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

Libraries

How to use imahwashere/TexttoSQLish_Code with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("imahwashere/TexttoSQLish_Code", dtype="auto")

llama-cpp-python

How to use imahwashere/TexttoSQLish_Code with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="imahwashere/TexttoSQLish_Code",
	filename="unsloth.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use imahwashere/TexttoSQLish_Code with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf imahwashere/TexttoSQLish_Code:Q8_0
# Run inference directly in the terminal:
llama-cli -hf imahwashere/TexttoSQLish_Code:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf imahwashere/TexttoSQLish_Code:Q8_0
# Run inference directly in the terminal:
llama-cli -hf imahwashere/TexttoSQLish_Code:Q8_0

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 imahwashere/TexttoSQLish_Code:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf imahwashere/TexttoSQLish_Code:Q8_0

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 imahwashere/TexttoSQLish_Code:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf imahwashere/TexttoSQLish_Code:Q8_0

Use Docker

docker model run hf.co/imahwashere/TexttoSQLish_Code:Q8_0

LM Studio
Jan
Ollama
How to use imahwashere/TexttoSQLish_Code with Ollama:
```
ollama run hf.co/imahwashere/TexttoSQLish_Code:Q8_0
```

Unsloth Studio

How to use imahwashere/TexttoSQLish_Code 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 imahwashere/TexttoSQLish_Code 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 imahwashere/TexttoSQLish_Code to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use imahwashere/TexttoSQLish_Code with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf imahwashere/TexttoSQLish_Code:Q8_0

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": "imahwashere/TexttoSQLish_Code:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use imahwashere/TexttoSQLish_Code with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf imahwashere/TexttoSQLish_Code:Q8_0

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 imahwashere/TexttoSQLish_Code:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use imahwashere/TexttoSQLish_Code with Docker Model Runner:
```
docker model run hf.co/imahwashere/TexttoSQLish_Code:Q8_0
```

Lemonade

How to use imahwashere/TexttoSQLish_Code with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull imahwashere/TexttoSQLish_Code:Q8_0

Run and chat with the model

lemonade run user.TexttoSQLish_Code-Q8_0

List all available models

lemonade list

Text-to-SQL Model

A fine-tuned Llama 3.2 3B model specialized for converting natural language queries into SQL statements. This model transforms everyday questions into precise database queries, making data accessible to everyone.

Desired Use Cases

Perfect for applications requiring natural language to SQL conversion:

Business Intelligence: "Show me sales by region for last quarter" Data Analytics: "Find customers who haven't purchased in 6 months" Reporting: "What are the top 10 products by revenue?" Database Querying: Making databases accessible to non-technical users

Training Data

Trained on the gretelai/synthetic_text_to_sql dataset, which provides:

High-quality synthetic text-to-SQL pairs Diverse query patterns and complexity levels Multiple database schemas and domains Robust coverage of SQL operations and functions

Model Performance

IDK MAN, it does what it does brev. -UPDATE- YEA I didnt cook. I reckon it underfit during training/. I may have to re-do the data_prep then training and see what results the model gives us during inference and try to evaluate the model.

Uploaded model

Developed by: imahwashere
License: apache-2.0
Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 54

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

8-bit

Inference Providers NEW

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

imahwashere
/

TexttoSQLish_Code

Text-to-SQL Model

Desired Use Cases

Training Data

Model Performance

Uploaded model

Dataset used to train imahwashere/TexttoSQLish_Code