Add recommended sampling defaults and reduce MoE GPU offload on dankserver
- frank-default-large: add TEMP=1.0, TOP_K=20, TOP_P=0.95, MIN_P=0.0 (per HauhauCS Qwen3.6-27B model card, thinking-general mode) - frank-default-small: add same sampling defaults (per HauhauCS Qwen3.6-35B-A3B model card) - frank-default-small: reduce N_CPU_MOE from 33 to 34 (6 expert layers on GPU instead of 7) to prevent OOM during high-load sessions - Both serve scripts pass --temp, --top-k, --top-p, --min-p to llama-server; clients can still override per-request via API
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@@ -33,5 +33,12 @@ CACHE_TYPE_V=q8_0
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# User-specified ubatch 4096 / batch 12288 is not feasible here (see bench).
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BATCH_SIZE=2048
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UBATCH_SIZE=512
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# Sampling settings — per HauhauCS Qwen3.6-27B model card.
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# Thinking-general: temp=1.0, top_p=0.95, top_k=20, min_p=0.
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# Clients may override per-request; these are the server-side defaults.
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TEMP=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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THREADS=12
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THREADS_HTTP=1
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@@ -8,15 +8,15 @@ PORT=7770
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# (Increased from 3×200k to add a 4th parallel stream.)
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CTX_SIZE=800000
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PARALLEL=4
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# Spec: ngram-mod. n_min=48, n_max=64, n_match=24 (defaults). 8 expert
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# layers back on GPU; rest on CPU. Batch 8192 / ubatch 2048.
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# Spec: ngram-mod. n_min=48, n_max=64, n_match=24 (defaults). 6 expert
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# layers back on GPU; 34 on CPU. Batch 8192 / ubatch 2048.
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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: 33 of 40 layers keep MoE weights on CPU; remaining
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# 7 layers have their expert weights resident on GPU for faster routing.
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N_CPU_MOE=33
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# Expert offload: 34 of 40 layers keep MoE weights on CPU; remaining
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# 6 layers have their expert weights resident on GPU for faster routing.
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N_CPU_MOE=34
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N_GPU_LAYERS=all
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# KV cache on GPU, K quantized to q8_0, V stays f16 (user-locked decision).
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CACHE_TYPE_K=q8_0
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@@ -31,5 +31,12 @@ UBATCH_SIZE=2048
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# CPU, so eager RAM load avoids page faults during decode (uses ~31GB RAM).
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NO_MMAP=1
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# Compute threads lowered to 7 so other processes on the box keep a core.
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# Sampling settings — per HauhauCS Qwen3.6-35B-A3B model card.
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# Thinking-general: temp=1.0, top_p=0.95, top_k=20, min_p=0.
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# Clients may override per-request; these are the server-side defaults.
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TEMP=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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THREADS=7
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THREADS_HTTP=8
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@@ -37,6 +37,10 @@ fi
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: "${CACHE_TYPE_V:=q8_0}"
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: "${BATCH_SIZE:=2048}"
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: "${UBATCH_SIZE:=512}"
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: "${TEMP:=1.0}"
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: "${TOP_K:=20}"
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: "${TOP_P:=0.95}"
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: "${MIN_P:=0.0}"
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: "${THREADS:=12}"
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: "${THREADS_HTTP:=1}"
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@@ -68,6 +72,10 @@ exec "$LLAMA_SERVER" \
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--parallel "$PARALLEL" \
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--batch-size "$BATCH_SIZE" \
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--ubatch-size "$UBATCH_SIZE" \
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--temp "$TEMP" \
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--top-k "$TOP_K" \
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--top-p "$TOP_P" \
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--min-p "$MIN_P" \
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--threads "$THREADS" \
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--threads-http "$THREADS_HTTP" \
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--alias "$ALIAS" \
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@@ -32,12 +32,16 @@ fi
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: "${NGRAM_MOD_N_MATCH:=24}"
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: "${CACHE_TYPE_K:=q8_0}"
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: "${CACHE_TYPE_V:=f16}"
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: "${N_CPU_MOE:=40}" # all 40 layers: MoE expert weights stay on CPU
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: "${N_CPU_MOE:=34}" # 34 of 40 layers: MoE expert weights stay on CPU; 6 on GPU
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: "${N_GPU_LAYERS:=all}" # every non-expert tensor goes to GPU
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: "${BATCH_SIZE:=2048}"
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: "${UBATCH_SIZE:=512}"
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: "${CTX_CHECKPOINTS:=0}"
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: "${NO_MMAP:=0}"
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: "${TEMP:=1.0}"
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: "${TOP_K:=20}"
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: "${TOP_P:=0.95}"
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: "${MIN_P:=0.0}"
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: "${THREADS:=8}" # 7700X physical cores
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: "${THREADS_HTTP:=8}"
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@@ -70,6 +74,10 @@ exec "$LLAMA_SERVER" \
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--ubatch-size "$UBATCH_SIZE" \
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--ctx-checkpoints "$CTX_CHECKPOINTS" \
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$([ "$NO_MMAP" = "1" ] && printf -- "--no-mmap") \
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--temp "$TEMP" \
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--top-k "$TOP_K" \
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--top-p "$TOP_P" \
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--min-p "$MIN_P" \
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--threads "$THREADS" \
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--threads-http "$THREADS_HTTP" \
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--alias "$ALIAS" \
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