Llama 4 Scout integrates text and image understanding, making it suitable for multimodal applications such as visual Q&A, content moderation, captioning, and analysis tasks involving images combined with textual data. It efficiently balances computational load via a mixture-of-experts architecture.
import openai
import weave
# Weave autopatches OpenAI to log LLM calls to W&B
weave.init("<team>/<project>")
client = openai.OpenAI(# The custom base URL points to W&B Inference
base_url='https://api.inference.wandb.ai/v1',# Get your API key from https://wandb.ai/authorize# Consider setting it in the environment as OPENAI_API_KEY instead for safety
api_key="<your-apikey>",# Team and project are required for usage tracking
project="<team>/<project>",)
response = client.chat.completions.create(
model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
messages=[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Tell me a joke."}],)print(response.choices[0].message.content)
