update README

README.md

@@ -7,7 +7,6 @@ tags:
 - sentence-transformers
 - sentence-similarity
 - feature-extraction
-- text-embeddings-inference
 ---
 # Qwen3-Embedding-0.6B
 
@@ -24,7 +23,6 @@ The Qwen3 Embedding model series is the latest proprietary model of the Qwen fam
 **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
 
 **Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.
-
 ## Model Overview
 
 **Qwen3-Embedding-0.6B** has the following features:
@@ -64,7 +62,6 @@ KeyError: 'qwen3'
 
 ```python
 # Requires transformers>=4.51.0
-# Requires sentence-transformers>=2.7.0
 
 from sentence_transformers import SentenceTransformer
 
@@ -168,66 +165,8 @@ scores = (embeddings[:2] @ embeddings[2:].T)
 print(scores.tolist())
 # [[0.7645568251609802, 0.14142508804798126], [0.13549736142158508, 0.5999549627304077]]
 ```
-
-### vLLM Usage
-
-```python
-# Requires vllm>=0.8.5
-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}'
-
-# 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-0.6B", task="embed")
-
-outputs = model.embed(input_texts)
-embeddings = torch.tensor([o.outputs.embedding for o in outputs])
-scores = (embeddings[:2] @ embeddings[2:].T)
-print(scores.tolist())
-# [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
-```
-
 📌 **Tip**: We recommend that developers customize the `instruct` according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an `instruct` on the query side can lead to a drop in retrieval performance by approximately 1% to 5%.
 
-### Text Embeddings Inference (TEI) Usage
-
-You can either run / deploy TEI on NVIDIA GPUs as:
-
-```bash
-docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.7.2 --model-id Qwen/Qwen3-Embedding-0.6B --dtype float16
-```
-
-Or on CPU devices as:
-
-```bash
-docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7.2 --model-id Qwen/Qwen3-Embedding-0.6B
-```
-
-And then, generate the embeddings sending a HTTP POST request as:
-
-```bash
-curl http://localhost:8080/embed \
-    -X POST \
-    -d '{"inputs": ["Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: What is the capital of China?", "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: Explain gravity"]}' \
-    -H "Content-Type: application/json"
-```
-
 ## Evaluation
 
 ### MTEB (Multilingual)
@@ -283,10 +222,11 @@ curl http://localhost:8080/embed \
 If you find our work helpful, feel free to give us a cite.
 
 ```
-@article{qwen3embedding,
-  title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
-  author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
-  journal={arXiv preprint arXiv:2506.05176},
-  year={2025}
+@misc{qwen3-embedding,
+    title  = {Qwen3-Embedding},
+    url    = {https://qwenlm.github.io/blog/qwen3/},
+    author = {Qwen Team},
+    month  = {May},
+    year   = {2025}
 }
 ```