Usage Examples

Learn how to use W&B Inference with practical code examples

  3 minute read  

These examples show how to use W&B Inference with Weave for tracing, evaluation, and comparison.

Basic example: Trace Llama 3.1 8B with Weave

This example shows how to send a prompt to the Llama 3.1 8B model and trace the call with Weave. Tracing captures the full input and output of the LLM call, monitors performance, and lets you analyze results in the Weave UI.

In this example:

  • You define a @weave.op()-decorated function that makes a chat completion request
  • Your traces are recorded and linked to your W&B entity and project
  • The function is automatically traced, logging inputs, outputs, latency, and metadata
  • The result prints in the terminal, and the trace appears in your Traces tab at https://wandb.ai

Before running this example, complete the prerequisites.

import weave
import openai

# Set the Weave team and project for tracing
weave.init("<your-team>/<your-project>")

client = openai.OpenAI(
    base_url='https://api.inference.wandb.ai/v1',

    # Get your API key from https://wandb.ai/authorize
    api_key="<your-api-key>",

    # Required for W&B inference usage tracking
    project="wandb/inference-demo",
)

# Trace the model call in Weave
@weave.op()
def run_chat():
    response = client.chat.completions.create(
        model="meta-llama/Llama-3.1-8B-Instruct",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Tell me a joke."}
        ],
    )
    return response.choices[0].message.content

# Run and log the traced call
output = run_chat()
print(output)

After running the code, view the trace in Weave by:

  1. Clicking the link printed in the terminal (for example: https://wandb.ai/<your-team>/<your-project>/r/call/01977f8f-839d-7dda-b0c2-27292ef0e04g)
  2. Or navigating to https://wandb.ai and selecting the Traces tab

Advanced example: Use Weave Evaluations and Leaderboards

Besides tracing model calls, you can also evaluate performance and publish leaderboards. This example compares two models on a question-answer dataset.

Before running this example, complete the prerequisites.

import os
import asyncio
import openai
import weave
from weave.flow import leaderboard
from weave.trace.ref_util import get_ref

# Set the Weave team and project for tracing
weave.init("<your-team>/<your-project>")

dataset = [
    {"input": "What is 2 + 2?", "target": "4"},
    {"input": "Name a primary color.", "target": "red"},
]

@weave.op
def exact_match(target: str, output: str) -> float:
    return float(target.strip().lower() == output.strip().lower())

class WBInferenceModel(weave.Model):
    model: str

    @weave.op
    def predict(self, prompt: str) -> str:
        client = openai.OpenAI(
            base_url="https://api.inference.wandb.ai/v1",
            # Get your API key from https://wandb.ai/authorize
            api_key="<your-api-key>",
            # Required for W&B inference usage tracking
            project="<your-team>/<your-project>",
        )
        resp = client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
        )
        return resp.choices[0].message.content

llama = WBInferenceModel(model="meta-llama/Llama-3.1-8B-Instruct")
deepseek = WBInferenceModel(model="deepseek-ai/DeepSeek-V3-0324")

def preprocess_model_input(example):
    return {"prompt": example["input"]}

evaluation = weave.Evaluation(
    name="QA",
    dataset=dataset,
    scorers=[exact_match],
    preprocess_model_input=preprocess_model_input,
)

async def run_eval():
    await evaluation.evaluate(llama)
    await evaluation.evaluate(deepseek)

asyncio.run(run_eval())

spec = leaderboard.Leaderboard(
    name="Inference Leaderboard",
    description="Compare models on a QA dataset",
    columns=[
        leaderboard.LeaderboardColumn(
            evaluation_object_ref=get_ref(evaluation).uri(),
            scorer_name="exact_match",
            summary_metric_path="mean",
        )
    ],
)

weave.publish(spec)

After running this code, go to your W&B account at https://wandb.ai/ and:

View your model evaluations View your leaderboard

Next steps


Last modified August 26, 2025