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Del Separate Tokenizer Conversion Step for Embeddings Pipeline #2738

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Del Separate Tokenizer Conversion Step for Embeddings Pipeline #2738

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562e1f2
Del Tokenizer Conversion Step
apaniukov Oct 15, 2024
a66f116
Fix dir structure
apaniukov Oct 15, 2024
66de685
Move Note
apaniukov Oct 15, 2024
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12 changes: 9 additions & 3 deletions demos/embeddings/README.md
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Expand Up @@ -29,12 +29,18 @@ pip3 install optimum-intel@git+https://github.com/huggingface/optimum-intel.git
Run optimum-cli to download and quantize the model:
```bash
cd demos/embeddings
convert_tokenizer -o models/gte-large-en-v1.5-tokenizer/1 Alibaba-NLP/gte-large-en-v1.5
optimum-cli export openvino --disable-convert-tokenizer --model Alibaba-NLP/gte-large-en-v1.5 --task feature-extraction --weight-format int8 --trust-remote-code --library sentence_transformers models/gte-large-en-v1.5-embeddings/1
rm models/gte-large-en-v1.5-embeddings/1/*.json models/gte-large-en-v1.5-embeddings/1/vocab.txt
optimum-cli export openvino --model Alibaba-NLP/gte-large-en-v1.5 --task feature-extraction --weight-format int8 --trust-remote-code --library sentence_transformers models/gte-large-en-v1.5-embeddings/1
rm models/gte-large-en-v1.5-embeddings/1/*.json models/gte-large-en-v1.5-embeddings/1/vocab.txt
```

> **Note** Change the `--weight-format` to quantize the model to `fp16`, `int8` or `int4` precision to reduce memory consumption and improve performance.

Move the tokenizer to a separate folder to create an embedding pipeline:
```bash
mkdir -p models/gte-large-en-v1.5-tokenizer/1
mv models/gte-large-en-v1.5-embeddings/1/*_tokenizer.* -t models/gte-large-en-v1.5-tokenizer/1
```

You should have a model folder like below:
```bash
tree models/
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