Swap fleet naming + restore Qwen3.6-35B-A3B Q6_K_P on Dankserver
- Restore the pre-2026-08-17 Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive Q6_K_P + ngram-mod setup as frank-default-small on Dankserver (port 7770, upstream llama.cpp b10121, MoE experts on CPU via --n-cpu-moe 36). - Rename the CIRU Qwen3.8-27B ActiveFPX setup from frank-default-small to frank-default-large (Dankclaw, port 52625); bump to CTX_SIZE=262144 (native max) and PARALLEL=2 (96 GiB unified pool). - Retire the Qwen3.8-27B IQ4_XS FastMTP frank-default-large (Dankserver) to .AGENTTRASH. - deploy.sh: SERVICE[dankserver]=frank-default-small, SERVICE[dankclaw]=frank-default-large; HEALTH_WAIT[dankserver]=600.
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@@ -1,71 +1,61 @@
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# Tunables for frank-default-small (CIRU llama.cpp, dankclaw, Strix Halo
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# gfx1151).
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# Tunables for frank-default-small (HauhauCS Qwen3.6-35B-A3B Q6_K_P,
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# ngram-mod spec, Dankserver, RTX 3090).
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#
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# Runtime: the PromptForge llama-server built from ciru-ai/ROCmFPX tag
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# qwen3.8-activefpx-promptforge-v2.3 serving
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# jcbtc/Qwen3.8-27B-CIRU-ActiveFPX-PromptForge (v2). ActiveFPX compact
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# GGUF with PromptForge companions for CIRU DualView prompt acceleration and
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# native depth-4 MTP. Alias frank-default-small, port 52625.
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# Runtime: upstream llama.cpp llama-server (b10121). RTX 3090 24 GB;
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# Ryzen 7 7700X; 62 GB RAM. MoE experts offloaded to CPU (--n-cpu-moe 36).
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# Restored 2026-08-18 (was the pre-2026-08-17-redo frank-default-small).
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#
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# This runtime replaced the retired vLLM service that previously occupied
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# this slot. See docs/ciru-activefpx-promptforge.md.
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#
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# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE. The ROCm
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# HIP runtime reads those bare env vars (see the 2026-08-13 TEMP incident).
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# llama.cpp reads --host/--port/--temp as CLI flags, never as env vars.
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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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# CIRU llama-server binary (ROCmFPX v2.3 build, see docs/ciru runtime page).
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# Built against the private TheRock ROCm 7.15 SDK kept in the retired
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# ling3-ciru venv. ROCm 7.15 is required for the advertised Strix Halo
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# performance; the system ROCm 7.2.4 selects slower/fallback kernels.
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LLAMA_BIN=/home/dank/ciru-qwen38/ROCmFPX/build-promptforge/bin/llama-server
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# Model files. The three-file payload (v2 GGUF + FFN + GDN) enables CIRU
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# DualView prompt acceleration; omit the two .pfs files to run the GGUF alone
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# and save ~19.7 GiB of memory at the cost of prompt throughput.
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MODEL_DIR=/home/dank/models/CIRU-Qwen3.8-27B-ActiveFPX
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MODEL_FILE=Qwen3.8-27B-CIRU-ActiveFPX-PromptForge-v2.gguf
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FFN_PFS=Qwen3.8-27B-CIRU-ActiveFPX-PromptForge-FFN.pfs
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GDN_PFS=Qwen3.8-27B-CIRU-ActiveFPX-PromptForge-GDN.pfs
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# ROCm 7.15 SDK paths (inside the retired ling3-ciru venv). The serve script
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# exports ROCM_PATH and LD_LIBRARY_PATH from these so the 7.15 libraries
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# precede any system ROCm 7.2.4.
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ROCM_DEVEL=/home/dank/ling3-ciru/Ling-3.0-Flash-CIRU-int4-Strix-native/.venv/lib/python3.12/site-packages/_rocm_sdk_devel
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ROCM_LIB=/home/dank/ling3-ciru/Ling-3.0-Flash-CIRU-int4-Strix-native/.venv/lib/python3.12/site-packages/_rocm_sdk_libraries
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ROCM_CORE=/home/dank/ling3-ciru/Ling-3.0-Flash-CIRU-int4-Strix-native/.venv/lib/python3.12/site-packages/_rocm_sdk_core
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# Model files (already on disk; Q6_K_P GGUF + f16 mmproj vision encoder).
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MODEL_DIR=/home/dank/models
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MODEL_FILE=Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf
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MMPROJ_FILE=mmproj-Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-f16.gguf
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# Upstream llama.cpp binary (b10121).
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LLAMA_SERVER=/home/dank/llama.cpp/build/bin/llama-server
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ALIAS=frank-default-small
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BIND_ADDR=0.0.0.0
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LISTEN_PORT=52625
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LISTEN_PORT=7770
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# Serving: 128K context (user request, 2026-08-18; model card default is
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# 64K), single slot, f16 KV, context checkpoints, prompt cache. Native MTP
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# depth-4. KV at 128K is 8 GiB (64 KiB/token); estimated total ~43 GiB of
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# the 64 GiB SVM pool, leaving ~21 GiB headroom.
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CTX_SIZE=131072
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PARALLEL=1
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BATCH_SIZE=2048
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UBATCH_SIZE=2048
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# 4 parallel slots x ~175104 ctx each = ~700000 total context pool.
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# Dual-f16 KV at 700000 cells leaves ~2.3 GiB free on the 3090.
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CTX_SIZE=700000
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PARALLEL=4
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# Spec: ngram-mod (n_min=48, n_max=64, n_match=24).
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SPEC_TYPE=ngram-mod
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NGRAM_MOD_N_MIN=48
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NGRAM_MOD_N_MAX=64
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NGRAM_MOD_N_MATCH=24
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# Expert offload: 36 of 40 layers keep MoE weights on CPU; 4 on GPU.
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N_CPU_MOE=36
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N_GPU_LAYERS=all
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# KV cache on GPU, both K and V at f16.
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CACHE_TYPE_K=f16
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CACHE_TYPE_V=f16
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CTX_CHECKPOINTS=32
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CACHE_RAM=8192
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# Native MTP draft (depth-4, greedy, ROCm-safe CPU argmax fast path).
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SPEC_DRAFT_N_MAX=4
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SPEC_DRAFT_N_MIN=0
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SPEC_DRAFT_P_MIN=0.0
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SPEC_DRAFT_P_SPLIT=0.10
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# Sampling (model card defaults).
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TEMPERATURE=0.7
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TOP_P=0.95
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# Thinking enabled (user request, 2026-08-18). deepseek format moves the
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# <think> reasoning into message.reasoning_content (OpenAI-compatible);
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# none would leave the thought tags unparsed in content.
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REASONING=on
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REASONING_FORMAT=deepseek
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VERBOSITY=4
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# Store SWA/context checkpoints in RAM to hold peak GPU memory down.
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CTX_CHECKPOINTS=4
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# Big prefill batches (ubatch 3072 is the top prefill tier).
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BATCH_SIZE=8192
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UBATCH_SIZE=3072
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# Eager RAM load (MoE experts on CPU; ~31 GB RAM).
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NO_MMAP=1
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# Sampling (HauhauCS Qwen3.6-35B-A3B 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 7 so other processes keep a core.
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THREADS=7
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THREADS_HTTP=8
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