> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Use your trained models

> Make inference requests to the models you've trained

After you train a model with Serverless RL, it's automatically available for inference. This page shows you how to construct the endpoint for a trained model and send inference requests to it. Use this endpoint to integrate your model into your application or evaluation workflows.

To send requests to your trained model, you need the following:

* Your [W\&B API key](https://wandb.ai/settings)
* The [Serverless Training API's](/serverless-training/api-reference) base URL, `https://api.training.wandb.ai/v1/`
* Your model's endpoint

The model's endpoint uses the following schema:

```text theme={null}
wandb-artifact:///[ENTITY]/[PROJECT]/[MODEL-NAME]:[STEP]
```

The schema consists of:

* Your W\&B entity's (team) name
* The name of the project associated with your model
* The trained model's name
* The training step of the model you want to deploy. This is usually the step where the model performed best in your evaluations.

For example, if your W\&B team is named `email-specialists`, your project is called `mail-search`, your trained model is named `agent-001`, and you want to deploy it on step 25, the endpoint looks like this:

```text theme={null}
wandb-artifact:///email-specialists/mail-search/agent-001:step25
```

After you have your endpoint, you can integrate it into your normal inference workflows. The following examples show how to make inference requests to your trained model using a cURL request or the [Python OpenAI SDK](https://github.com/openai/openai-python). Choose the example that matches your environment.

## cURL

```bash theme={null}
curl https://api.training.wandb.ai/v1/chat/completions \
    -H "Authorization: Bearer $WANDB_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
            "model": "wandb-artifact://[ENTITY]/[PROJECT]/[MODEL-NAME]:[STEP]",
            "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Summarize our training run."}
            ],
            "temperature": 0.7,
            "top_p": 0.95
        }'
```

## OpenAI SDK

```python theme={null}
from openai import OpenAI

WANDB_API_KEY = "your-wandb-api-key"
ENTITY = "my-entity"
PROJECT = "my-project"

client = OpenAI(
    base_url="https://api.training.wandb.ai/v1",
    api_key=WANDB_API_KEY
)

response = client.chat.completions.create(
    model=f"wandb-artifact:///{ENTITY}/{PROJECT}/my-model:step100",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Summarize our training run."},
    ],
    temperature=0.7,
    top_p=0.95,
)

print(response.choices[0].message.content)
```