# Tunables for frank-default-small (Ornith-1.5-35B-A3B CRACK Q8_0, MTP MoE,
# Dankserver, RTX 3090).
#
# Runtime: ik_llama.cpp llama-server (ikawrakow ik_llama.cpp, built
# 2026-08-26 from commit b166e269) on RTX 3090 24 GB; Ryzen 7 7700X; 62 GB
# RAM. Previous runtime was upstream llama.cpp b10121. MoE expert weights
# pinned to CPU with an explicit --override-tensor regex (not --n-cpu-moe).
# KV cache K q8_0 / V q5_0 with Hadamard transforms on both (see the KV
# section below). Split mode none (single GPU; graph would require 2+).
#
# Build flags (IK_FA note, see docs/LLM/ikllama-ornith-dankserver.md) must
# ALWAYS include: -DGGML_CUDA_FA_ALL_QUANTS=ON and -DGGML_IQK_FA_ALL_QUANTS=ON
# so the quantized + Hadamard KV cache combos compile into the CUDA/CPU FA
# kernels. Replacing the HauhauCS Qwen3.6-35B-A3B model on 2026-08-24.
# Switched to CRACK Q8_0 on 2026-08-26 (was Q6_K).
#
# 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 (Q8_0 CRACK GGUF + f16 mmproj vision encoder + MTP head bundled
# in the GGUF). Dealign2026-08-24.
MODEL_DIR=/home/dank/models/Ornith-1.5-35B-A3B-CRACK
MODEL_FILE=Ornith-1.5-35B-A3B-CRACK-Q8_0.gguf
MMPROJ_FILE=mmproj-Ornith-1.5-35B-A3B-f16.gguf
# ik_llama.cpp llama-server (built 2026-08-26 from ikawrakow/ik_llama.cpp @
# b166e269; CUDA 13.2, -DGGML_CUDA_FA_ALL_QUANTS=ON -DGGML_IQK_FA_ALL_QUANTS=ON,
# arch 86 for the RTX 3090). Uses the staged pip cu13 toolkit at
# /home/dank/cuda13-stage. Rollback: switch to
# /home/dank/llama.cpp/build/bin/llama-server (upstream b10121).
LLAMA_SERVER=/home/dank/ik_llama.cpp/build/bin/llama-server

# Qwen-Sharp chat template from peculiar-ragdoll (tracked in the repo,
# staged on the host).
# Source: https://huggingface.co/peculiar-ragdoll/Qwen-Sharp-Chat-Templates
CHAT_TEMPLATE_FILE=/home/dank/serverAppConfigs/scripts/chat-template.jinja
# Render the Qwen-Sharp template with the true Jinja engine (--jinja). Without
# it ik_llama falls back to built-in non-Jinja handlers and drops the custom
# template's thinking/image/tool formatting.
USE_JINJA=1

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

# Three parallel slots sharing a 491520 total context pool (491520/3 =
# 163840 per slot — the requested 160k). With PARALLEL=3 ik_llama limits
# speculative decoding to a single slot, so SPEC stays none (MTP is also
# disabled). The 3 slots + 480k pool make the KV cache large, so expert
# weights are CPU-pinned for the cache to fit on the 24 GB card.
CTX_SIZE=491520
PARALLEL=3

# Speculative decoding: DISABLED. ik_llama limits spec to a single slot; at
# PARALLEL=3 a configured spec stage hard-exits at load. The low-parameter
# MoE (3B active of 35B) does not need it anyway. MTP is also disabled.
SPEC_TYPE=none

# Expert offload via explicit tensor override (replaces --n-cpu-moe). The Q8_0
# audit found ~1088 MiB of expert tensors per block (4 tensor × 272 MiB). Keep
# every final/output-adjacent block 35-40 on CUDA; full-attention blocks 3,7,
# 11,15,19,23 have no exps and are untouched by this regex. All other expert
# tensors are pinned to CPU. Buffer type "CPU" is the CPU buft on this host.
#
# 2026-08-26: PARALLEL=3 (CTX 160000) triples the KV cache, so the blocks
# that were moved back to CUDA for fused-MoE (27-34) are pinned to CPU again
# to free GPU. Blocks 35-40 stay on CUDA. If it boot-loops, move the next
# lower block into the CPU regex one at a time.
OVERRIDE_EXPERT='blk\.(0|1|2|4|5|6|8|9|10|12|13|14|16|17|18|20|21|22|24|25|26|27|28|29|30|31|32|33|34)\.ffn_(up|down|gate|gate_up)_(ch|)exps'
OVERRIDE_BUFT=CPU
# ik_llama --gpu-layers/-ngl does NOT accept the literal "all"; use 999 to
# mean "load as many layers as possible". Expert exps are pinned to CPU by
# OVERRIDE_EXPERT below.
N_GPU_LAYERS=999

# KV cache on GPU: K back at the default f16 (no Hadamard on K; the K
# cache is kept full-precision so it does not need the transform), and V at
# q6_0 (6-bit k-quant) with Hadamard. Flash-attention is on by default.
CACHE_TYPE_K=f16
CACHE_TYPE_V=q6_0
K_CACHE_HADAMARD=0
V_CACHE_HADAMARD=1

# Split mode: none. Dankserver has a single RTX 3090 and ik_llama requires
# >= 2 devices for split-mode graph ('It is not possible to use split mode
# graph with less than 2 devices' is a hard load error). none is correct
# here and still works with OVERRIDE_EXPERT CPU expert offload.
SPLIT_MODE=none

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

# Prefill batch sizing. ubatch 1024 is selected for reduced compute-memory
# pressure and predictable latency under the ngram-mod deployment.
BATCH_SIZE=4096
UBATCH_SIZE=1024

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

# Sampling (Dealign Ornith model card, thinking-general).
TEMPERATURE=1.0
TOP_K=20
TOP_P=0.95
MIN_P=0.0

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