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causal-lm
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metadata
datasets:
  - ClusterlabAi/InstAr-500k
  - CohereForAI/aya_dataset
language:
  - ar
tags:
  - causal-lm
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Arabic StableLM 2 Chat 1.6B

Model Description

Arabic Stable LM 2 Chat 1.6B is a 1.6 billion parameter instruction tuned language model from the ar-stablelm-base. The model is trained on a mix of publicly available datasets and synthetic datasets.

Usage

StableLM 2 1.6B Chat uses the following ChatML format:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('stabilityai/ar-stablelm-2-chat')
model = AutoModelForCausalLM.from_pretrained(
    'stabilityai/ar-stablelm-2-chat',
    device_map="auto",
)

prompt = [{'role': 'user', 'content': 'ماهو إعراب الجملة: ذهبت إلى السوق'}]
inputs = tokenizer.apply_chat_template(
    prompt,
    add_generation_prompt=True,
    return_tensors='pt'
)

tokens = model.generate(
    inputs.to(model.device),
    max_new_tokens=100,
    temperature=0.7,
    do_sample=True
)
output = tokenizer.decode(tokens[:, inputs.shape[-1]:][0], skip_special_tokens=False)

print(output)

Model Details

Training Dataset

The dataset is comprised of a mixture of open datasets large-scale datasets available on the HuggingFace Hub:

Evaluation

We use the following datasets for evaluation :

Below we list the a comparison of our models against 28 models

Model Params CIDAR-MCQ ArabicMMLU ACVA AlGhafa Average
AraGPT2-base 135M 39.0 31.7 51.7 37.9 40.1
AraT5v2-base-1024 220M 38.0 28.3 37.3 36.8 35.1
AraGPT2-medium 370M 39.0 32.2 44.5 38.3 38.5
jais-family-590m 590M 33.0 35.7 51.2 38.8 39.7
jais-family-590m-chat 590M 28.0 31.4 51.8 36.8 37.0
AraGPT2-large 792M 37.0 32.6 46.2 37.9 38.4
AraGPT2-mega 1.46B 40.0 33.3 51.0 38.4 40.7
jais-family-1p3b 1.3B 41.0 37.5 51.3 39.6 42.3
jais-family-1p3b-chat 1.3B 30.0 34.6 56.0 44.7 41.3
Qwen2-1.5B 1.5B 30.0 30.5 61.1 38.7 40.1
Qwen2-1.5B-Instruct 1.5B 28.0 30.8 63.9 38.9 40.4
bloom-1b7 1.72B 37.0 33.0 52.2 39.1 40.3
bloomz-1b7 1.72B 25.0 33.5 45.3 42.1 36.5
jais-family-2p7b 2.7B 43.0 39.0 48.5 40.9 42.8
jais-family-2p7b-chat 2.7B 36.0 36.1 49.5 43.7 41.3
jais-family-6p7b 6.7B 44.0 40.7 55.8 41.3 45.4
jais-family-6p7b-chat 6.7B 38.0 38.7 64.1 39.9 45.2
AceGPT-7B 7B 46.0 40.9 63.1 40.7 47.7
AceGPT-7B-chat 7B 37.0 38.2 65.9 45.1 46.6
SILMA-9B-Instruct-v1.0 9B 27.0 30.8 56.2 42.0 39.0
AceGPT-13B 13B 41.0 40.7 65.3 38.8 46.4
AceGPT-13B-chat 13B 42.0 41.1 70.2 41.6 48.7
AceGPT-v1.5-13B 13B 40.0 40.3 66.1 39.5 46.5
AceGPT-v1.5-13B-chat 13B 36.0 40.9 69.0 39.8 46.4
jais-13b 13B 47.0 40.9 59.6 41.2 47.2
jais-13b-chat 13B 41.0 40.7 61.2 41.4 46.1
jais-family-13b 13B 45.0 41.9 58.3 40.5 46.4
jais-family-13b-chat 13B 37.0 39.9 63.2 41.4 45.4
ar-stablelm-2-base 1.64B 43.0 41.1 52.0 43.9 45.0
ar-stablelm-2-chat 1.64B 46.0 45.5 57.0 50.1 49.6

Use and Limitations

Intended Use

The model is intended to be used in chat-like applications for research only. Users should evaluate the model for safety performance in their specific use case and apply the necessary safeguards and fine-tune the model to facilitate safe performance in downstream applications. Read more about safety and limitations below.

Out-of-scope Use

Out-of-scope uses include use in any manner that violates applicable laws or regulations, Stability AI’s Acceptable Use Policy or license agreement, or use in languages outside of those explicitly supported by this model.

Limitations and Bias

As a base model, this model may exhibit unreliable or other undesirable behaviors that should be corrected through evaluation and fine-tuning prior to deployment. Given that each use case is unique, running a suite of tests may help facilitate proper performance of this model. Using this model will require guardrails around the user’s inputs and outputs to ensure that any outputs returned are not harmful. Pairing this model with an input and output classifier may help prevent harmful responses. Users should exercise caution when using these models in production systems and should not use the models if they are unsuitable for the user’s application.

How to Cite 

@misc{alyafeai2024arabicstablelmadapting,
      title={Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic}, 
      author={Zaid Alyafeai and Michael Pieler and Hannah Teufel and Jonathan Tow and Marco Bellagente and Duy Phung and Nikhil Pinnaparaju and Reshinth Adithyan and Paulo Rocha and Maksym Zhuravinskyi and Carlos Riquelme},
      year={2024},
      eprint={2412.04277},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.04277}, 
}