swarms/docs/swarms/models/cerebras.md

Using Cerebras LLaMA with Swarms

This guide demonstrates how to create and use an AI agent powered by the Cerebras LLaMA 3 70B model using the Swarms framework.

Prerequisites

• Python 3.7+

• Swarms library installed (pip install swarms)

• Set your ENV key CEREBRAS_API_KEY

Step-by-Step Guide

1. Import Required Module

from swarms.structs.agent import Agent

This imports the Agent class from Swarms, which is the core component for creating AI agents.

2. Create an Agent Instance

agent = Agent(
    agent_name="Financial-Analysis-Agent",
    agent_description="Personal finance advisor agent",
    max_loops=4,
    model_name="cerebras/llama3-70b-instruct",
    dynamic_temperature_enabled=True,
    interactive=False,
    output_type="all",
)

Let's break down each parameter:

• agent_name: A descriptive name for your agent (here, "Financial-Analysis-Agent")

• agent_description: A brief description of the agent's purpose

• max_loops: Maximum number of interaction loops the agent can perform (set to 4)

• model_name: Specifies the Cerebras LLaMA 3 70B model to use

• dynamic_temperature_enabled: Enables dynamic adjustment of temperature for varied responses

• interactive: When False, runs without requiring user interaction

• output_type: Set to "all" to return complete response information

3. Run the Agent

agent.run("Conduct an analysis of the best real undervalued ETFs")

This command:

1. Activates the agent

2. Processes the given prompt about ETF analysis

3. Returns the analysis based on the model's knowledge

Notes

• The Cerebras LLaMA 3 70B model is a powerful language model suitable for complex analysis tasks

• The agent can be customized further with additional parameters

• The max_loops=4 setting prevents infinite loops while allowing sufficient processing depth

• Setting interactive=False makes the agent run autonomously without user intervention

Example Output

The agent will provide a detailed analysis of undervalued ETFs, including:

• Market analysis

• Performance metrics

• Risk assessment

• Investment recommendations

Note: Actual output will vary based on current market conditions and the model's training data.