# frank-zealot — llama.cpp (PrismML fork build, CUDA) serving
# prism-ml/Ternary-Bonsai-2-27B-gguf at PQ2_0 on the RTX 3090, :7771.
#
# 2026-10-17 (Frank): swap from Qwen3.5-9B-Uncensored Q6_K to the Bonsai-2-27B
# ternary checkpoint at the SAME shape Frank asked for — TWO 200,704-token
# conversations — with q8_0 K + q8_0 V (f16 is arithmetically impossible here,
# see the KV law), MTP off (the file carries no nextn/mtp tensors), verbosity 4.
#
# THE RUNTIME IS A LOCK, NOT A PREFERENCE. The file is general.file_type 141
# with 402 tensors of the CUSTOM GGML type 142 (pq2_0) plus prism.hadamard.*
# metadata (normalized-Sylvester-Walsh transform, block 1024, 401 named
# weights, inverse on token_embd, gdn_v_grouped). Stock llama.cpp refuses type
# 142 at load; only the PrismML fork registers the type AND reads the Hadamard
# metadata. The vendor prebuilt build 10754 (commit 2459f68b5) is what every
# number in this file was measured on; sha256 12ae5baba033d8cd…
LLAMA_BIN=/home/dank/GitPackages/llama-prism/llama-server
MODEL_FILE=/home/dank/models/Bonsai2-PQ2_0/Ternary-Bonsai-2-27B-PQ2_0.gguf
MMPROJ_FILE=/home/dank/models/Bonsai2-PQ2_0/Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf
# The CRACK re-export (dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF, uncensored,
# already on disk at /home/dank/models/Bonsai2-CRACK/) is a ONE-LINE swap: the
# header census proves byte-identical geometry (851 tensors, same
# {pq2_0:402, f32:353, bf16:96} histogram, 64 blocks, vocab 248,320, same
# 8,952-char chat template) and the official file's sha256 is the Hub LFS oid.
# Bonsai-2 is the BASE (censored) Qwen3.8 family; if a compaction or subagent
# request ever comes back with a refusal, point MODEL_FILE at the CRACK file
# and restart — the VRAM ledger below does not move.
#
# Served model id — the harness/family name. The SERVICE, unit, scripts and
# this env file keep the frank-zealot name; only the alias on the wire is the
# model id. Consumers that key on the served id: OpenWebUI (model row,
# access_grant, api_configs model_ids) and the harness models.yml.
ALIAS=frank-zealot
BIND_ADDR=0.0.0.0
LISTEN_PORT=7771
# CTX_SIZE is the POOL llama.cpp divides across slots: 401,408 / 2 = 200,704
# per conversation. MUST stay divisible by PARALLEL (the seam enforces it) and
# the harness contextWindow quotes the PER-SLOT number — a longer prompt is
# answered with a 400, never truncated. The model's native window is 262,144;
# 2 × 262,144 at q8_0 projects 25,758 MiB and does NOT fit this card (measured
# f16 analogue: 41,118). One 262,144 slot fits with 5,995 MiB free but
# serializes compaction, so the pair stays.
CTX_SIZE=401408
PARALLEL=2
BATCH_SIZE=4096
UBATCH_SIZE=2048
# KV law (from the GGUF header, then confirmed by the boot ledger): 16
# full-attention layers (full_attention_interval 4 over 64 blocks) × 4 KV heads
# × 256 dim = 16,384 elements per token per cache. q8_0 bills 17,408 B per
# cache ⇒ 34,816 B/token = 34 KiB, so the 401,408 pool is 13,328.00 MiB — the
# ledger said exactly that. f16 would bill 25,088 MiB and the boot DIES:
# `cudaMalloc failed: out of memory` on the 25,088.00 MiB request, engine fit
# projection 33,198 vs 23,558 free. The 48 DeltaNet layers bill a fixed
# recurrent state per SLOT — ~150 MiB each (context 13,627 − KV 13,328 = 299
# for two slots), which is why pool arithmetic alone under-prices the shape.
CACHE_TYPE_K=q8_0
CACHE_TYPE_V=q8_0
# Boot ledger (2026-10-17, this exact shape, nvidia-smi settled):
#   22,664 MiB used / 1,461 MiB free of 24,123
#   fit projection 21,439 = 6,539 model + 13,627 context + 1,272 compute
#   the mtmd projector's 873.10 MiB worst-case is NOT inside that projection —
#   it lands after the fit check, which is the whole difference between
#   "2.6 GiB free on paper" and 1.4 GiB free in reality. Watch it.
# Fallback rung if the margin is ever eaten (desktop, co-tenant): q4_0 KV
# measured 16,392 used / 7,733 free at the same 2 × 200,704.
# 1024 is the ubatch fallback (−616 MiB, measured on the 9B predecessor at
# this card/arch; 2048 was the measured CUDA optimum, +4.6 % prefill).
SPEC_TYPE=none
NGL=99
# Log verbosity threshold (llama.cpp -lv). 4 = debug: KV layout, buffer sizes
# and graph capture land in the journal, which is what the VRAM-fit forensics
# read. Levels above 4 flood the journal.
VERBOSITY=4