Use --alias frank-default-large, multi-stream validation script

- Replace --override-kv with --alias for model naming
- Validation script tests 1, 2, 3 parallel streams sequentially
- Model name confirmed: frank-default-large
This commit is contained in:
Frank
2026-08-07 17:15:09 -07:00
parent 471e963350
commit a5f72627bb
3 changed files with 63 additions and 63 deletions
+1 -1
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@@ -6,7 +6,7 @@ After=network.target
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 131072 --batch-size 4096 --port 8080 --host 0.0.0.0 --threads 16 --load-mode none
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 --alias frank-default-large --load-mode none
Restart=on-failure
RestartSec=10
+1
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@@ -39,4 +39,5 @@ exec "${SERVER_BIN}" \
--port "${PORT}" \
--host "0.0.0.0" \
--threads 16 \
--alias frank-default-large \
--load-mode none
+61 -62
View File
@@ -1,11 +1,12 @@
#!/usr/bin/env bash
# Validation test for llama.cpp DFlash server
# Sends 2 parallel prompts and measures generation speed (tokens/sec)
# 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}"
@@ -26,78 +27,76 @@ for i in $(seq 1 300); do
sleep 2
done
# Send two parallel requests and capture timing
echo ""
echo "Sending 2 parallel validation prompts..."
START_TIME=$(date +%s%N)
# Test function: send N parallel prompts and measure speed
run_test() {
local num_streams=$1
local tmpfiles=()
local pids=()
TMP1=$(mktemp)
TMP2=$(mktemp)
trap "rm -f $TMP1 $TMP2" EXIT
echo ""
echo "--- Testing ${num_streams} parallel stream(s) ---"
START_TIME=$(date +%s%N)
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\": ${MAX_TOKENS},
\"temperature\": 0.7,
\"top_p\": 0.95
}" > "$TMP1" &
PID1=$!
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\": \"Write a detailed analysis of the geopolitical consequences of the fall of the Byzantine Empire in 1453. Trace the chain of events from the Ottoman conquest of Constantinople through the next two centuries, covering at minimum: the shift in Mediterranean trade routes, the Italian Renaissance's relationship with fleeing Greek scholars, the Russian claim to Roman succession, and the papal response. Then compare how the region might have developed differently had Constantinople remained independent.\"}
],
\"max_tokens\": ${MAX_TOKENS},
\"temperature\": 0.7,
\"top_p\": 0.95
}" > "$TMP2" &
PID2=$!
curl -s \
-X POST "http://${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 $PID1 $PID2
wait "${pids[@]}"
END_TIME=$(date +%s%N)
END_TIME=$(date +%s%N)
TOTAL_TIME_MS=$(( (END_TIME - START_TIME) / 1000000 ))
# Extract token counts and timing from responses
COMPLETION_TOKENS1=$(python3 -c "import json; d=json.load(open('$TMP1')); print(d['usage']['completion_tokens'])" 2>/dev/null || echo "0")
COMPLETION_TOKENS2=$(python3 -c "import json; d=json.load(open('$TMP2')); print(d['usage']['completion_tokens'])" 2>/dev/null || echo "0")
TOTAL_TOKENS=$((COMPLETION_TOKENS1 + COMPLETION_TOKENS2))
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 or response parse error"
echo "Response1: $(cat $TMP1)"
echo "Response2: $(cat $TMP2)"
exit 1
fi
if [ "$TOTAL_TOKENS" -eq 0 ]; then
echo "FAIL: No tokens generated"
return 1
fi
# Calculate tokens/sec
TOK_PER_SEC=$(python3 -c "print(f'{${TOTAL_TOKENS} * 1000 / ${TOTAL_TIME_MS}:.2f}')")
TOK_PER_SEC=$(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 "=== Results ==="
echo "Prompt 1 tokens: ${COMPLETION_TOKENS1}"
echo "Prompt 2 tokens: ${COMPLETION_TOKENS2}"
echo "Total tokens: ${TOTAL_TOKENS}"
echo "Total time: ${TOTAL_TIME_MS}ms"
echo "Speed: ${TOK_PER_SEC} tok/s"
echo ""
# Check minimum speed threshold
MIN_SPEED="${MIN_SPEED:-5.0}"
PASS=$(python3 -c "print('yes' if ${TOK_PER_SEC} >= ${MIN_SPEED} else 'no')")
if [ "$PASS" = "yes" ]; then
echo "PASS: Speed ${TOK_PER_SEC} tok/s >= ${MIN_SPEED} tok/s threshold"
echo "=== Summary ==="
if [ "$ALL_PASS" = "yes" ]; then
echo "ALL TESTS PASSED"
exit 0
else
echo "FAIL: Speed ${TOK_PER_SEC} tok/s < ${MIN_SPEED} tok/s threshold"
echo "SOME TESTS FAILED"
exit 1
fi