qwen4_exp: load modelopt MIXED_PRECISION (NVFP4 experts + block-FP8 dense) checkpoints - #320

What

Adds support for loading Qwen3.8-Flash-Next checkpoints exported with modelopt MIXED_PRECISION quantization - NVFP4 routed experts + 128x128 block-FP8 dense (attention / GDN) weights. The common community build of this shape is lovedheart/Qwen3.8-Flash-Next-NVFP4-FP8, the ~124GB quant that fits the model on a 24GB GPU via the offload backend.

Before this, such a checkpoint fails to load in two places:

1. Config detection - parse_config only recognises a top-level quant_algo of fp8 or *fp4*. MIXED_PRECISION declares the per-module algo in quantized_layers instead, so expert_quant fell through to "none" and the offload cache tried to load the NVFP4 experts as bf16 banks -> ValueError: Missing MoE expert source layers.
2. Dense weight loading - the dense attn / GDN projections are stored as 128x128 block-FP8 (.weight_scale_inv), but iter_weights assumed all non-expert weights are bf16 and passed them straight to _try_fuse -> RuntimeError: Promotion for Float8 Types is not supported, attempted to promote Float8_e4m3fn and BFloat16 on the GDN in_proj fuse.

Changes

• config.py: detect quant_algo == "mixed_precision", read quantized_layers to set expert_quant = "nvfp4" (experts stay native for the offload cache), and keep every dense module bf16.
• weight.py: _load_maybe_block_fp8 dequantizes a dense .weight to bf16 when a sibling .weight_scale_inv is present (reusing the existing dequant_block_fp8 kernel), and .weight_scale_inv is added to _SCALE_SUFFIXES so the scale tensor is not yielded as a weight. Only block-FP8 is added - it is the only dense quant these checkpoints use.

Testing

Loads and serves lovedheart/Qwen3.8-Flash-Next-NVFP4-FP8 (~124GB) on an RTX PRO 4000 Blackwell (24GB VRAM) backed by 172GB system RAM, with --moe-backend hybrid - the offload backend holds the ~124GB checkpoint resident in host RAM and streams experts to the GPU per token. Output is coherent and correct (reasoning / math / coding verified) and tool-calling works, ~22 tok/s single-stream. The new branches are gated on mixed_precision / a present .weight_scale_inv, so no other checkpoint path changes.