MetaLlamamodel

Bagian ini memberikan parameter inferensi dan contoh kode untuk menggunakan model berikut dariMeta.

Anda membuat permintaan inferensi ke Meta Llama model dengan InvokeModelatau InvokeModelWithResponseStream(streaming). Anda memerlukan ID model untuk model yang ingin Anda gunakan. Untuk mendapatkan ID model, lihatID model Amazon Bedrock.

Permintaan dan tanggapan

Badan permintaan diteruskan di body bidang permintaan ke InvokeModelatau InvokeModelWithResponseStream.

Contoh kode

Contoh ini menunjukkan cara memanggil model MetaLlama 2 Chat13B.

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to generate text with Meta Llama 2 Chat (on demand).
"""

import json
import logging
import boto3


from botocore.exceptions import ClientError


logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


def generate_text(model_id, body):
    """
    Generate an image using Meta Llama 2 Chat on demand.
    Args:
        model_id (str): The model ID to use.
        body (str) : The request body to use.
    Returns:
        response (JSON): The text that the model generated, token information, and the
        reason the model stopped generating text.
    """

    logger.info("Generating image with Meta Llama 2 Chat model %s", model_id)

    bedrock = boto3.client(service_name='bedrock-runtime')

    accept = "application/json"
    content_type = "application/json"

    response = bedrock.invoke_model(
        body=body, modelId=model_id, accept=accept, contentType=content_type
    )

    response_body = json.loads(response.get('body').read())

    return response_body


def main():
    """
    Entrypoint for Meta Llama 2 Chat example.
    """

    logging.basicConfig(level=logging.INFO,
                        format="%(levelname)s: %(message)s")

    model_id = 'meta.llama2-13b-chat-v1'
    prompt = """What is the average lifespan of a Llama?"""
    max_gen_len = 128
    temperature = 0.1
    top_p = 0.9


    # Create request body.
    body = json.dumps({
        "prompt": prompt,
        "max_gen_len": max_gen_len,
        "temperature": temperature,
        "top_p": top_p
    })


    try:

        response = generate_text(model_id, body)

        print(f"Generated Text: {response['generation']}")
        print(f"Prompt Token count:  {response['prompt_token_count']}")
        print(f"Generation Token count:  {response['generation_token_count']}")
        print(f"Stop reason:  {response['stop_reason']}")

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))

    else:
        print(
            f"Finished generating text with Meta Llama 2 Chat model {model_id}.")


if __name__ == "__main__":
    main()