2 Commits

Include renames

Author	SHA1	Message	Date
vh	9a916a759f	Swap the MeroMero A4B onto the erp-seat seat as char-rp-fast, retire the Pfish-6 alias ... Operator: "replace that a4b moe over pfish-6 -- remove the pfish-6 alias and create an alias for char-rp-fast." G4-MeroMero-26B-A4B-it-uncensored-heretic-NVFP4A16 is live on ana-ml2 :8021 under its own served name, behind the new gateway alias char-rp-fast. Pfish-6 is gone from the gateway and now returns an explicit 400 rather than a substitution; 0 of 17 LiteLLM keys scoped it, so nothing was orphaned. The compose project name stays erp-seat because asset-engine derives seat liveness from it. The first quant of that A4B served NaN and passed its healthcheck doing it. It was built with the dense v2-31B recipe, whose ignore list has no router regex, so all 30 MoE routers were quantized to 4 bits -- and a 4-bit router changes which experts run rather than degrading them. Quant rc=0, healthcheck green, correct KV pool, correct served name, and every completion returned finish_reason=length with the full token count and content: null. The model was emitting a full budget of tokens that decoded to the empty string. Raw /v1/completions was empty too, ruling out the chat template and the reasoning parser. The signal that named it was logprobs: vLLM refused to serialize the response, "Out of range float values are not JSON compliant: nan". The lesson is about the control rather than the router. That tree had already been structurally diffed and passed -- against a verified-good DENSE quant of the same Gemma-4 family. A dense model has no routers, so the single thing that was wrong was the single thing the control could not distinguish. Diffing instead against Pfish-6, a known-good quant of the same 26B-A4B MoE, gave it in one line: 222 ignore entries against 252, the 30 missing being layers.N.router.proj. A positive control is only worth what it can distinguish, and "same family" is not "same architecture class". Re-quantized with the MoE recipe, whose dry-run asserts 11,520 expert Linears and refuses a router in the quantize set before any GPU time. The live seat then passed prose with no channel-prefix leak, a solid-colour image read correctly, an auto tool call parsed, finite logprobs, and KV 534,649 tokens / 2.04x carried over from Pfish-6 unchanged. The broken tree is parked on ana-ml2 as ...-NVFP4A16.BROKEN-routers-quantized-20260910. Section 4.4's temp port was not reachable: 15.9 GiB of weights plus KV plus multimodal encoder-cache profiling does not fit in the ~19 GiB free beside GPU1's six other tenants -- 0.20 utilization refused admission, 0.185 OOM'd in encoder profiling. The substitute was reversibility and ordering: named .env backup, prove the seat on its real port while no alias points at it, move the alias last. That is why a NaN-serving seat never reached a consumer. The seat was down about 16 minutes across two attempts; no consumer saw a broken alias. Playbook gains the router-quant failure signature and the control-class rule in 3.15, and a logprobs check in 4.4. seat_verify.py carries that check as check 6. Quality is NOT established: no RP eval, no long-context check, no A/B against Pfish-6 or char-rp. Samplers are the author's card values, untuned here.	2026-09-10 11:34:05 -07:00
vh	1a5bc2ddf1	Land the MeroMero v2-31B NVFP4A16 quant and record the pinned-transformers trap ... The v2 dense quant had failed four times. Attempt 5 lands it at 19 G. The blocker was not what it looked like. `AmbiguousGlobalPerLayerAttributeError` on `head_dim` read as a malformed upload -- DogOnKeyboard's config carries a `per_layer_config` key zerofata's canonical one lacks -- and the standing fix was to force `allow_global_per_layer_attribute_access=True`. Both halves were wrong. `pip install llmcompressor==0.13.0` downgrades transformers 5.16.1 -> 5.14.1. The config was serialized by 5.16.1, which materializes `per_layer_config` from `global_head_dim` + `layer_types`; 5.14.1 carries the heterogeneity guard but not the gemma4 resolver. Under the image's own transformers the same config loads fine. `:latest` was also re-pulled during attempt 4 and no earlier run, so the toolchain moved mid-diagnosis. Two things separated "malformed upload" from "moved toolchain": reproducing the real failing call (a bare AutoConfig load does not reproduce it; the trigger is reached through AutoTokenizer) and keeping zerofata's canonical tree, quantized cleanly on 2026-08-21, as a positive control. The fix drops `per_layer_config` rather than forcing global access. It is exactly redundant -- keys are precisely the ten full_attention layer indices, sole value (512, 4), verbatim the global fields -- and forcing instead would make `config.head_dim` answer 256 to the callers building the 512-wide layers. patch_perlayer.py re-proves that redundancy at apply time and refuses if it ever stops holding. Verified on the tensor table rather than the exit code: the output is identical family-for-family and count-for-count to the August canonical quant, with 356 BF16 vision-tower tensors preserved and input_activations=None. A GPU-free load leaves 0 tensors on meta and generates coherent prose. The section 4.4 serve test has NOT run -- GPU1 has 19.9 GB free against 19.5 GB of weights, so it needs a live seat displaced. Also fixes the A4B output, which had a truncation cap baked into its tokenizer (max_length 8192) from being quantized with the calibration corpus. Playbook gains section 3.17 for the pinned-transformers class and sharpens 3.16 to say drop the dataset outright for any A16 scheme.	2026-09-10 10:56:16 -07:00