Feature/49 tool calling pipeline

[michalharakal]

feat: unified model pipeline with decoupled tool calling (#49)
Implements the unified inference pipeline for SKaiNET Transformers,
resolving #49 and building on the tool calling foundation from #46.

Summary

This branch decouples tool calling from the kllama runner, creates a
unified model pipeline with architecture auto-detection, and adds
comprehensive Antora documentation.

Phase 1: Decouple Tool Calling

• Enhance Tokenizer interface with eosTokenId, bosTokenId, vocabSize
• Create ChatSession abstraction in llm-agent (any runner gets tool
  calling for free)
• Refactor ToolCallingDemo and AgentCli to accept Tokenizer, not
  GGUFTokenizer
• Fix JavaAgentLoop instanceof hack

Phase 2: Model Registry

• Add ModelFamily enum (LLAMA, QWEN, GEMMA, APERTUS, BERT, VOXTRAL)
• Add ModelRegistry.detect() for GGUF architecture auto-detection
• Add UnifiedModelLoader.peek() to extract model info without loading

Phase 3: Tokenization Pipeline

• Move GGUFTokenizer from kllama to llm-core (all runners can use it)
• Create TokenizerFactory with fromGGUF(), fromTokenizerJson(),
  fromHuggingFace()

Phase 4: Unified CLI

• New skainet-cli module: single entry point for all GGUF models
• Auto-detects architecture, supports --chat/--agent/--demo modes

Smoke Tests

• Add tool calling test phase with [Tool Call] detection
• Add ToolCallingDemo.runSingleShot() for non-interactive testing
• Add Qwen3-8B-Q4 to smoke test config

Documentation (Antora + Divio)

• 19 AsciiDoc pages: tutorials, how-to, reference, explanation
• Mermaid diagrams via Kroki for pipeline, architecture, agent loop
• GitHub Actions workflow for docs build and GitHub Pages deployment