Instructions to use Tuana/qwen35-08b-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tuana/qwen35-08b-text2sql with Transformers:

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

pipe = pipeline("text-generation", model="Tuana/qwen35-08b-text2sql")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Tuana/qwen35-08b-text2sql")
model = AutoModelForImageTextToText.from_pretrained("Tuana/qwen35-08b-text2sql")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Tuana/qwen35-08b-text2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tuana/qwen35-08b-text2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tuana/qwen35-08b-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tuana/qwen35-08b-text2sql

SGLang

How to use Tuana/qwen35-08b-text2sql 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 "Tuana/qwen35-08b-text2sql" \
    --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": "Tuana/qwen35-08b-text2sql",
		"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 "Tuana/qwen35-08b-text2sql" \
        --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": "Tuana/qwen35-08b-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tuana/qwen35-08b-text2sql with Docker Model Runner:
```
docker model run hf.co/Tuana/qwen35-08b-text2sql
```

qwen35-08b-text2sql

Tuana/qwen35-08b-text2sql is a demo Text-to-SQL model fine-tuned from Qwen/Qwen3.5-0.8B.

It was fine-tuned on a small, specific synthetic SQL dataset for demonstration purposes. It is not intended to be a general Text-to-SQL model for arbitrary schemas or production databases.

Model Details

Base model: Qwen/Qwen3.5-0.8B
Task demo: Text-to-SQL style SQL generation
Fine-tuning method: LoRA SFT, merged into a full checkpoint
Training platform: Google Cloud Vertex AI
Training container: Hugging Face PyTorch Training Deep Learning Container
Dataset: Tuana/synthetic-sql-dataset
Model format: Merged transformers checkpoint

What This Model Demonstrates

This model demonstrates a small fine-tuning workflow:

Generate a synthetic SQL instruction dataset
Fine-tune a small Qwen base model on Vertex AI
Merge the LoRA adapter into the base checkpoint
Serve or compare the result in a small demo app

The demo dataset uses a small synthetic database domain with tables such as:

department
management
head

The model should be viewed as a demo artifact for this specific setup, not as a robust SQL assistant.

Example Prompt Format

Given this database schema:
CREATE TABLE department (
  department_id VARCHAR,
  name VARCHAR,
  creation VARCHAR
);
CREATE TABLE management (
  department_id VARCHAR,
  head_id VARCHAR,
  temporary_acting VARCHAR
);
CREATE TABLE head (
  head_id VARCHAR,
  name VARCHAR,
  born_state VARCHAR
);

Write a SQL query for:
List all department names.

SQL:

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Safetensors

Model size

0.9B params

Tensor type

BF16

Model tree for Tuana/qwen35-08b-text2sql

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qwen35-08b-text2sql

Model Details

What This Model Demonstrates

Example Prompt Format

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