feat: runtime model config from HuggingFace config.json

[Alexintosh]

Summary

• Replace ~54 hardcoded #define model constants with a runtime ModelConfig struct populated from HuggingFace config.json at startup via NSJSONSerialization. Switch between Qwen3.5 models (35B, 122B, 397B) with just --model <path> — no recompilation needed.
• Add model_manager.py utility to list local compatible models, search HuggingFace for MLX-quantized Qwen3.5 MoE models, download them, and validate compatibility.
• Update README with compatible models table, model manager docs, --model flag usage, and FLASH_MOE_MODEL env var.

What changed in infer.m

• ModelConfig struct + load_model_config() parses config.json (architecture, quantization, layer types, RoPE, EOS tokens) and tokenizer.json (think tokens)
• compute_expert_offsets() derives all expert byte offsets from dimensions + quantization params
• alloc_tracking_arrays() dynamically allocates all tracking arrays (expert freq, cache state, predictions, layer cache) previously sized by compile-time constants
• ~960 #define references replaced with cfg.* fields via helper macros (FREQ(), CACHE_SEEN(), PRED_EXPERT(), etc.)
• MetalCtx buffer arrays converted from fixed-size to dynamically allocated (__strong ARC pointers)
• Validated: compiles clean, runs 5.03 tok/s on 35B-A3B 4-bit, correct output

Test plan

• Compile with cd metal_infer && make
• Run 35B model: ./infer --model ~/.cache/huggingface/hub/models--mlx-community--Qwen3.5-35B-A3B-4bit --prompt "What is 2+2?" --tokens 20
• Verify config summary printed to stderr on startup
• Run python model_manager.py --local to list cached models
• Run python model_manager.py --search to find remote models
• Test FLASH_MOE_MODEL env var as default model path

🤖 Generated with Claude Code

[fiveangle]

Download complete: : 20.4GB [06:39, 47.6MB/s]              /Users/speedster/.cache/huggingface/hub/models--mlx-community--Qwen3.5-35B-A3B-4bit/snapshots/1e20fd8d42056f870933bf98ca6211024744f7ec
Download complete: : 20.4GB [06:39, 51.1MB/s]

Model is compatible with Flash-MoE!
  Architecture:  40 layers, hidden=2048, 256 experts (K=8)
  Quantization:  4-bit, group_size=64
  Expert data:   16.9 GB on disk, 13.5 MB active/token
  Parameters:    ~64B total
  Packed experts: NOT FOUND (run repack_experts.py)
  Weights file:   OK
  Status:        NEEDS PREPARATION (see above)

Next steps:
  1. python repack_experts.py --model /Users/speedster/.cache/huggingface/hub/models--mlx-community--Qwen3.5-35B-A3B-4bit
  2. python metal_infer/extract_weights.py --model /Users/speedster/.cache/huggingface/hub/models--mlx-community--Qwen3.5-35B-A3B-4bit
  3. ./metal_infer/infer --model /Users/speedster/.cache/huggingface/hub/models--mlx-community--Qwen3.5-35B-A3B-4bit --prompt 'Hello' --tokens 20
~/dev/flash-moe$ python repack_experts.py --model /Users/speedster/.cache/huggingface/hub/models--mlx-community--Qwen3.5-35B-A3B-4bit
zsh: command not found: python
~/dev/flash-moe$ python3 repack_experts.py --model /Users/speedster/.cache/huggingface/hub/models--mlx-community--Qwen3.5-35B-A3B-4bit
usage: repack_experts.py [-h] [--index INDEX] [--layers LAYERS] [--dry-run] [--verify-only LAYER]
repack_experts.py: error: unrecognized arguments: --model /Users/speedster/.cache/huggingface/hub/models--mlx-community--Qwen3.5-35B-A3B-4bit```