Offline inference

Offline inference with MAX allows you to run large language models directly in Python without relying on external API endpoints. This is in contrast to online inference, where you would send requests to a remote service.

When to use offline inference

You'll want to use offline inference in scenarios where you want to perform model inference without the need for a separate model inference server. Typically this includes where you have to process a batch of inputs concurrently.

This approach is beneficial for tasks that require high throughput and can be executed in a controlled environment, such as data preprocessing, model evaluation, or when working with large datasets that need to be processed in batches.

How offline inference works

The core of offline inference revolves around the the LLM class which provides a Python interface to load and run language models.

Specify the model from a Hugging Face repository or a local path and MAX handles the process of downloading the model. The PipelineConfig class allows you to specify parameters related to the inference pipeline, such as max_length and max_num_steps. The generate() function is used to generate text from the model.

The Python API for offline inference currently supports text-only input and does not support multi-modal models. If you need to work with vision capabilities, see the tutorial on Generate image descriptions with Llama 3.2 Vision.

Quickstart

This quickstart demonstrates how to use offline inference using a Hugging Face model with MAX in Python.

1. Set up your project:

   pip

   1. Create a project folder:

      mkdir quickstart && cd quickstart

      mkdir quickstart && cd quickstart

   2. Create and activate a virtual environment:

      python3 -m venv .venv \
        && source .venv/bin/activate

      python3 -m venv .venv \
        && source .venv/bin/activate

   3. Install the modular Python package:

      Nightly

      pip install modular \
        --index-url https://download.pytorch.org/whl/cpu \
        --extra-index-url https://dl.modular.com/public/max-nightly/python/simple/

      pip install modular \
        --index-url https://download.pytorch.org/whl/cpu \
        --extra-index-url https://dl.modular.com/public/max-nightly/python/simple/

      Stable

      pip install modular \
        --index-url https://download.pytorch.org/whl/cpu

      pip install modular \
        --index-url https://download.pytorch.org/whl/cpu

   uv

   1. Install uv:

      curl -LsSf https://astral.sh/uv/install.sh | sh

      curl -LsSf https://astral.sh/uv/install.sh | sh

      Then restart your terminal to make uv accessible.

   2. Create a project:

      uv init quickstart && cd quickstart

      uv init quickstart && cd quickstart

   3. Create and start a virtual environment:

      uv venv && source .venv/bin/activate

      uv venv && source .venv/bin/activate

   4. Install the modular Python package:

      Nightly

      uv pip install modular \
        --index-url https://download.pytorch.org/whl/cpu \
        --extra-index-url https://dl.modular.com/public/max-nightly/python/simple/

      uv pip install modular \
        --index-url https://download.pytorch.org/whl/cpu \
        --extra-index-url https://dl.modular.com/public/max-nightly/python/simple/

      Stable

      uv pip install modular \
        --index-url https://download.pytorch.org/whl/cpu

      uv pip install modular \
        --index-url https://download.pytorch.org/whl/cpu

   magic

   1. Install magic:

      curl -ssL https://magic.modular.com/ | bash

      curl -ssL https://magic.modular.com/ | bash

      Then run the source command that's printed in your terminal.

   2. Create a project:

      magic init quickstart --format pyproject && cd quickstart

      magic init quickstart --format pyproject && cd quickstart

   3. Install the max-pipelines conda package:

      Nightly

      magic add max-pipelines

      magic add max-pipelines

      Stable

      magic add "max-pipelines==25.3"

      magic add "max-pipelines==25.3"

   4. Start the virtual environment:

      magic shell

      magic shell

2. Create a file named main.py with the following code:

   from max.entrypoints.llm import LLM
   from max.pipelines import PipelineConfig
   from max.serve.config import Settings
   
   
   def main():
       model_path = "modularai/Llama-3.1-8B-Instruct-GGUF"
       print(f"Loading model: {model_path}")
   
       # Configure the inference pipeline
       pipeline_config = PipelineConfig(model_path=model_path)
   
       settings = Settings()
   
       # Initialize the LLM engine
       llm = LLM(settings, pipeline_config)
   
       prompts = [
           "In the beginning, there was",
           "I believe the meaning of life is",
           "The fastest way to learn python is",
       ]
   
       print("Generating responses...")
   
       responses = llm.generate(prompts, max_new_tokens=50)
   
       for i, (prompt, response) in enumerate(zip(prompts, responses)):
           print(f"========== Response {i} ==========")
           print(prompt + response)
           print()
   
   
   if __name__ == "__main__":
   
       main()

   from max.entrypoints.llm import LLM
   from max.pipelines import PipelineConfig
   from max.serve.config import Settings
   
   
   def main():
       model_path = "modularai/Llama-3.1-8B-Instruct-GGUF"
       print(f"Loading model: {model_path}")
   
       # Configure the inference pipeline
       pipeline_config = PipelineConfig(model_path=model_path)
   
       settings = Settings()
   
       # Initialize the LLM engine
       llm = LLM(settings, pipeline_config)
   
       prompts = [
           "In the beginning, there was",
           "I believe the meaning of life is",
           "The fastest way to learn python is",
       ]
   
       print("Generating responses...")
   
       responses = llm.generate(prompts, max_new_tokens=50)
   
       for i, (prompt, response) in enumerate(zip(prompts, responses)):
           print(f"========== Response {i} ==========")
           print(prompt + response)
           print()
   
   
   if __name__ == "__main__":
   
       main()

   This script downloads the modularai/Llama-3.1-8B-Instruct-GGUF model (if not already downloaded) and then run inference locally. While the initial model download requires internet access, the actual inference process is self-contained and does not send requests to a remote service for generating text.

   You can update the script to use a different model or modify the prompts to generate different responses. For a list of available models, see our Model repository. We chose the Llama-3.1-8B-Instruct-GGUF model for this example because it's not gated, meaning it's freely available without requiring special access permissions or authentication.

   For offline inference, MAX supports models in GGUF format. This includes most generative LLMs with "Chat" modality, but the specific configuration parameters might vary between models. Always refer to the model's documentation for compatibility details and optimal configuration settings.

3. Run the script:

   python main.py

   python main.py

   This command will download the model and generate responses for the prompts.

   You should see output like the following:

   Generating responses...
   ========== Response 0 ==========
   In the beginning, there was Andromeda. The Andromeda galaxy, that is. It's the
   closest major galaxy to our own Milky Way, and it's been a source of fascination
   for astronomers and space enthusiasts for centuries. But what if I told you that
   there's
   
   ========== Response 1 ==========
   I believe the meaning of life is to find your gift. The purpose of life is to give it away to others.
   I believe that the meaning of life is to find your gift. The purpose of life is to give it away to others.
   I believe that the meaning of life is
   
   ========== Response 2 ==========
   The fastest way to learn python is to practice with real-world projects. Here are
   some ideas for projects that you can use to learn Python:
   
   1. **Command Line Calculator**: Create a command line calculator that can perform
   basic arithmetic operations like addition, subtraction, multiplication, and
   division. 

   Generating responses...
   ========== Response 0 ==========
   In the beginning, there was Andromeda. The Andromeda galaxy, that is. It's the
   closest major galaxy to our own Milky Way, and it's been a source of fascination
   for astronomers and space enthusiasts for centuries. But what if I told you that
   there's
   
   ========== Response 1 ==========
   I believe the meaning of life is to find your gift. The purpose of life is to give it away to others.
   I believe that the meaning of life is to find your gift. The purpose of life is to give it away to others.
   I believe that the meaning of life is
   
   ========== Response 2 ==========
   The fastest way to learn python is to practice with real-world projects. Here are
   some ideas for projects that you can use to learn Python:
   
   1. **Command Line Calculator**: Create a command line calculator that can perform
   basic arithmetic operations like addition, subtraction, multiplication, and
   division. 

Next steps

For more information on offline inference, see the following:

• Offline inference example
• Offline inference recipe

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