Instructions to use aravula7/qwen-sql-finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aravula7/qwen-sql-finetuning with Transformers:

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

pipe = pipeline("text-generation", model="aravula7/qwen-sql-finetuning")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("aravula7/qwen-sql-finetuning", dtype="auto")

PEFT
How to use aravula7/qwen-sql-finetuning with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aravula7/qwen-sql-finetuning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aravula7/qwen-sql-finetuning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aravula7/qwen-sql-finetuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aravula7/qwen-sql-finetuning

SGLang

How to use aravula7/qwen-sql-finetuning 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 "aravula7/qwen-sql-finetuning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aravula7/qwen-sql-finetuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "aravula7/qwen-sql-finetuning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aravula7/qwen-sql-finetuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aravula7/qwen-sql-finetuning with Docker Model Runner:
```
docker model run hf.co/aravula7/qwen-sql-finetuning
```

aravula7 commited on Feb 9

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@@ -202,10 +202,10 @@ Training and evaluation were tracked with MLflow on DagsHub. The GitHub reposito
 ## Citation
 ```bibtex
-@misc{qwen-sql-finetuning-2025,
   author = {Anirudh Reddy Ravula},
   title = {Qwen2.5-3B Text-to-SQL Fine-Tuning for PostgreSQL},
-  year = {2025},
   publisher = {HuggingFace},
   howpublished = {\url{https://huggingface.co/aravula7/qwen-sql-finetuning}},
   note = {Fine-tuned with QLoRA for e-commerce SQL generation}

 ## Citation
 ```bibtex
+@misc{qwen-sql-finetuning-2026,
   author = {Anirudh Reddy Ravula},
   title = {Qwen2.5-3B Text-to-SQL Fine-Tuning for PostgreSQL},
+  year = {2026},
   publisher = {HuggingFace},
   howpublished = {\url{https://huggingface.co/aravula7/qwen-sql-finetuning}},
   note = {Fine-tuned with QLoRA for e-commerce SQL generation}