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)# 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") 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
| 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] " | |
| 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() |