Instructions to use chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF 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 chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16

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 chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16

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 chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16

Use Docker

docker model run hf.co/chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16

LM Studio
Jan
Ollama
How to use chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF with Ollama:
```
ollama run hf.co/chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16
```
Unsloth Desktop
Docker Model Runner
How to use chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF with Docker Model Runner:
```
docker model run hf.co/chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16
```

Lemonade

How to use chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF:F16

Run and chat with the model

lemonade run user.gemma-3-270m-text2sql-oracle-postgres-GGUF-F16

List all available models

lemonade list

Atomic Chat

gemma-3-270m-text2sql-oracle-postgres-GGUF / README.md

chabab

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metadata

base_model: chabab/gemma-3-270m-text2sql-oracle-postgres
tags:
  - gguf
  - text-to-sql
  - sql
  - oracle
  - postgresql
  - gemma3
license: apache-2.0

gemma-3-270m-text2sql-oracle-postgres-GGUF

GGUF builds of chabab/gemma-3-270m-text2sql-oracle-postgres — Gemma-3 270M fine-tuned for Oracle / PostgreSQL text-to-SQL.

File	Quant	Size
`gemma-3-270m-text2sql-f16.gguf`	f16	543 MB
`gemma-3-270m-text2sql-q8_0.gguf`	q8_0	292 MB

Ollama

huggingface-cli download chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF gemma-3-270m-text2sql-q8_0.gguf Modelfile --local-dir .
ollama create text2sql -f Modelfile
ollama run text2sql "Schema:
employees(employee_id INTEGER PK, first_name VARCHAR(50), last_name VARCHAR(50), salary NUMERIC(12,2))

Question:
Show the five employees with the largest salary. Return only the SQL."

llama.cpp

llama-cli -m gemma-3-270m-text2sql-q8_0.gguf --temp 0 -p "<your schema + question>"

Use greedy decoding (temperature 0). The model is trained to emit exactly one SQL statement with no fences or commentary.