Instructions to use cssupport/t5-small-awesome-text-to-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cssupport/t5-small-awesome-text-to-sql with Transformers:

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

pipe = pipeline("text-generation", model="cssupport/t5-small-awesome-text-to-sql")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("cssupport/t5-small-awesome-text-to-sql")
model = AutoModelForSeq2SeqLM.from_pretrained("cssupport/t5-small-awesome-text-to-sql", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cssupport/t5-small-awesome-text-to-sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cssupport/t5-small-awesome-text-to-sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cssupport/t5-small-awesome-text-to-sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cssupport/t5-small-awesome-text-to-sql

SGLang

How to use cssupport/t5-small-awesome-text-to-sql 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 "cssupport/t5-small-awesome-text-to-sql" \
    --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": "cssupport/t5-small-awesome-text-to-sql",
		"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 "cssupport/t5-small-awesome-text-to-sql" \
        --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": "cssupport/t5-small-awesome-text-to-sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cssupport/t5-small-awesome-text-to-sql with Docker Model Runner:
```
docker model run hf.co/cssupport/t5-small-awesome-text-to-sql
```

t5-small-awesome-text-to-sql / README.md

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	---
	license: apache-2.0
	datasets:
	- Clinton/Text-to-sql-v1
	- b-mc2/sql-create-context
	language:
	- en
	pipeline_tag: text2text-generation
	---
	# Model Card for Model ID

	<!-- Based on https://huggingface.co/t5-small, model generates SQL from text given table list with "CREATE TABLE" statements.
	This is a very light weigh model and could be used in multiple analytical applications. -->

	Based on [t5-small](https://huggingface.co/t5-small), model generates SQL from text given table list with "CREATE TABLE" statements. Supports multiple tables with joins.
	This is a very light weigh model and could be used in multiple analytical applications. Used combination of [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) and [Clinton/Text-to-sql-v1](https://huggingface.co/datasets/Clinton/Text-to-sql-v1) dataset.
	Contact us for more info: support@cloudsummary.com


	## Model Details

	### Model Description

	<!-- Provide a longer summary of what this model is. -->



	- Developed by: cssupport (support@cloudsummary.com)
	- Model type: Language model
	- Language(s) (NLP): English
	- License: Apache 2.0
	- Finetuned from model : [t5-small](https://huggingface.co/t5-small)

	### Model Sources

	<!-- Provide the basic links for the model. -->

	Please refer [t5-small](https://huggingface.co/t5-small) for Model Sources.

	## How to Get Started with the Model

	Use the code below to get started with the model.

	```python
	import torch
	from transformers import T5Tokenizer, T5ForConditionalGeneration

	# Initialize the tokenizer from Hugging Face Transformers library
	tokenizer = T5Tokenizer.from_pretrained('t5-small')

	# Load the model
	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	model = T5ForConditionalGeneration.from_pretrained('cssupport/t5-small-awesome-text-to-sql')
	model = model.to(device)
	model.eval()

	def generate_sql(input_prompt):
	# Tokenize the input prompt
	inputs = tokenizer(input_prompt, padding=True, truncation=True, return_tensors="pt").to(device)

	# Forward pass
	with torch.no_grad():
	outputs = model.generate(**inputs, max_length=512)

	# Decode the output IDs to a string (SQL query in this case)
	generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)

	return generated_sql

	# Test the function
	#input_prompt = "tables:\n" + "CREATE TABLE Catalogs (date_of_latest_revision VARCHAR)" + "\n" +"query for: Find the dates on which more than one revisions were made."
	#input_prompt = "tables:\n" + "CREATE TABLE table_22767 ( \"Year\" real, \"World\" real, \"Asia\" text, \"Africa\" text, \"Europe\" text, \"Latin America/Caribbean\" text, \"Northern America\" text, \"Oceania\" text )" + "\n" +"query for:what will the population of Asia be when Latin America/Caribbean is 783 (7.5%)?."
	#input_prompt = "tables:\n" + "CREATE TABLE procedures ( subject_id text, hadm_id text, icd9_code text, short_title text, long_title text ) CREATE TABLE diagnoses ( subject_id text, hadm_id text, icd9_code text, short_title text, long_title text ) CREATE TABLE lab ( subject_id text, hadm_id text, itemid text, charttime text, flag text, value_unit text, label text, fluid text ) CREATE TABLE demographic ( subject_id text, hadm_id text, name text, marital_status text, age text, dob text, gender text, language text, religion text, admission_type text, days_stay text, insurance text, ethnicity text, expire_flag text, admission_location text, discharge_location text, diagnosis text, dod text, dob_year text, dod_year text, admittime text, dischtime text, admityear text ) CREATE TABLE prescriptions ( subject_id text, hadm_id text, icustay_id text, drug_type text, drug text, formulary_drug_cd text, route text, drug_dose text )" + "\n" +"query for:" + "what is the total number of patients who were diagnosed with icd9 code 2254?"
	input_prompt = "tables:\n" + "CREATE TABLE student_course_attendance (student_id VARCHAR); CREATE TABLE students (student_id VARCHAR)" + "\n" + "query for:" + "List the id of students who never attends courses?"

	generated_sql = generate_sql(input_prompt)

	print(f"The generated SQL query is: {generated_sql}")
	#OUTPUT: The generated SQL query is: SELECT student_id FROM students WHERE NOT student_id IN (SELECT student_id FROM student_course_attendance)

	```


	## Uses

	<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

	[More Information Needed]

	### Direct Use

	<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
	Could used in application where natural language is to be converted into SQL queries.
	[More Information Needed]



	### Out-of-Scope Use

	<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

	[More Information Needed]

	## Bias, Risks, and Limitations

	<!-- This section is meant to convey both technical and sociotechnical limitations. -->

	[More Information Needed]

	### Recommendations

	<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

	Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.



	## Technical Specifications

	### Model Architecture and Objective

	[t5-small](https://huggingface.co/t5-small)

	### Compute Infrastructure



	#### Hardware

	one A100-80

	#### Software

	Pytorch and HuggingFace


	## Model Card Contact

	cssupport (support@cloudsummary.com)