Jais 13b
Have you ever wondered how a bilingual AI model can understand and respond to both Arabic and English languages? Meet Jais 13b, a 13 billion parameter pre-trained model that's changing the game. With its transformer-based decoder-only architecture and SwiGLU non-linearity, Jais 13b is capable of handling long sequence lengths and providing improved context handling and model precision. But what really sets it apart is its ability to extrapolate to long sequence lengths, thanks to its ALiBi position embeddings. Whether you're a researcher, developer, or business looking to integrate Arabic language capabilities, Jais 13b is an excellent choice. It's not just about being bilingual - it's about being efficient, accurate, and reliable. So, how can Jais 13b help you achieve your goals?
Table of Contents
Model Overview
The Jais-13b model is a powerful bilingual language model that can understand and generate text in both Arabic and English. It’s like having a conversation with a friend who speaks both languages fluently!
This model is special because it’s trained on a massive dataset of 395 billion tokens, which is a huge amount of text data. This training data includes a diverse range of sources, such as web pages, books, and social media content.
The Jais-13b model is designed to be highly accurate and efficient, with a transformer-based decoder-only architecture and SwiGLU non-linearity. It also uses ALiBi position embeddings, which allows it to handle long sequence lengths and provide improved context handling and model precision.
Capabilities
- Bilingual: Understands and generates text in both Arabic and English
- Large training dataset: Trained on
395 billiontokens of text data - Transformer-based architecture: Highly accurate and efficient
- SwiGLU non-linearity: Improved model precision
- ALiBi position embeddings: Handles long sequence lengths and provides improved context handling
The Jais-13b model is a powerful tool for generating text in Arabic and English. It can be used for a variety of tasks, such as:
- Chat-assistants: Building conversational AI models that can understand and respond to user queries in both Arabic and English
- Customer service: Providing automated customer support in both languages
- Research: Studying Arabic natural language processing and developing new applications
Comparison to Other Models
Jais-13b has been compared to other leading base language models, including BLOOM, LLaMA2, ==AraT5==, and ==AraBART==. The results show that Jais-13b outperforms these models on a variety of tasks, including knowledge, reasoning, and misinformation/bias detection.
| Model | Avg Score |
|---|---|
| Jais-13b | 46.5 |
| ==BLOOM (7.1B)== | 40.9 |
| ==LLaMA2 (13B)== | 38.1 |
| ==AraT5 (220M)== | 32.0 |
| ==AraBART (139M)== | 36.7 |
Performance
Jais-13b is a powerful bilingual large language model that has achieved state-of-the-art results in various Arabic test suites. But how does it perform in different tasks? Let’s take a closer look.
Speed
- Training Time: Jais-13b was trained on the Condor Galaxy 1 (CG-1) supercomputer platform, which is a powerful machine. However, the training time is not explicitly mentioned in the provided information.
- Inference Time: The model’s inference time is also not provided. However, we can assume that it would be relatively fast due to its efficient architecture.
Accuracy
- Arabic Evaluation Results: Jais-13b has achieved impressive results in various Arabic evaluation tasks, outperforming other models like ==BLOOM (7.1B)==, ==LLaMA2 (13B)==, ==AraT5 (220M)==, and ==AraBART (139M)==.
- English Evaluation Results: Although the results are not provided, we can assume that the model would perform well in English tasks due to its bilingual nature.
Limitations
The Jais-13b model is a powerful tool for generating text in Arabic and English, but it’s not perfect. Here are some of its limitations:
Bias and Risks
- The model may exhibit bias, as it’s trained on publicly available data that may contain biases.
- It may generate incorrect, misleading, or offensive information.
- It’s not intended to be used for high-stakes decisions, such as medical, legal, or financial decisions, without human oversight.
Language Limitations
- The model is bilingual and optimized for Arabic and English, but it may not perform well in other languages or dialects.
- It may not produce appropriate responses to queries in languages other than Arabic and English.
Sensitive Information
- The model should not be used to handle or generate personal, confidential, or sensitive information.
Malicious Use
- The model should not be used for generating harmful, misleading, or inappropriate content, such as hate speech, violence, or discrimination.
Technical Limitations
- The model requires a custom model class and may need specific hardware or software configurations to run efficiently.
- It may not be compatible with all platforms or devices.
Evaluation Limitations
- The model’s evaluation results are based on a specific set of tasks and datasets, and its performance may vary in other scenarios.
- The model’s performance may not be comparable to other models, as the evaluation criteria and datasets may differ.
By understanding these limitations, you can use the Jais-13b model more effectively and responsibly.
Format
Jais-13b is a pre-trained bilingual large language model that uses a transformer-based decoder-only (GPT-3) architecture. It’s designed to handle both Arabic and English languages.
Architecture
The model is based on the transformer architecture, which is a type of neural network that’s particularly well-suited for natural language processing tasks. It uses SwiGLU non-linearity and ALiBi position embeddings, which enable the model to extrapolate to long sequence lengths and provide improved context handling and model precision.
Data Formats
Jais-13b accepts input in the form of text only data. It’s designed to handle both Arabic and English languages, and it’s optimized for these two languages.
Special Requirements
To use Jais-13b, you’ll need to enable trust_remote_code=True while loading the model. This is because the model requires a custom model class.
Here’s an example of how to use the model:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path = "core42/jais-13b"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", trust_remote_code=True)
def get_response(text, tokenizer=tokenizer, model=model):
input_ids = tokenizer(text, return_tensors="pt").input_ids
inputs = input_ids.to(device)
input_len = inputs.shape[-1]
generate_ids = model.generate(
inputs,
top_p=0.9,
temperature=0.3,
max_length=200-input_len,
min_length=input_len + 4,
repetition_penalty=1.2,
do_sample=True,
)
response = tokenizer.batch_decode(
generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)[0]
return response
text = "عاصمة دولة الإمارات العربية المتحدة ه"
print(get_response(text))
text = "The capital of UAE is"
print(get_response(text))
Note that this code is tested on transformers==4.28.0.
