feat: add memory-aware model partitioning framework

- New ModelProfiler.swift: reads config.json, measures weight files
  (follows HF Hub symlinks), computes memory requirements (weights +
  KV cache + 20% overhead), and outputs a PartitionPlan with strategy
  (fullGPU/swapAssisted/layerPartitioned/tooLarge)

- New --info flag: dry-run profiler prints formatted memory analysis
  report and exits without loading the model

- New --gpu-layers option: accepts 'auto' or integer, ready for future
  GPU/CPU layer splitting (Phase 2)

- Pre-load profiling: automatically detects overcommit ratio and sets
  MLX cache limits (2MB cache for swap-assisted mode to let OS manage
  page caching, inspired by Flash-MoE research)

- Enhanced /health endpoint: includes partition data (strategy,
  overcommit_ratio, weight/kv/total GB, GPU layers, estimated tok/s)

- Ready event JSON: includes partition data for downstream integration

- Rename main.swift -> Server.swift (required by Swift compiler when
  adding second source file with @main attribute)

Author: simba