Meta-Llama-3.1-8B-Text-to-SQL-GGUF-q4

This model is a fine-tuned version of ruslanmv/Meta-Llama-3.1-8B-Text-to-SQL for Text-to-SQL generation. It is designed to convert natural language queries into SQL commands, optimized for efficient inference using GGUF (Grouped Quantization for Uniform Format).

Model Details

• Base Model: ruslanmv/Meta-Llama-3.1-8B-Text-to-SQL
• Task: Text-to-SQL generation
• Quantization: GGUF (Q4, 4-bit quantization)
• License: Apache-2.0

Installation

To use this model, you need to install llama-cpp-python and huggingface_hub for downloading and running the quantized model.

Step 1: Install Required Packages

# Install llama-cpp-python from the appropriate repository
!pip install llama-cpp-python \
  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/12.1 \
  --force-reinstall --upgrade --no-cache-dir --verbose

# Install huggingface_hub to download models from Hugging Face
!pip install huggingface_hub hf_transfer

Step 2: Set up Hugging Face Hub and Download the Model

Ensure that Hugging Face's transfer feature is enabled and download the quantized model from Hugging Face using the huggingface-cli.

import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

!huggingface-cli download \
 ruslanmv/Meta-Llama-3.1-8B-Text-to-SQL-GGUF-q4 \
 unsloth.Q4_K_M.gguf \
 --local-dir . \
 --local-dir-use-symlinks False

Make sure the downloaded model is stored in the local directory. Set the model path as follows:

MODEL_PATH = "/content/unsloth.Q4_K_M.gguf"

Usage Example

Here is an example that demonstrates how to generate an SQL query from a natural language prompt using the quantized GGUF model and the llama_cpp library.

Step 1: Define the User Query and Prompt

The user provides a natural language query, and we format the prompt using an Alpaca-style template.

user_query = "Seleziona tutte le colonne della tabella table1 dove la colonna anni è uguale a 2020"

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
"""

prompt = alpaca_prompt.format(
    "Provide the SQL query",
    user_query
)

Step 2: Load the Model and Generate SQL Query

To load the quantized model and perform inference, you will need the llama_cpp library.

from llama_cpp import Llama
import os

# Get the current directory
current_directory = os.getcwd()

# Construct the full model path
MODEL_PATH = os.path.join(current_directory, "unsloth.Q4_K_M.gguf")

# Ensure the model path exists
assert os.path.exists(MODEL_PATH), f"Model path {MODEL_PATH} does not exist."

# Create the prompt for SQL query generation
B_INST, E_INST = "<s>[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = """\
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
"""
SYSTEM_PROMPT = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS

def create_prompt(user_query):
    instruction = f"Provide the SQL query. User asks: {user_query}\n"
    prompt = B_INST + SYSTEM_PROMPT + instruction + E_INST
    return prompt.strip()

# Define user query
user_query = "Seleziona tutte le colonne della tabella table1 dove la colonna anni è uguale a 2020"  
prompt = create_prompt(user_query)
print(f"Prompt created:\n{prompt}")

# Load the model
try:
    llm = Llama(model_path=MODEL_PATH, n_gpu_layers=1)  # Adjust GPU layers as per your hardware
except AssertionError as e:
    raise RuntimeError(f"Failed to load the model. Check that the model is in the correct format: {e}")

# Perform inference
try:
    result = llm(
        prompt=prompt,
        max_tokens=200,
        echo=False
    )
    print(result['choices'][0]['text'])
except Exception as e:
    print(f"Error during inference: {e}")

Expected Output

The model will return the following SQL query:

SELECT * FROM table1 WHERE anni = 2020

Additional Notes

• Quantization: The model is quantized using GGUF to enable efficient inference, especially on systems with limited memory.
• Prompt: The prompt follows an Alpaca instruction style, which helps guide the model in generating SQL queries based on user input.
• Inference: The llama_cpp library is used to perform inference with this GGUF model. Adjust n_gpu_layers and max_tokens based on your hardware capabilities and the complexity of the SQL query.

License

This model is released under the Apache-2.0 license.

For more detailed information, visit the model card on Hugging Face.