Instructions to use gaussalgo/T5-LM-Large-text2sql-spider with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gaussalgo/T5-LM-Large-text2sql-spider with Transformers:

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

pipe = pipeline("text-generation", model="gaussalgo/T5-LM-Large-text2sql-spider")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("gaussalgo/T5-LM-Large-text2sql-spider")
model = AutoModelForSeq2SeqLM.from_pretrained("gaussalgo/T5-LM-Large-text2sql-spider", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gaussalgo/T5-LM-Large-text2sql-spider with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gaussalgo/T5-LM-Large-text2sql-spider"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gaussalgo/T5-LM-Large-text2sql-spider",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gaussalgo/T5-LM-Large-text2sql-spider

SGLang

How to use gaussalgo/T5-LM-Large-text2sql-spider 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 "gaussalgo/T5-LM-Large-text2sql-spider" \
    --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": "gaussalgo/T5-LM-Large-text2sql-spider",
		"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 "gaussalgo/T5-LM-Large-text2sql-spider" \
        --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": "gaussalgo/T5-LM-Large-text2sql-spider",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gaussalgo/T5-LM-Large-text2sql-spider with Docker Model Runner:
```
docker model run hf.co/gaussalgo/T5-LM-Large-text2sql-spider
```

T5-LM-Large-text2sql-spider

Commit History

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Commit History

Sep 8, 2025

Update README.md

Jun 12, 2025

Add chat_template to tokenizer_config.json (#4)⁠

Jun 5, 2024

Update README.md

Mar 19, 2024

Create train_txt2sql.py

Dec 20, 2023

Upload requirements.txt

Upload sqlite_parser.py⁠

Dec 19, 2023

Update README.md

Jul 6, 2023

Update README.md

Jun 16, 2023

Update README.md

add eval results

Update README.md

Update README.md

evaluate script

Jun 9, 2023

Update README.md

Update README.md

Update README.md

Update README.md

Update README.md

Update README.md

Update README.md

Create README.md⁠

Apr 27, 2023

Adding `safetensors` variant of this model (#1)⁠

Apr 25, 2023

Upload T5ForConditionalGeneration

Upload tokenizer

initial commit