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93 lines
7.5 KiB
Markdown
93 lines
7.5 KiB
Markdown
## 支持的模型
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|模型名|上下文长度|量化|所需卡数|部署命令|最低版本要求|
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|ERNIE-4.5-300B-A47B|32K|WINT8|8|export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 8 \ <br> --max-model-len 32768 \ <br> --max-num-seqs 64 \ <br> --quantization "wint8" \ <br> --gpu-memory-utilization 0.9|>=2.0.3|
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|ERNIE-4.5-300B-A47B|32K|WINT4|4 （推荐）|export XPU_VISIBLE_DEVICES="0,1,2,3" or "4,5,6,7"<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 4 \ <br> --max-model-len 32768 \ <br> --max-num-seqs 64 \ <br> --quantization "wint4" \ <br> --gpu-memory-utilization 0.9|>=2.0.0|
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|ERNIE-4.5-300B-A47B|32K|WINT4|8|export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 8 \ <br> --max-model-len 32768 \ <br> --max-num-seqs 64 \ <br> --quantization "wint4" \ <br> --gpu-memory-utilization 0.9|>=2.0.0|
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|ERNIE-4.5-300B-A47B|128K|WINT4|8 （推荐）|export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 8 \ <br> --max-model-len 131072 \ <br> --max-num-seqs 64 \ <br> --quantization "wint4" \ <br> --gpu-memory-utilization 0.9|>=2.0.0|
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|ERNIE-4.5-21B-A3B|32K|BF16|1|export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 32768 \ <br> --max-num-seqs 128 \ <br> --gpu-memory-utilization 0.9|>=2.1.0|
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|ERNIE-4.5-21B-A3B|32K|WINT8|1|export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 32768 \ <br> --max-num-seqs 128 \ <br> --quantization "wint8" \ <br> --gpu-memory-utilization 0.9|>=2.1.0|
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|ERNIE-4.5-21B-A3B|32K|WINT4|1|export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 32768 \ <br> --max-num-seqs 128 \ <br> --quantization "wint4" \ <br> --gpu-memory-utilization 0.9|>=2.1.0|
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|ERNIE-4.5-21B-A3B|128K|BF16|1|export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 131072 \ <br> --max-num-seqs 128 \ <br> --gpu-memory-utilization 0.9|>=2.1.0|
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|ERNIE-4.5-21B-A3B|128K|WINT8|1|export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 131072 \ <br> --max-num-seqs 128 \ <br> --quantization "wint8" \ <br> --gpu-memory-utilization 0.9|>=2.1.0|
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|ERNIE-4.5-21B-A3B|128K|WINT4|1|export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 131072 \ <br> --max-num-seqs 128 \ <br> --quantization "wint4" \ <br> --gpu-memory-utilization 0.9|>=2.1.0|
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|ERNIE-4.5-0.3B|32K|BF16|1|export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 32768 \ <br> --max-num-seqs 128 \ <br> --gpu-memory-utilization 0.9|>=2.0.3|
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|ERNIE-4.5-0.3B|32K|WINT8|1|export XPU_VISIBLE_DEVICES="x" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 32768 \ <br> --max-num-seqs 128 \ <br> --quantization "wint8" \ <br> --gpu-memory-utilization 0.9|>=2.0.3|
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|ERNIE-4.5-0.3B|128K|BF16|1|export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 131072 \ <br> --max-num-seqs 128 \ <br> --gpu-memory-utilization 0.9|>=2.0.3|
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|ERNIE-4.5-0.3B|128K|WINT8|1|export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡<br>python -m fastdeploy.entrypoints.openai.api_server \ <br> --model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \ <br> --port 8188 \ <br> --tensor-parallel-size 1 \ <br> --max-model-len 131072 \ <br> --max-num-seqs 128 \ <br> --quantization "wint8" \ <br> --gpu-memory-utilization 0.9|>=2.0.3|
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## 快速开始
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### OpenAI 兼容服务器
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您还可以通过如下命令，基于 FastDeploy 实现 OpenAI API 协议兼容的服务器部署。
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#### 启动服务
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**基于 WINT4 精度和 32K 上下文部署 ERNIE-4.5-300B-A47B-Paddle 模型到 4 卡 P800 服务器**
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```bash
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export XPU_VISIBLE_DEVICES="0,1,2,3" # 设置使用的 XPU 卡
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python -m fastdeploy.entrypoints.openai.api_server \
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--model baidu/ERNIE-4.5-300B-A47B-Paddle \
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--port 8188 \
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--tensor-parallel-size 4 \
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--max-model-len 32768 \
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--max-num-seqs 64 \
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--quantization "wint4" \
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--gpu-memory-utilization 0.9
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```
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**注意：** 使用 P800 在 4 块 XPU 上进行部署时，由于受到卡间互联拓扑等硬件限制，仅支持以下两种配置方式：
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`export XPU_VISIBLE_DEVICES="0,1,2,3"`
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or
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`export XPU_VISIBLE_DEVICES="4,5,6,7"`
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更多参数可以参考 [参数说明](../../parameters.md)。
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全部支持的模型可以在上方的 *支持的模型* 章节找到。
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#### 请求服务
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您可以基于 OpenAI 协议，通过 curl 和 python 两种方式请求服务。
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```bash
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curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
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-H "Content-Type: application/json" \
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-d '{
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"messages": [
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{"role": "user", "content": "Where is the capital of China?"}
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]
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}'
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```
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```python
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import openai
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host = "0.0.0.0"
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port = "8188"
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client = openai.Client(base_url=f"http://{host}:{port}/v1", api_key="null")
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response = client.completions.create(
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model="null",
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prompt="Where is the capital of China?",
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stream=True,
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)
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for chunk in response:
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print(chunk.choices[0].text, end='')
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print('\n')
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response = client.chat.completions.create(
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model="null",
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messages=[
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{"role": "user", "content": "Where is the capital of China?"},
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],
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stream=True,
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)
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for chunk in response:
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if chunk.choices[0].delta:
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print(chunk.choices[0].delta.content, end='')
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print('\n')
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```
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OpenAI 协议的更多说明可参考文档 [OpenAI Chat Compeltion API](https://platform.openai.com/docs/api-reference/chat/create)，以及与 OpenAI 协议的区别可以参考 [兼容 OpenAI 协议的服务化部署](../../online_serving/README.md)。
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