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