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")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

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

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 serve -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": "StellaYoon/data-sql-7b-oracle-postgresql:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "StellaYoon/data-sql-7b-oracle-postgresql:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

Hermes Agent

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

Start the llama.cpp server

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

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default StellaYoon/data-sql-7b-oracle-postgresql:Q4_K_M

Run Hermes

hermes

Atomic Chat

StellaYoon commited on Dec 19, 2025

Commit

d03aadb

verified ·

1 Parent(s): 0c885b2

End of training

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README.md +58 -0

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	@@ -0,0 +1,58 @@

+---
+library_name: peft
+base_model: Chinastark/DatA-SQL-7B
+tags:
+- trl
+- sft
+- generated_from_trainer
+model-index:
+- name: data-sql-7b-oracle-postgresql
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# data-sql-7b-oracle-postgresql
+This model is a fine-tuned version of [Chinastark/DatA-SQL-7B](https://huggingface.co/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