Enable DFlash speculative decoding, remove mlock
- Add --spec-type draft-dflash (was missing, DFlash was inactive) - Change --load-mode from mlock to none (RLIMIT_MEMLOCK too small for 2.7GB) - Increase validation max_tokens from 256 to 1024 with complex ML prompt - Baseline: 18.76 tok/s (was 7.62 tok/s without DFlash)
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@@ -6,7 +6,7 @@ After=network.target
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Type=simple
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User=dank
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Group=dank
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ExecStart=/home/dank/llama.cpp/build/bin/llama-server --hf-repo unsloth/Qwen3.6-27B-MTP-GGUF:Q8_0 --spec-draft-hf Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q8_0 --spec-draft-n-max 16 --device ROCm0 --spec-draft-device ROCm0 --gpu-layers all --fit on --fit-target 512 --parallel 3 --ctx-size 131072 --batch-size 4096 --port 8080 --host 0.0.0.0 --threads 16 --load-mode mlock
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ExecStart=/home/dank/llama.cpp/build/bin/llama-server --hf-repo unsloth/Qwen3.6-27B-MTP-GGUF:Q8_0 --spec-draft-hf Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q8_0 --spec-type draft-dflash --spec-draft-n-max 16 --device ROCm0 --spec-draft-device ROCm0 --gpu-layers all --fit on --fit-target 512 --parallel 3 --ctx-size 131072 --batch-size 4096 --port 8080 --host 0.0.0.0 --threads 16 --load-mode none
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Restart=on-failure
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RestartSec=10
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@@ -26,6 +26,7 @@ echo ""
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exec "${SERVER_BIN}" \
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--hf-repo "${TARGET_REPO}" \
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--spec-draft-hf "${DRAFT_REPO}" \
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--spec-type draft-dflash \
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--spec-draft-n-max "${DRAFT_N_MAX}" \
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--device ROCm0 \
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--spec-draft-device ROCm0 \
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@@ -38,4 +39,4 @@ exec "${SERVER_BIN}" \
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--port "${PORT}" \
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--host "0.0.0.0" \
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--threads 16 \
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--load-mode mlock
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--load-mode none
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+2
-2
@@ -5,7 +5,7 @@ set -euo pipefail
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PORT="${LLAMA_PORT:-8080}"
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HOST="${LLAMA_HOST:-127.0.0.1}"
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MAX_TOKENS="${MAX_TOKENS:-256}"
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MAX_TOKENS="${MAX_TOKENS:-1024}"
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echo "=== llama.cpp Validation Test ==="
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echo "Target: http://${HOST}:${PORT}"
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@@ -36,7 +36,7 @@ RESPONSE=$(curl -s \
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-H "Content-Type: application/json" \
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-d "{
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\"messages\": [
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{\"role\": \"user\", \"content\": \"What is 2+2? Answer in one sentence.\"}
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{\"role\": \"user\", \"content\": \"Explain the difference between supervised, unsupervised, and reinforcement learning in machine learning. For each type, provide a concrete real-world example, describe the training process, and discuss the advantages and limitations. Then compare them across three dimensions: data requirements, interpretability of results, and computational cost. Finally, describe a hybrid scenario where combining two of these approaches would outperform using either alone.\"}
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],
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\"max_tokens\": ${MAX_TOKENS},
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\"temperature\": 0.7,
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