Instructions to use Descant/SQL_Gen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Descant/SQL_Gen with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Descant/SQL_Gen", dtype="auto")

llama-cpp-python

How to use Descant/SQL_Gen with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Descant/SQL_Gen",
	filename="gemma-3-full-finetune.BF16.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 Descant/SQL_Gen 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 Descant/SQL_Gen:BF16
# Run inference directly in the terminal:
llama cli -hf Descant/SQL_Gen:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Descant/SQL_Gen:BF16
# Run inference directly in the terminal:
llama cli -hf Descant/SQL_Gen:BF16

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 Descant/SQL_Gen:BF16
# Run inference directly in the terminal:
./llama-cli -hf Descant/SQL_Gen:BF16

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 Descant/SQL_Gen:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Descant/SQL_Gen:BF16

Use Docker

docker model run hf.co/Descant/SQL_Gen:BF16

LM Studio
Jan
Ollama
How to use Descant/SQL_Gen with Ollama:
```
ollama run hf.co/Descant/SQL_Gen:BF16
```

Unsloth Studio

How to use Descant/SQL_Gen 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 Descant/SQL_Gen 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 Descant/SQL_Gen to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Descant/SQL_Gen to start chatting

Atomic Chat new
Docker Model Runner
How to use Descant/SQL_Gen with Docker Model Runner:
```
docker model run hf.co/Descant/SQL_Gen:BF16
```

Lemonade

How to use Descant/SQL_Gen with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Descant/SQL_Gen:BF16

Run and chat with the model

lemonade run user.SQL_Gen-BF16

List all available models

lemonade list

Uploaded finetuned model

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

This gemma3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

This model specializes in generating MSSQL TSQL queries.

Downloads last month: 2

GGUF

Model size

4B params

Architecture

gemma3

Hardware compatibility

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Descant/SQL_Gen

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Quantized

unsloth/gemma-3-4b-it-unsloth-bnb-4bit

Quantized

(79)

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