Root-caused the NVFP4 gibberish to a quant-namespace bug: quant_nvfp4.py loaded
via AutoModelForCausalLM -> text-only Qwen3_5ForCausalLM -> flat model.layers.* keys,
but vLLM 0.24 serves only Qwen3_5ForConditionalGeneration (whose weight mapper needs
model.language_model.*). Fixed by loading as AutoModelForImageTextToText; NVFP4 now
serves coherent (validated greedy on ana-ml2 GPU0).
Base NVFP4 (compressed-tensors) measured ~53 tok/s (~= GGUF at batch-1, no single-stream
win) and its MTP is 0% acceptance (vLLM's Qwen3_5MTP drafter loads the bf16 mtp head only
off a modelopt main-model checkpoint). Added quant_modelopt.py (nvidia-modelopt PTQ,
matches AEON's NVFP4 W4A4 g16 + lm_head/linear_attn/visual exclusions) as the path to
working native MTP; graft + splice + serve otherwise unchanged.
Captures the full pipeline recipe (graft->quant->splice->config->serve) with every
gotcha found this session, the 3 gibberish suspects, and the diagnostic ladder
(validate native-config no-MTP coherence FIRST) for a fresh session to finish the
chase. Also stages the NVFP4 scripts + 512-row calib. Recent decisions: NEO-CODE
seat swap (R36), webhook ALLOWED_HOST_LIST fix. Lessons: validate-tracer-bullet-first,
mtp-graft-dropped-at-load, gitea-204-red-herring.
render-verify caught it: Dvalin's calib tool_calls carry OpenAI wire-form JSON
string arguments, but the Qwen3.6 chat template does .items() on arguments (needs
a dict) → jinja TypeError. Parse string->dict in render_verify + the quant's
load_calib_chat. Confirmed: renders the exact qwen3_coder XML the seat emits
(prefixed bifrost.soong-lab.*, v0.3.13 generate_portrait, <think>, <tool_response>).
vh