Issue Accessing Meta-Llama-3.1-70B-Instruct Model in East US 2 Region

Raj Shukla 5 Reputation points
2024-12-21T04:03:12.6366667+00:00

I am experiencing an issue when trying to access the Meta-Llama-3.1-70B-Instruct model via the Azure OpenAI service in the East US 2 region. Below are the details of the problem:

  • Service: Azure OpenAI
  • Model: Meta-Llama-3.1-70B-Instruct
  • Region: East US 2
  • Issue Description: I am unable to successfully request completions from the Meta-Llama-3.1-70B-Instruct model. Every attempt results in an error (InternalServerError: Error code: 500 - {"error": {"code": "InternalServerError", "message": "Backend returned unexpected response. Please contact Microsoft for help."}}).
  • Steps to Reproduce:
    1. Configure the AzureOpenAIClient with the correct endpoint and API key.
      1. Attempt to create a chat completion using the Meta-Llama-3.1-70B-Instruct model.
      2. Error occurs upon submitting the request.
        • Expected Outcome: I expect to receive a response from the model without any errors.
        • Actual Outcome: The request fails with an error (specific error message and code).
        • Troubleshooting Steps Taken:
      • Verified API key and endpoint URL.
        • Checked the Azure status page for any reported outages in the East US 2 region.
          • Attempted the request with a different model to ensure the client setup is correct , this work fine.

Could you please assist in resolving this issue? Any insights or corrective actions you could recommend would be greatly appreciated.

Thank you for your assistance.

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  1. VINODH KUMAR T D 26,141 Reputation points MVP
    2024-12-21T15:28:54.2933333+00:00

    Hi ,

    Thanks for reaching out to Microsoft Q&A.

    Here’s how you can approach resolving the issue with accessing the Meta-Llama-3.1-70B-Instruct model in the East US 2 region via Azure OpenAI service:


    Steps to Investigate and Troubleshoot:

    1. Verify Model Availability in the Region:
      • Confirm that the Meta-Llama-3.1-70B-Instruct model is supported in the East US 2 region.
      • Check Azure OpenAI service documentation or portal for supported models and regions.
    2. Validate Configuration:
      • Ensure that the AzureOpenAIClient is initialized with the correct:
      • API endpoint: The region-specific endpoint, e.g., https://<your-resource-name>.openai.azure.com/.
      • API key: The key tied to the correct Azure OpenAI resource.
    3. Inspect Error Details:
      • Include the specific error code and message. Common errors might be related to:
      • Authentication (401 Unauthorized).
      • Model not found or misconfigured (404 Not Found).
      • Request throttling or limits (429 Too Many Requests).
      • General service issues (500 Internal Server Error).
    4. Service Quotas and Limits:
      • Verify the request aligns with quota limits:
      • Check token limits, batch size, and concurrent request limits for the model.
      • Validate that the input adheres to model specifications (e.g., token count).
    5. Test Connectivity and Basic Functionality:
      • Attempt a request to the OpenAI service using a simpler model like gpt-3.5-turbo or davinci.
      • If these models work, the issue may be specific to the Meta-Llama-3.1-70B-Instruct model.
    6. Logs and Diagnostics:
      • Enable diagnostic logs for the Azure OpenAI resource.
      • Review logs to pinpoint the issue (e.g., API calls, failed attempts).
    7. Check Resource Deployment:
      • Confirm the Azure OpenAI resource has been provisioned correctly.
      • Ensure the Meta-Llama model is explicitly enabled for your resource.

    If you're using Python with the Azure SDK, ensure your request code is structured correctly.

    from azure.ai.openai import OpenAIClient from azure.core.credentials import AzureKeyCredential api_key = "<your-api-key>" endpoint = "https://<your-resource-name>.openai.azure.com/" client = OpenAIClient(endpoint, AzureKeyCredential(api_key)) try: response = client.chat_completions.create( model="Meta-Llama-3.1-70B-Instruct", messages=[{"role": "system", "content": "You are an assistant."}, {"role": "user", "content": "Hello, how can you help me?"}], max_tokens=100 ) print(response.choices[0].message["content"]) except Exception as e: print(f"Error occurred: {e}")

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