Instructions to use mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder with Transformers:

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

pipe = pipeline("text-generation", model="mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder")
model = AutoModelForCausalLM.from_pretrained("mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder")
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 Settings

vLLM

How to use mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder

SGLang

How to use mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder 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 "mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder" \
    --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": "mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder",
		"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 "mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder" \
        --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": "mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder with Docker Model Runner:
```
docker model run hf.co/mohamedelmadany/Qwen2.5-Arabic-to-SQL-Coder
```

mohamedelmadany commited on Apr 27

Commit

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@@ -52,12 +52,7 @@ The cosine learning-rate schedule with 5% warmup:
 Evaluated on a hand-crafted held-out test suite covering 12 SQL skill categories
 (basic SELECT, JOIN, GROUP BY, HAVING, subqueries, LEFT JOIN with NULL, date
 filtering, DISTINCT COUNT, LIKE, ORDER BY + LIMIT, single + multi-table aggregations)
-with **real execution against an in-memory SQLite database**:
-| Language | Execution accuracy |
-|----------|-------------------:|
-| **English** | **100%** (18/18) |
-| **Arabic (MSA)** | **88.9%** (16/18) |
 Each test case includes seeded data, so accuracy is verified by running both the
 predicted SQL and the gold reference SQL and comparing result rows — not by string

 Evaluated on a hand-crafted held-out test suite covering 12 SQL skill categories
 (basic SELECT, JOIN, GROUP BY, HAVING, subqueries, LEFT JOIN with NULL, date
 filtering, DISTINCT COUNT, LIKE, ORDER BY + LIMIT, single + multi-table aggregations)
+with **real execution against an in-memory SQLite database**
 Each test case includes seeded data, so accuracy is verified by running both the
 predicted SQL and the gold reference SQL and comparing result rows — not by string