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Gonsoo/AWS-Neuron-llama-3-Korean-Bllossom-8B
AWS-Neuron-llama-3-Korean-Bllossom-8B is a machine learning model from Gonsoo. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This model is an AWS Neuron compiled version of the Korean fine-tuned model MLP-KTLim/llama-3-Korean-Bllossom-8B, available at https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B. It is intended for deployment…
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From the Hugging Face model README
This model is an AWS Neuron compiled version of the Korean fine-tuned model MLP-KTLim/llama-3-Korean-Bllossom-8B, available at https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B. It is intended for deployment on Amazon EC2 Inferentia2 and Amazon SageMaker. For detailed information about the model and its license, please refer to the original MLP-KTLim/llama-3-Korean-Bllossom-8B model page
This model is compiled with neuronx-cc version, 2.13.66.0. It can be deployed with neuronnx-tgi docker, ghcr.io/huggingface/neuronx-tgi:0.0.23
You can pull the docker image ghcr.io/huggingface/neuronx-tgi:0.0.23, downlaod this model and run the command like this example:
docker run \
-p 8080:80 \
-v $(pwd)/data:/data \
--privileged \
ghcr.io/huggingface/neuronx-tgi:latest \
--model-id /data/AWS-Neuron-llama-3-Korean-Bllossom-8B
After deployment, you can inference like this
curl 127.0.0.1:8080/generate \
-X POST \
-d '{"inputs":"딥러닝이 뭐야?","parameters":{"max_new_tokens":512}}' \
-H 'Content-Type: application/json'
or
curl localhost:8080/v1/chat/completions \
-X POST \
-d '{
"model": "tgi",
"messages": [
{
"role": "system",
"content": "당신은 인공지능 전문가 입니다."
},
{
"role": "user",
"content": "딥러닝이 무엇입니까?"
}
],
"stream": false,
"max_tokens": 512
}' \
-H 'Content-Type: application/json'
This model can be deployed to Amazon SageMaker Endtpoint with this guide, Deploy this model to SageMaker Endpoint
In order to do neuron-compilation and depoly in detail , you can refer to Amazon EC2 Inferentia2 기반 위에 한국어 파인 튜닝 모델을 서빙하기
At a minimum hardware, you can use Amazon EC2 inf2.xlarge and more powerful family such as inf2.8xlarge, inf2.24xlarge and inf2.48xlarge. The detailed information is Amazon EC2 Inf2 Instances
Gonsoo Moon, [email protected]