Both backbones (FP csm_fork + Q8 csm_quantized) now expose 7 LoRA hooks
per layer: q/k/v/o on attention plus gate/up/down (Llama w1/w3/w2) on
the SwiGLU MLP. LoraConfig::default() still returns q+v only (backward
compat for existing trained adapters); LoraConfig::extended() returns
the full 7-module set. lora_train + lora_finetune_step take a
--extended-lora flag.
Verified end-to-end on Metal: injection across all 16 backbone layers
× 7 modules = 224 adapter Vars, 5.6M trainable params (~6.6× q+v alone,
still tiny vs the 1B base). Step-0 loss matches the q+v baseline
exactly (B=0 init is also a no-op for the new hooks). Forward + backward
+ AdamW + refresh_lora cycle runs without errors.
LoRA test suite: 9 pass (added config_extended_targets_full_attn_and_mlp);
full lib suite still 92/92.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>