SILMA

🚀 Our Flagship Model: SILMA 1.0 🚀

• SILMA 1.0 was the TOP-RANKED open-weights Arabic LLM (Until February 2025) with an impressive 9 billion parameter size, surpassing models that are over seven times larger 🏆

Important Tip: 💡 For RAG use-cases please use SILMA Kashif v1.0 as it has been specifically trained for Question Answering tasks.

What makes SILMA exceptional?

• SIMLA is a small language model outperforming 72B models in most arabic language tasks, thus more practical for business use-cases
• SILMA is built over the robust foundational models of Google Gemma, combining the strengths of both to provide you with unparalleled performance
• SILMA is an open-weight model, free to use in accordance with our open license

👥 Our Team

We are a team of seasoned Arabic AI experts who understand the nuances of the language and cultural considerations, enabling us to build solutions that truly resonate with Arabic users.

Authors: silma.ai

Usage

Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:

pip install -U transformers sentencepiece

Then, copy the snippet from the section that is relevant for your usecase.

Running with the pipeline API

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="silma-ai/SILMA-9B-Instruct-v1.0",
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",  # replace with "mps" to run on a Mac device
)

messages = [
    {"role": "user", "content": "اكتب رسالة تعتذر فيها لمديري في العمل عن الحضور اليوم لأسباب مرضية."},
]

outputs = pipe(messages, max_new_tokens=256)
assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
print(assistant_response)

• Response:

السلام عليكم ورحمة الله وبركاته

أودّ أن أعتذر عن عدم الحضور إلى العمل اليوم بسبب مرضي. أشعر بالسوء الشديد وأحتاج إلى الراحة. سأعود إلى العمل فور تعافيي.
شكراً لتفهمكم.

مع تحياتي،
[اسمك]

Running the model on a single / multi GPU

pip install accelerate

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "silma-ai/SILMA-9B-Instruct-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

messages = [
    {"role": "system", "content": "أنت مساعد ذكي للإجابة عن أسئلة المستخدمين."},
    {"role": "user", "content": "أيهما أبعد عن الأرض, الشمس أم القمر؟"},
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=256)

print(tokenizer.decode(outputs[0]))

• Response:

الشمس

You can ensure the correct chat template is applied by using tokenizer.apply_chat_template as follows:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "silma-ai/SILMA-9B-Instruct-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

messages = [
    {"role": "system", "content": "أنت مساعد ذكي للإجابة عن أسئلة المستخدمين."},
    {"role": "user", "content": "اكتب كود بايثون لتوليد متسلسلة أرقام زوجية."},
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]).split("model")[-1])

• Response:

def generate_even_numbers(n):
	"""
	This function generates a list of even numbers from 1 to n.
	Args:
		n: The upper limit of the range.

	Returns:
		A list of even numbers.
	"""
	return [i for i in range(1, n + 1) if i % 2 == 0]

# Example usage
n = 10
even_numbers = generate_even_numbers(n)
print(f"The first {n} even numbers are: {even_numbers}")

Quantized Versions through bitsandbytes

Using 8-bit precision (int8)

pip install bitsandbytes accelerate

# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_id = "silma-ai/SILMA-9B-Instruct-v1.0"
quantization_config = BitsAndBytesConfig(load_in_8bit=True)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=quantization_config,
)

messages = [
    {"role": "system", "content": "أنت مساعد ذكي للإجابة عن أسئلة المستخدمين."},
    {"role": "user", "content": "اذكر خمس انواع فواكه بها نسب عالية من فيتامين ج."},
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]).split("model")[-1])

• Response:

الليمون، البرتقال، الموز، الكيوي، الفراولة

Using 4-bit precision

# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_id = "silma-ai/SILMA-9B-Instruct-v1.0"
quantization_config = BitsAndBytesConfig(load_in_4bit=True)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=quantization_config,
)

messages = [
    {"role": "system", "content": "أنت مساعد ذكي للإجابة عن أسئلة المستخدمين."},
    {"role": "user", "content": "في أي عام توفى صلاح الدين الأيوبي؟"},
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]).spl