Frank
04a8fb7806
Reconfigure to 3 parallel 160000-ctx slots (was 1 slot). KV cache is now asymmetric: K at q8_0, V at q5_0, both with Hadamard. The 3 slots triple the KV, so expert blocks 27-34 are pinned back to CPU (freed ~8.7 GiB) leaving output-adjacent 35-40 on CUDA. Q8_0 model, split-mode none, spec disabled (ik limits spec to 1 slot). AGENTS.md: drop the scratch-port rule (test/bench processes cannot share the single GPU with the running service anyway). Env, serve script, bats, docs updated.
110 lines
4.9 KiB
Bash
110 lines
4.9 KiB
Bash
# Tunables for frank-default-small (Ornith-1.5-35B-A3B CRACK Q8_0, MTP MoE,
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# Dankserver, RTX 3090).
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#
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# Runtime: ik_llama.cpp llama-server (ikawrakow ik_llama.cpp, built
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# 2026-08-26 from commit b166e269) on RTX 3090 24 GB; Ryzen 7 7700X; 62 GB
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# RAM. Previous runtime was upstream llama.cpp b10121. MoE expert weights
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# pinned to CPU with an explicit --override-tensor regex (not --n-cpu-moe).
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# KV cache K q8_0 / V q5_0 with Hadamard transforms on both (see the KV
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# section below). Split mode none (single GPU; graph would require 2+).
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#
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# Build flags (IK_FA note, see docs/LLM/ikllama-ornith-dankserver.md) must
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# ALWAYS include: -DGGML_CUDA_FA_ALL_QUANTS=ON and -DGGML_IQK_FA_ALL_QUANTS=ON
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# so the quantized + Hadamard KV cache combos compile into the CUDA/CPU FA
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# kernels. Replacing the HauhauCS Qwen3.6-35B-A3B model on 2026-08-24.
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# Switched to CRACK Q8_0 on 2026-08-26 (was Q6_K).
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#
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# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE. The
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# ROCm HIP runtime reads those bare env vars (see the 2026-08-13 TEMP
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# incident). This box is NVIDIA/CUDA, but the rule keeps the env portable.
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# Model files (Q8_0 CRACK GGUF + f16 mmproj vision encoder + MTP head bundled
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# in the GGUF). Dealign2026-08-24.
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MODEL_DIR=/home/dank/models/Ornith-1.5-35B-A3B-CRACK
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MODEL_FILE=Ornith-1.5-35B-A3B-CRACK-Q8_0.gguf
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MMPROJ_FILE=mmproj-Ornith-1.5-35B-A3B-f16.gguf
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# ik_llama.cpp llama-server (built 2026-08-26 from ikawrakow/ik_llama.cpp @
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# b166e269; CUDA 13.2, -DGGML_CUDA_FA_ALL_QUANTS=ON -DGGML_IQK_FA_ALL_QUANTS=ON,
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# arch 86 for the RTX 3090). Uses the staged pip cu13 toolkit at
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# /home/dank/cuda13-stage. Rollback: switch to
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# /home/dank/llama.cpp/build/bin/llama-server (upstream b10121).
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LLAMA_SERVER=/home/dank/ik_llama.cpp/build/bin/llama-server
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# Qwen-Sharp chat template from peculiar-ragdoll (tracked in the repo,
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# staged on the host).
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# Source: https://huggingface.co/peculiar-ragdoll/Qwen-Sharp-Chat-Templates
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CHAT_TEMPLATE_FILE=/home/dank/serverAppConfigs/scripts/chat-template.jinja
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# Render the Qwen-Sharp template with the true Jinja engine (--jinja). Without
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# it ik_llama falls back to built-in non-Jinja handlers and drops the custom
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# template's thinking/image/tool formatting.
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USE_JINJA=1
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ALIAS=frank-default-small
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BIND_ADDR=0.0.0.0
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LISTEN_PORT=7770
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# Three parallel slots, each with 160000 ctx. With PARALLEL=3 ik_llama
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# limits speculative decoding to a single slot, so SPEC stays none (MTP is
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# also disabled). The 3 slots triple the KV cache, so enough expert weights
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# are CPU-pinned for the cache to fit on the 24 GB card.
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CTX_SIZE=160000
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PARALLEL=3
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# Speculative decoding: DISABLED. ik_llama limits spec to a single slot; at
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# PARALLEL=3 a configured spec stage hard-exits at load. The low-parameter
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# MoE (3B active of 35B) does not need it anyway. MTP is also disabled.
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SPEC_TYPE=none
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# Expert offload via explicit tensor override (replaces --n-cpu-moe). The Q8_0
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# audit found ~1088 MiB of expert tensors per block (4 tensor × 272 MiB). Keep
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# every final/output-adjacent block 35-40 on CUDA; full-attention blocks 3,7,
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# 11,15,19,23 have no exps and are untouched by this regex. All other expert
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# tensors are pinned to CPU. Buffer type "CPU" is the CPU buft on this host.
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#
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# 2026-08-26: PARALLEL=3 (CTX 160000) triples the KV cache, so the blocks
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# that were moved back to CUDA for fused-MoE (27-34) are pinned to CPU again
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# to free GPU. Blocks 35-40 stay on CUDA. If it boot-loops, move the next
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# lower block into the CPU regex one at a time.
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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'
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OVERRIDE_BUFT=CPU
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# ik_llama --gpu-layers/-ngl does NOT accept the literal "all"; use 999 to
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# mean "load as many layers as possible". Expert exps are pinned to CPU by
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# OVERRIDE_EXPERT below.
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N_GPU_LAYERS=999
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# KV cache on GPU: K at q8_0 (8-bit) and V at q5_0 (5-bit), each with
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# Hadamard transforms. The V cache at lower precision than K is a common
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# asymmetric choice; q8_0 worked (plain q8_KV still crashes here, ggml.c
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# :5427). Flash-attention is on by default.
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CACHE_TYPE_K=q8_0
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CACHE_TYPE_V=q5_0
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K_CACHE_HADAMARD=1
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V_CACHE_HADAMARD=1
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# Split mode: none. Dankserver has a single RTX 3090 and ik_llama requires
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# >= 2 devices for split-mode graph ('It is not possible to use split mode
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# graph with less than 2 devices' is a hard load error). none is correct
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# here and still works with OVERRIDE_EXPERT CPU expert offload.
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SPLIT_MODE=none
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VERBOSITY=4
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# Store context checkpoints in RAM to hold peak GPU memory down.
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CTX_CHECKPOINTS=16
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# Prefill batch sizing. ubatch 1024 is selected for reduced compute-memory
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# pressure and predictable latency under the ngram-mod deployment.
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BATCH_SIZE=4096
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UBATCH_SIZE=1024
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# Eager RAM load (expert weights on CPU; ~31 GB RAM).
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NO_MMAP=1
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# Sampling (Dealign Ornith model card, thinking-general).
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TEMPERATURE=1.0
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TOP_K=20
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TOP_P=0.95
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MIN_P=0.0
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# Compute threads lowered to 8 so other processes keep a core.
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THREADS=7
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THREADS_HTTP=8 |