Frank
e23df8b229
The build default is 8192 MiB. dankclaw is memory-tight (frank-large's 70 GiB GTT plus the rest of the stack) and Gemma4 runs fully on the 3090 (-ngl 999), so the CPU-side cache is not the hot path. Cap it at 2048 to give back ~6 GiB of LPDDR. Gemma4 is not a DeltaNet model, so the >12.8 GiB CACHE_RAM floor does not apply. Wired through frank-small.env (CACHE_RAM) -> serve-frank-small.sh (--cache-ram), with a wiring seam so the cap cannot be silently unwired.
119 lines
5.6 KiB
Bash
119 lines
5.6 KiB
Bash
# Tunables for frank-small (HauhauCS/Gemma4-12B-QAT-Uncensored-HauhauCS-
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# Balanced, Q4_K_M, plus the BF16 vision projector and the separate MTP
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# sidecar) on mainline llama.cpp, Dankclaw's RTX 3090, since 2026-09-17.
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#
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# Runtime: llama.cpp master 37b53fd, CUDA build for sm_86:
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# cmake -B build-cuda -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=ON \
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# -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=86 -DGGML_CUDA_FA_ALL_QUANTS=ON
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# cmake --build build-cuda --target llama-server -j
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# GGML_CUDA_FA_ALL_QUANTS is ON: this service runs q8_0 KV with flash
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# attention, and without the flag quantized-KV prefill collapses ~12x on
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# CUDA (see the CUDA lesson in AGENTS.md).
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#
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# Model: HauhauCS/Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced at Q4_K_M
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# (6.87 GiB). Architecture (GGUF arch gemma4): 48 layers, MHA with 16 kv
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# heads x 512 head_dim, rope freq_base 1e6, final logit softcapping 30,
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# per-layer token embeddings (PLE) inside the file, and a sliding-window
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# attention pattern (attention.sliding_window = 1024) -- most layers cache
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# only a 1024-position window, which is why the KV pool is a fraction of
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# the full-ctx x all-layers number.
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#
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# MTP: the Multi-Token Prediction head ships as a SEPARATE file
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# (mtp-gemma-4-12B-it.gguf, arch gemma4-assistant, 4 nextn layers, 0.24
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# GiB). Unlike the Qwen3.8 -mtp build (head inside the main file), this one
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# is passed via --model-draft with --spec-type draft-mtp, so the draft
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# context loads the assistant model. The same --spec-type value chains the
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# draftless ngram-mod matcher, which attaches unconditionally; two
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# context-backed drafters cannot share one process, so draft-mtp,ngram-mod
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# is the only legal shape that carries both.
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#
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# SIZING (RTX 3090, 24576 MiB): CTX_SIZE = PARALLEL x 262144 -- four
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# FULL-window slots, measured to fit: the 4-slot boot idles at 22,346
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# MiB and each extra full-context slot costs 3,450 MiB (measured 1-slot
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# 11,996 -> 4-slot 22,346). A fifth slot projects to ~25.8 GiB, past the
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# card, and the KV pool allocates upfront -- so 4 is the max. Explicit
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# CTX_SIZE bypasses --fit on purpose: --fit maximises the TOTAL pool,
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# which would shrink each slot below the full 262144 window once
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# parallel > 1.
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#
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# Vision + audio: --mmproj loads the BF16 projector (11 tensors, 52M
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# params; it carries an audio encoder too). The fleet's image path lives
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# here, which is why halogen no longer sets HALOGEN_VISION_TOWER. Audio
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# WORKS: input_audio content parts transcribe verbatim (verified
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# 2026-09-17 with a 16k mono WAV).
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#
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# Sampling: greedy by default (the fleet contract); a client that sends a
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# temperature gets its own, and the harness applies per-model overrides
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# from models.yml.
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#
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# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE (HIP
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# reads those bare vars on the ROCm hosts -- see the 2026-08-13 incident in
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# AGENTS.md). This box is CUDA, but the rule keeps env files portable.
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# llama.cpp runtime binary: mainline master 37b53fd, CUDA sm_86,
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# FA_ALL_QUANTS=ON.
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LLAMA_BIN=/home/dank/GitPackages/llama.cpp-mainline/build-cuda/bin/llama-server
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# Model package. The MTP sidecar file is required for speculative decoding.
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MODEL_DIR=/home/dank/models/Gemma4-12B-QAT
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MODEL_FILE=Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf
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MMPROJ_PATH=/home/dank/models/Gemma4-12B-QAT/mmproj-Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-BF16.gguf
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DRAFT_FILE=mtp-gemma-4-12B-it.gguf
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# Fleet identity and network. 7771 is this service's own endpoint on
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# Dankclaw; 7770 stays frank-large's. The alias is the id at /v1/models and
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# what clients ask for -- the harness entry in models.yml names it.
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ALIAS=frank-small
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BIND_ADDR=0.0.0.0
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LISTEN_PORT=7771
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# Four FULL 262144-token contexts, one per slot (CTX_SIZE is the total
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# pool; --parallel divides it). See the SIZING note above for the fit
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# arithmetic; a fifth slot does not fit.
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CTX_SIZE=1048576
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PARALLEL=4
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CACHE_TYPE_K=q8_0
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CACHE_TYPE_V=q8_0
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# CPU RAM cache cap in MiB (build default 8192). This host is memory-tight
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# (frank-large's 70 GiB GTT plus the rest of the stack), and Gemma4 runs
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# fully on the 3090 (-ngl 999), so the CPU-side cache is not the hot path;
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# cap it at 2048 to give back ~6 GiB of LPDDR. Gemma4 is not a DeltaNet
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# model, so the >12.8 GiB CACHE_RAM floor does not apply.
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CACHE_RAM=2048
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# Batch geometry: 8192 logical / 1024 physical. Measured on this card at
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# max length (254k-token prefill / 253k-ctx decode, 4 slots, q8 KV):
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# ubatch 512 -> pp 915 t/s, tg@250k 90-204; ubatch 1024 -> pp 962,
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# tg 257-302. Larger ubatches (2048) OOM the MTP draft context's pp
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# compute buffer (~3.3 GiB against 2.2 GiB headroom) and must not be
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# tried. Idle VRAM is 23,300 MiB at this geometry.
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BATCH_SIZE=8192
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UBATCH_SIZE=1024
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# CPU threads: this service runs entirely on the GPU (NGL=999), so threads
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# cover tokenization, sampling and the HTTP layer only. Deliberately modest
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# -- this host's CPU is shared with halogen's engine, which is CPU-hungry.
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NUM_THREADS=8
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THREAD_BATCH=8
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# Every layer on the 3090, with flash attention (required for q8 KV).
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NGL=999
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FLASH_ATTN=on
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# Speculative decoding: the MTP sidecar (draft context) chained with the
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# draftless ngram-mod matcher. Draft depth and busy-wait level are made
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# explicit here (both were the implicit build defaults: 3 and 50).
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# Measured at 250k ctx: depth 4 + poll 100 DECODED WORSE (230-257 t/s)
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# than depth 3 + poll 50 (257-302 t/s) — the longer verification round
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# costs more attention re-read at long context than the extra accepted
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# token earns. Keep 3/50.
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SPEC_TYPE=draft-mtp,ngram-mod
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SPEC_DRAFT_N_MAX=3
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POLL_LEVEL=50
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# Greedy default (fleet contract); reasoning is extracted into
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# reasoning_content with an unlimited budget (the model decides).
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TEMPERATURE=0.0
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REASONING_FORMAT=deepseek
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REASONING_BUDGET=-1
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