Instructions to use sirunchained/text-to-sql-model-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sirunchained/text-to-sql-model-v1 with Transformers:

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

pipe = pipeline("text-generation", model="sirunchained/text-to-sql-model-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sirunchained/text-to-sql-model-v1", device_map="auto")

PEFT
How to use sirunchained/text-to-sql-model-v1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sirunchained/text-to-sql-model-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sirunchained/text-to-sql-model-v1

SGLang

How to use sirunchained/text-to-sql-model-v1 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 "sirunchained/text-to-sql-model-v1" \
    --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": "sirunchained/text-to-sql-model-v1",
		"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 "sirunchained/text-to-sql-model-v1" \
        --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": "sirunchained/text-to-sql-model-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sirunchained/text-to-sql-model-v1 with Docker Model Runner:
```
docker model run hf.co/sirunchained/text-to-sql-model-v1
```

text-to-sql-model-v1 / README.md

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	---
	base_model: google/gemma-3-270m-it
	library_name: transformers
	model_name: text-to-sql-model
	tags:
	- generated_from_trainer
	- trl
	- sft
	- peft
	- lora
	- gemma
	licence: license
	license: mit
	datasets:
	- sirunchained/text-to-sql-dataset
	language:
	- en
	pipeline_tag: text-generation
	---

	# Gemma 270M-IT Text-to-SQL Model (Version 1)

	This model is a fine-tuned version of `google/gemma-3-270m-it` specifically adapted for Text-to-SQL conversion. It takes natural language questions, optionally along with a database schema, and generates the corresponding SQL query.

	## Model Description

	- Base Model: `google/gemma-3-270m-it`
	- Task: Text-to-SQL Generation (Instruction-tuned language model)
	- Fine-tuning Framework: `trl` library with `SFTTrainer`
	- Optimization: LoRA (Low-Rank Adaptation) for Parameter-Efficient Fine-Tuning (PEFT) and 4-bit quantization using `bitsandbytes` to reduce memory footprint and enable training on resource-constrained environments (e.g., T4 GPU).

	## Training Data

	The model was fine-tuned on the `sirunchained/text-to-sql-dataset` from Hugging Face. This dataset consists of natural language queries, corresponding database schemas (optional), and the target SQL queries.

	## Model Performance

	The model's performance was evaluated on a test set, yielding the following results:

	- Evaluation Loss: `0.681`
	- Mean Token Accuracy: `0.836`

	During training, the model showed a consistent decrease in both training and validation loss, indicating successful learning and generalization. The validation mean token accuracy steadily improved, reaching approximately `84%` by the end of the training process, suggesting that the model is capable of generating accurate SQL queries given natural language input and schema information.

	## Version Information

	This is Version 1 of the `sirunchained/text-to-sql-model`. The model will be continuously updated and improved over time with more data and potentially different architectures.

	## Gradio Demo

	A Gradio web interface was developed to demonstrate the model's capabilities, allowing users to input natural language queries and provide database schema details table by table, then view the generated SQL output. You can find the Gradio app code within the accompanying notebook.
	You can test it [here](https://huggingface.co/spaces/sirunchained/text-to-sql-gradio).