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

Libraries

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

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Chinastark/DatA-SQL-7B")
model = PeftModel.from_pretrained(base_model, "StellaYoon/data-sql-7b-oracle-postgresql")

llama-cpp-python

How to use StellaYoon/data-sql-7b-oracle-postgresql 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",
	filename="data-sql-7b-oracle-postgresql-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 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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf StellaYoon/data-sql-7b-oracle-postgresql: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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf StellaYoon/data-sql-7b-oracle-postgresql: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:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf StellaYoon/data-sql-7b-oracle-postgresql: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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf StellaYoon/data-sql-7b-oracle-postgresql:Q4_K_M

Use Docker

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

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

Unsloth Studio new

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

Pi new

How to use StellaYoon/data-sql-7b-oracle-postgresql 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: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"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

data-sql-7b-oracle-postgresql

This model is a fine-tuned version of Chinastark/DatA-SQL-7B on an unknown dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Framework versions

PEFT 0.13.0
Transformers 4.45.0
Pytorch 2.9.0+cu126
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: 5

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for StellaYoon/data-sql-7b-oracle-postgresql

Base model

Chinastark/DatA-SQL-7B

Adapter

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