Text Generation
Transformers
PyTorch
Safetensors
English
t5
text2text-generation
text2sql
conversational
text-generation-inference
Instructions to use gaussalgo/T5-LM-Large-text2sql-spider with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 2,368 Bytes
698d7e3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import sqlite3
import json
import os
from typing import Union, List, Dict
from pathlib import Path
from itertools import chain
from simple_ddl_parser import parse_from_file
class DBParser:
def __init__(self, db_path:Union[str, Path]) -> None:
self.db_path = db_path
self.suffix:str =".sql"
self.primary_key_token:str = "primary key:"
self.foreign_key_token:str = "foreign_key:"
self.separator:str = " [SEP] "
@staticmethod
def dump_sqlite_to_sql(path_to_sqlite: Union[str, Path], output_path: Union[str, Path]) -> None:
assert path_to_sqlite.endswith('.sqlite')
con = sqlite3.connect(path_to_sqlite)
with open(output_path, 'w') as f:
for line in con.iterdump():
f.write('%s\n' % line)
def parse_table(self, table_schema:dict) -> str:
normal_keys = " ".join(list(chain.from_iterable((column["name"], column["type"], ",") for column in table_schema["columns"] if column["references"] is None)))
foreign_keys =" ".join(list(chain.from_iterable((column["name"], column["type"],"from", column["references"]["table"], column["references"]["column"], ",") for column in table_schema["columns"] if column["references"] is not None)))
primary_keys = " ".join(table_schema["primary_key"])
return " ".join([table_schema["table_name"], normal_keys, self.foreign_key_token, foreign_keys, self.primary_key_token, primary_keys])
def parse_schema(self, schema:List[dict]) -> str:
table_schemas: List[str] = [self.parse_table(table) for table in schema if 'columns' in table]
return self.separator.join(table_schemas)
def create_db_prompt_dict(self, output_file: str = 'db_schemas.json') -> Dict[str, str]:
db_schema_dict = {}
for dir in os.listdir(self.db_path):
print("Processing database: ", dir)
filenames = [i for i in os.listdir(Path(self.db_path, dir)) if i.endswith(self.suffix)]
path_to_db = Path(self.db_path, dir,filenames[0])
schema = parse_from_file(path_to_db)
db_schema_dict[dir]=self.parse_schema(schema)
with open(output_file, 'w') as f:
f.write(json.dumps(db_schema_dict))
return db_schema_dict
# # Usage
# db_parser = DBParser(<PATH_To_DATABASES>)
# db_parser.create_db_prompt_dict() |