Instructions to use StellaYoon/data-sql-7b-oracle-postgresql-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StellaYoon/data-sql-7b-oracle-postgresql-v2 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("StellaYoon/data-sql-7b-oracle-postgresql-v2", dtype="auto")

llama-cpp-python

How to use StellaYoon/data-sql-7b-oracle-postgresql-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="StellaYoon/data-sql-7b-oracle-postgresql-v2",
	filename="data-sql-7b-oracle-postgresql-v2-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use StellaYoon/data-sql-7b-oracle-postgresql-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M

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 StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M

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 StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M

Use Docker

docker model run hf.co/StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M

LM Studio
Jan
Ollama
How to use StellaYoon/data-sql-7b-oracle-postgresql-v2 with Ollama:
```
ollama run hf.co/StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M
```

Unsloth Studio new

How to use StellaYoon/data-sql-7b-oracle-postgresql-v2 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 StellaYoon/data-sql-7b-oracle-postgresql-v2 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 StellaYoon/data-sql-7b-oracle-postgresql-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for StellaYoon/data-sql-7b-oracle-postgresql-v2 to start chatting

Pi new

How to use StellaYoon/data-sql-7b-oracle-postgresql-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "data-sql-7b-oracle-postgresql-v2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use StellaYoon/data-sql-7b-oracle-postgresql-v2 with Docker Model Runner:
```
docker model run hf.co/StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M
```

Lemonade

How to use StellaYoon/data-sql-7b-oracle-postgresql-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull StellaYoon/data-sql-7b-oracle-postgresql-v2:Q4_K_M

Run and chat with the model

lemonade run user.data-sql-7b-oracle-postgresql-v2-Q4_K_M

List all available models

lemonade list

Model Card for data-sql-7b-oracle-postgresql-v2

This model is a fine-tuned version of Chinastark/DatA-SQL-7B. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="StellaYoon/data-sql-7b-oracle-postgresql-v2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.26.2
Transformers: 4.57.3
Pytorch: 2.9.0+cu126
Datasets: 4.4.2
Tokenizers: 0.22.1

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

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Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

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