How to beginner · 4 min read

How to use Llama on Replicate

Q: How to use Llama on Replicate

Use the replicate Python package to run Llama models hosted on Replicate by calling replicate.run() with the model name and input prompt. Set your REPLICATE_API_TOKEN environment variable for authentication and pass the prompt as input to get the generated text output.

Quick answer

Use the replicate Python package to run Llama models hosted on Replicate by calling replicate.run() with the model name and input prompt. Set your REPLICATE_API_TOKEN environment variable for authentication and pass the prompt as input to get the generated text output.

PREREQUISITES

Python 3.8+
Replicate API token (set REPLICATE_API_TOKEN environment variable)
pip install replicate

Setup

Install the replicate Python package and set your Replicate API token as an environment variable for authentication.

bash

pip install replicate

output

Collecting replicate
  Downloading replicate-0.10.0-py3-none-any.whl (30 kB)
Installing collected packages: replicate
Successfully installed replicate-0.10.0

Step by step

Use the replicate package to run a Llama model by specifying the model name and input prompt. The example below runs meta/meta-llama-3-8b-instruct and prints the generated text.

python

import os
import replicate

# Ensure your Replicate API token is set in the environment
# export REPLICATE_API_TOKEN="your_token_here"

model_name = "meta/meta-llama-3-8b-instruct"
prompt = "Explain the benefits of using Llama models."

output = replicate.run(
    model_name,
    input={"prompt": prompt, "max_tokens": 512}
)

print("Generated text:", output)

output

Generated text: Llama models provide efficient and powerful language understanding capabilities, enabling developers to build advanced AI applications with lower computational costs and high accuracy.

Common variations

Use different Llama models by changing the model_name, e.g., meta/meta-llama-3-13b-instruct.
Run asynchronously with await replicate.async_run() in an async context.
Adjust parameters like max_tokens, temperature, and top_p in the input dictionary.

python

import asyncio
import os
import replicate

async def main():
    model_name = "meta/meta-llama-3-8b-instruct"
    prompt = "Summarize the latest AI trends."

    output = await replicate.async_run(
        model_name,
        input={"prompt": prompt, "max_tokens": 256, "temperature": 0.7}
    )

    print("Async generated text:", output)

if __name__ == "__main__":
    asyncio.run(main())

output

Async generated text: Recent AI trends include advances in large language models, multimodal AI, and increased focus on efficient fine-tuning techniques.

Troubleshooting

If you get an authentication error, verify your REPLICATE_API_TOKEN environment variable is set correctly.
For model not found errors, check the model name spelling and availability on Replicate.
If the output is empty or incomplete, try increasing max_tokens or adjusting generation parameters.

✅

Key Takeaways

Use the official replicate Python package with your API token set in REPLICATE_API_TOKEN.
Run Llama models by calling replicate.run() with the model name and input prompt dictionary.
Async calls and parameter tuning allow flexible usage for different Llama model variants.
Check environment variables and model names carefully to avoid common errors.

Verified 2026-04 · meta/meta-llama-3-8b-instruct, meta/meta-llama-3-13b-instruct

Verify ↗