# NVFP4A16 serving pipeline — built, validated, and the MoE landmine it found

`[2026-08-25]`

Pipeline at `scripts/erp-tune-serve/` (commits `6a85829`, `ab980e9`).
Validated end-to-end against checkpoint-100 before the real adapter existed.

## ⚠⚠ THE LANDMINE: a `targets=["Linear"]` recipe misses EVERY MoE expert

    before linearize_moe:  427 Linears,    205 targeted, experts 0
    after  linearize_moe:  11,947 Linears, 11,725 targeted, experts 11,520
                           (30 layers x 128 experts x 3 projections)

Gemma-4 stores each layer's 128 experts as two fused 3-D `nn.Parameter` tensors
(`gate_up_proj` [128,1408,2816], `down_proj` [128,2816,704]) — note the absent
`.weight` suffix. A Linear-targeting recipe resolves 205 of 427 modules and
**zero experts**, leaving 22.84 B params (88.5% of the model) in BF16 with no
warning.

**This is the same defect that killed QLoRA here via bitsandbytes.** The blind
spot is in the *checkpoint layout*, not the tool. Fix:
`llmcompressor.modeling.moe.linearize.linearize_moe` — no registration needed,
Gemma-4 satisfies `FusedExpertsProtocol` structurally. Playbook §3.15.

## Scheme: NVFP4A16, deviating from the playbook default, on measured grounds

brokkr benched the W4A4 quant of this checkpoint at **12% on contradiction
detection with CoT off against gen's 81%** — the signature of 4-bit input
activations on a reasoning-dense task. Plus W4A4 KLD is 2-4x worse past ~10k ctx
on sm_120. This is a 16,384-ctx RP seat. Marlin's prefill cost accepted.

⚠ Several HF repos named `…-NVFP4A16` declare `input_activations num_bits 4` —
W4A4 wearing an A16 label. The script refuses if the emitted config says 4.

## Four silent defects the dry run found

1. **transformers 5.15 MIGRATES the config schema on save** — drops
   `global_head_dim`/`num_global_key_value_heads`, writes `per_layer_config`.
   transformers 5.10 (the llmcompressor venv) then reads `num_key_value_heads`
   as None and dies with `TypeError: unsupported operand type(s) for //`.
   Every working artifact on the box uses the OLD schema. Merge now downgrades it.
2. **llmcompressor cannot auto-init a processor for a multimodal checkpoint** —
   pass the tokenizer explicitly as `processor`.
3. **`save_pretrained` does not carry `processor_config.json`** — vLLM then fails
   with "Can't load feature extractor", which reads as a vision bug.
4. **The quant needs more than GPU1's free 32 GiB.** `quant_with_gen_down.sh`
   stops `vllm-gen` and restores it from a trap on EVERY exit path, using
   `docker start` not `compose up` so the container returns with its exact config.

## Verified on the emitted artifact

    49 GB -> 17 GB, format nvfp4-pack-quantized, a=null (genuine A16)
    weight_packed 11,725 of which expert 11,520
    tokenizer truncation: clean (§3.14 trap avoided by calibrating on the
                                 encode cache, so the tokenizer is never called
                                 with truncation=True at all)
    served: Marlin NVFP4 kernel + Marlin MoE backend, coherent generation

⚠ The reference `nvfp4a16` artifact triggers a vLLM warning that q/k/v carry
*different* weight global scales ("likely reduced accuracy"). **Ours does not** —
llmcompressor 0.12 links weight observers across fused groups automatically. The
in-house quant is better than the downloaded one on that axis.

## ⚠ MERGED WEIGHTS ARE MANDATORY — and not for the reason we assumed

The open question was whether LoRA-on-NVFP4 hot-swap still silently no-ops.
Retested on `vllm/vllm-openai:latest`: **it refuses to start.**

    AttributeError: To support LoRA for MoE model,
                    'get_expert_mapping' must be implemented

The check is in `vllm/lora/utils.py::process_packed_modules_mapping` and branches
on `is_moe_model()` — **quantization is not in the condition.** `gemma4.py`,
`gemma4_mm.py`, `gemma4_mtp.py`, `gemma4_unified.py` all have ZERO occurrences;
`deepseek_v2`, `mixtral`, `glm4_moe`, `ernie45_moe` implement it.

**vLLM cannot serve a LoRA on ANY Gemma-4, bf16 or quantized.** Merging is the
only path for this architecture, and it would have bitten identically on the
unquantized base. A loud refusal is strictly better than the 0.24.0 silent no-op,
which shipped a base model wearing the tune's name.

⚠ Base-viability pre-flight is now playbook §3.11 — three greps before picking a
base. **Grep the CLASS, not the file**: `mistral.py` greps as `SupportsLoRA=0`
and is fully LoRA-capable via inheritance from `LlamaForCausalLM`.
