---
title: 'Fanar-1-9B: Arabic-Centric 9B LLM'
url: https://www.emergentmind.com/topics/fanar-1-9b
type: topic
---

# Fanar-1-9B: Arabic-Centric 9B LLM

Fanar-1-9B is the first-generation 9B-class Fanar Arabic large language model line associated with Qatar Computing Research Institute’s Arabic-centric generative AI program. Across the literature, it appears under several closely related names—most prominently **Fanar Prime (9B)** in the Fanar 1.0 platform report, and **Fanar-1-9B**, **Fanar-1-9B-Instruct**, or **Fanar-1-9b-instruct** in later work. The model is presented as the stronger of the two main Fanar 1.0 text LLMs, built by continual pre-training on **Gemma-2-9B**, and later reused both as a general-purpose Arabic-centric instruct model and as the base model for specialized downstream systems, including Islamic inheritance reasoning [2501.13944] [2603.16397] [2508.15854].

## 1. Identity, naming, and model lineage

The designation **Fanar-1-9B** is not uniform across papers. In the Fanar platform report, the formal 9B model name is **Fanar Prime**, described as the platform’s larger reasoning-oriented LLM. In the Fanar 2.0 report, the same model line is referenced explicitly as **Fanar Prime (9B)**, **Fanar-1-9b-instruct**, **Fanar-1-9B**, and **Fanar-1-9B-Instruct**. The later Islamic inheritance paper uses **Fanar-1-9B** as the base model name and releases a task-adapted checkpoint as **Fanar-1-9B-Islamic-Inheritance-Reasoning** [2501.13944] [2603.16397].

| Name in the literature | Role or variant | Source |
|---|---|---|
| Fanar Prime (9B) | Fanar 1.0 9B model, continually pretrained on Gemma-2-9B | [2501.13944] |
| Fanar-1-9B | Pre-trained base model name used in later work | [2603.16397] |
| Fanar-1-9B-Instruct / Fanar-1-9b-instruct | Instruction-tuned or post-trained variant | [2603.16397] |
| Fanar-1-9B-Islamic-Inheritance-Reasoning | LoRA-adapted downstream checkpoint for inheritance reasoning | [2508.15854] |

This naming variability matters because the literature does not formally normalize all aliases. A cautious reading is that **Fanar-1-9B** denotes the 9B Fanar 1.x line whose platform identity is **Fanar Prime**, with both base and instruction-tuned forms. The Fanar 2.0 paper makes this lineage explicit by describing **Fanar-1-9b-instruct** as “the first generation Fanar model, built on top of gemma-2-9b base model using continual pre-training and instruction fine-tuning,” and by contrasting the Fanar 1.0 dual-LLM strategy—**Fanar Star (7B)** and **Fanar Prime (9B)**—with the later single-flagship **Fanar-27B** design [2603.16397].

## 2. Architecture and training lineage

Fanar-1-9B is described as a **decoder-only Transformer** derived from **Gemma-2-9B** through continual pre-training rather than from-scratch training. The Fanar platform report gives the most specific architectural parameters for the 9B line under the name **Fanar Prime**: **8.78B** total parameters, **8.32B** non-embedding parameters, **459M** embedding parameters, vocabulary size **128,256**, model dimension **3584**, **RMSNorm** for both pre- and post-normalization, **RoPE** positional embeddings, **GQA** attention, no biases, **Approximated GeGLU** activation, token batch size **~4.4M**, and **weight tying: False**. The same report states that vocabulary pruning reduced model size from **9.2B** to **8.78B** by shrinking the original Gemma-2 vocabulary from **250,000** to **128,256** [2501.13944].

The literature is explicit about some absences of detail. The provided Fanar platform text does not cleanly expose the number of layers, attention heads, hidden size or intermediate size, and context length because the relevant table rows are corrupted in the excerpt. The Fanar 2.0 report likewise says that, for Fanar-1-9B, it does not provide a full internal architectural specification such as tokenizer details, positional settings, or exact attention mechanism beyond backbone provenance. In the Islamic inheritance paper, the model is characterized only as a **“9-billion parameter causal decoder-only transformer”** designed for **Arabic and Islamic domain text**, and no layer count, hidden size, tokenizer design, pretraining corpus detail, or context length of the base model is supplied there [2501.13944] [2508.15854].

The pretraining recipe reported for the 9B line is continual pre-training on a large bilingual-plus-code corpus. For **Fanar Prime**, the training mixture is **45% Arabic**, **45% English**, and **10% code**. The reported continual pretraining volume is **650B tokens**, with results shown from a checkpoint around the **600B token mark**. The training schedule uses a two-stage curriculum comprising a **single-epoch pretraining phase** and a **cool-down phase**, with the cool-down stage using the same high-quality dataset curated for Fanar Star. The objective is the standard **auto-regressive language modeling objective**, though no explicit next-token loss formula is printed in the supplied text [2501.13944].

Optimization details are comparatively specific. The reported settings for Fanar Prime pretraining are **100** warmup steps, peak learning rate \(8 \times 10^{-6}\), minimum learning rate \(1 \times 10^{-6}\), **AdamW** with \( \beta_1 = 0.9 \), \( \beta_2 = 0.95 \), \( \epsilon = 1 \times 10^{-8} \), weight decay **0.01**, cosine learning-rate schedule, gradient clipping **1.0**, gradient-reduce dtype **bfloat16**, optimizer-state dtype **bfloat16**, global batch size **1071 samples**, and **~4.4M** tokens per step. Pretraining used **168 NVIDIA H100 80GB SXM5 GPUs** across **21 nodes** with **8 GPUs per node**, **NVLink / NVSwitch** intra-node interconnect, bidirectional GPU-GPU bandwidth **900 GB/s**, **InfiniBand** inter-node connectivity, and **LitGPT** for continual pretraining [2501.13944].

## 3. Arabic-centric data strategy

The Arabic-centric character of Fanar-1-9B is grounded primarily in data design rather than in a novel transformer block. The Fanar platform report describes an approximately **1 trillion token** pretraining corpus with headline composition **50% English**, **40% Arabic**, and **10% code**, corresponding to **513B** English tokens, **410B** Arabic tokens, and **102.6B** code tokens. Arabic data spans **Modern Standard Arabic**, **Classical Arabic**, and **dialectal Arabic**, and includes in-house crawled web documents, news articles, books, Arabic Wikipedia, classical and contemporary Arabic poetry, and in-house machine-translated books, STEM papers, and encyclopedic documents. This corpus design is presented as a response to Arabic data scarcity and to the need for stronger technical and cultural coverage in Arabic [2501.13944].

Arabic augmentation through machine translation is a central element of that strategy. The report states that machine translation was used to expand Arabic knowledge coverage, with **Shaheen** selected for **books**, and **NLLB 3.3B** selected for **STEM papers** and **Wiki Encyclopedia**. The same report also notes nearly **50B translated MSA and dialectal Arabic** tokens used to enhance Arabic coverage. This suggests that Fanar-1-9B’s Arabic specialization depends substantially on curated bilingual transfer and translation-based enrichment rather than on naturally occurring Arabic web data alone [2501.13944].

The data pipeline is heavily filtered. Heterogeneous raw inputs were normalized into a Dolma-style JSON-record format with fields `"id"`, `"text"`, `"metadata"`, and `"quality_signals"`. Syntactic filtering used **20 quality signals** adapted for Arabic-specific orthographic features such as RTL punctuation, Arabic/Hindi digits, diacritics, ligatures, special symbols, and Farsi/decorated characters. Semantic filtering used **ASAD** to detect offensive or profane language and adult content. Model-based filtering used **KenLM** perplexity filtering based on an MSA language model, removing the **highest 5% perplexity** documents per dataset and, in the last training epoch, also filtering low-perplexity documents that had become “too easy.” An Arabic-native education classifier inspired by FineWeb-Edu removed low-score documents and filtered approximately **20%** of the data [2501.13944].

Deduplication is described at substantial scale. The pipeline applies URL deduplication, exact-match deduplication, and approximate-match deduplication using min-wise locality sensitive hashing with gram size **8**, number of bands \(b = 12\), band length \(r = 11\), approximate Jaccard threshold \( \approx 0.8 \), and signature length **132**. The reported infrastructure for approximate deduplication used **350 CPU cores**, peak memory **1TB**, and runtime about **12 hours** [2501.13944].

The Fanar 2.0 paper reinterprets these first-generation design choices through a quality-over-quantity lens. It states that Fanar 1.0 taught the team that **“data quality dominates data quantity in the continual pretraining regime,”** and contrasts Fanar 1.0’s **~1T tokens** and **“Multi-epoch + cool-down”** strategy with Fanar 2.0’s **~120B high-quality tokens** and **“Recipe-based annealing + model merging.”** In that retrospective framing, Fanar-1-9B represents the first major Fanar attempt to construct a broad Arabic-centric base model under severe data scarcity constraints [2603.16397].

## 4. Platform role in Fanar 1.0 and transition to Fanar 2.0

Fanar-1-9B was not introduced as an isolated checkpoint but as one component in a larger **Arabic-centric multimodal generative AI platform** developed at **Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University**, and sponsored by **Qatar’s Ministry of Communications and Information Technology**. In Fanar 1.0, the two core text LLMs are **Fanar Star** and **Fanar Prime**, deployed concurrently and routed through a custom-built **Orchestrator**. The routing policy described in the platform report assigns **Fanar Prime** to **STEM and reasoning-related prompts**, while other requests may be routed to Fanar Star or to specialized services such as **Islamic RAG**, **Recency RAG**, **Biography RAG**, **Attribution RAG**, speech, image generation, translation, and embeddings [2501.13944].

This platform framing is important for interpreting the 9B model’s capabilities. Several functions often associated informally with “Fanar” are not intrinsic properties of Fanar-1-9B itself. The **Islamic RAG**, **Recency RAG**, **Biography RAG**, **Attribution RAG**, safety filters, and orchestrator belong to the surrounding system rather than to the 9B checkpoint. In particular, the platform report distinguishes the core model from external factuality and religious-answer safeguards: Islamic prompts are grounded by a dedicated **Islamic RAG**, while attribution is implemented as an opt-in post-generation service rather than as a modification of the base model [2501.13944].

The Fanar 2.0 paper presents the 9B line primarily as a first-generation baseline and predecessor. It states that the “most significant architectural shift” is the move from the Fanar 1.0 **dual-LLM strategy**—**Fanar Star at 7B** and **Fanar Prime at 9B**—to a single flagship **Fanar-27B** model built by continual pre-training on **Gemma-3-27B**. In this comparison, Fanar 1.0 is characterized by an **8K context window**, absence of **selective reasoning traces**, absence of **hallucination self-verification traces**, absence of **tool calling**, a post-training pipeline of **SFT + DPO**, **translation-based traces** for Arabic reasoning, and **SFT-based** cultural alignment. The 9B model therefore occupies a historically specific position: it is the stronger Fanar 1.0 core LLM, but one later superseded in the flagship role by Fanar-27B [2603.16397].

The same later paper also shows that the 9B line continued to be useful as an experimental substrate. In the agentic section, **Fanar-1-9B** and **Fanar-1-9B-Instruct** are explicitly fine-tuned to study tool-calling behavior. This does not imply that tool-calling was a native Fanar 1.0 capability; rather, it indicates that the first-generation 9B base and post-trained variants remained viable research models even after the platform’s main text role had shifted to Fanar-27B [2603.16397].

## 5. General-purpose benchmark profile

The benchmark evidence portrays Fanar-1-9B as a strong first-generation Arabic-centric model for its size, with particularly strong results on Arabic, cultural, and dialectal tasks, while still lagging the later Fanar-27B across most categories. In the Fanar 2.0 evaluation tables, **Fanar-1-9b-instruct** records **58.30** on **MMMLU/Ar (0-shot)**, **67.35** on **ArabicMMLU (3-shot)**, and **68.64** on **OALL-v2 (0-shot)** for Arabic knowledge; **40.00** on **Nahw-MCQ (3-shot)**, **60.60** on **AraLingBench (0-shot)**, **65.0** on **Phonology (0-shot)**, **63.9** on **Morphology (0-shot)**, **66.3** on **Almieyar Syntax (0-shot)**, **74.7** on **Semantics (0-shot)**, and **78.7** on **Pragmatics (0-shot)** for Arabic language; **83.26** on **Belebele (3-shot)** and roughly **59–63** on several AraDiCE and DialectalArabic-MMLU variants for dialectal evaluation; **79.66** on **ACVA (5-shot)**, **82.33** on **PalmX Islamic (0-shot)**, and **67.10** on **PalmX Culture (0-shot)** for Arabic and Islamic culture awareness; and **71.32** on **MMLU (5-shot)**, **82.37** on **PIQA (0-shot)**, **83.01** on **HellaSwag (0-shot)**, and **65.19** on **ARC Challenge (0-shot)** for English world knowledge. Its conversational and instruction-following profile in the same paper includes **MT-Bench: 5.58**, **IFEval: 74.70**, **Arabic Cultural: 3.86**, and **Internal: 9.14** [2603.16397].

The Fanar platform report presents closely related results for **Fanar Prime** and **Fanar Prime Instruct** and emphasizes gains over Gemma-2-9B and Gemma-2-9b-it on Arabic-focused benchmarks. For the base model, the paper reports **57.30** on **MMMLU(Ar)**, **67.35** on **ArabicMMLU, 3-shot**, **54.79** on **OALL**, **71.90** on **CulturalMCQ**, **79.37** on **Belebele(Ar)**, and **77.68** on **Almieyar(Ar)**, all exceeding the corresponding Gemma-2-9b scores listed in the paper. For **Fanar Prime Instruct**, the report gives **67.82** on **ArabicMMLU, 3-shot**, **63.81** on **OALL**, **70.60** on **CulturalMCQ**, **82.48** on **Belebele(Ar)**, **78.30** on **Almieyar(Ar)**, and **9.66** on **User-Chats**, again above the listed Gemma-2-9b-it baselines on those tasks [2501.13944].

The Fanar 2.0 paper reframes these numbers comparatively rather than absolutely. It states that Fanar-27B consistently outperforms the 9B first-generation model, with summary gains of **+9.1** on **MMMLU/Ar**, **+7.3** on **ArabicMMLU**, **+7.6** on **MMLU**, and **+3.5** on **Belebele**. This does not negate Fanar-1-9B’s earlier standing; rather, it places the model as a solid first-generation baseline that remained competitive across Arabic, dialectal, cultural, and bilingual evaluations while being clearly surpassed by the later 27B system [2603.16397].

A common misunderstanding is to treat these results as evidence that all “Fanar” capabilities were already present in the 9B checkpoint alone. The platform literature indicates otherwise: some of the strongest end-user behaviors attributed to Fanar were produced through orchestration, retrieval, or auxiliary services rather than through the base 9B model in isolation. Similarly, the benchmark tables describe a strong general-purpose model for its size, not a universally dominant model across all judged generation settings [2501.13944] [2603.16397].

## 6. Specialized adaptation for Islamic inheritance reasoning

The most detailed downstream case study of Fanar-1-9B is the QIAS 2025 shared-task system for **Islamic Inheritance Reasoning (Ilm al-Mawārīth)**. In that paper, **Fanar-1-9B** is the central model and is explicitly described as the **base model** for the submission, a **“9-billion parameter causal decoder-only transformer”** “specifically designed for Arabic and Islamic domain text.” The system fine-tunes the model with **Low-Rank Adaptation (LoRA)** and then uses the adapted checkpoint as the generator inside a **Retrieval-Augmented Generation (RAG)** pipeline. The authors release the resulting checkpoint as **`msmadi/Fanar-1-9B-Islamic-Inheritance-Reasoning`**, based on **`QCRI/Fanar-1-9B`** [2508.15854].

The task itself is a multiple-choice classification problem over **22,000** examples: **20,000 train**, **1,000 validation**, and **1,000 test**. Each example is an Islamic inheritance MCQ with question text and **up to six options (A–F)**, evenly split between **Beginner** and **Advanced** difficulty. The described reasoning demands include understanding inheritance scenarios stated in natural language, identifying eligible and ineligible heirs, applying fixed-share rules, managing residuary shares, handling **proportional reduction** (\(\text{‘awl}\)) and **redistribution** (**radd**), and performing precise share calculations. The model is not trained to emit long explanations; instead, examples are serialized as **system–user–assistant turns**, with the assistant target restricted to a single gold letter **A–F** [2508.15854].

The LoRA setup is parameter-efficient but concretely specified. LoRA is described conceptually as injecting **“trainable rank-decomposition matrices into specific layers while keeping the original weights frozen.”** The reported adapter targets are `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, and `down_proj`, with rank \(r = 32\), LoRA alpha \(\alpha = 64\), and LoRA dropout \(0.1\). The fine-tuning hyperparameters are **4 epochs**, per-device batch size **2** for train and eval, **32** gradient accumulation steps, learning rate \(3 \times 10^{-4}\), weight decay **0.01**, warmup ratio **0.1**, max gradient norm **1.0**, optimizer `adamw_torch`, cosine decay scheduler, and **FP16** precision. Memory-efficiency measures include **4-bit NormalFloat (NF4) quantization**, **gradient checkpointing**, attention implementation set to **eager**, and disabling `use_cache` when gradient checkpointing is enabled. The paper is equally explicit about what it does **not** specify: no formal LoRA equation, no causal-LM objective formula, no exact trainable-parameter count after LoRA, no exact sequence length used during fine-tuning, and no hardware inventory [2508.15854].

At inference time, the fine-tuned model is embedded in a practical RAG system. Questions are embedded with **`sentence-transformers/all-MiniLM-L6-v2`**, searched in a **FAISS** index, and augmented with the **top-\(k\)** retrieved passages where \(k = 5\). Those passages are concatenated with the question and answer options into an **“enriched Arabic chat prompt”** for Fanar-1-9B. The reported decoding settings are a maximum input length of **10,000 tokens**, maximum new tokens **15**, temperature **0.05**, and **greedy** decoding aimed at short, deterministic outputs, followed by **regex-based answer extraction** to force a single valid letter prediction [2508.15854].

The reported performance is **85.8%** overall accuracy on the final test set, with **74.0%** on **Beginner** and **97.6%** on **Advanced** questions. The paper compares this full QU-NLP system—LoRA-fine-tuned Fanar-1-9B plus RAG—against zero-shot baselines from the organizers: **o3** at **93.4** overall, **94.4** beginner, **92.4** advanced; **Gemini 2.5** at **90.6**, **91.6**, **89.6**; **GPT-4.5** at **74.0**, **86.8**, **61.2**; **LLaMA3** at **48.8**, **57.8**, **39.8**; **Fanar 7B** at **48.1**, **60.4**, **35.8**; **Mistral** at **44.5**, **58.6**, **30.4**; and **ALLaM7B** at **42.9**, **58.0**, **27.8**. The paper explicitly states that QU-NLP **“surpassed all models on the advanced subset”** with **97.6%** accuracy on **500** advanced MCQs, exceeding both **Gemini 2.5** and **OpenAI o3** on that subset [2508.15854].

That result requires careful interpretation. The paper does **not** show that the raw, untuned Fanar-1-9B base model surpasses frontier models zero-shot. Rather, it shows that **fine-tuned Fanar-1-9B within a retrieval-grounded pipeline** can outperform several zero-shot baselines, particularly on advanced Islamic inheritance MCQs. The same paper also does **not** provide a clean ablation isolating base zero-shot Fanar-1-9B, LoRA without RAG, or RAG without fine-tuning; therefore, the numeric contribution of retrieval is argued qualitatively rather than established by an explicit ablation table [2508.15854].

The error analysis reveals where the adapted 9B model remained brittle. Accuracy on **blocked-heir** questions is **64.5%** for \(n=299\), versus **94.9%** for \(n=701\) non-blocked cases, and the authors relate part of this to distribution shift: blocked items form **17/1000 = 1.70%** of development, **3491/20000 = 17.46%** of training, and **299/1000 = 29.90%** of test. Questions containing negation or exception markers such as \(\textAR{لا} / \textAR{ليس} / \textAR{لم} / \textAR{لن} / \textAR{غير} / \textAR{بدون}\) yield **83.5%** accuracy for \(n=807\), versus **95.3%** for \(n=193\) questions without negation. The authors also identify **10** near-duplicate answer-option cases—about **7% of all errors**—as orthographic evaluation artifacts rather than clear model failures. The full misclassification breakdown is **106** blocked-heir errors, **17** negation-exception errors, **10** near-duplicate-option errors, **9** other errors, and **142** total errors [2508.15854].

Taken together, this downstream study portrays Fanar-1-9B as a model whose strongest specialized behavior emerges when four conditions align: Arabic input, close match to its domain specialization, supervised adaptation on a substantial task dataset, and a constrained output space reinforced by retrieval grounding and post-processing. A plausible implication is that the model’s value lies less in frontier-scale open-ended generation than in highly structured Arabic reasoning settings where domain adaptation, retrieval, and controlled decoding can compensate for moderate model scale [2508.15854].

Source: https://www.emergentmind.com/topics/fanar-1-9b