Use your trained models

After training a model with Serverless RL, it is automatically available for inference.

To send requests to your trained model, you need:

• Your W&B API key
• The Training API’s base URL, https://api.training.wandb.ai/v1/
• Your model’s endpoint

The model’s endpoint uses the following schema:

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 wanted to deploy it on step 25, the endpoint looks like this:

wandb-artifact:///email-specialists/mail-search/agent-001:step25

Once 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.

cURL

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

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)