add tei back to readme

README.md

@@ -134,6 +134,57 @@ packed_embeddings = np.packbits(binary_embeddings != -1, axis=-1)
 
 </details>
 
+<details>
+<summary>Using Text Embeddings Inference (TEI)</summary>
+
+> [!NOTE]
+> Text Embeddings Inference v1.9.0 will be released stable soon, in the meantime
+> feel free to use the latest containers or rather via SHA ``.
+
+> [!IMPORTANT]
+> Currently, only int8-quantized embeddings are available via TEI. Remember to use cosine similarity with unnormalized int8 embeddings.
+
+- CPU w/ Candle:
+
+```bash
+docker run -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id perplexity-ai/pplx-embed-v1-0.6B --auto-truncate
+```
+
+- CPU w/ ORT (ONNX Runtime):
+
+```bash
+docker run -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id onnx-community/pplx-embed-v1-0.6B --auto-truncate
+```
+
+- GPU w/ CUDA:
+
+```bash
+docker run --gpus all --shm-size 1g -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id perplexity-ai/pplx-embed-v1-0.6B --auto-truncate
+```
+
+> Alternatively, when running in CUDA you can use the architecture / compute capability specific
+> container instead of the `cuda-latest`, as that includes the binaries for Turing, Ampere and
+> Hopper, so using a dedicated container will be lighter e.g., `ampere-latest`.
+
+And then you can send requests to it via cURL to `/embed`:
+
+```bash
+curl http://0.0.0.0:8080/embed \
+  -H "Content-Type: application/json" \
+  -d '{
+    "inputs": [
+      "Scientists explore the universe driven by curiosity.",
+      "Children learn through curious exploration.",
+      "Historical discoveries began with curious questions.",
+      "Animals use curiosity to adapt and survive.",
+      "Philosophy examines the nature of curiosity."
+    ],
+    "normalize": false
+  }'
+```
+
+</details>
+
 ## Technical Details
 
 For comprehensive technical details and evaluation results, see our paper on arXiv: https://arxiv.org/abs/2602.11151.