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
- llama.cpp
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 Studio
How to use chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF 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 chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF 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 chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF to start chatting
- 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
GGUF builds of chabab/gemma-3-270m-text2sql-oracle-postgres โ 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.
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Model tree for chabab/gemma-3-270m-text2sql-oracle-postgres-GGUF
Base model
google/gemma-3-270m Finetuned
google/gemma-3-270m-it