3d62430fd7f1c573b366b1e77d2c4ffb0a46c9bf
天数智芯 天垓100 文本生成引擎(基于 vLLM 优化适配Qwen3.6-35B-A3B)
# 本地构建
docker build -t enginex-iluvatar-vllm:bi100-qwen3.6 -f Dockerfile .
启动容器镜像
下载Qwen3.6-35B-A3B模型,并且需要将模型的config.json文件中architectures字段改成
"architectures": [
"Qwen3_5MoeForCausalLM"
]
docker run -dit --network=host --ipc=host \
-v /usr/src:/usr/src -v /lib/modules:/lib/modules -v /dev:/dev --privileged \
-v /mnt/disk1/models/Qwen3.6-35B-A3B:/model:ro --entrypoint=python3 \
-e CUDA_VISIBLE_DEVICES=4,5,6,7 -e VLLM_ENGINE_ITERATION_TIMEOUT_S=3600 \
enginex-iluvatar-vllm:bi100-qwen3.6 \
-m vllm.entrypoints.openai.api_server \
--model /model --port 1111 --served-model-name llm \
--max-model-len 100000 --trust-remote-code -tp 4 --gpu-memory-utilization 0.95 \
--max-num-seqs 1 --disable-log-requests --disable-frontend-multiprocessing \
--max-num-batched-tokens 4096 --enable-chunked-prefill \
--max-seq-len-to-capture 32768 --enable-auto-tool-choice \
--tool-call-parser qwen3_coder --reasoning-parser qwen3
请求
curl http://localhost:1111/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llm",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Can you tell me the story of Snow White?"}
],
"max_tokens": 200,
"temperature": 0.7
}'
Description
Languages
Python
99.8%
Shell
0.1%
C
0.1%