Fanar-1

Fanar-1-9B-Instruct is a powerful Arabic-English LLM developed by Qatar Computing Research Institute (QCRI) at Hamad Bin Khalifa University (HBKU), a member of Qatar Foundation for Education, Science, and Community Development. It is the instruction-tuned version of Fanar-1-9B. We continually pretrain the google/gemma-2-9b model on 1T Arabic and English tokens. We pay particular attention to the richness of the Arabic language by supporting Modern Standard Arabic (MSA) and a diverse set of Arabic dialects, including Gulf, Levantine, and Egyptian. Fanar models, through meticulous curation of the pretraining and instruction-tuning data, are aligned with Islamic values and Arab cultures.

Fanar-1-9B-Instruct is a core component of the Fanar GenAI platform that offers a suite of capabilities including image generation, video and image understanding, deep thinking, advanced text-to-speech (TTS) and automatic-speech-recognition (ASR), attribution and fact-checking, Islamic RAG, among several other features.

We have published a comprehensive report with all the details regarding our Fanar GenAI platform. We also provide an API to our models and the GenAI platform (request access here).

Model Details

Model Training

Pretraining

Fanar-1-9B-Instruct was continually pretrained on 1T tokens, with a balanced focus on Arabic and English: ~515B English tokens from a carefully curated subset of the Dolma dataset, 410B Arabic tokens that we collected, parsed, and filtered from a variety of sources, and 102B code tokens curated from The Stack dataset. Our codebase used the LitGPT framework.

Post-training

Fanar-1-9B-Instruct underwent a two-phase post-training pipeline:

Getting Started

Fanar-1-9B-Instruct is compatible with the Hugging Face transformers library (≥ v4.40.0). Here's how to load and use the model:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# message content may be in Arabic or English
messages = [
    {"role": "user", "content": "ما هي عاصمة قطر؟"},
]

inputs = tokenizer.apply_chat_template(messages, tokenize=False, return_tensors="pt")
outputs = model.generate(**tokenizer(inputs, return_tensors="pt", return_token_type_ids=False), max_new_tokens=256)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Inference using VLLM is also supported:

from vllm import LLM, SamplingParams

model_name = "QCRI/Fanar-1-9B-Instruct"

llm = LLM(model=model_name)
sampling_params = SamplingParams(temperature=0.7, max_tokens=256)

# message content may be in Arabic or English
messages = [
    {"role": "user", "content": "ما هي عاصمة قطر؟"},
]

outputs = llm.chat(messages, sampling_params)
print(outputs[0].outputs[0].text)

Intended Use

Fanar-1-9B-Instruct is built for:

• Conversational agents (Arabic only or bilingual)
• Cultural and dialectal question answering in Arabic
• Educational, governmental, and civic NLP applications focused on the Arab world or Arabic-speaking audiences
• Research on Arabic natural language generation and understanding

Fanar-1-9B-Instruct can be deployed as part of a broader AI system. Developers are encouraged to implement proper safeguards to ensure culturally respectful, accurate, and safe deployment. It should not be used to generate or spread harmful, illegal, or misleading content.

A version of this model can be accessed through Fanar Chat. We are continuously improving the Fanar’s models and capabilities, and answers can differ from what you get from Fanar-1-9B-Instruct.

Ethical Considerations & Limitations

Fanar-1-9B-Instruct is capable of generating fluent and contextually appropriate responses. However, as with any generative model there are uncertainities. The model may produce biased, offensive, or incorrect outputs. The model is not suitable for high-stakes decision-making (e.g., legal, medical, or financial advice). Though we have extensively tested Fanar-1-9B-Instruct and attempted to mitigate these issues, we cannot redress every possible scenario. Thus, we advise developers to implement safety checks and perform domain-specific fine-tuning for