# DeepSeekR1

### Overview <a href="#overview" id="overview"></a>

**DeepSeekR1 is a high-performance and scalable family of large language models developed by DeepSeek AI.**\
It is optimized for inference efficiency and supports both base and instruction-tuned variants, making it suitable for a wide range of production-grade NLP workloads such as coding, reasoning, and chat.

**DeepSeek is available through the vLLM application and is designed for instant deployment, reducing the need for manual configuration.**\
If you need to customize the deployment, use the configurable application variant. Examples of tunable parameters include `server-extra-args`, Ingress authentication, and more.

### Managing application via Apolo CLI <a href="#managing-application-via-apolo-cli" id="managing-application-via-apolo-cli"></a>

DeepSeekR1 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 deepseek-inference > deepseek.yaml
```

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

```yaml
# Application template configuration for: llama4-inference
# Application template configuration for: deepseek-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: deepseek-inference
template_version: v25.7.1
input:
  # Apolo Secret Configuration.
  hf_token:
    key: ''
  # Enable or disable autoscaling for the LLM.
  autoscaling_enabled: false
  size: R1
  llm_class: deepseek_r1


```

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 deepseek.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](#usage) section.

### Usage

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

1. Go to the **Installed Apps** tab.
2. You will see a list of all running apps, including the **DeepSeek** 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.

```python
import requests

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

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

data = {
    "model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",  # 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](https://github.com/vllm-project/vllm)
* [app-llm-inference Helm Chart Repository](https://github.com/neuro-inc/app-llm-inference)
* [Apolo Documentation](https://docs.apolo.us/apolo-cli/commands/shortcuts#usage-16) (for the usage of `apolo run` and resource presets)
* [Hugging Face Model Hub](https://huggingface.co/) (for discovering or hosting models)
* [Managing Apps](/index/apolo-concepts-cli/apps/installable-apps/managing-apps.md)
* [Apolo Hugging Face application management](/index/apolo-console/apps/installable-apps/available-apps/hugging-face.md)
* [DeepSeekR1 installation via Apolo UI](/index/apolo-console/apps/installable-apps/available-apps/deepseekr1.md)
* [vLLM inference install via CLI](/index/apolo-concepts-cli/apps/installable-apps/available-apps/vllm-inference.md)
* [DeepSeek HuggingFace collection](https://huggingface.co/deepseek-ai/DeepSeek-R1)


---

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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
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```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.