How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dacorvo/llama-test-upload-folder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dacorvo/llama-test-upload-folder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/dacorvo/llama-test-upload-folder
Quick Links

Neuronx model for meta-llama/Llama-2-7b-hf

This repository contains AWS Inferentia2 and neuronx compatible checkpoints for meta-llama/Llama-2-7b-hf. You can find detailed information about the base model on its Model Card.

This model has been exported to the neuron format using specific input_shapes and compiler parameters detailed in the paragraphs below.

Please refer to the πŸ€— optimum-neuron documentation for an explanation of these parameters.

Usage on Amazon SageMaker

coming soon

Usage with πŸ€— optimum-neuron

>>> from optimum.neuron import pipeline

>>> p = pipeline('text-generation', 'aws-neuron/Llama-2-7b-hf-neuron-latency')
>>> p("My favorite place on earth is", max_new_tokens=64, do_sample=True, top_k=50)
[{'generated_text': 'My favorite place on earth is the ocean. It is where I feel most
at peace. I love to travel and see new places. I have a'}]

This repository contains tags specific to versions of neuronx. When using with πŸ€— optimum-neuron, use the repo revision specific to the version of neuronx you are using, to load the right serialized checkpoints.

Arguments passed during export

input_shapes

{
  "batch_size": 1,
  "sequence_length": 2048,
}

compiler_args

{
  "auto_cast_type": "fp16",
  "num_cores": 24,
}
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