metadata

frameworks:
  - Pytorch
license: other
tasks:
  - text-generation

Model Card for NL2SQL-StarCoder-15B

Model Inro

NL2SQL-StarCoder-15B is a NLP-SQL model fintuned by QLoRAbased on StarCoder 15B Code-LLM。

Requirements

python>=3.8
pytorch>=2.0.0
transformers==4.32.0
CUDA 11.4

Data Format

The data is in the form of a string spliced by the model in the training data format, which is also how the input PROMPT is spliced during inference:

"""
<|user|>
/* Given the following database schema: */
CREATE TABLE "table_name" (
"col1" int,
...
...
)

/* Write a sql to answer the following question: {Question} */
<|assistant|>
```sql
{Output SQL}
```<|end|>
"""

Quick Start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_dir = "gabrielpondc/NL2SQL-StarCoder-15B"
tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", 
                              trust_remote_code=True, torch_dtype=torch.float16)
tokenizer.padding_side = "left"
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("<fim_pad>")
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("<|endoftext|>")
tokenizer.pad_token = "<fim_pad>"
tokenizer.eos_token = "<|endoftext|>"

model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", 
                              trust_remote_code=True, torch_dtype=torch.float16)
model.eval()

text = '<|user|>\n/* Given the following database schema: */\nCREATE TABLE "singer" (\n"Singer_ID" int,\n"Name" text,\n"Country" text,\n"Song_Name" text,\n"Song_release_year" text,\n"Age" int,\n"Is_male" bool,\nPRIMARY KEY ("Singer_ID")\n)\n\n/* Write a sql to answer the following question: Show countries where a singer above age 40 and a singer below 30 are from. */<|end|>\n'
inputs = tokenizer(text, return_tensors='pt', padding=True, add_special_tokens=False).to("cuda")
outputs = model.generate(
        inputs=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        max_new_tokens=512,
        top_p=0.95,
        temperature=0.1,
        do_sample=False,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id
    )
gen_text = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(gen_text)