Instructions to use andhiyaulhaq/natural2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use andhiyaulhaq/natural2sql 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 andhiyaulhaq/natural2sql:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andhiyaulhaq/natural2sql:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf andhiyaulhaq/natural2sql:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andhiyaulhaq/natural2sql: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 andhiyaulhaq/natural2sql:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf andhiyaulhaq/natural2sql: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 andhiyaulhaq/natural2sql:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf andhiyaulhaq/natural2sql:Q4_K_M

Use Docker

docker model run hf.co/andhiyaulhaq/natural2sql:Q4_K_M

LM Studio
Jan

vLLM

How to use andhiyaulhaq/natural2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andhiyaulhaq/natural2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andhiyaulhaq/natural2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/andhiyaulhaq/natural2sql:Q4_K_M

Ollama
How to use andhiyaulhaq/natural2sql with Ollama:
```
ollama run hf.co/andhiyaulhaq/natural2sql:Q4_K_M
```

Unsloth Studio

How to use andhiyaulhaq/natural2sql 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 andhiyaulhaq/natural2sql 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 andhiyaulhaq/natural2sql to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for andhiyaulhaq/natural2sql to start chatting

How to use andhiyaulhaq/natural2sql with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andhiyaulhaq/natural2sql: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": "andhiyaulhaq/natural2sql:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use andhiyaulhaq/natural2sql with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andhiyaulhaq/natural2sql: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 andhiyaulhaq/natural2sql:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use andhiyaulhaq/natural2sql with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andhiyaulhaq/natural2sql: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 "andhiyaulhaq/natural2sql: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 andhiyaulhaq/natural2sql with Docker Model Runner:
```
docker model run hf.co/andhiyaulhaq/natural2sql:Q4_K_M
```

Lemonade

How to use andhiyaulhaq/natural2sql with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull andhiyaulhaq/natural2sql:Q4_K_M

Run and chat with the model

lemonade run user.natural2sql-Q4_K_M

List all available models

lemonade list

natural2sql / README.md

andhiyaulhaq

Upload README.md

aa8603b verified 3 months ago

preview code

Raw

History Blame Contribute Delete

2.57 kB

	---
	language:
	- en
	tags:
	- text-to-sql
	- sql
	- llama-3.2
	- postgresql
	- gguf
	- text-generation
	license: llama3.2
	base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
	---

	# Natural Language to SQL (NL2SQL) Llama 3.2 3B - GGUF

	This repository contains a fine-tuned version of `unsloth/Llama-3.2-3B-Instruct-bnb-4bit`, specifically trained to translate natural language questions into accurate, executable PostgreSQL queries.

	The model was fine-tuned using [Unsloth](https://github.com/unslothai/unsloth) for 2x faster training and is provided in the GGUF format, making it highly optimized for local inference on consumer hardware using tools like [Ollama](https://ollama.com/) or [llama.cpp](https://github.com/ggerganov/llama.cpp).

	## Model Details
	* Base Model: [unsloth/Llama-3.2-3B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-bnb-4bit)
	* Task: Text-to-SQL (focusing on PostgreSQL syntax)
	* Architecture: Llama 3
	* Format: GGUF
	* Quantization: `Q4_K_M` (4-bit quantization. This provides an excellent balance between memory usage, inference speed, and model quality).

	## How to run with Ollama

	1. Download the `saved_finetuned_q4_k_m.gguf` file to your local machine.
	2. Create a file named `Modelfile` in the same directory with the following content:

	```dockerfile
	FROM ./saved_finetuned_q4_k_m.gguf

	SYSTEM """You are SQL-Llama, a specialized Text-to-SQL assistant.
	For all inputs, you MUST output ONLY valid SQL code. Do not include markdown blocks or conversational filler."""

	TEMPLATE """<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>

	{{ .System }}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>

	{{ .Prompt }}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>

	"""
	```

	3. Build and run the model in your terminal:

	```bash
	# Build the model
	ollama create natural2sql -f Modelfile

	# Run the model
	ollama run natural2sql
	```

	## Prompt Format
	To get the best results, provide the database schema as `Context` and your inquiry as `Question`:

	```text
	Context: CREATE TABLE sales (transaction_id INT, product_name TEXT, amount DECIMAL, sale_date DATE, region TEXT);
	Question: What was the total revenue from the 'North' region for transactions occurring after January 2023?
	```

	## License
	The base model weights are subject to the [Meta Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE). Any custom datasets or fine-tuning code associated with this project are provided under the MIT License (see `LICENSE` file).