Bedrock 模型微调

在 AWS Bedrock 上微调 Claude

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Finetuning Claude 3 Haiku on Bedrock

In this notebook, we’ll walk you through the process of finetuning Claude 3 Haiku on Amazon Bedrock

What You’ll Need

Install Dependencies

!pip install boto3
import boto3

Prep a Dataset

Your dataset for bedrock finetuning needs to be a JSONL file (i.e. a file with a json object on each line).

Each line in the JSONL file should be a JSON object with the following structure:

{
  "system": "<optional_system_message>",
  "messages": [
    {"role": "user", "content": "user message"},
    {"role": "assistant", "content": "assistant response"},
    ...
  ]
}
  • The system field is optional.
  • There must be at least two messages.
  • The first message must be from the “user”.
  • The last message must be from the “assistant”.
  • User and assistant messages must alternate.
  • No extraneous keys are allowed.

Sample Dataset - JSON Mode

We’ve included a sample dataset that teaches a model to respond to all questions with JSON. Here’s what that dataset looks like:

import json

sample_dataset = []
dataset_path = "datasets/json_mode_dataset.jsonl"
with open(dataset_path) as f:
    for line in f:
        sample_dataset.append(json.loads(line))

print(json.dumps(sample_dataset[0], indent=2))

Upload your dataset to S3

Your dataset for finetuning should be available on s3; for this demo we’ll write the sample dataset to an s3 bucket you control

bucket_name = "YOUR_BUCKET_NAME"
s3_path = "json_mode_dataset.jsonl"

s3 = boto3.client("s3")
s3.upload_file(dataset_path, bucket_name, s3_path)

Launch Bedrock Finetuning Job

Now that you have your dataset ready, you can launch a finetuning job using boto3. First we’ll configure a few parameters for the job:

# Configuration
job_name = "anthropic-finetuning-cookbook-training"
custom_model_name = "anthropic_finetuning_cookbook"
role = "YOUR_AWS_SERVICE_ROLE_ARN"
output_path = f"s3://{bucket_name}/finetuning_example_results/"
base_model_id = (
    "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-haiku-4-5-20251001-v1:0:200k"
)

# Hyperparameters
epoch_count = 5
batch_size = 4
learning_rate_multiplier = 1.0

Then we can launch the job with boto3

bedrock = boto3.client(service_name="bedrock")
bedrock_runtime = boto3.client(service_name="bedrock-runtime")

bedrock.create_model_customization_job(
    customizationType="FINE_TUNING",
    jobName=job_name,
    customModelName=custom_model_name,
    roleArn=role,
    baseModelIdentifier=base_model_id,
    hyperParameters={
        "epochCount": f"{epoch_count}",
        "batchSize": f"{batch_size}",
        "learningRateMultiplier": f"{learning_rate_multiplier}",
    },
    trainingDataConfig={"s3Uri": f"s3://{bucket_name}/{s3_path}"},
    outputDataConfig={"s3Uri": output_path},
)

You can use this to check the status of your job while its training:

# Check for the job status
status = bedrock.get_model_customization_job(jobIdentifier=job_name)["status"]

Use your finetuned model!

To use your finetuned model, you’ll need to host it using Provisioned Throughput in Amazon Bedrock. Once your model is ready with Provisioned Throughput, you can invoked your model via the Bedrock API.

provisioned_throughput_arn = "YOUR_PROVISIONED_THROUGHPUT_ARN"
bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")
body = json.dumps(
    {
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 1000,
        "system": "JSON Mode: Enabled",
        "messages": [
            {
                "role": "user",
                "content": [{"type": "text", "text": "What is a large language model?"}],
            }
        ],
    }
)
response = bedrock_runtime.invoke_model(modelId=provisioned_throughput_arn, body=body)
body = json.loads(response["body"].read().decode("utf-8"))
print(body["content"][0]["text"])
Tip

中文读者提示：本章节的代码和输出为英文原文。如需理解具体实现细节，可参考上方中文导读和代码注释。如有疑问，欢迎在 GitHub Issues 讨论。