The world of AI is ever-evolving, and at the forefront is the Gorilla-OpenFunctions v2, providing cutting-edge capabilities comparable to models like GPT-4. This guide will take you through the steps needed to set up and use GGUF quantized models locally.

Introduction to Gorilla-OpenFunctions v2

Gorilla-OpenFunctions extends the capabilities of large language models (LLMs) through enhanced chat completion features that can formulate executable API calls from natural language instructions. With multiple function support and native REST capabilities, it makes integrating AI into your applications smoother than ever.

What You Need to Get Started

• Python installed on your machine.
• Access to the internet for downloading models.
• The Hugging Face CLI installed.
• A compatible hardware setup for optimal performance.

Download the GGUF Models

To begin using GGUF locally, the first step is to download the models. Here’s how you can do that:

• Open your terminal.
• Run the following command, replacing QUANTIZATION_METHOD with your desired quantization option:

bash
huggingface-cli download gorilla-llm/gorilla-openfunctions-v2-gguf gorilla-openfunctions-v2-QUANTIZATION_METHOD.gguf --local-dir gorilla-openfunctions-v2-GGUF

This command will store the specified GGUF file in your local directory, named gorilla-openfunctions-v2-GGUF.

Supported Quantization Methods

Here are the supported quantization methods that you can use:

• q2_K
• q3_K_S
• q3_K_M
• q3_K_L
• q4_K_S
• q4_K_M
• q5_K_S
• q5_K_M
• q6_K

Setting Up for Local Inference

After downloading the models, you will need to install the llama-cpp-python package. Follow the instructions on their GitHub page for the installation process.

Example Script for Local Inference

Fill in your directory in the code snippet below to set up the local inference:

python
from llama_cpp import Llama
import json

llm = Llama(model_path=YOUR_DIRECTORY/gorilla-openfunctions-v2-GGUF/gorilla-openfunctions-v2-q2_K.gguf, n_threads=8, n_gpu_layers=35)

def get_prompt(user_query: str, functions: list = []) -> str:
    # Generates a conversation prompt based on the user's query and a list of functions.
    parameters...
    
user_prompt = get_prompt(query, functions)
output = llm(user_prompt, max_tokens=512, stop="EOT", echo=True)
print("Output:", output)

This code snippet sets up a basic structure to interact with the model. You just need to replace YOUR_DIRECTORY with the path where your model is stored.

Expected Output

Once you run the script, you can expect the output to include responses generated by the Gorilla LLM model based on your provided query. Pay attention to the logs for any relevant information regarding model loading and execution times.

Troubleshooting Common Issues

If you encounter any issues while using the GGUF models, here are some troubleshooting ideas:

• Make sure you have a stable internet connection while downloading models.
• Ensure that you are running the correct version of Python and have all dependencies installed.
• If the output is unexpected, check your input prompt and ensure it is formatted properly.
• Review your system resources; make sure you have enough memory available to run the model.
• If you face any specific errors, consider checking the logs for hints or search for solutions online.

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

Conclusion

With Gorilla-OpenFunctions v2, harnessing the power of advanced AI models locally is more accessible than ever. Dive into the world of quantized models and see how they can revolutionize your projects.

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.