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How to use Llama for code generation

Q: How to use Llama for code generation

Use the llama_cpp Python package to load a Llama GGUF model locally and generate code by calling create_chat_completion with a code prompt. This approach enables efficient, offline code generation with Llama models like llama-3.1-8b-instruct.

Quick answer

Use the llama_cpp Python package to load a Llama GGUF model locally and generate code by calling create_chat_completion with a code prompt. This approach enables efficient, offline code generation with Llama models like llama-3.1-8b-instruct.

PREREQUISITES

Python 3.8+
pip install llama-cpp-python
Download a Llama GGUF model file (e.g., llama-3.1-8b-instruct.gguf)
Basic Python knowledge

Setup

Install the llama-cpp-python package and download a Llama GGUF model from Hugging Face or a trusted source. Ensure you have Python 3.8 or higher.

bash

pip install llama-cpp-python

Step by step

Load the Llama model locally and generate code by sending a prompt in chat format. The example below uses llama-3.1-8b-instruct.gguf and generates Python code for a factorial function.

python

from llama_cpp import Llama

# Initialize the Llama model with the local GGUF file
llm = Llama(model_path="./llama-3.1-8b-instruct.gguf", n_ctx=2048)

# Create a chat completion with a code generation prompt
response = llm.create_chat_completion(messages=[
    {"role": "user", "content": "Write a Python function to compute factorial recursively."}
], max_tokens=256)

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

output

def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n - 1)

Common variations

Async usage: The llama-cpp-python package currently does not support async calls natively; wrap calls in async functions if needed.
Streaming output: Streaming is not supported in the Python API; generate full completions instead.
Different models: Use other GGUF Llama models like llama-3.3-70b.gguf for larger capacity, adjusting model_path accordingly.

Troubleshooting

If you see FileNotFoundError, verify the model_path points to the correct GGUF model file.
If memory errors occur, reduce n_ctx or use a smaller model.
For slow inference, ensure you have GPU support configured or use a smaller model.

✅

Key Takeaways

Use llama-cpp-python with GGUF models for local, efficient code generation.
Call create_chat_completion with a code prompt to generate code snippets.
Adjust model size and context length based on hardware capabilities.
Streaming and async are not currently supported in the official Python API.
Ensure correct model file path and sufficient memory to avoid errors.

Verified 2026-04 · llama-3.1-8b-instruct, llama-3.3-70b

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