Fanar-1-9B-Instruct
Fanar-1-9B-Instruct is a 9B-parameter instruction-tuned LLM developed by Qatar Computing Research Institute (QCRI) for Arabic-English bilingual conversational tasks. It was continually pretrained on 1 trillion tokens (Arabic, English, and code) and fine-tuned with 4.5M supervised instructions and 250K DPO preference pairs. The model supports Modern Standard Arabic (MSA) and multiple dialects (Gulf, Levantine, Egyptian). It is open-source under Apache 2.0 license, not gated, and compatible with HuggingFace transformers and vLLM. Intended use includes conversational agents, cultural QA, and Arabic NLP research; not suitable for high-stakes decisions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | QCRI |
| Parameters | 8.8B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 65k |
| Likes | 33 |
| Last updated | 2025-07-15 |
| Source | QCRI/Fanar-1-9B-Instruct |
What Fanar-1-9B-Instruct is
Fanar-1-9B-Instruct is an autoregressive transformer derived from google/gemma-2-9b via continual pretraining. Architecture: 8.783B parameters, 4096 token context length, text-only I/O. Training: 1T mixed-language tokens (515B English, 410B Arabic, 102B code), using LitGPT framework. Post-training: 2-phase (SFT + DPO). Inference: Compatible with transformers (≥v4.40.0) and vLLM. Format: SafeTensors, PyTorch. MMLU (5-shot): 71.53%; strong performance on Arabic-specific benchmarks (MMMLU-Arabic 58.89%, ArabicMMLU 67.69%, Belebele-Arabic 83.22%). Model last modified 2025-07-15.
Run Fanar-1-9B-Instruct locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="QCRI/Fanar-1-9B-Instruct")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated: 18–36 GB VRAM (bfloat16/fp16 inference, single GPU). Quantized versions (int8, int4 via bitsandbytes or similar) would reduce to 9–18 GB. Training on full 1T tokens used LitGPT; per-step memory scales with batch size and sequence length. Inference tested/compatible with vLLM (efficient batching) and standard transformers. No precision specification provided in model card; assume bfloat16 as standard for gemma-2-9b-derived models.
LoRA/QLoRA feasibility: High. Model is Gemma-2-based transformer with standard architecture; well-suited for parameter-efficient tuning via LoRA (rank 8–16, alpha 16–32 typical). No explicit LoRA weights provided, but standard transformers integration enables easy integration with peft library. Supervised fine-tuning already performed at scale (4.5M instructions); domain-specific fine-tuning on 10K–100K examples is practical. DPO tuning also feasible but requires paired preference data. Quantization (4-bit) + LoRA recommended for resource-constrained setups.
When to avoid it — and what to weigh
- High-Stakes Decision-Making — Model card explicitly states unsuitability for legal, medical, or financial advice. Risk of biased, offensive, or factually incorrect outputs. Do not use for compliance-critical applications or scenarios requiring guaranteed accuracy.
- Long-Context Applications — Context length is 4096 tokens. Avoid use cases requiring multi-document summarization, extended dialogue history, or long-form content processing without chunking/sliding window strategies.
- Non-English/Non-Arabic Languages — Trained exclusively on Arabic and English (plus ~102B code tokens). Performance on other languages is not stated or evaluated. Do not assume multilingual capability beyond these two languages.
- Real-Time, Low-Latency Requirements — 9B model requires significant VRAM (estimated 18–36 GB in fp16/bfloat16). Inference speed depends heavily on hardware and batch size. Not ideal for sub-100ms latency SLAs without quantization or distillation.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Permits commercial use, modification, distribution, and private use without restriction, provided original license and copyright notice are included. No copyleft requirement. Clear and non-restrictive for both open-source and proprietary deployments.
Commercial use is permitted under Apache 2.0. No gating, no restricted API key requirement for model download. However, model card stipulates ethical deployment responsibilities: developers must implement safety checks and avoid generating harmful, illegal, or misleading content. Output is not attributed to QCRI, HBKU, Qatar Foundation, or MCIT—full liability rests with deploying organization. Consult model card's Terms of Service and Privacy Policy for hosted Fanar Chat service (separate from open-source model). For commercial SaaS or high-risk applications, strongly recommend domain-specific safety validation and fine-tuning.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model capability to generate text means risk of prompt injection, adversarial inputs, and jailbreaking. No explicit adversarial robustness testing or red-teaming results published. Model card notes capacity for biased/offensive outputs; mitigation relies on deployer's safety checks. Training data (Dolma, curated Arabic sources, The Stack) not fully attributed—supply-chain transparency is partial. Recommend: rate-limiting, input validation, output filtering for sensitive domains. No claimed vulnerability disclosure process; security issues should be reported to QCRI directly (contact method not specified in provided data).
Alternatives to consider
ALLaM-7B-Instruct-preview
Saudi/regional alternative; smaller (7B vs 9B), supports Arabic+English. MMLU 60.72% vs Fanar 71.53%; stronger on ArabicMMLU (68.59% vs 67.69%) but weaker on most other benchmarks. Lighter resource footprint; check license/gating status independently.
aya-expanse-8b
Multilingual model (100+ languages including Arabic); 8B parameters, no gating. MMLU 62.85%; weaker on Arabic-specific benchmarks (MMMLU-Arabic 47.14%) but broader language coverage. Choose if supporting non-MENA locales required.
gemma-2-9b-it (base)
Underlying base model; no Arabic-specific tuning. MMLU 71.65% (slightly higher than Fanar), but Belebele-Arabic 78.31% vs Fanar 83.22%. Lighter maintenance (Google-backed). If Arabic dialect support not critical, use base gemma-2-9b-it + custom fine-tuning.
Ship Fanar-1-9B-Instruct with senior software developers
Start with vLLM or HuggingFace transformers—Apache 2.0 licensing means no gating. Need guidance on fine-tuning for your Arabic NLP domain, integration, or safety validation? Contact our AI engineering team to architect a custom implementation.
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Fanar-1-9B-Instruct FAQ
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Software developers & web developers for hire
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Ready to Deploy Fanar-1-9B-Instruct?
Start with vLLM or HuggingFace transformers—Apache 2.0 licensing means no gating. Need guidance on fine-tuning for your Arabic NLP domain, integration, or safety validation? Contact our AI engineering team to architect a custom implementation.