From 2a7571abdc0384c7c0d204fb10ad7a56f3eafb76 Mon Sep 17 00:00:00 2001
From: Frank <frank@dankware>
Date: Mon, 10 Aug 2026 21:12:48 -0700
Subject: [PATCH] chore: remove stale root-level duplicates of scripts/ files

- validate-vllm.sh (root) superseded by scripts/validate-vllm.sh
- vllm-qwen36.sh superseded by scripts/serve-vllm-qwen36-dflash.sh
---
 validate-vllm.sh | 85 ------------------------------------------------
 vllm-qwen36.sh   | 31 ------------------
 2 files changed, 116 deletions(-)
 delete mode 100755 validate-vllm.sh
 delete mode 100755 vllm-qwen36.sh

diff --git a/validate-vllm.sh b/validate-vllm.sh
deleted file mode 100755
index e97e163..0000000
--- a/validate-vllm.sh
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/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
-
-echo "=== Validating vLLM on ${HOST}:${PORT} ==="
-
-# Test health endpoint
-echo "Health check..."
-if ! curl -sf "http://${HOST}:${PORT}/health" > /dev/null 2>&1; then
-    echo "FAIL: Health check failed"
-    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": "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 ))
-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}:${PORT}/v1/chat/completions" \
-        -H "Content-Type: application/json" \
-        -d '{
-            "model": "frank-default-small",
-            "messages": [
-                {"role": "user", "content": "Explain the difference between supervised, unsupervised, and reinforcement learning in machine learning. For each type, provide a concrete real-world example, describe the training process, and discuss the advantages and limitations. Then compare them across three dimensions: data requirements, interpretability of results, and computational cost. Finally, describe a hybrid scenario where combining two of these approaches would outperform using either alone."}
-            ],
-            "max_tokens": 256
-        }' > "$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 "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 ""
-
-# 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 ${PARALLEL_SPEED} tok/s is below 5 tok/s threshold"
-    exit 1
-fi
diff --git a/vllm-qwen36.sh b/vllm-qwen36.sh
deleted file mode 100755
index 5426845..0000000
--- a/vllm-qwen36.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-MODEL="Qwen/Qwen3.6-35B-A3B-FP8"
-PORT=8001
-HOST="0.0.0.0"
-
-exec docker run --rm --name vllm-qwen36 \
-    --gpus all \
-    --privileged \
-    --ipc=host \
-    -p "${PORT}:${PORT}" \
-    -e PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
-    -v /home/dank/.cache/huggingface:/root/.cache/huggingface \
-    docker.io/vllm/vllm-openai:latest \
-    "${MODEL}" \
-    --port "${PORT}" \
-    --host "${HOST}" \
-    --kv-cache-dtype fp8 \
-    --gpu-memory-utilization 0.95 \
-    --max-num-seqs 4 \
-    --max-model-len 16384 \
-    --max-num-batched-tokens 4096 \
-    --trust-remote-code \
-    --cpu-offload-gb 30 \
-    --cpu-offload-params "experts" \
-    --enable-chunked-prefill \
-    --enable-prefix-caching \
-    --served-model-name frank-default-small \
-    --enable-auto-tool-choice \
-    --tool-call-parser qwen3_xml