Instructions to use Congi-libya/BayanSimplify-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Congi-libya/BayanSimplify-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Congi-libya/BayanSimplify-v0.2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Congi-libya/BayanSimplify-v0.2")
model = AutoModelForSeq2SeqLM.from_pretrained("Congi-libya/BayanSimplify-v0.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Congi-libya/BayanSimplify-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Congi-libya/BayanSimplify-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Congi-libya/BayanSimplify-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Congi-libya/BayanSimplify-v0.2

SGLang

How to use Congi-libya/BayanSimplify-v0.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Congi-libya/BayanSimplify-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Congi-libya/BayanSimplify-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Congi-libya/BayanSimplify-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Congi-libya/BayanSimplify-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Congi-libya/BayanSimplify-v0.2 with Docker Model Runner:
```
docker model run hf.co/Congi-libya/BayanSimplify-v0.2
```

BayanSimplify-v0.2

Model 2 of the Bayan project: AraT5v2 trained on a 22,733-pair mix with strength tags.

What it does

Trained on SAMER, Baseet and DAASI pairs, each tagged with the style it teaches: [S0] SAMER (minimal word swaps), [S1] light, [S2] medium, [S3] strong (Baseet levels 3, 2, 1), [SA] everyday and administrative (DAASI). The tag sets the strength at run time; with no tag the model is most careful with meaning.

Input: بسّط: (with the shadda), then an optional tag such as [S2] , then the text.

BayanBench v2.0, test, core items (meaning kept = Gemma 4 31B P(same) ≥ 0.5 and every number kept):

Setting	Meaning kept	Longest clause, words cut
`[S2]`, float	59.5%	6.3
no tag, float	68.3%	4.5
no tag, int8 in the app	78.6%	1.9

It was the app's large model until BayanSimplify-v0.3 replaced it, and stays available as "Earlier large model".

Use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

repo = "Congi-libya/BayanSimplify-v0.2"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSeq2SeqLM.from_pretrained(repo)

text = "وقد أدى التوسع العمراني السريع الذي شهدته المدينة خلال العقدين الماضيين إلى ازدحام مروري خانق في ساعات الذروة."
x = tok("بسّط: " + text, return_tensors="pt", max_length=256, truncation=True)
y = model.generate(**x, num_beams=1, no_repeat_ngram_size=3, max_length=256)
print(tok.decode(y[0], skip_special_tokens=True))

The BayanSimplify family

Model	What it is	Base model	Training data	In the Bayan app
BayanSimplify-v0.1	Model 1	AraT5v2-base-1024	SAMER, level 5 → 3	not shipped
BayanSimplify-v0.2	Model 2	AraT5v2-base-1024	22,733-pair mix (SAMER, Baseet, DAASI) with strength tags	"Earlier large model"
BayanSimplify-v0.2-Fast	Model 2, compact	AraBART	the same mix	"Fast model", the default download
BayanSimplify-v0.3	Model 3, our best	AraT5v2-base-1024	Bayan corpus v1, 14,975 rows	"Large model", four beams
BayanSimplify-ONNX	The int8 bundles the app downloads			v0.2-Fast, v0.2, v0.3

Licence and data

CC BY-NC 4.0 (non-commercial), in line with the training data's terms. SAMER is used under the CAMeL Lab's permission to fine-tune and share weights for non-commercial use; its text is not redistributed here.

About Bayan

Bayan (بيان) simplifies Arabic text for readers with dyslexia, entirely on an Android phone: select text in any app, choose «تبسيط» (Simplify), and a simpler version appears over the page. Built by Team Cogni for the Samsung Innovation Campus AI capstone, 2026. Every number on this card comes from the team's final report and BayanBench v2.0, the benchmark built for the task (meaning scored by Gemma 4 31B, checked against human raters, AUC 0.85).

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Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for Congi-libya/BayanSimplify-v0.2

Base model

UBC-NLP/AraT5v2-base-1024

Finetuned

(33)

this model