Instructions to use RealMati/t2sql_v6_structured with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RealMati/t2sql_v6_structured with Transformers:

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

pipe = pipeline("text-generation", model="RealMati/t2sql_v6_structured")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("RealMati/t2sql_v6_structured")
model = AutoModelForSeq2SeqLM.from_pretrained("RealMati/t2sql_v6_structured", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RealMati/t2sql_v6_structured with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RealMati/t2sql_v6_structured"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RealMati/t2sql_v6_structured",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RealMati/t2sql_v6_structured

SGLang

How to use RealMati/t2sql_v6_structured 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 "RealMati/t2sql_v6_structured" \
    --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": "RealMati/t2sql_v6_structured",
		"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 "RealMati/t2sql_v6_structured" \
        --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": "RealMati/t2sql_v6_structured",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RealMati/t2sql_v6_structured with Docker Model Runner:
```
docker model run hf.co/RealMati/t2sql_v6_structured
```

T2SQL V6 Structured - Text to SQL

Fine-tuned T5 model that converts natural language questions to SQL queries.

Usage

from transformers import pipeline

pipe = pipeline("text2text-generation", model="RealMati/t2sql_v6_structured")
result = pipe("translate to SQL: list all users older than 18 | schema: users(id, name, age, email)")
print(result[0]["generated_text"])

Training

Base model: T5-base
Dataset: WikiSQL (56k train / 8k val / 15k test)
Task: Natural language to structured SQL output

Downloads last month: 14

Safetensors

Model size

0.2B params

Tensor type

F32

RealMati
/

t2sql_v6_structured

T2SQL V6 Structured - Text to SQL

Usage

Training

Space using RealMati/t2sql_v6_structured 1