Welcome to the Jais Family Model Card! This series of bilingual English-Arabic large language models (LLMs) is designed to push the boundaries of NLP for Arabic and English speakers alike. Whether you’re a researcher or a developer, these models offer robust functionality to enhance your projects.

What Are the Jais Models?

The Jais family consists of two primary types of foundation models:

• Models pre-trained from scratch (jais-family-*).
• Models adapted from Llama-2 (**Llama-2**) (jais-adapted-*).

These models span a variety of sizes, from 590M to 70B parameters, and are optimized to excel in Arabic, while maintaining strong capabilities in English. With up to 1.6 trillion tokens of training data, the potential for research and application is vast.

Getting Started with Jais Models

To use Jais models, you’ll need to follow a few simple steps. Below is sample code to help you get started:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = "inceptionai/jais-family-30b-16k-chat"  # your model path
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", trust_remote_code=True)

def get_response(text):
    input_ids = tokenizer(text, return_tensors="pt").input_ids
    inputs = input_ids.to("cuda" if torch.cuda.is_available() else "cpu")
    generate_ids = model.generate(inputs, top_p=0.9, temperature=0.3, max_length=2048, do_sample=True)

    return tokenizer.decode(generate_ids[0], skip_special_tokens=True)

Analogy: Think of Jais as an Assistant

Imagine having a personal assistant who is fluent in both Arabic and English. When you ask this assistant (the model) a question, it quickly analyzes your request and formulates an answer based on its extensive knowledge and training. The Jais models function similarly: they interpret user input and generate coherent and contextually relevant responses, all while maintaining an understanding of cultural nuances across languages.

Key Features of Jais Models

• Multi-Application: Beneficial for research, chat applications, sentiment analysis, and more.
• Adaptive Learning: Effectively utilizes feedback to enhance its understanding of language subtleties.
• Extensible Framework: Techniques used for Arabic are adaptable for other low and medium resource languages.

Troubleshooting Tips

If you encounter issues while using Jais models, try these steps:

• Check Dependencies: Ensure that all required libraries such as PyTorch and Transformers are installed correctly.
• Model Path Validation: Double-check the model path to ensure it’s correct and accessible.
• CUDA Compatibility: If using a GPU, confirm that CUDA is installed and operational.
• Input Format: Ensure that the text being sent to the model is properly formatted and meets the model’s input requirements.

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

Conclusion

The Jais family of models represents a significant leap forward in bilingual NLP. By offering flexible and powerful tools for Arabic and English, these models expand opportunities for developers and researchers. 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.