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LLaMA4

Overview

LLaMA4 is a high-performance and scalable family of large language models developed by Meta AI. It is optimized for inference efficiency and supports both instruction-tuned and base variants, making it suitable for a wide range of production-level NLP tasks and deployments.

LLaMA 4 is part of the vLLM application and is designed for instant deployment, minimizing configuration effort. If you need to customize the deployment, please use the configurable application variant. Examples of tunable parameters include server-extra-args, Ingress authentication, and more.

Managing application via Apolo CLI

LLaMA4 can be installed on Apolo either via the CLI or the Web Console. Below are the detailed instructions for installing using Apolo CLI.

Install via Apolo CLI

Step 1 — Use the CLI command to get the application configuration file template:

apolo app-template get llama4-inference > llama4.yaml

Step 2 — Customize the application parameters. Below is an example configuration file:

# Application template configuration for: llama4-inference
# Fill in the values below to configure your application.
# To use values from another app, use the following format:
# my_param:
#   type: "app-instance-ref"
#   instance_id: "<app-instance-id>"
#   path: "<path-from-get-values-response>"

template_name: llama4-inference
template_version: v25.7.1
input:
  # Apolo Secret Configuration.
  hf_token:
    key: ''
  # Enable or disable autoscaling for the LLM.
  autoscaling_enabled: false
  size: <ONE_OF_THE_AVAILABLE  >
  llm_class: llama4

Explanation of configuration parameters:

  1. HuggingFace token: Set HuggingFace model that you want to deploy.

  2. Autoscaling: enable if needed true/false

  3. Size: One of the available model sizes

Step 3 — Deploy the application in your Apolo project:

apolo app install -f llama4.yaml

Monitor the application status using:

apolo app list

To uninstall the application, use:

apolo app uninstall <app-id>

If you want to see logs of the application, use:

apolo app logs <app-id>

For instructions on how to access the application, please refer to the Usage section.

Usage

After installation, you can utilize LLaMA4 for different kind of workflows:

  1. Go to the Installed Apps tab.

  2. You will see a list of all running apps, including the LLaMA4 app you just installed. To open the detailed information & uninstall the app, click the Details button.

Once in the Details" page, scroll down to the Outputs sections. To launch the applications, find the HTTP API output with with the public domain address, copy and open it and paste to the script.

import requests

API_URL = "<APP_HOST>/v1/chat/completions"

headers = {
    "Content-Type": "application/json",
}

data = {
    "model": "meta-llama/Llama-4-Scout-17B-16E",  # Must match the model name loaded by vLLM
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant that gives concise and clear answers.",
        },
        {
            "role": "user",
            "content": (
                "I'm preparing a presentation for non-technical stakeholders "
                "about the benefits and limitations of using large language models in our customer support workflows. "
                "Can you help me outline the key points I should include, with clear, jargon-free explanations and practical examples?"
            ),
        },
    ]
}


if __name__ == '__main__':

    response = requests.post(API_URL, headers=headers, json=data)
    response.raise_for_status()

    reply = response.text
    status_code = response.status_code
    print("Assistant:", reply)
    print("Status Code:", status_code)

References:

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