Jais-3-0.9B-GGUF

This repository contains GGUF versions of inception42/Jais-3-0.9B for use with llama.cpp.

Multiple precision and quantization variants are available: BF16, Q8_0, Q6_K, Q5_K_M, Q5_0, Q4_K_M.

Deployment notes: K2 Horizon architecture support is merged into llama.cpp (ggml-org/llama.cpp#29535). Use a recent llama.cpp build, build it yourself from master, or wait for the next official llama.cpp release. Start llama-server with --jinja so that reasoning and tool calls are parsed.

Jais-3-0.9B is the compact dense member of the Jais-3 family: a 0.9B-class decoder-only model with a 128K context window.

Other formats are available in the IFM/Jais-3 collection:

• MLX for Apple silicon:
  • IFM/Jais-3-0.9B-MLX-4bit
  • IFM/Jais-3-0.9B-MLX-8bit
  • IFM/Jais-3-0.9B-MLX-bf16

Benchmark Results

		Reference models
	Jais-3-0.9B	Qwen3.5-2B	Falcon-H1-1.5B-Deep-Instruct	Hala 1.2B	tiny-aya-global
# Params	0.9B	2B	1.5B	1.2B	3B
# Activated params	0.9B	2B	1.5B	1.2B	3B
Architecture	Dense	Dense	Dense	Dense	Dense
Open-ended Generation
AraGen
AraGen v3 (3C3H) 09-2025	32.7	15.3	12.2	25.0	21.4
Correctness	39.4	20.0	15.5	28.2	27.3
Completeness	38.6	19.3	15.0	27.1	25.2
Conciseness	10.9	2.6	3.1	14.4	6.5
Helpfulness	33.2	15.4	11.4	25.8	21.1
Honesty	35.8	15.4	13.1	26.8	22.1
Harmlessness	38.7	19.0	15.4	27.5	26.5
Knowledge & Reasoning
General Knowledge & Comprehension
Arabic MMLU* Accuracy	70.4	63.1	48.7	45.0	55.9
Belebele Accuracy	81.6	67.6	57.8	39.9	62.2
STEM & Math
AraSTEM* Accuracy	81.6	83.9	62.5	48.3	58.5
Arabic GSM8K* Accuracy	80.9	75.6	65.4	57.3	59.6
GATmath* Accuracy	87.4	87.4	40.2	29.6	31.4
Professional Domains
MedAraBench* Accuracy	48.1	54.3	32.3	32.3	39.5
Arabic Legal (HELM)* Accuracy	58.7	49.1	48.8	41.1	52.2
Arabic Language & Culture
Islamic Knowledge & Arab Culture
IslamicQA Accuracy (mean of the 4 below)	66.2	43.0	48.2	35.9	44.2
PaLM-X 2025-2* Accuracy	78.1	62.0	62.1	50.0	71.6
QIAS-1* Accuracy	61.1	26.1	32.1	16.2	23.1
QIAS-2* Accuracy	65.0	51.5	56.0	41.9	54.7
IslamicEval2025-1B* Accuracy	60.6	32.5	42.4	35.5	27.3
Arab Culture Accuracy (mean of the 3 below)	73.3	54.8	54.4	46.9	61.5
ArabCulture* Accuracy	74.0	63.8	61.7	60.6	68.6
AraDiCE-Culture* Accuracy	72.6	46.9	49.2	30.7	55.3
Arabic Cuisine^ Accuracy	73.4	53.6	52.4	49.5	60.6
Dialects
Dialectal Arabic MMLU* Accuracy	63.7	59.5	42.3	40.3	47.7
Dialect Identification (QADI) Accuracy	65.9	24.2	23.4	23.0	38.8
Alyah* Accuracy	73.8	53.2	54.8	53.2	68.1
Linguistics & Literature
Arabic MMLU with diacritics Accuracy	65.1	49.4	34.7	31.3	45.6
FannOrFlop-MCQ Accuracy	59.3	43.6	38.5	31.3	48.5
Arabic Linguistics^ Accuracy (mean of the 2 below)	85.5	54.7	33.3	32.6	38.2
Diacritization Accuracy	83.6	65.1	26.0	32.9	36.7
Parsing Accuracy	87.4	44.4	40.6	32.2	39.6
Assistant Capabilities
Instruction Following
Arabic IFBench^ Accuracy (prompt-level strict)	91.1	33.1	16.2	14.6	21.5
Arabic IFEval Accuracy	71.3	62.6	49.3	44.1	58.2
Factuality & Hallucination
HalluScore Accuracy	28.0	25.8	6.0	10.1	20.1
SimpleQA Verified (Ar) Aggregate (2/3 F-score + 1/3 non-hallucination rate)	27.1	7.9	2.6	3.1	3.1
F-score	1.0	2.7	2.0	3.1	3.1
Non-hallucination rate	79.3	18.2	3.8	3.2	3.2

Starred (*) benchmarks have been audited for sample quality. Items with ambiguous questions, invalid answer options, or indefensible gold labels were excluded, since they can penalize a correct answer or reward a lucky guess. Scores on these benchmarks are reported on the cleaned sets. The audit methodology, the excluded samples and the verified benchmarks are available in the IFM/Arabic-Benchmark-Auditing collection.

Benchmarks marked with ^ will be released by IFM soon.

Scores above are for the BF16 source checkpoint. Quantized variants have not been separately evaluated yet.

Files

File	Type	Size
Jais-3-0.9B-BF16.gguf	BF16	2.16 GB
Jais-3-0.9B-Q8_0.gguf	Q8_0	1.15 GB
Jais-3-0.9B-Q6_K.gguf	Q6_K	0.89 GB
Jais-3-0.9B-Q5_K_M.gguf	Q5_K_M	0.77 GB
Jais-3-0.9B-Q5_0.gguf	Q5_0	0.76 GB
Jais-3-0.9B-Q4_K_M.gguf	Q4_K_M	0.67 GB

Quickstart

llama.cpp

cmake -B build -DGGML_CUDA=ON
cmake --build build -j --target llama-server
./build/bin/llama-server -hf IFM/Jais-3-0.9B-GGUF:Q8_0 --jinja -ngl 99 -c 32768

Pick a variant with :BF16, :Q8_0, :Q6_K, :Q5_K_M, :Q5_0 or :Q4_K_M, or pass a local file with -m. --jinja enables the embedded chat template, which handles reasoning_effort and K2 Horizon tool calls; reasoning_content is returned separately from content.

API Usage

Recommended settings: reasoning_effort="high", the sampling hyperparameters in the table below, and at least 32,768 output tokens. Reasoning depth is selected per request through chat_template_kwargs. Thinking is returned in reasoning_content and the answer in content.

Parameter	Default	Thinking mode, English	Thinking mode, Arabic
temperature	0.2	1.0	0.7
top_p	0.95	0.95	0.95
top_k	20	20	20

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8080/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="Jais-3-0.9B",
    messages=[{"role": "user", "content": "لدينا عددان مجموعهما 50 وحاصل ضربهما 600. ما هما العددان؟ اشرح خطوات الحل."}],
    temperature=0.7,
    top_p=0.95,
    max_tokens=32768,
    extra_body={"top_k": 20, "chat_template_kwargs": {"reasoning_effort": "high"}},
)
message = response.choices[0].message
print("Reasoning:", getattr(message, "reasoning_content", None))
print("Answer:", message.content)

Best Practices

1. Reasoning effort: always high. All reported results use high reasoning effort. Pass {"chat_template_kwargs": {"reasoning_effort": "high"}} on every request. No reasoning ({"reasoning_effort": "low"}) is not recommended except for research on reasoning efforts.
2. Sampling parameters. Use the values in the table above: temperature=0.2, top_p=0.95, top_k=20 by default. In thinking mode, set temperature=1.0 for English and temperature=0.7 for Arabic. Keep min_p=0.0, presence_penalty=0.0 and repetition_penalty=1.0.
3. Parsers. With --jinja, llama.cpp detects the K2 Horizon template and parses reasoning and tool calls itself; no parser flags are needed.

Intended Use and Limitations

Intended use: The model is designed for Arabic and English assistant and agentic applications, including reasoning and tool-use tasks, as well as for research and experimentation.

Out of scope: The model should not be used in ways that violate applicable laws or regulations. The following uses are also considered out of scope:

• Harmful or malicious use: generating hateful, extremist, discriminatory, or deliberately misleading content, or using the model to support illegal or harmful activities.
• Sensitive information: using the model to generate, expose, or infer personal, confidential, or otherwise sensitive information about individuals or organizations.
• Unsupported languages: applications that depend on reliable performance in languages other than Arabic or English.
• High-stakes decisions: relying on the model to make medical, legal, financial, or safety-critical decisions without appropriate human oversight.

Limitations: The model can produce inaccurate or made-up content and may reflect biases present in its training data. Evaluate it for your use case and add safeguards before deploying it.

Citation

@misc{ifm2026jais3introducingarabiccentric,
      title={Jais 3: Introducing Arabic-centric Agentic AI},
      author={Team IFM and Haonan Li and Hadi Abdine and Nikhil Ranjan and Mohamed Anwar and Ahmad Chamma and Amr Mohamed and Ahmed Frikha and Dani Bouch and Maaz Maqsood and George Ibrahim and Mostafa Awad and Nurkhan Laiyk and Hasan Iqbal and Abhishek Maiti and Bilal Bouardi and Shivam Chauhan and Ahmer Tabassum and Owais Aijaz and Omar Arbi and Neha Sengupta and Aaryamonvikram Singh and Abdelhakim Freihat and Sarath Chandran and Rahul Pal and Mohammed Bellam and Sajid Siddiki and Sarah AlBarri and Debopriyo Banerjee and Ali El Filali and Samta Kamboj and Rifo Genadi and Ahmed Attia and Go Inoue and Shuonan Dong and Amine Khelif Khelif and Evan Dufraisse and Florian Valade and Tapas Sen and Guowei He and Sunil Sahu and Onkar Pandit and Kareem Magdy and Sheikh Khalid bin Khalifa Al Nahyan and Samujjwal Ghosh and Zainul Abedien and Ahmed Quraishi and Larry Murray and Avraham Sheinin and Natalia Vassilieva and Preslav Nakov and Guokan Shang and Xuguang Hector Ren and Zhengzhong Liu and Eric Xing},
      year={2026},
      eprint={2611.10224},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2611.10224},
}