Live GEN_MODEL is now qwen38-27b-coldfusion-h300-nvfp4-mixed (ana-ml2 GPU0
:8015). Served-name left unchanged so all 7 LiteLLM aliases route without a
gateway edit.
Verification:
KV pool 401,550 tok / 1.53x (baseline 403k / 1.54x)
LiteLLM aliases 7/7 green
vision 3/3 shapes, colour+form+position correct
MTP acceptance 59.7% median @ 118.37 tok/s
quality gens 4/4 correct
abliteration 4/4 compliance
PPL NOT measured (see below)
The roadmap predicted ~47% acceptance for a pristine MTP graft versus L35's
59.1% in-band edit. Measured 59.7% on the same harness: there is no acceptance
penalty, which removes the throughput argument for reimplementing MPOA.
A single long-prose generation read 47.5% off the same counters -- below the
8-run minimum of 49.0% -- and would have "confirmed" the prediction by
coincidence. Acceptance must be read from quickbench.py, never one sample.
PPL is blocked on VRAM, not on the model: eval_quality.py aborts with
"prompt_logprobs look uniform" under --speculative-config, and the probe-seat
workaround needs ~22 GB while both cards sit at ~96% committed.
Also normalizes the quant dir from root:0600 to llmuser:llmuser 0664 to match
every other model dir, and records that config.json sha256 is byte-identical
across the h300 and L35 quants and is therefore useless for confirming which
weights are mounted (mtime and a head-hash are the discriminating views).
Rollback is one line to .env.bak-pre-h300-20260820.
vh
e3ce713f7f