# Tunables for frank-small (HauhauCS/Gemma4-12B-QAT-Uncensored-HauhauCS-
# Balanced, Q4_K_M, plus the BF16 vision projector and the separate MTP
# sidecar) on mainline llama.cpp, Dankclaw's RTX 3090, since 2026-09-17.
#
# Runtime: llama.cpp master 37b53fd, CUDA build for sm_86:
#   cmake -B build-cuda -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=ON \
#     -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=86 -DGGML_CUDA_FA_ALL_QUANTS=ON
#   cmake --build build-cuda --target llama-server -j
# GGML_CUDA_FA_ALL_QUANTS is ON: this service runs q8_0 KV with flash
# attention, and without the flag quantized-KV prefill collapses ~12x on
# CUDA (see the CUDA lesson in AGENTS.md).
#
# Model: HauhauCS/Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced at Q4_K_M
# (6.87 GiB). Architecture (GGUF arch gemma4): 48 layers, MHA with 16 kv
# heads x 512 head_dim, rope freq_base 1e6, final logit softcapping 30,
# per-layer token embeddings (PLE) inside the file, and a sliding-window
# attention pattern (attention.sliding_window = 1024) -- most layers cache
# only a 1024-position window, which is why the KV pool is a fraction of
# the full-ctx x all-layers number.
#
# MTP: the Multi-Token Prediction head ships as a SEPARATE file
# (mtp-gemma-4-12B-it.gguf, arch gemma4-assistant, 4 nextn layers, 0.24
# GiB). Unlike the Qwen3.8 -mtp build (head inside the main file), this one
# is passed via --model-draft with --spec-type draft-mtp, so the draft
# context loads the assistant model. The same --spec-type value chains the
# draftless ngram-mod matcher, which attaches unconditionally; two
# context-backed drafters cannot share one process, so draft-mtp,ngram-mod
# is the only legal shape that carries both.
#
# SIZING (RTX 3090, 24576 MiB): CTX_SIZE = PARALLEL x 262144 -- four
# FULL-window slots, measured to fit: the 4-slot boot idles at 22,346
# MiB and each extra full-context slot costs 3,450 MiB (measured 1-slot
# 11,996 -> 4-slot 22,346). A fifth slot projects to ~25.8 GiB, past the
# card, and the KV pool allocates upfront -- so 4 is the max. Explicit
# CTX_SIZE bypasses --fit on purpose: --fit maximises the TOTAL pool,
# which would shrink each slot below the full 262144 window once
# parallel > 1.
#
# Vision + audio: --mmproj loads the BF16 projector (11 tensors, 52M
# params; it carries an audio encoder too). The fleet's image path lives
# here, which is why halogen no longer sets HALOGEN_VISION_TOWER. Audio
# WORKS: input_audio content parts transcribe verbatim (verified
# 2026-09-17 with a 16k mono WAV).
#
# Sampling: greedy by default (the fleet contract); a client that sends a
# temperature gets its own, and the harness applies per-model overrides
# from models.yml.
#
# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE (HIP
# reads those bare vars on the ROCm hosts -- see the 2026-08-13 incident in
# AGENTS.md). This box is CUDA, but the rule keeps env files portable.

# llama.cpp runtime binary: mainline master 37b53fd, CUDA sm_86,
# FA_ALL_QUANTS=ON.
LLAMA_BIN=/home/dank/GitPackages/llama.cpp-mainline/build-cuda/bin/llama-server

# Model package. The MTP sidecar file is required for speculative decoding.
MODEL_DIR=/home/dank/models/Gemma4-12B-QAT
MODEL_FILE=Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf
MMPROJ_PATH=/home/dank/models/Gemma4-12B-QAT/mmproj-Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-BF16.gguf
DRAFT_FILE=mtp-gemma-4-12B-it.gguf

# Fleet identity and network. 7771 is this service's own endpoint on
# Dankclaw; 7770 stays frank-large's. The alias is the id at /v1/models and
# what clients ask for -- the harness entry in models.yml names it.
ALIAS=frank-small
BIND_ADDR=0.0.0.0
LISTEN_PORT=7771

# Four FULL 262144-token contexts, one per slot (CTX_SIZE is the total
# pool; --parallel divides it). See the SIZING note above for the fit
# arithmetic; a fifth slot does not fit.
CTX_SIZE=1048576
PARALLEL=4
CACHE_TYPE_K=q8_0
CACHE_TYPE_V=q8_0

# Batch geometry: 8192 logical / 1024 physical. Measured on this card at
# max length (254k-token prefill / 253k-ctx decode, 4 slots, q8 KV):
# ubatch 512 -> pp 915 t/s, tg@250k 90-204; ubatch 1024 -> pp 962,
# tg 257-302. Larger ubatches (2048) OOM the MTP draft context's pp
# compute buffer (~3.3 GiB against 2.2 GiB headroom) and must not be
# tried. Idle VRAM is 23,300 MiB at this geometry.
BATCH_SIZE=8192
UBATCH_SIZE=1024

# CPU threads: this service runs entirely on the GPU (NGL=999), so threads
# cover tokenization, sampling and the HTTP layer only. Deliberately modest
# -- this host's CPU is shared with halogen's engine, which is CPU-hungry.
NUM_THREADS=8
THREAD_BATCH=8

# Every layer on the 3090, with flash attention (required for q8 KV).
NGL=999
FLASH_ATTN=on

# Speculative decoding: the MTP sidecar (draft context) chained with the
# draftless ngram-mod matcher. Draft depth and busy-wait level are made
# explicit here (both were the implicit build defaults: 3 and 50).
# Measured at 250k ctx: depth 4 + poll 100 DECODED WORSE (230-257 t/s)
# than depth 3 + poll 50 (257-302 t/s) — the longer verification round
# costs more attention re-read at long context than the extra accepted
# token earns. Keep 3/50.
SPEC_TYPE=draft-mtp,ngram-mod
SPEC_DRAFT_N_MAX=3
POLL_LEVEL=50

# Greedy default (fleet contract); reasoning is extracted into
# reasoning_content with an unlimited budget (the model decides).
TEMPERATURE=0.0
REASONING_FORMAT=deepseek
REASONING_BUDGET=-1