=========== embedding ===========

docker run -d --gpus all \
  --name tei-embedding \
  -p 16001:80 \
  -v /DATA/exlink/models:/data \
  --pull never \
  --restart always \
  ghcr.io/huggingface/text-embeddings-inference:hopper-1.8 \
  --model-id /data/qwen3-embedding-8b \
  --port 80 \
  --max-batch-tokens 40960


curl 127.0.0.1:16001/embed \
  -X POST \
  -d '{"inputs":"안녕하세요, 임베딩 테스트 중입니다."}' \
  -H 'Content-Type: application/json'
=========== embedding ===========

=========== reranker ===========

docker rm -f vllm-reranker

docker rm -f vllm-reranker

docker run -d --gpus all \
  --name vllm-reranker \
  -p 16002:8000 \
  -v /DATA/exlink/models:/data \
  --ipc=host \
  --restart always \
  -e HF_HUB_OFFLINE=1 \
  -e TRANSFORMERS_OFFLINE=1 \
  vllm/vllm-openai:latest \
  python3 /data/reranker_server.py

curl http://127.0.0.1:16002/v1/rerank \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{
"model": "/data/qwen3-reranker-4b",
"query": "사과는 어떤 과일인가요?",
"documents": [
"사과는 빨간색 과일이며 비타민이 풍부합니다.",
"서울은 대한민국의 수도입니다."
]
}'
=========== reranker ===========


=========== LLM ===========

docker run -d --gpus all \ 
  --name vllm-server \ 
  --memory 32g \ 
  -p 16000:8000 \ 
  -v /DATA/exlink/models:/data \ 
  --ipc=host \ --restart always \ 
  --network bridge \ 
  -e HF_HUB_OFFLINE=1 \ 
  -e HF_HOME=/data/cache \ 
  -e TRANSFORMERS_OFFLINE=1 \ 
  --entrypoint python3 \ 
  vllm/vllm-openai:latest \ 
  -m vllm.entrypoints.openai.api_server \ 
  --model /data/Qwen3.5-27B-FP8 \ 
  --host 0.0.0.0 \ 
  --port 8000 \ 
  --gpu-memory-utilization 0.65 \ 
  --max-model-len 5120 \ 
  --max-num-seqs 8 \ 
  --enable-chunked-prefill \ 
  --max-num-batched-tokens 4096 \ 

  =========== LLM ===========