Instructions to use parole-study-viper/gemma3-text-to-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use parole-study-viper/gemma3-text-to-sql with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="parole-study-viper/gemma3-text-to-sql",
	filename="gemma-sql.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use parole-study-viper/gemma3-text-to-sql with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf parole-study-viper/gemma3-text-to-sql
# Run inference directly in the terminal:
llama cli -hf parole-study-viper/gemma3-text-to-sql

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf parole-study-viper/gemma3-text-to-sql
# Run inference directly in the terminal:
llama cli -hf parole-study-viper/gemma3-text-to-sql

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf parole-study-viper/gemma3-text-to-sql
# Run inference directly in the terminal:
./llama-cli -hf parole-study-viper/gemma3-text-to-sql

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf parole-study-viper/gemma3-text-to-sql
# Run inference directly in the terminal:
./build/bin/llama-cli -hf parole-study-viper/gemma3-text-to-sql

Use Docker

docker model run hf.co/parole-study-viper/gemma3-text-to-sql

LM Studio
Jan
Ollama
How to use parole-study-viper/gemma3-text-to-sql with Ollama:
```
ollama run hf.co/parole-study-viper/gemma3-text-to-sql
```

Unsloth Studio

How to use parole-study-viper/gemma3-text-to-sql with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for parole-study-viper/gemma3-text-to-sql to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for parole-study-viper/gemma3-text-to-sql to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for parole-study-viper/gemma3-text-to-sql to start chatting

Atomic Chat new
Docker Model Runner
How to use parole-study-viper/gemma3-text-to-sql with Docker Model Runner:
```
docker model run hf.co/parole-study-viper/gemma3-text-to-sql
```

Lemonade

How to use parole-study-viper/gemma3-text-to-sql with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull parole-study-viper/gemma3-text-to-sql

Run and chat with the model

lemonade run user.gemma3-text-to-sql-{{QUANT_TAG}}

List all available models

lemonade list

parole-study-viper commited on Mar 13, 2025

Commit

6ed4adf

verified ·

1 Parent(s): df80a2b

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -10

README.md CHANGED Viewed

@@ -10,10 +10,8 @@ pipeline_tag: reinforcement-learning
 tags:
 - text-to-sql
 ---
-        ---
-        library: gemma3-text-to-sql
-        ---
 # Gemma 3 Text-to-SQL
 A powerful LoRA-fine-tuned adapter for Gemma 3 that converts natural language questions into SQL queries with high accuracy and contextual understanding.
@@ -58,7 +56,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 # Load adapter
-adapter_path = "your-username/gemma-3-text-to-sql"  # Replace with your HF model path
 model = PeftModel.from_pretrained(model, adapter_path)
 # Format prompt
@@ -87,7 +85,7 @@ from mlx_lm.utils import get_model_path
 # Setup paths
 model_path = "lmstudio-community/gemma-3-27b-it-GGUF"
-adapter_path = "your-username/gemma-3-text-to-sql/adapter_model.safetensors"
 # Run generation
 prompt = "Convert the following natural language query to SQL: Find all customers in New York"
@@ -108,7 +106,7 @@ You can also use the Hugging Face Inference API:
 ```python
 import requests
-API_URL = "https://api-inference.huggingface.co/models/your-username/gemma-3-text-to-sql"
 headers = {"Authorization": f"Bearer {API_TOKEN}"}
 def query(payload):
@@ -214,11 +212,11 @@ If you use this model in your research or applications, please cite:
 ```bibtex
 @misc{gemma3-text-to-sql,
-  author = {Your Name},
   title = {Gemma 3 Text-to-SQL: A LoRA-fine-tuned adapter for natural language to SQL conversion},
   year = {2025},
   publisher = {HuggingFace},
-  howpublished = {\url{https://huggingface.co/your-username/gemma-3-text-to-sql}}
 }
 ```
@@ -235,4 +233,4 @@ We thank Google for releasing the Gemma 3 models and the Hugging Face team for t
 If you find any issues or have suggestions for improvement, please open an issue on the GitHub repository or reach out on the Hugging Face community forums.
-        This model created by [@parole-study-viper]

 tags:
 - text-to-sql
 ---
+library: gemma3-text-to-sql
+---
 # Gemma 3 Text-to-SQL
 A powerful LoRA-fine-tuned adapter for Gemma 3 that converts natural language questions into SQL queries with high accuracy and contextual understanding.
 model = AutoModelForCausalLM.from_pretrained(model_id)
 # Load adapter
+adapter_path = "parole-study-viper/gemma-3-text-to-sql"  # Replace with your HF model path
 model = PeftModel.from_pretrained(model, adapter_path)
 # Format prompt
 # Setup paths
 model_path = "lmstudio-community/gemma-3-27b-it-GGUF"
+adapter_path = "parole-study-viper/gemma-3-text-to-sql/adapter_model.safetensors"
 # Run generation
 prompt = "Convert the following natural language query to SQL: Find all customers in New York"
 ```python
 import requests
+API_URL = "https://api-inference.huggingface.co/models/parole-study-viper/gemma-3-text-to-sql"
 headers = {"Authorization": f"Bearer {API_TOKEN}"}
 def query(payload):
 ```bibtex
 @misc{gemma3-text-to-sql,
+  author = {parole-study-viper},
   title = {Gemma 3 Text-to-SQL: A LoRA-fine-tuned adapter for natural language to SQL conversion},
   year = {2025},
   publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/parole-study-viper/gemma-3-text-to-sql}}
 }
 ```
 If you find any issues or have suggestions for improvement, please open an issue on the GitHub repository or reach out on the Hugging Face community forums.
+This model created by [@parole-study-viper]