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NeMo
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nvidia
steerlm
llama2
zhilinw commited on
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@@ -87,7 +87,7 @@ Pre-requisite: you would need at least a machine with 4 40GB or 2 80GB NVIDIA GP
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  ```
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  docker run --gpus all -it --rm --shm-size=300g -p 8000:8000 -v ${PWD}/Llama2-70B-SteerLM-Chat.nemo:/opt/checkpoints/Llama2-70B-SteerLM-Chat.nemo -w /opt/NeMo nvcr.io/ea-bignlp/ga-participants/nemofw-inference:23.10
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  ```
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- 8. Within the container, start the server in the background. This step does both conversion of the nemo checkpoint to TRT-LLM and then deployment using TRTLLM. For an explanation of each argument and advanced usage, please refer to [https://docs.nvidia.com/nemo-framework/user-guide/latest/deployingthenemoframeworkmodel.html]
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  ```
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  python scripts/deploy/deploy_triton.py --nemo_checkpoint /opt/checkpoints/Llama2-70B-SteerLM-Chat.nemo --model_type="llama" --triton_model_name Llama2-70B-SteerLM-Chat --triton_http_address 0.0.0.0 --triton_port 8000 --num_gpus 2 --max_input_len 3072 --max_output_len 1024 --max_batch_size 1 &
 
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  ```
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  docker run --gpus all -it --rm --shm-size=300g -p 8000:8000 -v ${PWD}/Llama2-70B-SteerLM-Chat.nemo:/opt/checkpoints/Llama2-70B-SteerLM-Chat.nemo -w /opt/NeMo nvcr.io/ea-bignlp/ga-participants/nemofw-inference:23.10
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  ```
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+ 8. Within the container, start the server in the background. This step does both conversion of the nemo checkpoint to TRT-LLM and then deployment using TRTLLM. For an explanation of each argument and advanced usage, please refer to [NeMo FW Deployment Guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/deployingthenemoframeworkmodel.html)
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  ```
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  python scripts/deploy/deploy_triton.py --nemo_checkpoint /opt/checkpoints/Llama2-70B-SteerLM-Chat.nemo --model_type="llama" --triton_model_name Llama2-70B-SteerLM-Chat --triton_http_address 0.0.0.0 --triton_port 8000 --num_gpus 2 --max_input_len 3072 --max_output_len 1024 --max_batch_size 1 &