aws-neuron/CodeLlama-7b-hf-neuron-8xlarge

Neuronx model for codellama/CodeLlama-7b-hf

This repository contains AWS Inferentia2 and neuronx compatible checkpoints for codellama/CodeLlama-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.

It has been compiled to run on an inf2.8xlarge instance on AWS. It also runs on an inf2.xlarge (the smallest Inferentia2 instance), but it pretty much maxes out the RAM. Be sure to test before using in production on the smaller instance.

Please refer to the 🤗 optimum-neuron documentation for an explanation of these parameters.

Usage on Amazon SageMaker

coming soon

Usage with 🤗 optimum-neuron

from optimum.neuron import pipeline

p = pipeline('text-generation', 'aws-neuron/CodeLlama-7b-hf-neuron-8xlarge')
p("import socket\n\ndef ping_exponential_backoff(host: str):",
    do_sample=True,
    top_k=10,
    temperature=0.1,
    top_p=0.95,
    num_return_sequences=1,
    max_length=200,
)

[{'generated_text': 'import socket\n\ndef ping_exponential_backoff(host: str):\n    """\n    Ping a host with exponential backoff.\n\n    :param host: Host to ping\n    :return: True if host is reachable, False otherwise\n    """\n    for i in range(1, 10):\n        try:\n            socket.create_connection((host, 80), 1).close()\n            return True\n        except OSError:\n            time.sleep(2 ** i)\n    return False\n\n\ndef ping_exponential_backoff_with_timeout(host: str, timeout: int):\n    """\n    Ping a host with exponential backoff and timeout.\n\n    :param host: Host to ping\n    :param timeout: Timeout in seconds\n    :return: True if host is reachable, False otherwise\n    """\n    for'}]

This repository contains tags specific to versions of neuronx. When using with 🤗 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": 2,
}