# Tunables for frank-default-small (HauhauCS Qwen3.6-35B-A3B Q6_K_P,
# ngram-mod spec, Dankserver, RTX 3090).
#
# Runtime: upstream llama.cpp llama-server (b10121). RTX 3090 24 GB;
# Ryzen 7 7700X; 62 GB RAM. MoE experts offloaded to CPU (--n-cpu-moe 36).
# Restored 2026-08-18 (was the pre-2026-08-17-redo frank-default-small).
#
# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE. The
# ROCm HIP runtime reads those bare env vars (see the 2026-08-13 TEMP
# incident). This box is NVIDIA/CUDA, but the rule keeps the env portable.

# Model files (already on disk; Q6_K_P GGUF + f16 mmproj vision encoder).
MODEL_DIR=/home/dank/models
MODEL_FILE=Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf
MMPROJ_FILE=mmproj-Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-f16.gguf
# Upstream llama.cpp binary (b10121).
LLAMA_SERVER=/home/dank/llama.cpp/build/bin/llama-server

ALIAS=frank-default-small
BIND_ADDR=0.0.0.0
LISTEN_PORT=7770

# 3 parallel slots x 262144 ctx each = ~786432 total context pool (max per-slot).
# Dual-f16 KV at 786432 cells leaves ~15 GiB free on the 3090.
CTX_SIZE=786432
PARALLEL=3

# Spec: ngram-mod (n_min=48, n_max=64, n_match=24).
SPEC_TYPE=ngram-mod
NGRAM_MOD_N_MIN=48
NGRAM_MOD_N_MAX=64
NGRAM_MOD_N_MATCH=24

# Expert offload: 32 of 40 layers keep MoE weights on CPU; 8 on GPU.
N_CPU_MOE=32
N_GPU_LAYERS=all

# KV cache on GPU, both K and V at f16.
CACHE_TYPE_K=f16
CACHE_TYPE_V=f16

VERBOSITY=4
# Store SWA/context checkpoints in RAM to hold peak GPU memory down.
CTX_CHECKPOINTS=16

# Big prefill batches (ubatch 2048, batch 4096).
BATCH_SIZE=4096
UBATCH_SIZE=2048

# Eager RAM load (MoE experts on CPU; ~31 GB RAM).
NO_MMAP=1

# Sampling (HauhauCS Qwen3.6-35B-A3B model card, thinking-general).
TEMPERATURE=1.0
TOP_K=20
TOP_P=0.95
MIN_P=0.0

# Compute threads lowered to 7 so other processes keep a core.
THREADS=7
THREADS_HTTP=8