README.md

metadata

license: apache-2.0
base_model: unsloth/gemma-3-27b-it-bnb-4bit
tags:
  - text-to-sql
  - gemma-3
  - unsloth
  - trl
  - finance
  - real-estate
  - nlp
datasets:
  - dan-text2sql/seoul-realestate-sql-v1
library_name: transformers

seoul-realestate-sql-agent-v2

Developed by: dan-text2sql
License: apache-2.0
Finetuned from model: unsloth/gemma-3-27b-it-bnb-4bit

This model is a Text-to-SQL agent specialized in Korean Real Estate (Seoul) data.
It was trained 2x faster with Unsloth and Huggingface's TRL library.

Model Description (v2)

This is the v2 version of the Seoul Real Estate SQL Agent.

• Base Model: Gemma-3 27B (IT)
• Improvement: Unlike v1 (Mistral-7B), this model leverages the massive 27B parameter size of Gemma-3.
• Objective: Translate natural language queries about Seoul apartment real estate data into executable SQL queries.

Usage Example

from unsloth import FastLanguageModel

# Load the model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "dan-text2sql/seoul-realestate-sql-agent-v2",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

# Test Prompt
prompt = """아래 질문에 대한 올바른 SQL 쿼리를 작성해주세요.

### 질문:
서울시 강남구 삼성동의 20억 이하 아파트 매물을 찾아줘.

### SQL:
"""

inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)
print(tokenizer.batch_decode(outputs)[0])