference runtime

In development
Explore

Learn more about Kaniskten Programming

Discover the company, explore KailStream in depth, or get in touch.

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