Instructions to use Etherll/Tashkeel-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Etherll/Tashkeel-350M with Transformers:

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

pipe = pipeline("text-generation", model="Etherll/Tashkeel-350M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Etherll/Tashkeel-350M")
model = AutoModelForCausalLM.from_pretrained("Etherll/Tashkeel-350M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Etherll/Tashkeel-350M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Etherll/Tashkeel-350M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Etherll/Tashkeel-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Etherll/Tashkeel-350M

SGLang

How to use Etherll/Tashkeel-350M 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 "Etherll/Tashkeel-350M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Etherll/Tashkeel-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Etherll/Tashkeel-350M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Etherll/Tashkeel-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Etherll/Tashkeel-350M with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Etherll/Tashkeel-350M to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Etherll/Tashkeel-350M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Etherll/Tashkeel-350M to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Etherll/Tashkeel-350M",
    max_seq_length=2048,
)

Docker Model Runner
How to use Etherll/Tashkeel-350M with Docker Model Runner:
```
docker model run hf.co/Etherll/Tashkeel-350M
```

Tashkeel-350M

Arabic Diacritization Model | نَمُوذَجَ تَشْكِيلِ النُصُوصِ الْعَرَبِيَةِ

نموذج بحجم 350 مليون بارامتر مخصص لتشكيل النصوص العربية. تم تدريب هذا النموذج بضبط نموذج

LiquidAI/LFM2-350M

على مجموعة البيانات

arbml/tashkeela.

النموذج الأساسي: LiquidAI/LFM2-350M
مجموعة البيانات: arbml/tashkeela

كيفية الاستخدام

from transformers import AutoModelForCausalLM, AutoTokenizer

#تحميل النموذج
model_id = "Etherll/Tashkeel-350M"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# إضافة التشكيل
prompt = "السلام عليكم" 
input_ids = tokenizer.apply_chat_template(
    [
      {'role': 'system','content': "أنت نموذج لتشكيل النصوص العربية."},
      {"role": "user", "content": prompt}
    ],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
).to(model.device)

output = model.generate(
    input_ids,
    do_sample=False,  
)

print(tokenizer.decode(output[0, input_ids.shape[-1]:], skip_special_tokens=True))

مثال

النص المدخل: السلام عليكم
الناتج: اَلسَلَامُ عَلَيْكُمْ

Tashkeel-350M (English)

A 350M parameter model for Arabic diacritization (Tashkeel). This model is a fine-tune of LiquidAI/LFM2-350M on the arbml/tashkeela dataset.

Base Model: LiquidAI/LFM2-350M
Dataset: arbml/tashkeela

How to Use

The Python code for usage is the same as listed in the Arabic section above.

Example

Input: السلام عليكم
Output: اَلسَلَامُ عَلَيْكُمْ

This lfm2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 4

Safetensors

Model size

0.4B params

Tensor type

BF16

Model tree for Etherll/Tashkeel-350M

Base model

LiquidAI/LFM2-350M

Finetuned

(59)

this model

Etherll
/

Tashkeel-350M