Initial commit: Create v2 model card (Gemma-3)

d8bdce3 verified 10 months ago

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---
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](https://huggingface.co/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](https://github.com/unslothai/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

```python
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])

	---
	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](https://huggingface.co/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](https://github.com/unslothai/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

	```python
	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])