Audit of the nvidia text loader (cuda.py) vs the shared hf_loading.py found real
config drift — the same model config behaved differently on the text path:

1. precision IGNORED — cuda.py hardcoded torch_dtype=float16. Now the manager
   passes the per-model  and cuda.py resolves it via resolve_dtype()
   (default fp16 on CUDA / fp32 on CPU when unset, preserving current behaviour).
2. 4-bit compute dtype hardcoded float16 → now follows the resolved precision
   (_make_bnb_config takes a compute_dtype).
3.  key was ignored — the manager only read . Now it
   honours flash_attn / flash_attention (per-model) and the global
   global_args.flash_attn (offload.flash_attention).
4. offload_buffers was only set on the disk-spill path → now also on the GPU+CPU
   device_map ladder (with offload_folder), so CPU offload doesn't pin activation
   buffers on the GPU and OOM the forward pass.
5. global max_ram_gb now clamps the CPU offload budget (central
   _get_gpu_memory_map_with_limit + the disk fallback), matching hf_loading.

Diffusers-only items (component_quantization, GGUF components, sdcpp flash flags)
are correctly N/A to the AutoModelForCausalLM text path.

Bump version to 0.1.9.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RdMufYvtTbtGDWsiZVoXce