Meta Llama models - Amazon Bedrock
Request and responseExample code

Meta Llama models

This section describes the request parameters and response fields for Meta Llama models. Use this information to make inference calls to Meta Llama models with the InvokeModel and InvokeModelWithResponseStream (streaming) operations. This section also includes Python code examples that shows how to call Meta Llama models. To use a model in an inference operation, you need the model ID for the model. To get the model ID, see Supported foundation models in Amazon Bedrock. Some models also work with the Converse API. To check if the Converse API supports a specific Meta Llama model, see Supported models and model features. For more code examples, see Code examples for Amazon Bedrock using AWS SDKs.

Foundation models in Amazon Bedrock support input and output modalities, which vary from model to model. To check the modalities that Meta Llama models support, see Supported foundation models in Amazon Bedrock. To check which Amazon Bedrock features the Meta Llama models support, see Supported foundation models in Amazon Bedrock. To check which AWS Regions that Meta Llama models are available in, see Supported foundation models in Amazon Bedrock.

When you make inference calls with Meta Llama models, you include a prompt for the model. For general information about creating prompts for the models that Amazon Bedrock supports, see Prompt engineering concepts. For Meta Llama specific prompt information, see the Meta Llama prompt engineering guide.

Note

Llama 3.2 Instruct and Llama 3.3 Instruct models use geofencing. This means that these models cannot be used outside the AWS Regions available for these models listed in the Regions table.

This section provides information for using the following models from Meta.

  • Llama 3 Instruct

  • Llama 3.1 Instruct

  • Llama 3.2 Instruct

  • Llama 3.3 Instruct

Request and response

The request body is passed in the body field of a request to InvokeModel or InvokeModelWithResponseStream.

Request

The Llama 3 Instruct, Llama 3.1 Instruct, and Llama 3.2 Instruct models have the following inference parameters. 

{
    "prompt": string,
    "temperature": float,
    "top_p": float,
    "max_gen_len": int
}

NOTE: Llama 3.2 models adds images to the request structure, which is a list of strings. Example: images: Optional[List[str]]

The following are required parameters.

  • prompt – (Required) The prompt that you want to pass to the model. For optimal results, format the conversation with the following template.

    <|begin_of_text|><|start_header_id|>user<|end_header_id|>

What can you help me with?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

    Example template with system prompt

    The following is an example prompt that includes a system prompt.

    <|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are a helpful AI assistant for travel tips and recommendations<|eot_id|><|start_header_id|>user<|end_header_id|>

What can you help me with?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

    Multi-turn conversation example

    The following is an example prompt of a multi-turn conversation.

    <|begin_of_text|><|start_header_id|>user<|end_header_id|>

What is the capital of France?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

The capital of France is Paris!<|eot_id|><|start_header_id|>user<|end_header_id|>

What is the weather like in Paris?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

    Example template with system prompt

    For more information, see Meta Llama 3.

The following are optional parameters.

  • temperature – Use a lower value to decrease randomness in the response.

    Default Minimum Maximum

    0.5

    0

    1

  • top_p – Use a lower value to ignore less probable options. Set to 0 or 1.0 to disable.

    Default Minimum Maximum

    0.9

    0

    1

  • max_gen_len – Specify the maximum number of tokens to use in the generated response. The model truncates the response once the generated text exceeds max_gen_len.

    Default Minimum Maximum

    512

    1

    2048
Response

The Llama 3 Instruct models return the following fields for a text completion inference call. 

{
    "generation": "\n\n<response>",
    "prompt_token_count": int,
    "generation_token_count": int,
    "stop_reason" : string
}

More information about each field is provided below.

  • generation – The generated text.

  • prompt_token_count – The number of tokens in the prompt.

  • generation_token_count – The number of tokens in the generated text.

  • stop_reason – The reason why the response stopped generating text. Possible values are:

    • stop – The model has finished generating text for the input prompt.

    • length – The length of the tokens for the generated text exceeds the value of max_gen_len in the call to InvokeModel (InvokeModelWithResponseStream, if you are streaming output). The response is truncated to max_gen_len tokens. Consider increasing the value of max_gen_len and trying again.

Example code

This example shows how to call the Llama 3 Instruct model.

# Use the native inference API to send a text message to Meta Llama 3.

import boto3
import json

from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-west-2")

# Set the model ID, e.g., Llama 3 70b Instruct.
model_id = "meta.llama3-70b-instruct-v1:0"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Embed the prompt in Llama 3's instruction format.
formatted_prompt = f"""
<|begin_of_text|><|start_header_id|>user<|end_header_id|>
{prompt}
<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
"""

# Format the request payload using the model's native structure.
native_request = {
    "prompt": formatted_prompt,
    "max_gen_len": 512,
    "temperature": 0.5,
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    response = client.invoke_model(modelId=model_id, body=request)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract and print the response text.
response_text = model_response["generation"]
print(response_text)