Instructions to use salmane11/Darija-to-SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use salmane11/Darija-to-SQL with Transformers:

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

pipe = pipeline("text-generation", model="salmane11/Darija-to-SQL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("salmane11/Darija-to-SQL")
model = AutoModelForCausalLM.from_pretrained("salmane11/Darija-to-SQL")
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 salmane11/Darija-to-SQL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "salmane11/Darija-to-SQL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "salmane11/Darija-to-SQL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/salmane11/Darija-to-SQL

SGLang

How to use salmane11/Darija-to-SQL 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 "salmane11/Darija-to-SQL" \
    --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": "salmane11/Darija-to-SQL",
		"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 "salmane11/Darija-to-SQL" \
        --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": "salmane11/Darija-to-SQL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use salmane11/Darija-to-SQL with Docker Model Runner:
```
docker model run hf.co/salmane11/Darija-to-SQL
```

salmane11 commited on Jan 30

Commit

2f56881

verified ·

1 Parent(s): 3f639ad

Update README.md

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README.md +11 -6

README.md CHANGED Viewed

@@ -113,10 +113,15 @@ print(line[query_beginning:])
   year={2025}
 }
-@article{chafik2026darijadb,
-  title={DarijaDB: Unlocking Text-to-SQL for Arabic Dialects},
-  author={Chafik, Salmane and Ezzini, Saad and Berrada, Ismail},
-  journal={ACM Transactions on Asian and Low-Resource Language Information Processing},
-  year={2026},
-  publisher={ACM New York, NY}
 }

   year={2025}
 }
+@article{10.1145/3787497,
+  author = {Chafik, Salmane and Ezzini, Saad and Berrada, Ismail},
+  title = {DarijaDB: Unlocking Text-to-SQL for Arabic Dialects},
+  year = {2026},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  issn = {2375-4699},
+  url = {https://doi.org/10.1145/3787497},
+  doi = {10.1145/3787497},
+  journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
+  month = jan
 }