Instructions to use Komma-LuisMiSanVe/LangToSQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Komma-LuisMiSanVe/LangToSQL with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Komma-LuisMiSanVe/LangToSQL",
	filename="LangToSQL-1.5B-F16.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 Komma-LuisMiSanVe/LangToSQL with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Komma-LuisMiSanVe/LangToSQL:F16
# Run inference directly in the terminal:
llama-cli -hf Komma-LuisMiSanVe/LangToSQL:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Komma-LuisMiSanVe/LangToSQL:F16
# Run inference directly in the terminal:
llama-cli -hf Komma-LuisMiSanVe/LangToSQL:F16

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 Komma-LuisMiSanVe/LangToSQL:F16
# Run inference directly in the terminal:
./llama-cli -hf Komma-LuisMiSanVe/LangToSQL:F16

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 Komma-LuisMiSanVe/LangToSQL:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Komma-LuisMiSanVe/LangToSQL:F16

Use Docker

docker model run hf.co/Komma-LuisMiSanVe/LangToSQL:F16

LM Studio
Jan
Ollama
How to use Komma-LuisMiSanVe/LangToSQL with Ollama:
```
ollama run hf.co/Komma-LuisMiSanVe/LangToSQL:F16
```

Unsloth Studio new

How to use Komma-LuisMiSanVe/LangToSQL 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 Komma-LuisMiSanVe/LangToSQL 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 Komma-LuisMiSanVe/LangToSQL to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Komma-LuisMiSanVe/LangToSQL to start chatting

Pi new

How to use Komma-LuisMiSanVe/LangToSQL with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Komma-LuisMiSanVe/LangToSQL:F16

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": "Komma-LuisMiSanVe/LangToSQL:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Komma-LuisMiSanVe/LangToSQL with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Komma-LuisMiSanVe/LangToSQL:F16

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 Komma-LuisMiSanVe/LangToSQL:F16

Run Hermes

hermes

Docker Model Runner
How to use Komma-LuisMiSanVe/LangToSQL with Docker Model Runner:
```
docker model run hf.co/Komma-LuisMiSanVe/LangToSQL:F16
```

Lemonade

How to use Komma-LuisMiSanVe/LangToSQL with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Komma-LuisMiSanVe/LangToSQL:F16

Run and chat with the model

lemonade run user.LangToSQL-F16

List all available models

lemonade list

See in spanish/Ver en español

🤖 AI Model for PostgreSQL queries

Check out other versions of this program:

WinForms

REST API

ChatBot

NuGet

Android

The AI model has been trained for turning natural language to PostgreSQL queries.

📝 Technology Explanation

This model uses Gwen Coder as a base and then is fine tuned with Spider datasets.

The JSON dataset file contains Spider's train_spider.json as is the main dataset.

The model is exported to GGUF with llama.cpp so it can be used by programs like LM Studio.

🛠️ Setup

In order to execute the training script for your own, you first need to install Python and run this command:

pip install transformers datasets peft accelerate bitsandbytes trl==1.0.0

Depending on the version, you may have to use this instead:

py -m pip install transformers datasets peft accelerate bitsandbytes trl==1.0.0

Make sure the TRL library version is 1.0.0, as is the only version supported by the trainer script.

📂 Files

This repository includes the trained LLM model's files, its training script, the training dataset and a tester script to test the .safetensors model.

You can download the final GGUF in the Releases.

🚀 Releases

The version will be released using these versioning policies:
New major features and critical bug fixes will cause the immediate release of a new version, while other minor changes or fixes will wait one week since the time the change is introduced in the repository before being included in the new version, so that other potential changes can be added.

These potencial new changes will not increase the wait time for the new version beyond one week.

The version number will follow this format:
[Major Feature].[Minor Feature].[Bug Fixes]

💻 Technologies Used

Programming Language: Python
Libraries:
- transformers
- datasets
- peft
- acceletare
- bitsandbytes
- trl (1.0.0)
Other:
Recommended IDE: VS Code

Downloads last month: 4

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Komma-LuisMiSanVe/LangToSQL

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Quantized

(90)

this model

Komma-LuisMiSanVe
/

LangToSQL