Prasyarat

6. Pilih tab Izin.

7. Pilih Edit.

8. [
   {
       "AllowedHeaders": [
           "*"
       ],
       "AllowedMethods": [
           "GET",
           "PUT",
           "POST",
           "DELETE"
       ],
       "AllowedOrigins": [
           "*"
       ],
       "ExposeHeaders": [
           "Access-Control-Allow-Origin"
       ]
   }
   ]

9. Pilih Simpan perubahan.

6. Pilih Lampirkan.

8. Pilih Lampirkan kebijakan.

10. Pilih Pilih.

11. !pip install fmeval

    catatan

1. import sagemaker
   from sagemaker.jumpstart.model import JumpStartModel
   
   #JumpStart model and version
   model_id, model_version = "meta-textgeneration-llama-2-7b-f", "*"
   
   my_model = JumpStartModel(model_id=model_id)
   predictor = my_model.deploy()
   endpoint_name = predictor.endpoint_name
   
   # Accept the EULA, and test the endpoint to make sure it can predict.
   predictor.predict({"inputs": [[{"role":"user", "content": "Hello how are you?"}]]}, custom_attributes='accept_eula=true')

2. from fmeval.model_runners.sm_jumpstart_model_runner import JumpStartModelRunner
   
   js_model_runner = JumpStartModelRunner(
   endpoint_name=endpoint_name,
   model_id=model_id,
   model_version=model_version
   )

3. js_model_runner.predict("What is the capital of London")

1. import boto3
   import json
   bedrock = boto3.client(service_name='bedrock')
   bedrock_runtime = boto3.client(service_name='bedrock-runtime')
   
   model_id = "amazon.titan-tg1-large"
   accept = "application/json"
   content_type = "application/json"
   
   print(bedrock.get_foundation_model(modelIdentifier=modelId).get('modelDetails'))

   Untuk informasi selengkapnya tentang parameter ini, lihat InvokeModelWithResponseStream.

2. from fmeval.model_runners.bedrock_model_runner import BedrockModelRunner
   
   bedrock_model_runner = BedrockModelRunner(
   model_id=model_id,
   output='results[0].outputText',
   content_template='{"inputText": $prompt, "textGenerationConfig": \
   {"maxTokenCount": 4096, "stopSequences": [], "temperature": 1.0, "topP": 1.0}}',
   )

   • "content_template": "{\"prompt\": $prompt, \"max_tokens_to_sample\": 500}"

     "content_template": "{\"inputs\": $prompt, \"parameters\":{\"max_new_tokens\": \
     10, \"top_p\": 0.9, \"temperature\": 0.8}}"

     bedrock_model_runner.predict("What is the capital of London?")