Instructions to use andhiyaulhaq/natural2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use andhiyaulhaq/natural2sql with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="andhiyaulhaq/natural2sql", filename="saved_finetuned_q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
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
- Pi
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
- 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
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 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 or llama.cpp.
Model Details
- Base Model: 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
- Download the
saved_finetuned_q4_k_m.gguffile to your local machine. - Create a file named
Modelfilein the same directory with the following content:
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|>
"""
- Build and run the model in your terminal:
# 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:
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. Any custom datasets or fine-tuning code associated with this project are provided under the MIT License (see LICENSE file).
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meta-llama/Llama-3.2-3B-Instruct