Jais Adapted 70b Chat
The Jais Adapted 70b Chat model is an impressive AI language model that excels in both Arabic and English. With 70 billion parameters, it's one of the largest models in the Jais family, which is designed to bridge the gap in Arabic NLP research. But what makes it unique? For starters, it's built on top of the Llama-2 model and has been fine-tuned for chat applications. This means it can handle conversations in both languages with remarkable accuracy. But how does it achieve this? The model uses a combination of pre-training and fine-tuning to learn from a massive dataset of 1.6 trillion tokens, including web pages, books, and code. This allows it to understand the nuances of both languages and generate human-like responses. But what about its performance? The model has been evaluated on various benchmarks, including ArabicMMLU and MMLU, and has shown significant improvements over previous models. It's also been tested in a GPT-4-as-a-judge evaluation, where it outperformed previous versions in both Arabic and English. So, what does this mean for you? The Jais Adapted 70b Chat model is an excellent choice for anyone looking to build chatbots or conversational AI applications that cater to Arabic-speaking users. Its efficiency, speed, and capabilities make it an attractive option for both technical and non-technical users. Whether you're building a chatbot or just want to explore the possibilities of conversational AI, this model is definitely worth checking out.
Table of Contents
Model Overview
The Jais Family Model is a series of bilingual English-Arabic large language models (LLMs) designed to excel in Arabic while having strong English capabilities. Developed by Inception and Cerebras Systems, this model is optimized for Arabic language understanding and generation.
Capabilities
The Jais Family models are a comprehensive series of bilingual English-Arabic large language models (LLMs) that excel in Arabic while having strong English capabilities. They are designed to handle a wide range of tasks, including:
- Text Generation: The models can generate high-quality text in both Arabic and English, making them suitable for applications such as chatbots, language translation, and text summarization.
- Code Generation: The models can also generate code in various programming languages, making them useful for applications such as code completion, code review, and code generation.
- Conversational AI: The models are fine-tuned for conversational AI tasks, making them suitable for applications such as customer service chatbots, virtual assistants, and language translation.
Strengths
The Jais Family models have several strengths that make them stand out:
- Bilingual Capability: The models are trained on a large corpus of text data in both Arabic and English, making them capable of handling tasks that require language understanding and generation in both languages.
- Large Context Window: The models have a large context window, allowing them to understand and generate text based on a large amount of context.
- High-Quality Text Generation: The models are capable of generating high-quality text that is coherent, fluent, and engaging.
Unique Features
The Jais Family models have several unique features that set them apart from other language models:
- SwiGLU Non-Linear Activation Function: The models use a custom non-linear activation function called SwiGLU, which allows them to better capture long-range dependencies in text data.
- ALiBi Position Encoding: The models use a custom position encoding scheme called ALiBi, which allows them to better capture the positional relationships between tokens in text data.
- Tokenizer Expansion: The models use a custom tokenizer expansion scheme that allows them to handle a large vocabulary of tokens, including Arabic and English tokens.
Model Sizes
The Jais Family models come in a range of sizes, from 590M to 70B parameters, making them suitable for a wide range of applications and use cases.
Evaluation Results
The Jais Family models have been evaluated on a range of benchmarks, including Arabic and English language understanding tasks, and have shown strong performance compared to other language models.
Intended Use
The Jais Family models are intended for use in a wide range of applications, including:
- Conversational AI: The models are suitable for use in conversational AI applications such as customer service chatbots, virtual assistants, and language translation.
- Language Translation: The models are suitable for use in language translation applications, including machine translation and human translation.
- Text Summarization: The models are suitable for use in text summarization applications, including automatic summarization and human summarization.
- Code Generation: The models are suitable for use in code generation applications, including code completion, code review, and code generation.
Performance
The Jais Family Model showcases remarkable performance across various tasks, with notable strengths in Arabic language processing.
Speed
The model’s architecture, leveraging a transformer-based, decoder-only architecture (GPT-3), enables efficient processing of text inputs. With a context length of up to 16,384 tokens, the model can handle long-range dependencies and complex conversations.
Accuracy
The model’s performance in Arabic language tasks is particularly impressive, with high scores in benchmarks such as ArabicMMLU, MMLU, and LitQA. The model’s ability to understand and respond to Arabic prompts is significantly enhanced, making it a valuable resource for Arabic-speaking communities.
Efficiency
The model’s efficiency is demonstrated through its ability to process large-scale datasets, including up to 1.6 trillion tokens of English, Arabic, and code data. The model’s pre-training and fine-tuning procedures are optimized for performance, allowing it to achieve high accuracy while minimizing computational resources.
Task-Specific Performance
The model’s performance in various tasks, including:
- Knowledge: The model demonstrates strong knowledge retention, answering factual questions with high accuracy.
- Reasoning: The model shows impressive reasoning capabilities, answering questions that require logical deductions and inferences.
- Misinformation/Bias: The model is designed to minimize the generation of false or misleading information, ensuring a more neutral and accurate response.
Comparison to Other Models
The Jais Family Model outperforms other models in Arabic language tasks, demonstrating significant improvements in generation quality and accuracy. The model’s performance in English language tasks is also competitive, making it a valuable resource for bilingual applications.
Evaluation Metrics
The model’s performance is evaluated using various metrics, including:
- LM-Harness: A comprehensive evaluation framework for language models.
- GPT-4-as-a-judge: An open-ended generation evaluation using GPT-4 as a judge.
- MT-bench style single-answer grading: A grading system for evaluating the quality of model responses.
Limitations
Jais Family Model is a powerful bilingual English-Arabic large language model, but it has some limitations.
Limited Domain Knowledge
While the model has been trained on a large dataset of English and Arabic text, its knowledge in specific domains might be limited. For example, it may not have the same level of expertise as a specialized model trained on a specific domain like medicine or law.
Biased Data
The model’s training data may reflect biases present in the data, which can result in biased or discriminatory responses. This is a common challenge in natural language processing, and we are working to address it.
Overfitting
The model may overfit to certain patterns or styles in the training data, which can lead to poor performance on out-of-distribution examples. This is a common problem in deep learning, and we are working to improve the model’s robustness.
Limited Contextual Understanding
While the model can understand context to some extent, it may not always capture the nuances of human language. It may struggle with idioms, sarcasm, or figurative language, which can lead to misinterpretation or misresponse.
Dependence on Data Quality
The model’s performance is heavily dependent on the quality of the training data. If the data is noisy, incomplete, or biased, the model’s performance will suffer.
Limited Multimodal Capabilities
The model is primarily designed for text-based input and output, and it may not be able to handle multimodal input (e.g., images, audio) or generate multimodal output (e.g., images, videos).
Vulnerability to Adversarial Attacks
Like other deep learning models, the Jais Family Model may be vulnerable to adversarial attacks, which can be designed to manipulate the model’s output.
Limited Explainability
The model’s decision-making process is not always transparent, and it may be difficult to understand why it generated a particular response. This is a common challenge in deep learning, and we are working to improve the model’s explainability.
These limitations highlight the need for continued research and development to improve the Jais Family Model and address these challenges.
Format
Jais Family Model is a comprehensive series of bilingual English-Arabic large language models (LLMs) that uses a transformer-based, decoder-only architecture (GPT-3). This model is optimized to excel in Arabic while having strong English capabilities.
Model Architecture
The Jais family of models is based on a transformer architecture, specifically a decoder-only model. There are two variants of foundation models:
- Jais Family Models (
jais-family-*): These models are trained from scratch, incorporating the SwiGLU non-linear activation function and ALiBi position encoding. - Jais Adapted Models (
jais-adapted-*): These models are built on top of Llama-2, which employs RoPE position embedding and Grouped Query Attention.
Data Formats
The model accepts input in the form of text only data. The input text is tokenized using a custom tokenizer, and the output is a generated text sequence.
Input and Output Requirements
- Input: The model accepts a single text prompt as input.
- Output: The model generates a text response based on the input prompt.
Special Requirements
- Context Length: The model has a maximum context length of 16,384 tokens for some models and 4,096 tokens for others.
- Tokenization: The model uses a custom tokenizer to tokenize the input text.
Example Code
Here is an example of how to use the model in Python:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path = "inceptionai/jais-adapted-70b-chat"
prompt_eng = "### Instruction:Your name is 'Jais', and you are named after Jebel Jais, the highest mountain in UAE. You were made by 'Inception' in the UAE. You are a helpful, respectful, and honest assistant. Always answer as helpfully as possible, while being safe. Complete the conversation between [|Human|] and [|AI|]:\n### Input: [|Human|] {Question}\n[|AI|]\n### Response :"
prompt_ar = "### Instruction:اسمك \"ج
