Welcome to the world of Fauno, the pioneering Italian language model developed by the RSTLess Research Group at the Sapienza University of Rome. In this article, we’ll explore how Fauno operates and how you can leverage its capabilities for your innovative projects.

Why Choose Fauno?

Fauno is not just any language model. If Baize represents a mythical creature, then Fauno draws inspiration from Roman mythology to create a uniquely Italian identity. With abilities ranging from answering questions to understanding medical literature, Fauno has been finely tuned using extensive datasets. This model can engage with a variety of topics, making it a versatile tool for your needs.

Getting Started with Fauno

To harness the power of Fauno, follow these straightforward steps:

1. Navigate to the Colab notebook by following this link: Open in Colab.
2. Once the notebook is open, you’ll see a cell for model initialization and another for inference.
3. Run the code in the model initialization section, which loads the necessary libraries and the Fauno model.
4. After initialization, proceed to the inference section where you can pose your questions to the model.

The Model Initialization: An Analogy

Think of initializing the Fauno model like setting up a restaurant kitchen. You begin by gathering all the essential ingredients (in our case, the libraries and model files) and arranging your tools (the coding commands). Once everything is prepped, your kitchen is set to create delicious meals (inference) that will satisfy your patrons (users). In our code, we utilize libraries like transformers and peft to serve up opportunities to engage with natural language queries.

from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
from peft import PeftModel

tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
model = LlamaForCausalLM.from_pretrained(
    "decapoda-research/llama-7b-hf",
    load_in_8bit=True,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, "andreabac3/Fauno-Italian-LLM-7B")
model.eval()

Inference: Engaging with Fauno

After model initialization, let’s explore setting up a query:

def evaluate(question: str) -> str:
    prompt = f"The conversation between human and AI assistant.\n[Human] {question}\n[AI]"
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].cuda()
    generation_output = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=256
    )
    output = tokenizer.decode(generation_output.sequences[0]).split("[AI]")[1]
    return output

your_question: str = "Qual è il significato della vita?"
print(evaluate(your_question))

Troubleshooting

While utilizing Fauno, you may encounter a few hurdles. Here are some common issues and their solutions:

• Model not loading: Ensure that you have access to the necessary resources in your Google Colab or local environment. Check for correct library installations.
• CUDA device errors: If you see errors related to CUDA, it may indicate an issue with your GPU settings. Make sure you’re running the notebook with a GPU runtime.
• Unexpected outputs: Fauno may produce outputs that seem off-topic or nonsensical. Remember, this is a generative model, and it can sometimes experience hallucinations.
  For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Always refer back to the documentation when you encounter issues and do not hesitate to experiment with your queries for better results!

Conclusion

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.

By following these guidelines, you’ll be ready to explore the vast capabilities of Fauno. Happy coding!