Loading custom-architecture checkpoints (Nemotron Omni family) against
transformers 5.14 surfaced API drift and wrapper mismatches:
- flash_attention_2 pinned in config but flash-attn not installed:
recursively fall back to eager attention, incl. nested sub-configs
(multimodal llm_config).
- all_tied_weights_keys shim: expected by the accelerate device-map
integration and assigned by 5.x post_init, but absent on older remote
code. Settable property aggregating legacy _tied_weights_keys,
filtering keys that don't resolve on multimodal wrappers.
- prepare_inputs_for_generation: 5.x may pass cache_position=None;
older remote code assumes a tensor. Wrap and synthesize it.
- Legacy list-style _tied_weights_keys normalized post-load so
save_pretrained works (emptied when tie_word_embeddings is false).
- Multimodal wrappers whose forward() requires media inputs are
unwrapped to their language_model submodule for text-only surgery.
- Register nemotron_h architecture (hybrid Mamba/attention/MoE, all
layer content under layer.mixer) in strategies/utils.py.
Retain the unsuperseded macOS RAM-detection contribution from PR #13 while making every optional probe fail closed. The shared MPS device, loader, cache, and dtype work is already present on current main and is intentionally not duplicated.
Cover legacy console fallbacks, platform temp paths, local UI disk probing, and lazy analysis exports. Bind the new local UI test to the executable source-to-test risk map.
Salvage the still-relevant functional work from PR #48: add non-UTF-8 console fallbacks, use platform temporary directories, make pipeline log output encoding-safe, and defer heavyweight analysis imports. The obsolete contributed CI workflow and already-corrected remote URL are intentionally excluded.
Add a --gpu-memory-utilization flag (0.0-1.0, default 0.85) that controls
the fraction of GPU VRAM available for model loading. Plumbed from CLI
through AbliterationPipeline to load_model's max_memory calculation.
Useful on dedicated GPU setups where the default 15% reserve is wasteful
and causes unnecessary CPU offloading on models that would otherwise fit.
Brian Bell