GPT OSS

Overview

GPT OSS is a family of open-source large language models developed for broad accessibility and performance. They are optimized for high-quality text generation and support both base and instruction-tuned variants, making them suitable for a wide range of NLP workloads such as chat, reasoning, and content creation.

GPT OSS models are available through the vLLM application and are designed for instant deployment, minimizing the need for manual setup. For advanced use cases, you can switch to the configurable application variant, which allows customization of parameters such as server-extra-args, Ingress authentication, and more.

Managing application via Apolo CLI

GPT OSS 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 gpt-inference > gpt-oss.yaml

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

# Application template configuration for: gpt-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>"
# yaml-language-server: $schema=https://api.dev.apolo.us/apis/apps/v2/templates/gpt-inference/v25.7.1/schema

template_name: gpt-inference
template_version: v25.7.1
input:
  # Apolo Secret Configuration.
  hf_token:
    key: ''
  # Enable or disable autoscaling for the LLM.
  autoscaling_enabled: false
  size: gpt-oss-20b
  llm_class: gpt-oss

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 gpt-oss.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 GPT OSS for different kind of workflows:

1. Go to the Installed Apps tab.

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

Once in the Details page, find the API endpoint URL in the Outputs section. Use this URL in your API calls as shown in the script below.

import requests

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

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

data = {
    "model": "openai/gpt-oss-20b",  # 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:

• vLLM Official GitHub Repo

• app-llm-inference Helm Chart Repository

• Apolo Documentation (for the usage of apolo run and resource presets)

• Hugging Face Model Hub (for discovering or hosting models)

• Managing Apps

• Apolo Hugging Face application management

• GPT OSS installation via Apolo UI

• vLLM inference install via CLI

• GPT OSS HuggingFace Collection