Update validation script to use frank-default-small model name

validate-vllm.sh

@@ -1,10 +1,11 @@
 #!/usr/bin/env bash
-# End-to-end validation test for vLLM Qwen3.6-35B-A3B-FP8 on dankserver
+# End-to-end validation test for vLLM Qwen3.6-35B-A3B-FP8
-# Measures prompt processing speed (tokens/second)
+# Tests single stream and 4 parallel streams
 set -euo pipefail
 
 HOST="${1:-127.0.0.1}"
 PORT="${2:-8001}"
+NUM_STREAMS=4
 
 echo "=== Validating vLLM on ${HOST}:${PORT} ==="
 
@@ -15,54 +16,70 @@ if ! curl -sf "http://${HOST}:${PORT}/health" > /dev/null 2>&1; then
     exit 1
 fi
 
-# Test chat completions with timing
+# Single stream test
-echo "Running prompt 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",
+        "model": "frank-default-small",
         "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 ))
-
-# Extract metrics from response
 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'])")
-CONTENT=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['choices'][0]['message']['content'][:100])")
+SINGLE_SPEED=$(python3 -c "print(f'{${PROMPT_TOKENS}/${ELAPSED_MS}*1000:.2f}')")
+echo "Single stream: ${SINGLE_SPEED} tok/s (prompt)"
 
-# Calculate speed
+# Parallel streams test
-if [ "$ELAPSED_MS" -gt 0 ]; then
+echo "Parallel streams test (${NUM_STREAMS} streams)..."
-    PROMPT_SPEED=$(python3 -c "print(f'{${PROMPT_TOKENS}/${ELAPSED_MS}*1000:.2f}')")
+START=$(date +%s%N)
-    COMPLETION_SPEED=$(python3 -c "print(f'{${COMPLETION_TOKENS}/${ELAPSED_MS}*1000:.2f}')")
+TMPDIR=$(mktemp -d)
-else
+for i in $(seq 1 $NUM_STREAMS); do
-    PROMPT_SPEED="N/A"
+    curl -s -X POST "http://${HOST}:${PORT}/v1/chat/completions" \
-    COMPLETION_SPEED="N/A"
+        -H "Content-Type: application/json" \
-fi
+        -d '{
+            "model": "frank-default-small",
+            "messages": [
+                {"role": "user", "content": "Explain the difference between supervised, unsupervised, and reinforcement learning in machine learning. For each type, provide a concrete real-world example, describe the training process, and discuss the advantages and limitations. Then compare them across three dimensions: data requirements, interpretability of results, and computational cost. Finally, describe a hybrid scenario where combining two of these approaches would outperform using either alone."}
+            ],
+            "max_tokens": 256
+        }' > "$TMPDIR/$i.json" &
+done
+wait
+END=$(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 "Prompt tokens:      ${PROMPT_TOKENS}"
+echo "Single stream prompt speed:   ${SINGLE_SPEED} tok/s"
-echo "Completion tokens:  ${COMPLETION_TOKENS}"
+echo "Parallel streams prompt speed: ${PARALLEL_SPEED} tok/s (${NUM_STREAMS} streams, ${TOTAL_PROMPT} total prompt tokens)"
-echo "Total time:         ${ELAPSED_MS}ms"
+echo "Parallel completion speed:    $(python3 -c "print(f'{${TOTAL_COMPLETION}/${ELAPSED_MS}*1000:.2f}')") tok/s"
-echo "Prompt speed:       ${PROMPT_SPEED} tok/s"
+echo "Total parallel time:          ${ELAPSED_MS}ms"
-echo "Completion speed:   ${COMPLETION_SPEED} tok/s"
-echo "Sample output:      ${CONTENT}..."
 echo ""
 
-# Fail if prompt speed < 5 tok/s
+# Fail if parallel speed < 5 tok/s
-if [ "$ELAPSED_MS" -gt 0 ] && [ "$PROMPT_TOKENS" -gt 0 ]; then
+if python3 -c "import sys; sys.exit(0 if ${TOTAL_PROMPT}/${ELAPSED_MS}*1000 >= 5 else 1)"; then
-    TOO_SLOW=$(python3 -c "print('yes' if ${PROMPT_TOKENS}/${ELAPSED_MS}*1000 < 5 else 'no')")
+    echo "PASS: Validation successful"
-    if [ "$TOO_SLOW" = "yes" ]; then
+    echo "$PARALLEL_SPEED" > /tmp/vllm_prompt_speed.txt
-        echo "FAIL: Prompt speed ${PROMPT_SPEED} tok/s is below 5 tok/s threshold"
+else
-        exit 1
+    echo "FAIL: Parallel prompt speed ${PARALLEL_SPEED} tok/s is below 5 tok/s threshold"
-    fi
+    exit 1
 fi
-
-echo "PASS: Validation successful"
-echo "$PROMPT_SPEED" > /tmp/vllm_prompt_speed.txt