Meta Llama 模型

本节介绍的请求参数和响应字段 Meta Llama 模型。使用此信息进行推理调用 Meta Llama 使用InvokeModel和 InvokeModelWithResponseStream（流式传输）操作的模型。本节还包括 Python 显示如何调用的代码示例 Meta Llama 模型。要在推理操作中使用模型，您需要模型的模型 ID。要获取模型 ID，请参阅Amazon Bedrock 模型 IDs。有些型号也适用于匡威。API检查匡威是否API支持特定的 Meta Llama 模型，请参阅支持的型号和型号功能。有关更多代码示例，请参阅使用 Amazon Bedrock 的代码示例 AWS SDKs。

Amazon Bedrock 中的基础模型支持输入和输出模式，这些模式因模型而异。要检查一下模式 Meta Llama 模型支持，请参阅Amazon Bedrock 中支持的根基模型。要查看 Amazon Bedrock 有哪些 Meta Llama 模型支持，请参阅按功能划分的型号支持。要查看哪些 AWS 区域 Meta Llama 型号可在中找到，请参阅按 AWS 地区划分的模型支持。

当你使用进行推理调用时 Meta Llama 模型，则需要包含模型的提示。有关为 Amazon Bedrock 支持的模型创建提示的一般信息，请参阅 提示工程概念。对于 Meta Llama 具体的提示信息，请参阅 Meta Llama 及时的工程指南。

本节提供有关使用以下模型的信息 Meta.

请求和回应

请求正文在请求body字段中传递给InvokeModel或InvokeModelWithResponseStream。

代码示例

此示例说明如何调用 Meta Llama 2 Chat 13B 型号。

# 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')

    response = bedrock.invoke_model(
        body=body, modelId=model_id)

    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 = """<s>[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>

There's a llama in my garden  What should I do? [/INST]"""
    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()