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Update README.md

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@@ -33,7 +33,7 @@ Training is light-weight and can be completed in only a few days depending on ba
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  _Note: For all samples, your environment must have access to cuda_
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- ### Production Server Sample
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  *To try this out running in a production-like environment, please use the pre-built docker image:*
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@@ -101,6 +101,27 @@ python sample_client.py
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  _Note: first prompt may be slower as there is a slight warmup time_
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  ### Minimal Sample
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  *To try this out with the fms-native compiled model, please execute the following:*
 
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  _Note: For all samples, your environment must have access to cuda_
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+ ### Use in IBM Production TGIS
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  *To try this out running in a production-like environment, please use the pre-built docker image:*
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  _Note: first prompt may be slower as there is a slight warmup time_
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+ ### Use in Huggingface TGI
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+
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+ #### start the server
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+
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+ ```bash
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+ model=ibm-fms/codellama-13b-accelerator
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+ volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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+ docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model
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+ ```
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+
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+ _note: for tensor parallel, add --num-shard_
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+
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+ #### make a request
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+
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+ ```bash
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+ curl 127.0.0.1:8080/generate_stream \
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+ -X POST \
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+ -d '{"inputs":"Write a simple bubble sort in python.","parameters":{"max_new_tokens":20}}' \
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+ -H 'Content-Type: application/json'
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+ ```
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+
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  ### Minimal Sample
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  *To try this out with the fms-native compiled model, please execute the following:*