Instructions to use juierror/flan-t5-text2sql-with-schema-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use juierror/flan-t5-text2sql-with-schema-v2 with Transformers:

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

pipe = pipeline("text-generation", model="juierror/flan-t5-text2sql-with-schema-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("juierror/flan-t5-text2sql-with-schema-v2")
model = AutoModelForSeq2SeqLM.from_pretrained("juierror/flan-t5-text2sql-with-schema-v2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use juierror/flan-t5-text2sql-with-schema-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "juierror/flan-t5-text2sql-with-schema-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "juierror/flan-t5-text2sql-with-schema-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/juierror/flan-t5-text2sql-with-schema-v2

SGLang

How to use juierror/flan-t5-text2sql-with-schema-v2 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 "juierror/flan-t5-text2sql-with-schema-v2" \
    --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": "juierror/flan-t5-text2sql-with-schema-v2",
		"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 "juierror/flan-t5-text2sql-with-schema-v2" \
        --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": "juierror/flan-t5-text2sql-with-schema-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use juierror/flan-t5-text2sql-with-schema-v2 with Docker Model Runner:
```
docker model run hf.co/juierror/flan-t5-text2sql-with-schema-v2
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

This is an upgraded version of https://huggingface.co/juierror/flan-t5-text2sql-with-schema.

It supports the '<' sign and can handle multiple tables.

How to use

from typing import List
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("juierror/flan-t5-text2sql-with-schema-v2")
model = AutoModelForSeq2SeqLM.from_pretrained("juierror/flan-t5-text2sql-with-schema-v2")

def get_prompt(tables, question):
    prompt = f"""convert question and table into SQL query. tables: {tables}. question: {question}"""
    return prompt

def prepare_input(question: str, tables: Dict[str, List[str]]):
    tables = [f"""{table_name}({",".join(tables[table_name])})""" for table_name in tables]
    tables = ", ".join(tables)
    prompt = get_prompt(tables, question)
    input_ids = tokenizer(prompt, max_length=512, return_tensors="pt").input_ids
    return input_ids

def inference(question: str, tables: Dict[str, List[str]]) -> str:
    input_data = prepare_input(question=question, tables=tables)
    input_data = input_data.to(model.device)
    outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=512)
    result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
    return result

print(inference("how many people with name jui and age less than 25", {
    "people_name": ["id", "name"],
    "people_age": ["people_id", "age"]
}))

print(inference("what is id with name jui and age less than 25", {
    "people_name": ["id", "name", "age"]
})))

Dataset

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Safetensors

Model size

0.2B params

Tensor type

F32

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