[Doc] Add qwen3 embedding 8b guide (#1734)

1. Add the tutorials for qwen3-embedding-8b
2. Remove VLLM_USE_V1=1  in docs, it's useless any more from 0.9.2


- vLLM version: v0.9.2
- vLLM main:
vllm-project/vllm@5923ab9

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

Author: wangxiyuan

docs/source/developer_guide/contribution/testing.md

@@ -156,7 +156,7 @@ There are several principles to follow when writing unit tests:
 ```bash
 # Run unit tests
 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -m)-linux/devlib
-VLLM_USE_V1=1 TORCH_DEVICE_BACKEND_AUTOLOAD=0 pytest -sv tests/ut
+TORCH_DEVICE_BACKEND_AUTOLOAD=0 pytest -sv tests/ut
 ```
 
 ::::

docs/source/tutorials/index.md

@@ -6,6 +6,7 @@
 single_npu
 single_npu_multimodal
 single_npu_audio
+single_npu_qwen3_embedding
 multi_npu
 multi_npu_moge
 multi_npu_qwen3_moe

docs/source/tutorials/multi_node.md

@@ -108,7 +108,6 @@ export TP_SOCKET_IFNAME=$nic_name
 export HCCL_SOCKET_IFNAME=$nic_name
 export OMP_PROC_BIND=false
 export OMP_NUM_THREADS=100
-export VLLM_USE_V1=1
 export HCCL_BUFFSIZE=1024
 
 # The w8a8 weight can obtained from https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V3-W8A8

docs/source/tutorials/multi_npu_quantization.md

@@ -1,6 +1,6 @@
 # Multi-NPU (QwQ 32B W8A8)
 
-## Run docker container:
+## Run docker container
 :::{note}
 w8a8 quantization feature is supported by v0.8.4rc2 or higher
 :::

docs/source/tutorials/multi_npu_qwen3_moe.md

@@ -35,9 +35,6 @@ export VLLM_USE_MODELSCOPE=True
 
 # Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
 export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
-
-# For vllm-ascend 0.9.2+, the V1 engine is enabled by default and no longer needs to be explicitly specified.
-export VLLM_USE_V1=1
 ```
 
 ### Online Inference on Multi-NPU

docs/source/tutorials/single_node_300i.md

@@ -60,7 +60,6 @@ Run the following command to start the vLLM server:
 
 ```{code-block} bash
    :substitutions:
-export VLLM_USE_V1=1
 vllm serve Qwen/Qwen3-0.6B \
     --tensor-parallel-size 1 \
     --enforce-eager \
@@ -90,7 +89,6 @@ Run the following command to start the vLLM server:
 
 ```{code-block} bash
    :substitutions:
-export VLLM_USE_V1=1
 vllm serve Qwen/Qwen2.5-7B-Instruct \
     --tensor-parallel-size 2 \
     --enforce-eager \
@@ -129,7 +127,7 @@ Run the following command to start the vLLM server:
 ```{code-block} bash
    :substitutions:
 
-VLLM_USE_V1=1 vllm serve /home/pangu-pro-moe-mode/ \
+vllm serve /home/pangu-pro-moe-mode/ \
 --tensor-parallel-size 4 \
 --enable-expert-parallel \
 --dtype "float16" \
@@ -321,7 +319,7 @@ if __name__ == "__main__":
 
 Run script:
 ```bash
-VLLM_USE_V1=1 python example.py
+python example.py
 ```
 
 If you run this script successfully, you can see the info shown below:

docs/source/tutorials/single_npu.md

@@ -50,8 +50,6 @@ Run the following script to execute offline inference on a single NPU:
 import os
 from vllm import LLM, SamplingParams
 
-os.environ["VLLM_USE_V1"] = "1"
-
 prompts = [
     "Hello, my name is",
     "The future of AI is",
@@ -77,8 +75,6 @@ for output in outputs:
 import os
 from vllm import LLM, SamplingParams
 
-os.environ["VLLM_USE_V1"] = "1"
-
 prompts = [
     "Hello, my name is",
     "The future of AI is",
@@ -133,7 +129,7 @@ docker run --rm \
 -e VLLM_USE_MODELSCOPE=True \
 -e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
 -it $IMAGE \
-VLLM_USE_V1=1 vllm serve Qwen/Qwen3-8B --max_model_len 26240
+vllm serve Qwen/Qwen3-8B --max_model_len 26240
 ```
 ::::
 
@@ -158,7 +154,7 @@ docker run --rm \
 -e VLLM_USE_MODELSCOPE=True \
 -e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
 -it $IMAGE \
-VLLM_USE_V1=1 vllm serve Qwen/Qwen3-8B --max_model_len 26240 --enforce-eager
+vllm serve Qwen/Qwen3-8B --max_model_len 26240 --enforce-eager
 ```
 ::::
 :::::

docs/source/tutorials/single_npu_audio.md

@@ -29,9 +29,6 @@ docker run --rm \
 Setup environment variables:
 
 ```bash
-# Use vllm v1 engine
-export VLLM_USE_V1=1
-
 # Load model from ModelScope to speed up download
 export VLLM_USE_MODELSCOPE=True
 

docs/source/tutorials/single_npu_multimodal.md

@@ -29,9 +29,6 @@ docker run --rm \
 Setup environment variables:
 
 ```bash
-# Use vllm v1 engine
-export VLLM_USE_V1=1
-
 # Load model from ModelScope to speed up download
 export VLLM_USE_MODELSCOPE=True
 
@@ -143,7 +140,6 @@ docker run --rm \
 -v /etc/ascend_install.info:/etc/ascend_install.info \
 -v /root/.cache:/root/.cache \
 -p 8000:8000 \
--e VLLM_USE_V1=1 \
 -e VLLM_USE_MODELSCOPE=True \
 -e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
 -it $IMAGE \

docs/source/tutorials/single_npu_qwen3_embedding.md

@@ -0,0 +1,99 @@
+# Single NPU (Qwen3-Embedding-8B)
+
+The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This guide describes how to run the model with vLLM Ascend. Note that only 0.9.2rc1 and higher versions of vLLM Ascend support the model.
+
+## Run docker container
+
+Take Qwen3-Embedding-8B model as an example, first run the docker container with the following command:
+
+```{code-block} bash
+   :substitutions:
+# Update the vllm-ascend image
+export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
+docker run --rm \
+--name vllm-ascend \
+--device /dev/davinci0 \
+--device /dev/davinci_manager \
+--device /dev/devmm_svm \
+--device /dev/hisi_hdc \
+-v /usr/local/dcmi:/usr/local/dcmi \
+-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+-v /etc/ascend_install.info:/etc/ascend_install.info \
+-v /root/.cache:/root/.cache \
+-p 8000:8000 \
+-it $IMAGE bash
+```
+
+Setup environment variables:
+
+```bash
+# Load model from ModelScope to speed up download
+export VLLM_USE_MODELSCOPE=True
+
+# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
+export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
+```
+
+### Online Inference
+
+```bash
+vllm serve Qwen/Qwen3-Embedding-8B --task embed
+```
+
+Once your server is started, you can query the model with input prompts
+
+```bash
+curl http://localhost:8000/v1/embeddings -H "Content-Type: application/json" -d '{
+  "model": "Qwen/Qwen3-Embedding-8B",
+  "messages": [
+    {"role": "user", "content": "Hello"}
+  ]
+}'
+```
+
+### Offline Inference
+
+```python
+import torch
+import vllm
+from vllm import LLM
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery:{query}'
+
+
+if __name__=="__main__":
+    # Each query must come with a one-sentence instruction that describes the task
+    task = 'Given a web search query, retrieve relevant passages that answer the query'
+
+    queries = [
+        get_detailed_instruct(task, 'What is the capital of China?'),
+        get_detailed_instruct(task, 'Explain gravity')
+    ]
+    # No need to add instruction for retrieval documents
+    documents = [
+        "The capital of China is Beijing.",
+        "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
+    ]
+    input_texts = queries + documents
+
+    model = LLM(model="Qwen/Qwen3-Embedding-8B",
+                task="embed",
+                distributed_executor_backend="mp")
+
+    outputs = model.embed(input_texts)
+    embeddings = torch.tensor([o.outputs.embedding for o in outputs])
+    scores = (embeddings[:2] @ embeddings[2:].T)
+    print(scores.tolist())
+```
+
+If you run this script successfully, you can see the info shown below:
+
+```bash
+Adding requests: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 282.22it/s]
+Processed prompts:   0%|                                                                                                                                    | 0/4 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s](VllmWorker rank=0 pid=4074750) ('Warning: torch.save with "_use_new_zipfile_serialization = False" is not recommended for npu tensor, which may bring unexpected errors and hopefully set "_use_new_zipfile_serialization = True"', 'if it is necessary to use this, please convert the npu tensor to cpu tensor for saving')
+Processed prompts: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 31.95it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
+[[0.7477798461914062, 0.07548339664936066], [0.0886271521449089, 0.6311039924621582]]
+```