license: llama2
language:
- en
pipeline_tag: text-generation
widget:
- text: >-
[INST] Write SQLite query to answer the following question given the
database schema. Please wrap your code answer using ```: Schema: CREATE
TABLE head (age INTEGER) Question: How many heads of the departments are
older than 56 ? [/INST] Here is the SQLite query to answer to the
question: How many heads of the departments are older than 56 ?: ```
example_title: Example 1
- text: >-
[INST] Write SQLite query to answer the following question given the
database schema. Please wrap your code answer using ```: Schema: CREATE
TABLE people (first_name VARCHAR) Question: List the first names of people
in alphabetical order? [/INST] Here is the SQLite query to answer to the
question: List the first names of people in alphabetical order?: ```
example_title: Example 2
- text: >-
[INST] Write SQLite query to answer the following question given the
database schema. Please wrap your code answer using ```: Schema: CREATE
TABLE weather (zip_code VARCHAR, mean_sea_level_pressure_inches INTEGER)
Question: What is the zip code in which the average mean sea level
pressure is the lowest? [/INST] Here is the SQLite query to answer to the
question: What is the zip code in which the average mean sea level
pressure is the lowest?: ```
example_title: Example 3
- text: >-
[INST] Write SQLite query to answer the following question given the
database schema. Please wrap your code answer using ```: Schema: CREATE
TABLE weather (date VARCHAR, mean_temperature_f VARCHAR, mean_humidity
VARCHAR, max_gust_speed_mph VARCHAR) Question: What are the date, mean
temperature and mean humidity for the top 3 days with the largest max gust
speeds? [/INST] Here is the SQLite query to answer to the question: What
are the date, mean temperature and mean humidity for the top 3 days with
the largest max gust speeds?: ```
example_title: Example 4
- text: >-
[INST] Write SQLite query to answer the following question given the
database schema. Please wrap your code answer using ```: Schema: CREATE
TABLE trip (end_station_id VARCHAR); CREATE TABLE station (id VARCHAR,
city VARCHAR) Question: Count the number of trips that did not end in San
Francisco city. [/INST] Here is the SQLite query to answer to the
question: Count the number of trips that did not end in San Francisco
city.: ```
example_title: Example 5
Llama-2-7B-instruct-text2sql-GGUF Model Card
Model Name: Llama-2-7B-instruct-text2sql-GGUF
Description: This model is a GGUF quantisation of a fine-tuned version of the Llama 2 with 7 billion parameters, specifically tailored for text-to-SQL tasks. It has been trained to generate SQL queries given a database schema and a natural language question. The GGUF quantisation was performed with llama.cpp.
Model Information
- Base Model: support-pvelocity/Llama-2-7B-instruct-text2sql
GGUF Parameters
- Quant method: Q4_K_M
- bits: 4
License
This model is governed by a custom commercial license from Llama. For details, please visit: Custom Commercial License
Intended Use
Intended Use Cases: This model is intended for commercial and research use in English. It is designed for text-to-SQL tasks, enabling users to generate SQL queries from natural language questions.
Out-of-Scope Uses: Any use that violates applicable laws or regulations, use in languages other than English, or any other use prohibited by the Acceptable Use Policy and Licensing Agreement for Llama and its variants.
Example Code
You can use the Llama-2-7B-instruct-text2sql-GGUF model to generate SQL queries from natural language questions, as demonstrated in the following code snippet:
pip install -q torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 ctransformers==0.2.27
from ctransformers import AutoModelForCausalLM
model_name = 'support-pvelocity/Llama-2-7B-instruct-text2sql-GGUF'
model = AutoModelForCausalLM.from_pretrained(
model_name,
model_file=model_name.split('/')[1].replace('-GGUF', '.q4_k_m.gguf'),
model_type="llama",
gpu_layers=50,
context_length=4048
)
table = "CREATE TABLE sales ( sale_id number PRIMARY KEY, product_id number, customer_id number, salesperson_id number, sale_date DATE, quantity number, FOREIGN KEY (product_id) REFERENCES products(product_id), FOREIGN KEY (customer_id) REFERENCES customers(customer_id), FOREIGN KEY (salesperson_id) REFERENCES salespeople(salesperson_id)); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number, FOREIGN KEY (product_id) REFERENCES products(product_id)); CREATE TABLE customers ( customer_id number PRIMARY KEY, name text, address text ); CREATE TABLE salespeople ( salesperson_id number PRIMARY KEY, name text, region text ); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number );"
question = 'Find the salesperson who made the most sales.'
prompt = f"[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: {table} Question: {question} [/INST] Here is the SQLite query to answer to the question: {question}: ``` "
output = model(prompt)
output = output.split('```')[0]
print(output.strip())
This code demonstrates how to utilize the model for generating SQL queries based on a provided database schema and a natural language question. It showcases the model's capability to assist in SQL query generation for text-to-SQL tasks.