Frank
a4fe5f6839
The scrape has never worked: lastError is "server returned HTTP status 501 Not Implemented" on http://host.containers.internal:7771/metrics, and the oldest retained sample of up{job="frank-zealot"} (2026-10-11) is already 0 -- the target has been red across four payload generations while /health answered 200 the whole time, keeping the critical LlmEndpointDown alert permanently active. llama.cpp gates /metrics behind --metrics; halogen serves it natively, which is why the asymmetry looked like a 3090-only problem. METRICS=1 in the shipped env, --metrics reaches the argv through EXTRA_ARGS, and the seam asserts both directions (default ships the flag, METRICS=0 strips it). Diagnosed via /api/v1/targets lastError rather than the alert list, which is also the lesson: Alertmanager here has no persistence, so a reboot re-stamps startsAt and a weeks-old fault looks fresh.
82 lines
4.8 KiB
Bash
82 lines
4.8 KiB
Bash
# frank-zealot — llama.cpp (PrismML fork build, CUDA) serving
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# prism-ml/Ternary-Bonsai-2-27B-gguf at PQ2_0 on the RTX 3090, :7771.
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#
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# 2026-10-17 (Frank): swap from Qwen3.5-9B-Uncensored Q6_K to the Bonsai-2-27B
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# ternary checkpoint at the SAME shape Frank asked for — TWO 200,704-token
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# conversations — with q8_0 K + q8_0 V (f16 is arithmetically impossible here,
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# see the KV law), MTP off (the file carries no nextn/mtp tensors), verbosity 4.
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#
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# THE RUNTIME IS A LOCK, NOT A PREFERENCE. The file is general.file_type 141
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# with 402 tensors of the CUSTOM GGML type 142 (pq2_0) plus prism.hadamard.*
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# metadata (normalized-Sylvester-Walsh transform, block 1024, 401 named
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# weights, inverse on token_embd, gdn_v_grouped). Stock llama.cpp refuses type
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# 142 at load; only the PrismML fork registers the type AND reads the Hadamard
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# metadata. The vendor prebuilt build 10754 (commit 2459f68b5) is what every
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# number in this file was measured on; sha256 12ae5baba033d8cd…
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LLAMA_BIN=/home/dank/GitPackages/llama-prism/llama-server
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MODEL_FILE=/home/dank/models/Bonsai2-PQ2_0/Ternary-Bonsai-2-27B-PQ2_0.gguf
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MMPROJ_FILE=/home/dank/models/Bonsai2-PQ2_0/Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf
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# The CRACK re-export (dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF, uncensored,
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# already on disk at /home/dank/models/Bonsai2-CRACK/) is a ONE-LINE swap: the
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# header census proves byte-identical geometry (851 tensors, same
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# {pq2_0:402, f32:353, bf16:96} histogram, 64 blocks, vocab 248,320, same
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# 8,952-char chat template) and the official file's sha256 is the Hub LFS oid.
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# Bonsai-2 is the BASE (censored) Qwen3.8 family; if a compaction or subagent
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# request ever comes back with a refusal, point MODEL_FILE at the CRACK file
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# and restart — the VRAM ledger below does not move.
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#
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# Served model id — the harness/family name. The SERVICE, unit, scripts and
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# this env file keep the frank-zealot name; only the alias on the wire is the
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# model id. Consumers that key on the served id: OpenWebUI (model row,
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# access_grant, api_configs model_ids) and the harness models.yml.
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ALIAS=frank-zealot
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BIND_ADDR=0.0.0.0
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LISTEN_PORT=7771
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# CTX_SIZE is the POOL llama.cpp divides across slots: 401,408 / 2 = 200,704
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# per conversation. MUST stay divisible by PARALLEL (the seam enforces it) and
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# the harness contextWindow quotes the PER-SLOT number — a longer prompt is
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# answered with a 400, never truncated. The model's native window is 262,144;
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# 2 × 262,144 at q8_0 projects 25,758 MiB and does NOT fit this card (measured
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# f16 analogue: 41,118). One 262,144 slot fits with 5,995 MiB free but
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# serializes compaction, so the pair stays.
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CTX_SIZE=401408
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PARALLEL=2
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BATCH_SIZE=4096
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UBATCH_SIZE=2048
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# KV law (from the GGUF header, then confirmed by the boot ledger): 16
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# full-attention layers (full_attention_interval 4 over 64 blocks) × 4 KV heads
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# × 256 dim = 16,384 elements per token per cache. q8_0 bills 17,408 B per
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# cache ⇒ 34,816 B/token = 34 KiB, so the 401,408 pool is 13,328.00 MiB — the
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# ledger said exactly that. f16 would bill 25,088 MiB and the boot DIES:
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# `cudaMalloc failed: out of memory` on the 25,088.00 MiB request, engine fit
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# projection 33,198 vs 23,558 free. The 48 DeltaNet layers bill a fixed
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# recurrent state per SLOT — ~150 MiB each (context 13,627 − KV 13,328 = 299
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# for two slots), which is why pool arithmetic alone under-prices the shape.
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CACHE_TYPE_K=q8_0
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CACHE_TYPE_V=q8_0
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# Boot ledger (2026-10-17, this exact shape, nvidia-smi settled):
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# 22,664 MiB used / 1,461 MiB free of 24,123
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# fit projection 21,439 = 6,539 model + 13,627 context + 1,272 compute
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# the mtmd projector's 873.10 MiB worst-case is NOT inside that projection —
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# it lands after the fit check, which is the whole difference between
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# "2.6 GiB free on paper" and 1.4 GiB free in reality. Watch it.
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# Fallback rung if the margin is ever eaten (desktop, co-tenant): q4_0 KV
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# measured 16,392 used / 7,733 free at the same 2 × 200,704.
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# 1024 is the ubatch fallback (−616 MiB, measured on the 9B predecessor at
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# this card/arch; 2048 was the measured CUDA optimum, +4.6 % prefill).
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SPEC_TYPE=none
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NGL=99
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# Log verbosity threshold (llama.cpp -lv). 4 = debug: KV layout, buffer sizes
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# and graph capture land in the journal, which is what the VRAM-fit forensics
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# read. Levels above 4 flood the journal.
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VERBOSITY=4
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# Serve /metrics. llama.cpp answers 501 without --metrics, and Dankclaw's
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# Prometheus scrapes /metrics on every fleet endpoint — a 501 reads as DOWN, so
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# this service has held the critical LlmEndpointDown alert for as long as the
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# metric has history (verified 2026-10-18: the oldest retained sample of
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# up{job="frank-zealot"}, 2026-10-11, is already 0; scrapeUrl
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# http://host.containers.internal:7771/metrics, lastError "server returned HTTP
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# status 501 Not Implemented"). 0 opts out and re-breaks the scrape.
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METRICS=1
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