Jais-3-7B-GGUF

[!NOTE] This repository contains GGUF versions of inception42/Jais-3-7B 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.

[!WARNING] 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-3-7B is the medium dense member of the Jais-3 family: a 7B-class decoder-only model with a 512K context window.

[!TIP] Other formats are available in the IFM/Jais-3 collection:

• Quantization: IFM/Jais-3-7B-FP8
• MLX for Apple silicon:
  • IFM/Jais-3-7B-MLX-4bit
  • IFM/Jais-3-7B-MLX-8bit
  • IFM/Jais-3-7B-MLX-bf16

Benchmark Results

[!NOTE] 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.

[!NOTE] Benchmarks marked with ^ will be released by IFM soon.

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

Files

Quickstart

llama.cpp

cmake -B build -DGGML_CUDA=ON
cmake --build build -j --target llama-server
./build/bin/llama-server -hf IFM/Jais-3-7B-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

[!TIP] 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.

Reasoning ExampleTool Calling Example

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8080/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="Jais-3-7B",
    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)

import json
from datetime import datetime
from zoneinfo import ZoneInfo
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8080/v1", api_key="EMPTY")
TIMEZONES = {"أبوظبي": "Asia/Dubai", "الرياض": "Asia/Riyadh", "القاهرة": "Africa/Cairo", "لندن": "Europe/London"}

def get_current_time(city: str) -> dict:
    if city not in TIMEZONES:
        return {"error": f"مدينة غير معروفة: {city}"}
    return {"city": city, "time": datetime.now(ZoneInfo(TIMEZONES[city])).strftime("%H:%M")}

tools = [{
    "type": "function",
    "function": {
        "name": "get_current_time",
        "description": "يعيد الوقت الحالي في مدينة معينة",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string", "description": "اسم المدينة بالعربية"}},
            "required": ["city"],
        },
    },
}]
available_tools = {"get_current_time": get_current_time}
messages = [{"role": "user", "content": "كم الساعة الآن في القاهرة؟"}]
for _ in range(5):  # call the model, run any requested tools, repeat until it answers
    message = client.chat.completions.create(
        model="Jais-3-7B",
        messages=messages,
        tools=tools,
        temperature=0.7,
        top_p=0.95,
        max_tokens=32768,
        extra_body={
            "top_k": 20,
            "chat_template_kwargs": {"reasoning_effort": "high", "tool_call_format": "xml"},
        },
    ).choices[0].message
    messages.append(message)
    if not message.tool_calls:
        break
    for call in message.tool_calls:
        tool = available_tools.get(call.function.name)
        arguments = json.loads(call.function.arguments)
        result = tool(**arguments)
        messages.append({"role": "tool", "tool_call_id": call.id,
                         "content": json.dumps(result, ensure_ascii=False)})

print(message.content)

Our model supports multiple tool-call formats, which can be changed with chat_template_kwargs. The supported values are json, xml, and xml_typed. The default is xml. Keep --tool-call-parser k2_horizon enabled to parse the selected format.

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{jais3_2026,
  title  = {Jais 3: Introducing Arabic-centric Agentic AI},
  author = {{IFM Team, Inception42 Team, and Cerebras Team}},
  year   = {2026},
  url    = {https://ifm.ai/blog/jais3/},
}