Llama4 Model Integration¶

Prerequisites

Python 3.8 or higher
swarms library installed
Access to Llama4 model
Valid environment variables configured

Quick Start¶

Here's a simple example of integrating Llama4 model for crypto risk analysis:

from dotenv import load_dotenv
from swarms import Agent
from swarms.utils.vllm_wrapper import VLLM

load_dotenv()
model = VLLM(model_name="meta-llama/Llama-4-Maverick-17B-128E")

Available Models¶

Model Name	Description	Type
meta-llama/Llama-4-Maverick-17B-128E	Base model with 128 experts	Base
meta-llama/Llama-4-Maverick-17B-128E-Instruct	Instruction-tuned version with 128 experts	Instruct
meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8	FP8 quantized instruction model	Instruct (Optimized)
meta-llama/Llama-4-Scout-17B-16E	Base model with 16 experts	Base
meta-llama/Llama-4-Scout-17B-16E-Instruct	Instruction-tuned version with 16 experts	Instruct

Model Selection

Choose Instruct models for better performance on instruction-following tasks
FP8 models offer better memory efficiency with minimal performance impact
Scout models (16E) are lighter but still powerful
Maverick models (128E) offer maximum performance but require more resources

Detailed Implementation¶

1. Define Custom System Prompt¶

CRYPTO_RISK_ANALYSIS_PROMPT = """
You are a cryptocurrency risk analysis expert. Your role is to:

1. Analyze market risks:
   - Volatility assessment
   - Market sentiment analysis
   - Trading volume patterns
   - Price trend evaluation

2. Evaluate technical risks:
   - Network security
   - Protocol vulnerabilities
   - Smart contract risks
   - Technical scalability

3. Consider regulatory risks:
   - Current regulations
   - Potential regulatory changes
   - Compliance requirements
   - Geographic restrictions

4. Assess fundamental risks:
   - Team background
   - Project development status
   - Competition analysis
   - Use case viability

Provide detailed, balanced analysis with both risks and potential mitigations.
Base your analysis on established crypto market principles and current market conditions.
"""

2. Initialize Agent¶

agent = Agent(
    agent_name="Crypto-Risk-Analysis-Agent",
    agent_description="Agent for analyzing risks in cryptocurrency investments",
    system_prompt=CRYPTO_RISK_ANALYSIS_PROMPT,
    max_loops=1,
    llm=model,
)

Full Code¶

from dotenv import load_dotenv

from swarms import Agent
from swarms.utils.vllm_wrapper import VLLM

load_dotenv()

# Define custom system prompt for crypto risk analysis
CRYPTO_RISK_ANALYSIS_PROMPT = """
You are a cryptocurrency risk analysis expert. Your role is to:

1. Analyze market risks:
   - Volatility assessment
   - Market sentiment analysis
   - Trading volume patterns
   - Price trend evaluation

2. Evaluate technical risks:
   - Network security
   - Protocol vulnerabilities
   - Smart contract risks
   - Technical scalability

3. Consider regulatory risks:
   - Current regulations
   - Potential regulatory changes
   - Compliance requirements
   - Geographic restrictions

4. Assess fundamental risks:
   - Team background
   - Project development status
   - Competition analysis
   - Use case viability

Provide detailed, balanced analysis with both risks and potential mitigations.
Base your analysis on established crypto market principles and current market conditions.
"""

model = VLLM(model_name="meta-llama/Llama-4-Maverick-17B-128E")

# Initialize the agent with custom prompt
agent = Agent(
    agent_name="Crypto-Risk-Analysis-Agent",
    agent_description="Agent for analyzing risks in cryptocurrency investments",
    system_prompt=CRYPTO_RISK_ANALYSIS_PROMPT,
    max_loops=1,
    llm=model,
)

print(
    agent.run(
        "Conduct a risk analysis of the top cryptocurrencies. Think for 2 loops internally"
    )
)

Resource Usage

The Llama4 model requires significant computational resources. Ensure your system meets the minimum requirements.

FAQ¶

What is the purpose of max_loops parameter?

The max_loops parameter determines how many times the agent will iterate through its thinking process. In this example, it's set to 1 for a single pass analysis.

Can I use a different model?

Yes, you can replace the VLLM wrapper with other compatible models. Just ensure you update the model initialization accordingly.

How do I customize the system prompt?

You can modify the CRYPTO_RISK_ANALYSIS_PROMPT string to match your specific use case while maintaining the structured format.

Best Practices

Always handle API errors gracefully
Monitor model performance and resource usage
Keep your prompts clear and specific
Test thoroughly before production deployment

Sample Usage

response = agent.run(
    "Conduct a risk analysis of the top cryptocurrencies. Think for 2 loops internally"
)
print(response)