Frank
23e905c712
Fused MoE (fused_moe / fused_up_gate / fused_mmad) is on by default in ik_llama - no flag needed; only -no-fmoe disables it. Slide two more expert blocks (28,29) from CPU onto the RTX 3090 (~1.26 GiB) for the fused-MoE run, leaving ~1.6 GiB headroom. Update override regex, test, and KB (including a note that the headroom is now tighter than the 2-3 GiB ideal).
116 lines
5.2 KiB
Bash
116 lines
5.2 KiB
Bash
# Tunables for frank-default-small (Ornith-1.5-35B-A3B CRACK Q6_K, 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 quantized to q8_0 with Hadamard transforms on both K and V.
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# Split mode none (single GPU; graph would require 2+ devices).
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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 from CRACK Q8_0 to Q6_K on 2026-08-25.
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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 (Q6_K 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-Q6_K.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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# 3 parallel slots x 163840 ctx each (262144 lowered by 96k per stream for
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# cache and checkpoint headroom) = 491520 total context pool. The per-slot
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# 163840 is advertised in models.json.
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CTX_SIZE=491520
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PARALLEL=3
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# Speculative decoding: DISABLED. ik_llama limits speculative decoding
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# (ngram-mod / MTP / etc.) to a single server slot (-np 1) and hard-exits if
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# --parallel > 1 with a spec stage configured ("Speculative decoding is
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# currently limited to a single server slot (-np 1)."). This service keeps
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# 3 parallel slots (one per user), so SPEC_TYPE is disabled (none). It is a
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# low-parameter MoE (3B active of 35B), so throughput is fine without
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# speculation. MTP is also intentionally disabled.
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SPEC_TYPE=none
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# Expert offload via explicit tensor override (replaces --n-cpu-moe). The Q6_K
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# layer audit found 630 MiB of expert tensors per block. Keep every
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# full-attention block plus every block 35-40 on CUDA. The unique GPU set is
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# 3,7,11,15,19,23,27,31,35,36,37,38,39,40. Move all other expert tensors to
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# CPU. All non-expert tensors stay on the 24 GB card. Buffer type "CPU" is the
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# CPU buft name on this host.
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#
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# 2026-08-26: brought back expert blocks 30,32,33,34 to CUDA (~2.5 GiB) to
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# use the ~5.4 GiB GPU headroom for lower latency; then 28,29 (~1.26 GiB)
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# for the fused-MoE run. Left ~1.6 GiB headroom. The remaining larger-CPU
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# range below those stays on CPU.
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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)\.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, both K and V at q8_0 plus Hadamard transforms on both
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# caches to preserve quality at 8-bit. NOTE: ik_llama's fast q8_KV type
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# crashes at model init on this MTP/multimodal arch with a ggml_view size
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# assert (ggml.c:5427) at commit b166e269; plain q8_0 works. Docs recommend
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# --k-cache-hadamard for quantized KV below Q6_0; we apply --v-cache-hadamard
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# to V as well. Flash-attention is on by default.
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CACHE_TYPE_K=q8_0
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CACHE_TYPE_V=q8_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 |