Jais-30b-v1

This is a 30 billion parameter pre-trained bilingual large language model for both Arabic and English, trained on a dataset containing 126 billion Arabic tokens, 251 billion English, and 50 billion code tokens.

This model, similar to our previous model releases Jais-13b, is based on transformer-based decoder-only (GPT-3) architecture and uses SwiGLU non-linearity. It implements ALiBi position embeddings, enabling the model to extrapolate to long sequence lengths, providing improved context handling and model precision.

Getting started

Below is sample code to use the model. Note that the model requires a custom model class, so users must enable trust_remote_code=True while loading the model. Also, note that this code is tested on transformers==4.32.0.

# -*- coding: utf-8 -*-

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path = "core42/jais-30b-v1"

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,
        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))

Model Details

Developed by: Core42 (Inception), Cerebras Systems.
Language(s) (NLP): Arabic and English
License: Apache 2.0
Input: Text only data.
Output: Model generates text.
Paper : Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
Blog : Access here
Demo : Access here

Intended Use

We release the Jais 30B model under a full open source license. We welcome all feedback and opportunities to collaborate.

This is the second release after Jais-13b from Core42, and at the time of release, achieved state of the art across a comprehensive Arabic test suite as noted in the evaluation section. Some potential downstream uses include:

Research: This model can be used by researchers and developers.
Commercial Use: It can be used as a base model to further fine-tune for specific use cases (similar to jais-13b-chat). Some potential use cases include:
- Chat-assistants.
- Customer service.

Audiences that we hope will benefit from our model:

Academics: For those researching Arabic natural language processing.
Businesses: Companies targeting Arabic-speaking audiences.
Developers: Those integrating Arabic language capabilities in apps.

Out-of-Scope Use

While Jais-30b is a powerful Arabic and English bilingual model, it's essential to understand its limitations and the potential of misuse. It is prohibited to use the model in any manner that violates applicable laws or regulations. The following are some example scenarios where the model should not be used.

Malicious Use: The model should not be used for generating harmful, misleading, or inappropriate content. This includes but is not limited to:
- Generating or promoting hate speech, violence, or discrimination.
- Spreading misinformation or fake news.
- Engaging in or promoting illegal activities.
Sensitive Information: The model should not be used to handle or generate personal, confidential, or sensitive information.
Generalization Across All Languages: Jais-30b is bilingual and optimized for Arabic and English, it should not be assumed to have equal proficiency in other languages or dialects.
High-Stakes Decisions: The model should not be used to make high-stakes decisions without human oversight. This includes medical, legal, financial, or safety-critical decisions.

Bias, Risks, and Limitations

The model is trained on publicly available data which was in part curated by Inception. We have employed different techniqes to reduce bias in the model. While efforts have been made to minimize biases, it is likely that the model, as with all LLM models, will exhibit some bias.

The model is trained as an AI assistant for Arabic and English speakers. The model is limited to produce responses for queries in these two languages and may not produce appropriate responses to other language queries.

By using Jais, you acknowledge and accept that, as with any large language model, it may generate incorrect, misleading and/or offensive information or content. The information is not intended as advice and should not be relied upon in any way, nor are we responsible for any of the content or consequences resulting from its use. We are continuously working to develop models with greater capabilities, and as such, welcome any feedback on the model

Training Details

Training Data

For the pre-training of Jais-30b, we used a diverse bilingual corpus sourced from the Web and other sources. We also used publicly available English and code datasets. To collect Arabic data, we use multiple sources including web pages, wikipedia articles, news articles, Arabic books, and social network content. We augment the volume of Arabic data by translating English to Arabic using an in-house machine translation system. We restrict this to high quality English resources such as English Wikipedia and English books. Our data acquisition strategy is similar to as mentioned in Jais-13b.

Training Procedure

Training was performed on the Condor Galaxy 1 (CG-1) supercomputer platform.

Training Hyperparameters

Hyperparameter	Value
Precision	fp32
Optimizer	AdamW
Learning rate	0 to 0.012 (<= 69 steps)
	0.012 to 0.005 (> 69 & < 70k steps)
	0.005 to 0.0008 (>70k-79k)
Weight decay	0.1
Batch size	2640
Steps	79k

Evaluation

We conducted a comprehensive evaluation of Jais and benchmarked it against other leading base language models, focusing on both English and Arabic. The evaluation criteria spanned various dimensions, including:

Knowledge: How well the model answers factual questions.
Reasoning: The model's ability to answer questions requiring reasoning.
Misinformation/Bias: Assessment of the model's susceptibility to generating false or misleading information, and its neutrality.

Arabic evaluation results:

Models	Avg	EXAMS	MMLU (M)	LitQA	Hellaswag	PIQA	BoolQA	SituatedQA	ARC-C	OpenBookQA	TruthfulQA	CrowS-Pairs
Jais (30B)	47.8	40	30.8	58.3	60.1	70	68.7	43.3	38.5	32.2	42.6	56.9
Jais (13B)	46.5	40.4	30.0	58.3	57.7	67.6	62.6	42.5	35.8	32.4	41.1	58.4
acegpt-13b	42.5	34.7	29.9	42.3	45.6	60.3	63.2	38.1	32.8	32.2	45.1	56.4
acegpt-7b	42.4	35.4	29	46.3	43.8	60.4	63.4	37.2	31.1	32	45.3	55.4
BLOOM (7.1B)	40.9	34.0	28.2	37.1	40.9	58.4	59.9	39.1	27.3	28.0	44.4	53.5
LLaMA (30B)	38.8	27.9	28.5	32.6	35	52.7	63.7	34.9	25.7	28.6	47.2	49.8
LLaMA2 (13B)	38.1	29.2	28.4	32.0	34.3	52.9	63.8	36.4	24.3	30.0	45.5	49.9

English evaluation results:

Models	Avg	MMLU	RACE	Hellaswag	PIQA	BoolQA	SituatedQA	ARC-C	OpenBookQA	Winogrande	TruthfulQA	CrowS-Pairs
Jais (30B)	56.2	34.5	39.8	75.1	79.5	74.3	49.9	45.9	41.2	68.4	36.5	73.3
Jais (13B)	53.9	31.5	38.3	71.8	77.9	67.6	48.2	41.9	40.6	68.4	35.4	71.5
OPT-30b	59.4	38.6	45.2	71.7	78.5	87.3	63.4	44.8	40.2	72.2	38.7	72.7
MPT-30b	57.3	38.8	39.7	80	80.8	73.9	45.6	49.2	43.2	71.1	38.3	69.3
Llama-30b	55.4	37	40.2	79.2	80.1	68.3	44	45.3	42	72.7	42.3	58.2
Falcon (40B)	54.8	31.3	37.1	76.4	80.5	73.7	43.2	43.6	44.2	67.2	34.3	72.3

Citation

@misc{sengupta2023jais,
      title={Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models}, 
      author={Neha Sengupta and Sunil Kumar Sahu and Bokang Jia and Satheesh Katipomu and Haonan Li and Fajri Koto and Osama Mohammed Afzal and Samta Kamboj and Onkar Pandit and Rahul Pal and Lalit Pradhan and Zain Muhammad Mujahid and Massa Baali and Alham Fikri Aji and Zhengzhong Liu and Andy Hock and Andrew Feldman and Jonathan Lee and Andrew Jackson and Preslav Nakov and Timothy Baldwin and Eric Xing},
      year={2023},
      eprint={2308.16149},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}