[English](../../usage/kunlunxin_xpu_deployment.md)
## 支持的模型
| 模型名称 | 上下文长度 | 量化 | 所需卡数 | 适用版本 |
|----------|-----------|------|---------|---------|
| ERNIE-4.5-300B-A47B | 32K | WINT8 | 8 | 2.5.0 |
| ERNIE-4.5-300B-A47B | 32K | WINT4 | 4 | 2.5.0 |
| ERNIE-4.5-300B-A47B | 32K | WINT4 | 8 | 2.5.0 |
| ERNIE-4.5-300B-A47B | 128K | WINT4 | 8 | 2.5.0 |
| ERNIE-4.5-21B-A3B | 32K | BF16 | 1 | 2.5.0 |
| ERNIE-4.5-21B-A3B | 32K | WINT8 | 1 | 2.5.0 |
| ERNIE-4.5-21B-A3B | 32K | WINT4 | 1 | 2.5.0 |
| ERNIE-4.5-21B-A3B | 128K | BF16 | 1 | 2.5.0 |
| ERNIE-4.5-21B-A3B | 128K | WINT8 | 1 | 2.5.0 |
| ERNIE-4.5-21B-A3B | 128K | WINT4 | 1 | 2.5.0 |
| ERNIE-4.5-0.3B | 32K | BF16 | 1 | 2.5.0 |
| ERNIE-4.5-0.3B | 32K | WINT8 | 1 | 2.5.0 |
| ERNIE-4.5-0.3B | 128K | BF16 | 1 | 2.5.0 |
| ERNIE-4.5-0.3B | 128K | WINT8 | 1 | 2.5.0 |
| ERNIE-4.5-300B-A47B-W4A8C8-TP4 | 32K | W4A8 | 4 | 2.5.0 |
| ERNIE-4.5-VL-28B-A3B | 32K | WINT8 | 1 | 2.5.0 |
| ERNIE-4.5-VL-424B-A47B | 32K | WINT8 | 8 | 2.5.0 |
| PaddleOCR-VL-0.9B | 32K | BF16 | 1 | 2.5.0 |
| ERNIE-4.5-VL-28B-A3B-Thinking | 128K | WINT8 | 1 | 2.5.0 |
ERNIE-4.5-300B-A47B (32K, WINT8, 8 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 8 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization "wint8" \
--gpu-memory-utilization 0.9
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
export BKCL_ENABLE_XDR=1
export BKCL_RDMA_NICS=eth1,eth1,eth3,eth4 # 与线上网卡名一致
export BKCL_TRACE_TOPO=1
export BKCL_PCIE_RING=1
export XSHMEM_MODE=1
export XSHMEM_QP_NUM_PER_RANK=32
export BKCL_RDMA_VERBS=1
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--engine-worker-queue-port 8124 \
--metrics-port 8125 \
--cache-queue-port 55996 \
--tensor-parallel-size 8 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization "wint8" \
--gpu-memory-utilization 0.9 \
--enable-expert-parallel \
--enable-prefix-caching \
--data-parallel-size 1 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "${mtp_model_path}"}'
```
ERNIE-4.5-300B-A47B (32K, WINT4, 4 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3" # 或 "4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 4 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization "wint4" \
--gpu-memory-utilization 0.9
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3" # 或 "4,5,6,7"
export BKCL_ENABLE_XDR=1
export BKCL_RDMA_NICS=eth1,eth1,eth3,eth4 # 与线上网卡名一致
export BKCL_TRACE_TOPO=1
export BKCL_PCIE_RING=1
export XSHMEM_MODE=1
export XSHMEM_QP_NUM_PER_RANK=32
export BKCL_RDMA_VERBS=1
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--engine-worker-queue-port 8124 \
--metrics-port 8125 \
--cache-queue-port 55996 \
--tensor-parallel-size 4 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization wint4 \
--gpu-memory-utilization 0.9 \
--enable-expert-parallel \
--enable-prefix-caching \
--data-parallel-size 1 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "${mtp_model_path}"}'
```
ERNIE-4.5-300B-A47B (32K, WINT4, 8 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 8 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization "wint4" \
--gpu-memory-utilization 0.95
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
export BKCL_ENABLE_XDR=1
export BKCL_RDMA_NICS=eth1,eth1,eth3,eth4 # 与线上网卡名一致
export BKCL_TRACE_TOPO=1
export BKCL_PCIE_RING=1
export XSHMEM_MODE=1
export XSHMEM_QP_NUM_PER_RANK=32
export BKCL_RDMA_VERBS=1
python -m fastdeploy.entrypoints.openai.api_server \
--model /home/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--engine-worker-queue-port 8124 \
--metrics-port 8125 \
--cache-queue-port 55996 \
--tensor-parallel-size 8 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization wint4 \
--gpu-memory-utilization 0.95 \
--enable-expert-parallel \
--enable-prefix-caching \
--data-parallel-size 1 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "${mtp_model_path}"}'
```
ERNIE-4.5-300B-A47B (128K, WINT4, 8 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 8 \
--max-model-len 131072 \
--max-num-seqs 64 \
--quantization "wint4" \
--gpu-memory-utilization 0.9
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
export BKCL_ENABLE_XDR=1
export BKCL_RDMA_NICS=eth1,eth1,eth3,eth4 # 与线上网卡名一致
export BKCL_TRACE_TOPO=1
export BKCL_PCIE_RING=1
export XSHMEM_MODE=1
export XSHMEM_QP_NUM_PER_RANK=32
export BKCL_RDMA_VERBS=1
python -m fastdeploy.entrypoints.openai.api_server \
--model /home/ERNIE-4.5-300B-A47B-Paddle \
--port 8123 \
--engine-worker-queue-port 8124 \
--metrics-port 8125 \
--cache-queue-port 55996 \
--tensor-parallel-size 8 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization wint4 \
--gpu-memory-utilization 0.9 \
--enable-expert-parallel \
--enable-prefix-caching \
--data-parallel-size 1 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "${mtp_model_path}"}'
```
ERNIE-4.5-21B-A3B (32K, BF16, 1 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "'${mtp_model_path}'"}'
```
ERNIE-4.5-21B-A3B (32K, WINT8, 1 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "'${mtp_model_path}'"}'
```
ERNIE-4.5-21B-A3B (32K, WINT4, 1 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--quantization "wint4" \
--gpu-memory-utilization 0.9
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--quantization "wint4" \
--gpu-memory-utilization 0.9 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "'${mtp_model_path}'"}'
```
ERNIE-4.5-21B-A3B (128K, BF16, 1 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "'${mtp_model_path}'"}'
```
ERNIE-4.5-21B-A3B (128K, WINT8, 1 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "'${mtp_model_path}'"}'
```
ERNIE-4.5-21B-A3B (128K, WINT4, 1 卡) - 点击查看部署命令
**快速部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--quantization "wint4" \
--gpu-memory-utilization 0.9
```
**最优部署：**
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--quantization "wint4" \
--gpu-memory-utilization 0.9 \
--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "'${mtp_model_path}'"}'
```
ERNIE-4.5-0.3B (32K, BF16, 1 卡) - 点击查看部署命令
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9
```
ERNIE-4.5-0.3B (32K, WINT8, 1 卡) - 点击查看部署命令
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9
```
ERNIE-4.5-0.3B (128K, BF16, 1 卡) - 点击查看部署命令
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9
```
ERNIE-4.5-0.3B (128K, WINT8, 1 卡) - 点击查看部署命令
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9
```
ERNIE-4.5-300B-A47B-W4A8C8-TP4 (32K, W4A8, 4 卡) - 点击查看部署命令
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3" # 或 "4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-W4A8C8-TP4-Paddle \
--port 8188 \
--tensor-parallel-size 4 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization "W4A8" \
--gpu-memory-utilization 0.9
```
ERNIE-4.5-VL-28B-A3B (32K, WINT8, 1 卡) - 点击查看部署命令
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-VL-28B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--quantization "wint8" \
--max-model-len 32768 \
--max-num-seqs 10 \
--enable-mm \
--mm-processor-kwargs '{"video_max_frames": 30}' \
--limit-mm-per-prompt '{"image": 10, "video": 3}' \
--reasoning-parser ernie-45-vl
```
ERNIE-4.5-VL-424B-A47B (32K, WINT8, 8 卡) - 点击查看部署命令
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-VL-424B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 8 \
--quantization "wint8" \
--max-model-len 32768 \
--max-num-seqs 8 \
--enable-mm \
--mm-processor-kwargs '{"video_max_frames": 30}' \
--limit-mm-per-prompt '{"image": 10, "video": 3}' \
--reasoning-parser ernie-45-vl \
--gpu-memory-utilization 0.7
```
PaddleOCR-VL-0.9B (32K, BF16, 1 卡) - 点击查看部署命令
```bash
export FD_ENABLE_MAX_PREFILL=1
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/PaddleOCR-VL \
--port 8188 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--max-model-len 16384 \
--max-num-batched-tokens 16384 \
--gpu-memory-utilization 0.8 \
--max-num-seqs 256
```
ERNIE-4.5-VL-28B-A3B-Thinking (128K, WINT8, 1 卡) - 点击查看部署命令
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-VL-28B-A3B-Thinking \
--port 8188 \
--tensor-parallel-size 1 \
--quantization "wint8" \
--max-model-len 131072 \
--max-num-seqs 32 \
--engine-worker-queue-port 8189 \
--metrics-port 8190 \
--cache-queue-port 8191 \
--reasoning-parser ernie-45-vl-thinking \
--tool-call-parser ernie-45-vl-thinking \
--mm-processor-kwargs '{"image_max_pixels": 12845056}'
```
## 快速开始
### 基于ERNIE-4.5-300B-A47B-Paddle模型部署在线服务
#### 启动服务
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
```bash
export XPU_VISIBLE_DEVICES="0,1,2,3" # 设置使用的 XPU 卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 4 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization "wint4" \
--gpu-memory-utilization 0.9
```
**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：
`export XPU_VISIBLE_DEVICES="0,1,2,3"`
or
`export XPU_VISIBLE_DEVICES="4,5,6,7"`
更多参数可以参考 [参数说明](../parameters.md)。
全部支持的模型可以在上方的 *支持的模型* 章节找到。
#### 请求服务
您可以基于 OpenAI 协议，通过 curl 和 python 两种方式请求服务。
```bash
curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": "Where is the capital of China?"}
]
}'
```
```python
import openai
host = "0.0.0.0"
port = "8188"
client = openai.Client(base_url=f"http://{host}:{port}/v1", api_key="null")
response = client.completions.create(
model="null",
prompt="Where is the capital of China?",
stream=True,
)
for chunk in response:
print(chunk.choices[0].text, end='')
print('\n')
response = client.chat.completions.create(
model="null",
messages=[
{"role": "user", "content": "Where is the capital of China?"},
],
stream=True,
)
for chunk in response:
if chunk.choices[0].delta:
print(chunk.choices[0].delta.content, end='')
print('\n')
```
OpenAI 协议的更多说明可参考文档 [OpenAI Chat Completion 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)。
### 基于ERNIE-4.5-VL-28B-A3B-Paddle模型部署在线服务
#### 启动服务
基于 WINT8 精度和 32K 上下文部署 ERNIE-4.5-VL-28B-A3B-Paddle 模型到 单卡 P800 服务器
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-VL-28B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--quantization "wint8" \
--max-model-len 32768 \
--max-num-seqs 10 \
--enable-mm \
--mm-processor-kwargs '{"video_max_frames": 30}' \
--limit-mm-per-prompt '{"image": 10, "video": 3}' \
--reasoning-parser ernie-45-vl
```
#### 请求服务
```bash
curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg", "detail": "high"}},
{"type": "text", "text": "请描述图片内容"}
]}
],
"metadata": {"enable_thinking": false}
}'
```
```python
import openai
ip = "0.0.0.0"
service_http_port = "8188"
client = openai.Client(base_url=f"http://{ip}:{service_http_port}/v1", api_key="EMPTY_API_KEY")
response = client.chat.completions.create(
model="default",
messages=[
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg", "detail": "high"}},
{"type": "text", "text": "请描述图片内容"}
]
},
],
temperature=0.0001,
max_tokens=10000,
stream=True,
top_p=0,
metadata={"enable_thinking": False},
)
def get_str(content_raw):
content_str = str(content_raw) if content_raw is not None else ''
return content_str
for chunk in response:
if chunk.choices[0].delta is not None and chunk.choices[0].delta.role != 'assistant':
reasoning_content = get_str(chunk.choices[0].delta.reasoning_content)
content = get_str(chunk.choices[0].delta.content)
print(reasoning_content + content, end='', flush=True)
print('\n')
```
### 基于PaddleOCR-VL-0.9B模型部署在线服务
#### 启动服务
基于 BF16 精度和 16K 上下文部署 PaddleOCR-VL-0.9B 模型到 单卡 P800 服务器
```bash
export FD_ENABLE_MAX_PREFILL=1
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/PaddleOCR-VL \
--port 8188 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--max-model-len 16384 \
--max-num-batched-tokens 16384 \
--gpu-memory-utilization 0.8 \
--max-num-seqs 256
```
#### 请求服务
```bash
curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/ocr_demo.jpg"}},
{"type": "text", "text": "OCR:"}
]}
],
"metadata": {"enable_thinking": false}
}'
```
```python
import openai
ip = "0.0.0.0"
service_http_port = "8188"
client = openai.Client(base_url=f"http://{ip}:{service_http_port}/v1", api_key="EMPTY_API_KEY")
response = client.chat.completions.create(
model="default",
messages=[
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/ocr_demo.jpg"}},
{"type": "text", "text": "OCR:"}
]
},
],
temperature=0.0001,
max_tokens=4096,
stream=True,
top_p=0,
metadata={"enable_thinking": False},
)
def get_str(content_raw):
content_str = str(content_raw) if content_raw is not None else ''
return content_str
for chunk in response:
if chunk.choices[0].delta is not None and chunk.choices[0].delta.role != 'assistant':
reasoning_content = get_str(chunk.choices[0].delta.reasoning_content)
content = get_str(chunk.choices[0].delta.content)
print(reasoning_content + content, end='', flush=True)
print('\n')
```
### 基于ERNIE-4.5-VL-28B-A3B-Thinking模型部署在线服务
#### 启动服务
基于 WINT8 精度和 128K 上下文部署 ERNIE-4.5-VL-28B-A3B-Thinking 模型到 单卡 P800 服务器
```bash
export XPU_VISIBLE_DEVICES="0" # 指定任意一张卡
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-VL-28B-A3B-Thinking \
--port 8188 \
--tensor-parallel-size 1 \
--quantization "wint8" \
--max-model-len 131072 \
--max-num-seqs 32 \
--engine-worker-queue-port 8189 \
--metrics-port 8190 \
--cache-queue-port 8191 \
--reasoning-parser ernie-45-vl-thinking \
--tool-call-parser ernie-45-vl-thinking \
--mm-processor-kwargs '{"image_max_pixels": 12845056 }'
```
### 请求服务
通过如下命令发起服务请求
```bash
curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": "把李白的静夜思改写为现代诗"}
]
}'
```
输入包含图片时，按如下命令发起请求
```
curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": [
{"type":"image_url", "image_url": {"url":"https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
{"type":"text", "text":"图中的文物属于哪个年代?"}
]}
]
}'
```
输入包含视频时，按如下命令发起请求
```
curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": [
{"type":"video_url", "video_url": {"url":"https://bj.bcebos.com/v1/paddlenlp/datasets/paddlemix/demo_video/example_video.mp4"}},
{"type":"text", "text":"画面中有几个苹果?"}
]}
]
}'
```
输入包含工具调用时，按如下命令发起请求
```
curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
-H "Content-Type: application/json" \
-d $'{
"tools": [
{
"type": "function",
"function": {
"name": "image_zoom_in_tool",
"description": "Zoom in on a specific region of an image by cropping it based on a bounding box (bbox) and an optional object label.",
"parameters": {
"type": "object",
"properties": {
"bbox_2d": {
"type": "array",
"items": {
"type": "number"
},
"minItems": 4,
"maxItems": 4,
"description": "The bounding box of the region to zoom in, as [x1, y1, x2, y2], where (x1, y1) is the top-left corner and (x2, y2) is the bottom-right corner, and the values of x1, y1, x2, y2 are all normalized to the range 0–1000 based on the original image dimensions."
},
"label": {
"type": "string",
"description": "The name or label of the object in the specified bounding box (optional)."
}
},
"required": [
"bbox_2d"
]
},
"strict": false
}
}
],
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Is the old lady on the left side of the empty table behind older couple?"
}
]
}
],
"stream": false
}'
```
多轮请求， 历史上下文中包含工具返回结果时，按如下命令发起请求
```
curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
-H "Content-Type: application/json" \
-d $'{
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Get the current weather in Beijing"
}
]
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_1",
"type": "function",
"function": {
"name": "get_weather",
"arguments": {
"location": "Beijing",
"unit": "c"
}
}
}
],
"content": ""
},
{
"role": "tool",
"content": [
{
"type": "text",
"text": "location: Beijing，temperature: 23，weather: sunny，unit: c"
}
]
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Determine weather in my location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"c",
"f"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
},
"strict": true
}
}
],
"stream": false
}'
```