---
title: Fanar-1-9B-Instruct Model Overview
url: https://www.emergentmind.com/topics/fanar-1-9b-instruct
type: topic
---

# Fanar-1-9B-Instruct Model Overview

Fanar-1-9B-Instruct is the first-generation 9B instruct-tuned model in the Fanar family of Arabic-centric generative models. In the Fanar 2.0 paper it is identified explicitly as “the first generation Fanar model, built on top of gemma-2-9b base model using continual pre-training and instruction fine-tuning,” whereas the original Fanar platform paper uses the name “Fanar Prime Instruct” for the corresponding 9B-class aligned model rather than the exact string “Fanar-1-9B-Instruct” [2603.16397][2501.13944]. This suggests that “Fanar-1-9B-Instruct” is the checkpoint-style name used in later benchmarking, while “Fanar Prime Instruct” is the earlier family/platform name for the same 9B instruct line.

## 1. Position within the Fanar lineage

Within the Fanar family, Fanar-1-9B-Instruct occupies the role of the stronger first-generation 9B model. The Fanar 2.0 paper places it inside Fanar 1.0’s dual-LLM strategy, consisting of **Fanar Star (7B, trained from scratch)** and **Fanar Prime (9B, continual on Gemma-2-9B)**, before the architectural shift in Fanar 2.0 to a single 27B model [2603.16397].

In that later comparison framework, Fanar-1-9B-Instruct is not treated as a minor ablation or auxiliary checkpoint. It serves as the principal first-generation baseline against which **Fanar-27B** is measured across Arabic knowledge, Arabic language, dialectal Arabic, Arabic and Islamic culture, English world knowledge, and conversational instruction-following tasks [2603.16397]. This historical placement matters because it fixes the model’s significance: it is the reference point for the transition from Fanar 1.0’s smaller Arabic-centric Gemma-2-derived line to Fanar 2.0’s larger Gemma-3-derived line.

The naming discrepancy between **Fanar Prime Instruct** and **Fanar-1-9B-Instruct** is central to the literature. The original platform paper does not use the latter string, but it does identify **Fanar Prime Instruct** as the 9B-class aligned model in the same family [2501.13944]. A plausible implication is that later benchmark-oriented naming standardized the model as **Fanar-1-9B-Instruct**, while the original system paper used the internal family names **Star** and **Prime**.

## 2. Architecture, initialization, and pretraining basis

Under the earlier Fanar platform nomenclature, the most detailed technical specification is given for **Fanar Prime**, the 9B-class model whose aligned form is **Fanar Prime Instruct**. Fanar Prime comprises **8.78B parameters total**, specifically **459M embedding parameters**, **8.32B non-embedding parameters**, and **8.78B total parameters**. It is built through the continual pre-training of the **Gemma-2-9B-Base** model rather than from-scratch training [2501.13944].

The architecture is described as a **decoder-only Transformer**. For the 9B model, the paper reports **model dimension 3584**, **RMSNorm** for both pre- and post-normalization, **RoPE**, **GQA**, **Approximated GeGLU**, **no biases**, **vocabulary size 128,256**, and **weight tying: False**. A major initialization step is **vocabulary pruning**: the original **250,000-token** Gemma vocabulary is reduced to **128,256 tokens**, which in turn reduces total parameters from **9.2 billion** to **8.78 billion** [2501.13944]. By contrast, several architectural items remain unspecified or unreadable in the extracted description, including the exact number of layers, attention-head count, intermediate size, and the exact context-length entry for Fanar Prime [2501.13944].

The pretraining basis is a cleaned and deduplicated multilingual corpus of **1 trillion tokens** spanning Arabic, English, and code. The platform paper reports the overall corpus as roughly **50% English**, **40% Arabic**, and **10% code**, and states that continual pretraining for Fanar Prime uses a similar balanced data composition of **45% Arabic, 45% English, and 10% code** [2501.13944]. The Fanar 2.0 retrospective summarizes Fanar 1.0 more coarsely as **~1T tokens (40% Ar / 50% En / 10% Code)** [2603.16397]. The papers therefore report the data mixture at different levels of granularity.

Arabic coverage in the corpus is broad by design. The Arabic portion is said to cover **MSA**, **Classical Arabic**, and **dialectal Arabic**, and to draw from web documents, news articles, books on literature, religion, politics, culture, and history, Arabic Wikipedia, classical and contemporary poetry, and machine-translated books, STEM papers, and encyclopedic documents [2501.13944]. The platform paper also emphasizes translation-augmented Arabic coverage, including **Shaheen** for books and **NLLB 3.3B** for STEM papers and Wiki Encyclopedia [2501.13944].

Data quality control is a major part of the model’s construction. Records are standardized into JSON; cleaning removes HTML and JavaScript tags and normalizes whitespace; filtering proceeds through **syntactic**, **semantic**, and **model-based** stages; semantic filtering uses **ASAD**; model-based filtering uses **KenLM** perplexity filtering and an education classifier; and deduplication includes URL-level, exact-match, and approximate-match stages. For the approximate deduplication stage, the paper reports **gram size \(= 8\)**, **number of bands \(b = 12\)**, **band length \(r = 11\)**, **approximated Jaccard threshold \(\approx 0.8\)**, and **signature length \(= 132\)** [2501.13944].

## 3. Instruction tuning and alignment stack

The “Instruct” suffix is grounded in explicit post-training rather than deployment-time prompting alone. The platform paper states that the same post-training stages were applied to **Fanar Star** and **Fanar Prime**, and that both models were trained to support a **system prompt** as part of their chat template. The post-training pipeline consists of **supervised fine-tuning (SFT)** and **Direct Preference Optimization (DPO)** for safety and value alignment [2501.13944].

The SFT corpus combines three sources: curated public instruction/dialog datasets, synthetic data generation, and new capability data. The public-source curation process produced about **2.5 million instructions and dialogues** across **11 capability categories** in Arabic and English. Synthetic generation produced **close to a million samples** in both languages, mainly for cultural contextualization and value alignment, using stronger external instruct models such as **Gemma-2-27B-it**, **Qwen2.5-72B-Instruct**, **c4ai-command-r-plus**, **Llama-3.1-70B-Instruct**, and **Llama-3.1-405B-Instruct** [2501.13944]. Additional capability data covered closed-set QA behavior expansion, IslamQ&A, poetry, humor, diacritization, question generation, grammar correction, dialectal dialogues, and expert DPO data on nuanced and controversial Islamic issues [2501.13944].

The SFT pipeline is two-stage. For **Fanar Prime**, **SFT Stage 1** uses **3.6M samples**, **batch 640**, and learning rate **\(5.0\times10^{-7}\)**, while **SFT Stage 2** uses **834K samples**, **batch 640**, and learning rate **\(1.0\times10^{-7}\)** [2501.13944]. Preference learning then adds **250K preference samples**, balanced across Arabic and English with **about 20% on-policy**, and Table 8 reports **batch 640** and learning rate **\(1.0\times10^{-7}\)** for **DPO** [2501.13944].

The Fanar 2.0 retrospective compresses the first-generation 9B alignment profile into a concise systems description: **SFT + DPO**, **8K tokens** context, **no** native reasoning traces, **no** tool calling, **translation-based traces**, **SFT-based cultural alignment**, and **knowledge probing** as hallucination mitigation [2603.16397]. This makes the historical contrast with Fanar 2.0 explicit: Fanar-1-9B-Instruct is an aligned instruct model, but it predates later additions such as native Arabic reasoning traces, self-verification traces, longer context, and integrated tool calling.

## 4. Empirical profile across benchmark families

In the Fanar 2.0 comparison, Fanar-1-9B-Instruct functions as a broad predecessor baseline rather than a single-task model, and its reported scores span Arabic knowledge, Arabic language, dialectal Arabic, Arabic and Islamic culture, English world knowledge, and conversational instruction following [2603.16397].

| Evaluation family | Fanar-1-9B-Instruct | Fanar-27B |
|---|---|---|
| Arabic knowledge | MMMLU/Ar 58.30; ArabicMMLU 67.35; OALL-v2 68.64 | 67.40; 74.67; 69.40 |
| Arabic language | Nahw-MCQ 40.00; AraLingBench 60.60 | 46.88; 68.67 |
| Dialectal Arabic | Belebele 83.26; DialectalArabicMMLU 59.91 | 86.81; 67.40 |
| Arabic and Islamic culture | ACVA 79.66; PalmX Islamic 82.33; PalmX Culture 67.10 | 82.70; 85.38; 72.70 |
| English world knowledge | MMLU 71.32; PIQA 82.37; HellaSwag 83.01; ARC Challenge 65.19 | 78.89; 85.91; 85.32; 65.61 |
| Conversational / instruction | MT-Bench 5.58; IFEval 74.70 | 6.12; 82.97 |

These results establish two points. First, the model is already strong on Arabic and Islamic culture, with **79.66** on **ACVA** and **82.33** on **PalmX Islamic** [2603.16397]. Second, the benchmark profile is bilingual rather than Arabic-only: the same model records **71.32** on **MMLU**, **82.37** on **PIQA**, and **83.01** on **HellaSwag** [2603.16397].

Under the earlier **Fanar Prime Instruct** naming, the same 9B line is also reported as strong on Arabic automatic and generative evaluations, including **MT-Bench(Ar) 8.93**, **User-Chats 9.66**, **Belebele(Ar) 82.48**, **OALL 63.81**, **ArabicMMLU(Egy) 59.22**, and **ArabicMMLU(Lev) 60.14**. The platform paper summarizes the 9B instruct line as “a strong competitor in Arabic, producing top scores in many automated and generative tasks” [2501.13944].

## 5. Downstream evaluation and task-specific adaptation

A direct downstream evaluation of **Fanar-1-9B-Instruct** appears in Arabic automatic essay scoring. On the **QAES** dataset, the model is evaluated under **Level 1: standard prompting**, **Level 2: hybrid prompting**, and **Level 3: rubric-guided prompting with scored exemplars**, across the traits **Organization**, **Vocabulary**, **Style**, **Development**, **Mechanics**, **Structure**, **Relevance**, and the summed **Final Score** [2603.19668]. Its total **QWK** rises from **0.008** under Level 1 to **0.161** under Level 2 and **0.284** under Level 3, which the paper identifies as the best total score in the study [2603.19668].

The per-trait pattern is also informative. Under **Level 3**, Fanar-1-9B-Instruct reaches **Development 0.204**, **Mechanics 0.280**, **Relevance 0.227**, and **Total 0.284**, while its strongest **Style** result appears under **Level 2** at **0.132** rather than under Level 3 [2603.19668]. The study states that **Fanar-1-9B-Instruct achieves the highest trait-level agreement in both zero- and few-shot prompting**, reports **QWK = 0.28** and **CI upper bound = 0.41**, and interprets the results as evidence that strong instruction tuning, robust Arabic coverage, and structured prompting matter more than parameter scale alone for Arabic AES [2603.19668].

A closely related but distinct 9B lineage appears in the QIAS 2025 Islamic inheritance reasoning system. That work fine-tunes the **Fanar-1-9B causal language model**, not explicitly the instruct checkpoint, using **LoRA** over `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, and `down_proj`, with **rank \(r = 32\)**, **LoRA alpha \(\alpha = 64\)**, **dropout \(0.1\)**, **4 epochs**, **batch size 2**, **gradient accumulation 32**, learning rate **\(3\times10^{-4}\)**, **weight decay 0.01**, **warmup ratio 0.1**, **cosine decay**, and **FP16** [2508.15854]. Integrated into a RAG pipeline using **all-MiniLM-L6-v2**, **FAISS**, **top-\(k = 5\)** retrieval, **maximum input length 10,000 tokens**, **maximum 15 new tokens**, **temperature 0.05**, and **greedy decoding**, the adapted system reaches **85.8%** overall accuracy and **97.6%** on the **Advanced** subset [2508.15854]. Although this result is not a direct evaluation of Fanar-1-9B-Instruct itself, it documents the broader adaptability of the first-generation 9B Fanar line in a highly structured Arabic-Islamic reasoning domain.

## 6. Ambiguities, misconceptions, and scope of claims

Two recurrent ambiguities structure the literature on this model. First, the foundational Fanar paper does **not** use the exact string **“Fanar-1-9B-Instruct”**; it uses **Fanar Prime** and **Fanar Prime Instruct** [2501.13944]. Second, not every later reference to **Fanar-1-9B-Instruct** is reliable evidence about the 9B instruct model. In the CultranAI PalmX submission paper, the abstract says that **Fanar-1-9B-Instruct** achieved the highest performance, but the detailed notes report that the paper’s tables and appendix repeatedly refer to **Fanar-7B / Fanar-7B-Instruct**, and that the most accurate reading is that the model actually benchmarked, fine-tuned, and submitted is **Fanar-7B-Instruct** rather than the 9B model [2508.17324].

System-level claims likewise require disentanglement from checkpoint-level claims. **Fanar-Sadiq** is a bilingual multi-agent Islamic assistant and a core component of the Fanar AI platform; it states that the system uses **“Fanar” as the LLM agent** and compares against **Fanar-2-27B**, but it does **not** explicitly mention **Fanar-1-9B-Instruct** anywhere [2603.08501]. Its reported performance therefore characterizes an agentic system with routing, retrieval, deterministic calculators, citation normalization, verification traces, exact verse lookup, and tool use, not a standalone 9B instruct checkpoint [2603.08501].

The first-generation 9B line also has substantive technical limits in the primary Fanar papers. Some core architectural items remain unspecified in the original platform description, including the exact number of layers, attention-head count, intermediate size, and exact context-length entry for Fanar Prime [2501.13944]. The same paper reports a **dislike rate around 13–15%**, attributes most dislikes to **factuality-related errors**, and introduces **Recency RAG**, **Islamic RAG**, **Biography RAG**, and **Attribution RAG** precisely because recency, verification, and some forms of grounded answering are not native model properties [2501.13944]. It also states that the platform has multimodal capabilities, but that these capabilities are **not intrinsic to the autoregressive model** [2501.13944]. This suggests that Fanar-1-9B-Instruct is best understood as a strong first-generation Arabic-centric instruct model whose deployed behavior was often coupled to a larger orchestration, retrieval, and safety stack.

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