promptfoo/examples/cerebras

cerebras (Cerebras Example (High-Performance LLM Inference))

This example demonstrates how to use the Cerebras provider with promptfoo to evaluate Cerebras Inference API models, which offer high-performance inference for Llama and other LLM models.

You can run this example with:

npx promptfoo@latest init --example cerebras

Prerequisites

API Key Setup

1. Sign up for an account at Cerebras AI
2. Navigate to your account settings to generate an API key
3. Set your Cerebras API key as an environment variable:

export CEREBRAS_API_KEY="your-api-key-here"

Alternatively, you can add it to your .env file:

CEREBRAS_API_KEY=your-api-key-here

Example Configurations

This repository contains three example configurations demonstrating different Cerebras features:

1. Basic Model Evaluation (promptfooconfig.yaml)

This configuration evaluates two Cerebras models on their ability to explain complex concepts in simple terms.

promptfoo eval

Expected output: You'll see a comparison of how each model explains concepts from different domains, with metrics on clarity, accuracy, and response time.

2. Structured Outputs (promptfooconfig-structured.yaml)

The structured output example demonstrates Cerebras's JSON schema enforcement capabilities, ensuring the model returns consistent, structured recipe data with proper types and required fields.

promptfoo eval -c promptfooconfig-structured.yaml

Expected output: You'll receive structured JSON outputs for different recipes, with consistent fields like cuisine type, difficulty level, ingredients, and cooking instructions - all following the defined schema.

Example output:

{
  "name": "Traditional Pasta Carbonara",
  "cuisine": "Italian",
  "difficulty": "medium",
  "prepTime": 15,
  "cookTime": 20,
  "ingredients": [
    { "name": "spaghetti", "amount": "400g" },
    { "name": "pancetta", "amount": "150g" },
    { "name": "eggs", "amount": "3 large" },
    { "name": "parmesan cheese", "amount": "50g" }
  ],
  "instructions": [
    "Bring a large pot of salted water to boil",
    "Cook spaghetti according to package instructions",
    "In a separate pan, cook pancetta until crispy",
    "In a bowl, whisk eggs and grated parmesan cheese",
    "Drain pasta, reserving some pasta water",
    "Toss hot pasta with pancetta, then quickly mix in egg mixture",
    "Add pasta water as needed to create a silky sauce"
  ]
}

3. Tool Use (promptfooconfig-tools.yaml)

The tool use example demonstrates Cerebras's function calling capabilities with a calculator tool that the model can use to solve math problems.

promptfoo eval -c promptfooconfig-tools.yaml

Expected output: The model will use the calculator tool to solve math problems and provide step-by-step explanations of the solution process. For example, when given "15 × 7", it will calculate 105 and explain multiplication concepts.

Model Capabilities

Cerebras supports several powerful models:

• llama-4-scout-17b-16e-instruct - Llama 4 Scout 17B model with 16 expert MoE (featured in examples)
• llama3.1-8b - Llama 3.1 8B model
• llama-3.3-70b - Llama 3.3 70B model
• deepSeek-r1-distill-llama-70B (private preview)

Pricing & Usage

Cerebras Inference API offers competitive pricing compared to other inference services. Check the official pricing page for the most current rates. Usage is billed based on input and output tokens.

Learn More

• Cerebras Provider Documentation
• Cerebras API Reference
• Cerebras Structured Outputs Guide
• Cerebras Tool Use Guide