sync scripts from remotes: ROCmFPX llama-server.service (dankclaw), dankserver vLLM serve/validate; drop failed-attempt scripts
- llama-server.service: dankclaw migrated to ROCmFPX runtime (Ling-3.0-flash, draft-mtp, no-mmap/direct-io) from regular llama.cpp Qwen3.6-27B-DFlash - serve-vllm-qwen36-dflash.sh: dankserver version (relies on image 'vllm serve' entrypoint; HF cache mounted read-write) - validate-vllm.sh: dankserver multi-stream vLLM validation (4 unique prompts, 1-4 streams, prompt+completion speed) - remove superseded scripts: serve-llamacpp-dflash.sh, validate.sh, vllm-awq-qwen36-dflash-ampere.sh, vllm-qwen36.service
This commit is contained in:
Frank
@@ -1,12 +1,12 @@
|
||||
[Unit]
|
||||
Description=llama.cpp DFlash+MTP Inference Server (Qwen3.6-27B on ROCm)
|
||||
Description=llama.cpp Inference Runtime
|
||||
After=network.target
|
||||
|
||||
[Service]
|
||||
Type=simple
|
||||
User=dank
|
||||
Group=dank
|
||||
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 600000 --batch-size 4096 --port 8080 --host 0.0.0.0 --threads 16 --alias frank-default-large --load-mode none
|
||||
ExecStart=/home/dank/GitPackages/ROCmFPX/build-strix-rocmfp4/bin/llama-server -hf raulvidis/Ling-3.0-flash-ROCmFP4-STRIX-MTP-GGUF:Q4_0 --spec-type draft-mtp --spec-draft-n-max 2 --device ROCm0 --gpu-layers all --fit on --fit-target 512 --parallel 2 --ctx-size 401400 --batch-size 8192 --ubatch-size 4096 --port 8080 --host 0.0.0.0 --threads 16 --alias frank-default-large --no-mmap --direct-io -lv 4 --timeout 7200 --chat-template-file /home/dank/LingFlash/chat_template.jinja
|
||||
Restart=on-failure
|
||||
RestartSec=10
|
||||
|
||||
|
||||
@@ -1,43 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
# Serve Qwen3.6-27B with DFlash + MTP speculative decoding via llama.cpp on AMD Strix Halo (gfx1151)
|
||||
# Uses llama.cpp built with ROCm support
|
||||
# 3 parallel streams, 131072 context each
|
||||
set -euo pipefail
|
||||
|
||||
SERVER_BIN="/home/dank/llama.cpp/build/bin/llama-server"
|
||||
PORT="${LLAMA_PORT:-8080}"
|
||||
N_PARALLEL="${LLAMA_N_PARALLEL:-3}"
|
||||
CTX_SIZE="${LLAMA_CTX_SIZE:-600000}"
|
||||
BATCH_SIZE="${LLAMA_BATCH_SIZE:-4096}"
|
||||
DRAFT_N_MAX="${LLAMA_DRAFT_N_MAX:-16}"
|
||||
|
||||
# Models via Hugging Face
|
||||
TARGET_REPO="unsloth/Qwen3.6-27B-MTP-GGUF:Q8_0"
|
||||
DRAFT_REPO="Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q8_0"
|
||||
|
||||
echo "Starting llama.cpp server..."
|
||||
echo " Target model: ${TARGET_REPO}"
|
||||
echo " Draft model: ${DRAFT_REPO}"
|
||||
echo " Parallel streams: ${N_PARALLEL}"
|
||||
echo " Context/stream: ${CTX_SIZE}"
|
||||
echo " Draft tokens: ${DRAFT_N_MAX}"
|
||||
echo ""
|
||||
|
||||
exec "${SERVER_BIN}" \
|
||||
--hf-repo "${TARGET_REPO}" \
|
||||
--spec-draft-hf "${DRAFT_REPO}" \
|
||||
--spec-type draft-dflash \
|
||||
--spec-draft-n-max "${DRAFT_N_MAX}" \
|
||||
--device ROCm0 \
|
||||
--spec-draft-device ROCm0 \
|
||||
--gpu-layers all \
|
||||
--fit on \
|
||||
--fit-target 512 \
|
||||
--parallel "${N_PARALLEL}" \
|
||||
--ctx-size "${CTX_SIZE}" \
|
||||
--batch-size "${BATCH_SIZE}" \
|
||||
--port "${PORT}" \
|
||||
--host "0.0.0.0" \
|
||||
--threads 16 \
|
||||
--alias frank-default-large \
|
||||
--load-mode none
|
||||
@@ -32,6 +32,7 @@ echo ""
|
||||
podman rm -f "${CONTAINER_NAME}" 2>/dev/null || true
|
||||
|
||||
# Run vLLM in Podman container (foreground mode for systemd)
|
||||
# Note: vllm/vllm-openai image has entrypoint "vllm serve", so we pass args directly
|
||||
exec podman run --name "${CONTAINER_NAME}" \
|
||||
--device /dev/nvidia0 \
|
||||
--device /dev/nvidiactl \
|
||||
@@ -39,10 +40,10 @@ exec podman run --name "${CONTAINER_NAME}" \
|
||||
--device /dev/nvidia-uvm-tools \
|
||||
--device /dev/nvidia-modeset \
|
||||
-v "${GPU_LIBS}:/usr/lib/x86_64-linux-gnu:ro" \
|
||||
-v "${HF_CACHE}:/root/.cache/huggingface:ro" \
|
||||
-v "${HF_CACHE}:/root/.cache/huggingface" \
|
||||
-p "${PORT}:${PORT}" \
|
||||
docker.io/vllm/vllm-openai:latest \
|
||||
vllm serve "${MODEL}" \
|
||||
"${MODEL}" \
|
||||
--port "${PORT}" \
|
||||
--host "0.0.0.0" \
|
||||
--kv-cache-dtype "${KV_CACHE_DTYPE}" \
|
||||
|
||||
+71
-68
@@ -1,11 +1,15 @@
|
||||
#!/usr/bin/env bash
|
||||
# End-to-end validation test for vLLM Qwen3.6-35B-A3B-FP8
|
||||
# Tests single stream and 4 parallel streams
|
||||
set -euo pipefail
|
||||
|
||||
HOST="${1:-127.0.0.1}"
|
||||
PORT="${2:-8001}"
|
||||
NUM_STREAMS=4
|
||||
HOST="127.0.0.1"
|
||||
PORT=8001
|
||||
MODEL="frank-default-small"
|
||||
|
||||
# Different prompts for each test to avoid prefix caching
|
||||
PROMPT1="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."
|
||||
PROMPT2="Describe the architectural differences between RISC-V and x86 instruction set architectures. Include a discussion of register files, addressing modes, and instruction encoding. Compare their performance for embedded systems and server workloads."
|
||||
PROMPT3="Analyze the economic impact of blockchain technology on supply chain management. Discuss the key benefits of transparency, immutability, and smart contracts. Compare implementation challenges of Bitcoin, Ethereum, and Hyperledger for enterprise use cases."
|
||||
PROMPT4="Explain the principles of evolutionary algorithms in artificial intelligence. Describe how genetic algorithms, genetic programming, and evolutionary strategies differ in their approach to problem solving. Compare their performance on optimization problems and neural network architecture search."
|
||||
|
||||
echo "=== Validating vLLM on ${HOST}:${PORT} ==="
|
||||
|
||||
@@ -16,70 +20,69 @@ if ! curl -sf "http://${HOST}:${PORT}/health" > /dev/null 2>&1; then
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Single stream test
|
||||
echo "Single stream test..."
|
||||
START=$(date +%s%N)
|
||||
RESPONSE=$(curl -s -X POST "http://${HOST}:${PORT}/v1/chat/completions" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "Qwen/Qwen3.6-35B-A3B-FP8",
|
||||
"messages": [
|
||||
{"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."}
|
||||
],
|
||||
"max_tokens": 256
|
||||
}')
|
||||
END=$(date +%s%N)
|
||||
ELAPSED_MS=$(( (END - START) / 1000000 ))
|
||||
PROMPT_TOKENS=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['usage']['prompt_tokens'])")
|
||||
COMPLETION_TOKENS=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['usage']['completion_tokens'])")
|
||||
SINGLE_SPEED=$(python3 -c "print(f'{${PROMPT_TOKENS}/${ELAPSED_MS}*1000:.2f}')")
|
||||
echo "Single stream: ${SINGLE_SPEED} tok/s (prompt)"
|
||||
|
||||
# Parallel streams test
|
||||
echo "Parallel streams test (${NUM_STREAMS} streams)..."
|
||||
START=$(date +%s%N)
|
||||
TMPDIR=$(mktemp -d)
|
||||
for i in $(seq 1 $NUM_STREAMS); do
|
||||
curl -s -X POST "http://${HOST}:${PORT}/v1/chat/completions" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "Qwen/Qwen3.6-35B-A3B-FP8",
|
||||
"messages": [
|
||||
{"role": "user", "content": "Explain the difference between supervised, unsupervised, and reinforcement learning in machine learning. For each type, provide a concrete real-world example, describe the training process, and discuss the advantages and limitations. Then compare them across three dimensions: data requirements, interpretability of results, and computational cost. Finally, describe a hybrid scenario where combining two of these approaches would outperform using either alone."}
|
||||
],
|
||||
"max_tokens": 256
|
||||
}' > "$TMPDIR/$i.json" &
|
||||
# Test N streams sequentially (1, 2, 3, 4)
|
||||
for NUM_STREAMS in 1 2 3 4; do
|
||||
echo ""
|
||||
echo "--- Testing ${NUM_STREAMS} stream(s) ---"
|
||||
|
||||
TMPDIR=$(mktemp -d)
|
||||
START=$(date +%s%N)
|
||||
|
||||
for i in $(seq 1 $NUM_STREAMS); do
|
||||
case $(( (i - 1) % 4 + 1 )) in
|
||||
1) PROMPT="${PROMPT1}" ;;
|
||||
2) PROMPT="${PROMPT2}" ;;
|
||||
3) PROMPT="${PROMPT3}" ;;
|
||||
4) PROMPT="${PROMPT4}" ;;
|
||||
esac
|
||||
|
||||
curl -s -X POST "http://${HOST}:${PORT}/v1/chat/completions" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d "{
|
||||
\"model\": \"${MODEL}\",
|
||||
\"messages\": [
|
||||
{\"role\": \"user\", \"content\": \"${PROMPT}\"}
|
||||
],
|
||||
\"max_tokens\": 256
|
||||
}" > "$TMPDIR/$i.json" &
|
||||
done
|
||||
wait
|
||||
|
||||
END=$(date +%s%N)
|
||||
ELAPSED_MS=$(( (END - START) / 1000000 ))
|
||||
|
||||
# Calculate speed from the first stream's response
|
||||
PROMPT_TOKENS=$(python3 -c "import json; print(json.load(open('$TMPDIR/1.json'))['usage']['prompt_tokens'])")
|
||||
COMPLETION_TOKENS=$(python3 -c "import json; print(json.load(open('$TMPDIR/1.json'))['usage']['completion_tokens'])")
|
||||
|
||||
# Calculate speeds
|
||||
PROMPT_SPEED=$(python3 -c "print(f'{${PROMPT_TOKENS}/${ELAPSED_MS}*1000:.2f}')")
|
||||
COMPLETION_SPEED=$(python3 -c "print(f'{${COMPLETION_TOKENS}/${ELAPSED_MS}*1000:.2f}')")
|
||||
|
||||
echo " Elapsed: ${ELAPSED_MS}ms"
|
||||
echo " Prompt tokens: ${PROMPT_TOKENS}"
|
||||
echo " Prompt speed: ${PROMPT_SPEED} tok/s"
|
||||
echo " Completion tokens: ${COMPLETION_TOKENS}"
|
||||
echo " Completion speed: ${COMPLETION_SPEED} tok/s"
|
||||
|
||||
# Save results
|
||||
echo "${NUM_STREAMS},${PROMPT_SPEED},${COMPLETION_SPEED},${ELAPSED_MS}" > "/tmp/vllm-${NUM_STREAMS}.csv"
|
||||
|
||||
# Clean up temp files
|
||||
rm -rf "$TMPDIR"
|
||||
done
|
||||
wait
|
||||
END=$(date +%s%N)
|
||||
ELAPSED_MS=$(( (END - START) / 1000000 ))
|
||||
|
||||
# Aggregate results from all streams
|
||||
TOTAL_PROMPT=0
|
||||
TOTAL_COMPLETION=0
|
||||
for i in $(seq 1 $NUM_STREAMS); do
|
||||
PT=$(python3 -c "import json; print(json.load(open('$TMPDIR/$i.json'))['usage']['prompt_tokens'])")
|
||||
CT=$(python3 -c "import json; print(json.load(open('$TMPDIR/$i.json'))['usage']['completion_tokens'])")
|
||||
TOTAL_PROMPT=$((TOTAL_PROMPT + PT))
|
||||
TOTAL_COMPLETION=$((TOTAL_COMPLETION + CT))
|
||||
done
|
||||
rm -rf "$TMPDIR"
|
||||
|
||||
PARALLEL_SPEED=$(python3 -c "print(f'{${TOTAL_PROMPT}/${ELAPSED_MS}*1000:.2f}')")
|
||||
|
||||
echo ""
|
||||
echo "=== Results ==="
|
||||
echo "Single stream prompt speed: ${SINGLE_SPEED} tok/s"
|
||||
echo "Parallel streams prompt speed: ${PARALLEL_SPEED} tok/s (${NUM_STREAMS} streams, ${TOTAL_PROMPT} total prompt tokens)"
|
||||
echo "Parallel completion speed: $(python3 -c "print(f'{${TOTAL_COMPLETION}/${ELAPSED_MS}*1000:.2f}')") tok/s"
|
||||
echo "Total parallel time: ${ELAPSED_MS}ms"
|
||||
echo ""
|
||||
echo "=== Results Summary ==="
|
||||
for n in 1 2 3 4; do
|
||||
if [ -f "/tmp/vllm-${n}.csv" ]; then
|
||||
IFS=',' read -r streams p_speed c_speed elapsed < "/tmp/vllm-${n}.csv"
|
||||
echo " ${n} stream(s): ${p_speed} tok/s (prompt), ${c_speed} tok/s (completion) - ${elapsed}ms"
|
||||
fi
|
||||
done
|
||||
|
||||
# Fail if parallel speed < 5 tok/s
|
||||
if python3 -c "import sys; sys.exit(0 if ${TOTAL_PROMPT}/${ELAPSED_MS}*1000 >= 5 else 1)"; then
|
||||
echo "PASS: Validation successful"
|
||||
echo "$PARALLEL_SPEED" > /tmp/vllm_prompt_speed.txt
|
||||
else
|
||||
echo "FAIL: Parallel prompt speed ${PARALLEL_SPEED} tok/s is below 5 tok/s threshold"
|
||||
exit 1
|
||||
fi
|
||||
# Check if single stream speed is >= 5 tok/s
|
||||
SINGLE_SPEED=$(head -1 /tmp/vllm-1.csv | cut -d',' -f2)
|
||||
python3 -c "import sys; sys.exit(0 if float('${SINGLE_SPEED}') >= 5 else 1)"
|
||||
echo ""
|
||||
echo "PASS: Validation successful"
|
||||
|
||||
@@ -1,102 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
# Validation test for llama.cpp DFlash server
|
||||
# Tests 1, 2, and 3 parallel prompts sequentially
|
||||
set -euo pipefail
|
||||
|
||||
PORT="${LLAMA_PORT:-8080}"
|
||||
HOST="${LLAMA_HOST:-127.0.0.1}"
|
||||
MAX_TOKENS="${MAX_TOKENS:-1024}"
|
||||
MIN_SPEED="${MIN_SPEED:-5.0}"
|
||||
|
||||
echo "=== llama.cpp Validation Test ==="
|
||||
echo "Target: http://${HOST}:${PORT}"
|
||||
echo "Max tokens: ${MAX_TOKENS}"
|
||||
echo ""
|
||||
|
||||
# Wait for server to be ready
|
||||
echo "Waiting for server..."
|
||||
for i in $(seq 1 300); do
|
||||
if curl -sf "http://${HOST}:${PORT}/health" > /dev/null 2>&1; then
|
||||
echo "Server ready after $((i * 2))s"
|
||||
break
|
||||
fi
|
||||
if [ "$i" -eq 300 ]; then
|
||||
echo "FAIL: Server did not become ready in 600s"
|
||||
exit 1
|
||||
fi
|
||||
sleep 2
|
||||
done
|
||||
|
||||
# Test function: send N parallel prompts and measure speed
|
||||
run_test() {
|
||||
local num_streams=$1
|
||||
local tmpfiles=()
|
||||
local pids=()
|
||||
|
||||
echo ""
|
||||
echo "--- Testing ${num_streams} parallel stream(s) ---"
|
||||
START_TIME=$(date +%s%N)
|
||||
|
||||
for s in $(seq 1 "$num_streams"); do
|
||||
tmpf=$(mktemp)
|
||||
tmpfiles+=("$tmpf")
|
||||
|
||||
curl -s \
|
||||
-X POST "http://${HOST}:${PORT}/v1/chat/completions" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d "{
|
||||
\"messages\": [
|
||||
{\"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.\"}
|
||||
],
|
||||
\"max_tokens\": ${MAX_TOKENS},
|
||||
\"temperature\": 0.7,
|
||||
\"top_p\": 0.95
|
||||
}" > "$tmpf" &
|
||||
pids+=($!)
|
||||
done
|
||||
|
||||
wait "${pids[@]}"
|
||||
|
||||
END_TIME=$(date +%s%N)
|
||||
TOTAL_TIME_MS=$(( (END_TIME - START_TIME) / 1000000 ))
|
||||
|
||||
TOTAL_TOKENS=0
|
||||
for tmpf in "${tmpfiles[@]}"; do
|
||||
tokens=$(python3 -c "import json; d=json.load(open('$tmpf')); print(d['usage']['completion_tokens'])" 2>/dev/null || echo "0")
|
||||
TOTAL_TOKENS=$((TOTAL_TOKENS + tokens))
|
||||
rm -f "$tmpf"
|
||||
done
|
||||
|
||||
if [ "$TOTAL_TOKENS" -eq 0 ]; then
|
||||
echo "FAIL: No tokens generated"
|
||||
return 1
|
||||
fi
|
||||
|
||||
TOK_PER_SEC=$(python3 -c "print(f'{${TOTAL_TOKENS} * 1000 / ${TOTAL_TIME_MS}:.2f}')")
|
||||
echo " Tokens: ${TOTAL_TOKENS} / ${TOTAL_TIME_MS}ms = ${TOK_PER_SEC} tok/s"
|
||||
|
||||
PASS=$(python3 -c "print('yes' if ${TOK_PER_SEC} >= ${MIN_SPEED} else 'no')")
|
||||
if [ "$PASS" = "yes" ]; then
|
||||
echo " PASS: >= ${MIN_SPEED} tok/s"
|
||||
else
|
||||
echo " FAIL: < ${MIN_SPEED} tok/s"
|
||||
fi
|
||||
}
|
||||
|
||||
# Run tests with 1, 2, and 3 parallel streams
|
||||
ALL_PASS="yes"
|
||||
for n in 1 2 3; do
|
||||
if ! run_test "$n"; then
|
||||
ALL_PASS="no"
|
||||
fi
|
||||
done
|
||||
|
||||
echo ""
|
||||
echo "=== Summary ==="
|
||||
if [ "$ALL_PASS" = "yes" ]; then
|
||||
echo "ALL TESTS PASSED"
|
||||
exit 0
|
||||
else
|
||||
echo "SOME TESTS FAILED"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,44 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
# vllm-awq-qwen36-dflash-ampere.sh
|
||||
#
|
||||
# Qwen3.6-35B-A3B AWQ 4bit with DFlash speculative decoding.
|
||||
# Optimized for Ampere-class GPUs.
|
||||
#
|
||||
# Model: cyankiwi/Qwen3.6-35B-A3B-AWQ-4bit
|
||||
# Draft: z-lab/Qwen3.6-35B-A3B-DFlash
|
||||
# Port: 34401
|
||||
#
|
||||
set -euo pipefail
|
||||
|
||||
MODEL="Avesed/Qwen3.6-35B-A3B-INT8-W8A8"
|
||||
PORT=34401
|
||||
|
||||
exec docker run --rm --name vllm-awq-qwen36-dflash-ampere \
|
||||
--gpus all \
|
||||
--privileged --ipc=host \
|
||||
-p "${PORT}:8000" \
|
||||
-e OMP_NUM_THREADS=8 \
|
||||
-e VLLM_CPU_OMP_THREADS_BIND=8 \
|
||||
-e PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
|
||||
-e VLLM_USE_V2_MODEL_RUNNER=0 \
|
||||
-v ~/.cache/huggingface:/root/.cache/huggingface \
|
||||
vllm/vllm-openai:nightly "${MODEL}" \
|
||||
--served-model-name "frank-default-small" \
|
||||
--max-num-seqs 3 \
|
||||
--max-model-len 200700 \
|
||||
--tensor-parallel-size 1 \
|
||||
--enable-chunked-prefill \
|
||||
--enable-prefix-caching \
|
||||
--max-num-batched-tokens 3072 \
|
||||
--performance-mode throughput \
|
||||
--enable-auto-tool-choice --tool-call-parser qwen3_xml \
|
||||
--reasoning-parser qwen3 \
|
||||
--trust-remote-code \
|
||||
--gpu-memory-utilization 0.97 \
|
||||
--kv-cache-dtype auto \
|
||||
--cpu-offload-gb 30 \
|
||||
--cpu-offload-params "experts" \
|
||||
--default-chat-template-kwargs '{"enable_thinking": true, "preserve_thinking": true}' \
|
||||
-O3 \
|
||||
--speculative-config '{"method": "mtp", "num_speculative_tokens": 2}'
|
||||
|
||||
@@ -1,24 +0,0 @@
|
||||
[Unit]
|
||||
Description=vLLM Qwen3.6-35B-A3B-FP8 with DFlash Drafting (Podman)
|
||||
After=network-online.target
|
||||
Wants=network-online.target
|
||||
|
||||
[Service]
|
||||
Type=simple
|
||||
User=dank
|
||||
Group=dank
|
||||
WorkingDirectory=/home/dank/Projects/vllmScripts
|
||||
Environment="PORT=8000"
|
||||
Environment="GPU_MEM_UTIL=0.95"
|
||||
Environment="MAX_NUM_SEQS=4"
|
||||
Environment="MAX_MODEL_LEN=262144"
|
||||
Environment="KV_CACHE_DTYPE=fp8"
|
||||
Environment="NUM_SPEC_TOKENS=8"
|
||||
ExecStart=/home/dank/Projects/vllmScripts/scripts/serve-vllm-qwen36-dflash.sh
|
||||
ExecStop=/usr/bin/podman stop vllm-qwen36-dflash
|
||||
ExecStopPost=/usr/bin/podman rm -f vllm-qwen36-dflash
|
||||
Restart=on-failure
|
||||
RestartSec=5
|
||||
|
||||
[Install]
|
||||
WantedBy=multi-user.target
|
||||
Reference in New Issue
Block a user