How to beginner · 4 min read

How to use Llama for code generation

Q: How to use Llama for code generation

Use the OpenAI SDK with a third-party provider that hosts Llama models, such as Groq or Together AI, by setting base_url and model parameters. Send your code generation prompt via client.chat.completions.create() to generate code snippets with Llama models like llama-3.3-70b-versatile.

Quick answer

Use the OpenAI SDK with a third-party provider that hosts Llama models, such as Groq or Together AI, by setting base_url and model parameters. Send your code generation prompt via client.chat.completions.create() to generate code snippets with Llama models like llama-3.3-70b-versatile.

PREREQUISITES

Python 3.8+
API key from a Llama model provider (e.g., Groq, Together AI)
pip install openai>=1.0
Set environment variable for API key (e.g., GROQ_API_KEY)

Setup

Install the openai Python package and set your API key environment variable for the Llama provider you choose. For example, Groq hosts Llama models accessible via OpenAI-compatible API endpoints.

bash

pip install openai>=1.0

Step by step

Use the OpenAI SDK with the provider's base_url and specify a Llama model for code generation. Send a prompt describing the code you want generated.

python

import os
from openai import OpenAI

# Initialize client with Groq API key and base URL
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")

# Define the prompt for code generation
prompt = "Write a Python function that returns the Fibonacci sequence up to n."

# Create chat completion request
response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": prompt}]
)

# Extract generated code
generated_code = response.choices[0].message.content
print(generated_code)

output

def fibonacci(n):
    sequence = [0, 1]
    while sequence[-1] + sequence[-2] < n:
        sequence.append(sequence[-1] + sequence[-2])
    return sequence

Common variations

Use other Llama providers like Together AI by changing base_url and api_key.
Switch models to smaller or specialized Llama variants for faster or more focused code generation.
Implement streaming completions by iterating over the response if supported by the provider.

python

from openai import OpenAI
import os

# Together AI example
client = OpenAI(api_key=os.environ["TOGETHER_API_KEY"], base_url="https://api.together.xyz/v1")

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[{"role": "user", "content": "Generate a JavaScript function to reverse a string."}]
)

print(response.choices[0].message.content)

output

function reverseString(str) {
    return str.split('').reverse().join('');
}

Troubleshooting

If you get authentication errors, verify your API key environment variable is set correctly.
If the model is not found, confirm the model name matches the provider's current Llama model offerings.
For slow responses, try smaller Llama models or check your network connectivity.

✅

Key Takeaways

Use OpenAI-compatible SDKs with third-party Llama providers for code generation.
Set base_url and api_key correctly to access Llama models.
Choose Llama models like llama-3.3-70b-versatile for powerful code generation.
Test prompts with clear instructions for best code output.
Check provider docs for model updates and streaming support.

Verified 2026-04 · llama-3.3-70b-versatile, meta-llama/Llama-3.3-70B-Instruct-Turbo

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