Welcome to the world of Pruna AI, where our mission is to make AI models cheaper, smaller, faster, and greener! In this blog post, we will guide you through the process of downloading and running compressed AI models using the Pruna AI framework. Think of us as your friendly neighborhood mechanic, making your hefty AI models light as a feather while ensuring peak performance!

Why Compress AI Models?

AI models can become quite large, consuming significant memory and computational resources. By compressing these models, you optimize their performance while minimizing their carbon footprint. Imagine carrying a suitcase stuffed with clothes on a long journey; you wouldn’t prefer dragging it along with you when you could easily carry a lighter version!

Downloading and Running the Compressed Models

Downloading GGUF Files

Before you begin, let’s quickly navigate how to download the required GGUF files.

• Option A – Using the text-generation-webui:
  1. In the model download section, enter the model repository: PrunaAI/microsoft_WizardLM-2-7B-GGUF-smashed-smashed.
  2. Specify the filename to download (e.g., phi-2.IQ3_M.gguf).
  3. Click on Download.
• Option B – Command Line Download:
  1. Install the Hugging Face Hub library using the command:

     pip3 install huggingface-hub

  2. Use the following command to download the model file:

     huggingface-cli download PrunaAI/microsoft_WizardLM-2-7B-GGUF-smashed-smashed microsoft_WizardLM-2-7B.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False

Running the Model

Option A – Using Llama.cpp

Make sure you are utilizing llama.cpp from the recommended commit. Your command might look something like this:

./main -ngl 35 -m microsoft_WizardLM-2-7B.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p s[INST] prompt [INST]

Here, -ngl specifies the number of layers, while -c denotes the sequence length. Adjust these parameters according to your computational resources, just like a lap runner adjusting their pace based on track conditions!

Option B – Running from Python

You can also run the model in Python using libraries like llama-cpp-python. Start by installing the package.

pip install llama-cpp-python

Here’s a simple example of how you can leverage the model:

from llama_cpp import Llama

llm = Llama(
    model_path=".microsoft_WizardLM-2-7B.IQ3_M.gguf",
    n_ctx=32768,
    n_threads=8,
    n_gpu_layers=35
)

output = llm(s[INST] prompt [INST], max_tokens=512, stop=[s], echo=True)

This code snippet illustrates how to initialize the model and generate some output with it. It’s like setting up a conversation between you and a chatbot – only this one is supercharged with the ability to learn!

Troubleshooting

If you experience issues during downloading or running the models, consider the following troubleshooting steps:

• Ensure that you have correctly installed the required libraries and frameworks.
• Check your internet connection if you encounter downloading errors.
• If your command does not run, ensure that the specified paths for your model files are correct.
• Explore available resources and documentation for additional details.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Transforming bulky AI models into agile performers is now within your grasp! By following the steps outlined above, you can utilize Pruna AI to optimize your machine learning capabilities with efficiency and sustainability in mind.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.