Instructions to use unige-fti/Aladdin-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unige-fti/Aladdin-3B with Transformers:

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

pipe = pipeline("text-generation", model="unige-fti/Aladdin-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unige-fti/Aladdin-3B")
model = AutoModelForCausalLM.from_pretrained("unige-fti/Aladdin-3B")
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 unige-fti/Aladdin-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unige-fti/Aladdin-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unige-fti/Aladdin-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unige-fti/Aladdin-3B

SGLang

How to use unige-fti/Aladdin-3B 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 "unige-fti/Aladdin-3B" \
    --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": "unige-fti/Aladdin-3B",
		"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 "unige-fti/Aladdin-3B" \
        --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": "unige-fti/Aladdin-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use unige-fti/Aladdin-3B with Docker Model Runner:
```
docker model run hf.co/unige-fti/Aladdin-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for unige-fti/Aladdin-3B

Multidialectal Arabic generation and translation model fine-tuned for dialect fidelity and diglossia.

Model Details

Model Description

Base model: SmolLM3-3B
Architecture: Decoder-only causal transformer (SmolLM architecture)
Parameters: ~3B
Language coverage: Arabic dialects, Modern Standard Arabic (MSA), English

Primary tasks:

Dialectal Arabic generation
Bidirectional translation (DA ↔ MSA ↔ English)
Controlled generation conditioned on dialect instructions

This model was fine-tuned by the Aladdin-FTI team for the AMIYA shared task to jointly optimize:

Machine translation (semantic adequacy & diglossia)

Instruction-formatted prompts:

Translate from English into Egyptian Arabic:
<SOURCE>

Instruction-conditioned generation (dialect fidelity)

Complete the sentence in Moroccan Arabic:
<PREFIX>

The objective balances meaning preservation and dialect naturalness in Arabic diglossia settings.

Model Sources

Repository: Github repository
Paper: https://arxiv.org/abs/2602.16290

How to Get Started with the Model

TODO

Training Details

Training Data: Closed-track training data only.

Datasets span multiple dialect regions and domains

Parallel corpora:

SauDial
Casablanca corpus
JODA
UFAL Levantine
DODA
Atlas

Monolingual dialect corpora:

MADAR
Shami
Saudi Tweets
EDGAD / EDC
HABIBI lyrics

Citation

If you use this model in your research, please cite the following paper:

@inproceedings{mutal2026aladdinfti,
  title     = {Aladdin-FTI @ AMIYA: Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation},
  author    = {Mutal, Jonathan and Al Almaoui, Perla and Hengchen, Simon and Bouillon, Pierrette},
  booktitle = {Proceedings of the AMIYA Shared Task, co-located with VarDial at EACL 2026},
  year      = {2026},
  address   = {Rabat, Morocco},
  publisher = {Association for Computational Linguistics},
}