Introducing the Hermes 2 Pro, a remarkable AI model based on the Mistral 7B architecture. This powerful AI combines advanced language capabilities with a focus on function calling and structured JSON outputs, making it a valuable tool for developers and AI enthusiasts alike. In this article, we’ll walk you through the process of using Hermes 2 Pro, with some troubleshooting tips along the way.

Understanding the Basics

Think of Hermes 2 Pro as a sophisticated assistant that can perform tasks for you—just like having a highly trained chef in a kitchen. If you have a recipe (a function call), you simply provide the chef with the ingredients (the input parameters), and they whip up a delicious dish (the output). With Hermes, your ingredients and recipe can involve various types of data and functions!

Getting Started with Hermes 2 Pro

1. **Installation**: First of all, ensure you have the necessary libraries installed. You will need PyTorch and HuggingFace Transformers. Follow the installation instructions specific to your setup.
2. **Load the Model**: Import the necessary classes to set up the model and tokenizer:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B")

3. **Craft Your Prompts**: Use the ChatML format for effective interaction. This is essential for function calling and structured outputs.
4. **Generate Outputs**: Call the model using your prompts. For instance:

user_input = "Write a short story about Goku discovering Kirby has teamed up with Majin Buu to destroy the world."
input_ids = tokenizer.encode(user_input, return_tensors='pt')

generated_ids = model.generate(input_ids, max_length=300)
response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(response)

Function Calling in Hermes 2 Pro

Function calling lets you execute predefined functions to get structured data responses, like fetching stock information. Like a digital waiter taking food orders, Hermes listens and serves the data you request.

Example of Function Calling

1. Define a function signature:

def get_stock_data(symbol: str):
    # Implementation of stock data fetching
    pass

2. Invoke the function using Hermes:

function_call = {
    "name": "get_stock_data",
    "arguments": {"symbol": "AAPL"}
}

Troubleshooting Tips

• If you encounter errors while loading the model, ensure that you have the correct version of all dependencies installed.
• Check your internet connection; model loading requires access to HuggingFace repositories.
• Ensure your tokens are within the model’s input size limits; otherwise, it may throw an error.
• If the model does not respond as expected, review your prompt formatting in the ChatML style.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Hermes 2 Pro offers a formidable platform for interacting with AI models, enabling advanced function calls and JSON outputs. By following this guide, you can effectively harness its capabilities. 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.