Remove TEI snippet temporarily

README.md

@@ -90,56 +90,6 @@ embeddings = model.encode(texts, quantization="binary") # Shape: (5, 2560), quan
 
 </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-v1-4B --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-v1-4B --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-v1-4B --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>
 <details>
 <summary> Using ONNX models </summary>