Instructions to use OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B")
model = AutoModelWithLMHead.from_pretrained("OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B")
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 OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B

SGLang

How to use OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B 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 "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B" \
    --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": "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B",
		"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 "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B" \
        --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": "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B with Docker Model Runner:
```
docker model run hf.co/OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B
```

Arabic_Text-To-SQL_using_Qwen2.5-1.5B / LLaMA-Factory /tests /model /test_pissa.py

OsamaMo

Training in progress, step 500

93adfea verified about 1 year ago

raw

history blame

2.24 kB

	# Copyright 2025 the LlamaFactory team.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import os

	import pytest

	from llamafactory.train.test_utils import compare_model, load_infer_model, load_reference_model, load_train_model


	TINY_LLAMA = os.getenv("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")

	TINY_LLAMA_PISSA = os.getenv("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-pissa")

	TRAIN_ARGS = {
	"model_name_or_path": TINY_LLAMA,
	"stage": "sft",
	"do_train": True,
	"finetuning_type": "lora",
	"pissa_init": True,
	"pissa_iter": -1,
	"dataset": "llamafactory/tiny-supervised-dataset",
	"dataset_dir": "ONLINE",
	"template": "llama3",
	"cutoff_len": 1024,
	"overwrite_cache": True,
	"output_dir": "dummy_dir",
	"overwrite_output_dir": True,
	"fp16": True,
	}

	INFER_ARGS = {
	"model_name_or_path": TINY_LLAMA_PISSA,
	"adapter_name_or_path": TINY_LLAMA_PISSA,
	"adapter_folder": "pissa_init",
	"finetuning_type": "lora",
	"template": "llama3",
	"infer_dtype": "float16",
	}

	OS_NAME = os.getenv("OS_NAME", "")


	@pytest.mark.xfail(reason="PiSSA initialization is not stable in different platform.")
	def test_pissa_train():
	model = load_train_model(**TRAIN_ARGS)
	ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=True)
	compare_model(model, ref_model)


	@pytest.mark.xfail(OS_NAME.startswith("windows"), reason="Known connection error on Windows.")
	def test_pissa_inference():
	model = load_infer_model(**INFER_ARGS)
	ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=False)
	ref_model = ref_model.merge_and_unload()
	compare_model(model, ref_model)