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---
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
tags:
- SOLAR
- instruct
- finetune
model-index:
- name: NaturalQuery-Solar-6.7B-v0.1
results: []
license: apache-2.0
language:
- en
datasets:
- wikisql
---
# **NaturalQuery-Solar-6.7B-v0.1**
**NaturalQuery** is a LLM that can translate natural language queries to SQL based on your schema.
NaturalQuery-v0.1 is finetuned on 8k text to PostgreSQL Natural Language <> SQL pairs.
**Future Improvements**:
- Much larger training set
- More complex schemas, questions, and queries
- Reward modeling via DPO
- Benchmarking
# **Usage**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("cfahlgren1/NaturalSQL-6.7B-v0")
model = AutoModelForCausalLM.from_pretrained(
"cfahlgren1/NaturalSQL-6.7B-v0",
device_map="auto",
torch_dtype=torch.float16,
)
```
### **Generating Text**
To generate text, use the following Python code. [Here](https://gist.github.com/cfahlgren1/ba17f01cf688c4229686dc3dfb4d4549) is a full notebook with the SQL table prompt format to use.
```python
messages=[
{ 'role': 'user', 'content': prompt}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# 32023 is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32023)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
# **SQL Generation Template**
```
### Task
Generate a SQL query to answer the following question: `{natural language question}`
### Database Schema
The query will run on a database with the following schema:
'''
<SQL Table DDL Statements>
'''
### Answer
Here is the SQL query that answers the question: `{natural language question}`
'''sql
```
# **Example SQL Output**
### **Example Schemas**
```sql
CREATE TABLE users (
user_id SERIAL PRIMARY KEY,
username VARCHAR(50) NOT NULL,
email VARCHAR(100) NOT NULL,
password_hash TEXT NOT NULL,
created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
);
CREATE TABLE projects (
project_id SERIAL PRIMARY KEY,
project_name VARCHAR(100) NOT NULL,
description TEXT,
start_date DATE,
end_date DATE,
owner_id INTEGER REFERENCES users(user_id)
);
CREATE TABLE tasks (
task_id SERIAL PRIMARY KEY,
task_name VARCHAR(100) NOT NULL,
description TEXT,
due_date DATE,
status VARCHAR(50),
project_id INTEGER REFERENCES projects(project_id)
);
CREATE TABLE taskassignments (
assignment_id SERIAL PRIMARY KEY,
task_id INTEGER REFERENCES tasks(task_id),
user_id INTEGER REFERENCES users(user_id),
assigned_date DATE NOT NULL DEFAULT CURRENT_TIMESTAMP
);
CREATE TABLE comments (
comment_id SERIAL PRIMARY KEY,
content TEXT NOT NULL,
created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
task_id INTEGER REFERENCES tasks(task_id),
user_id INTEGER REFERENCES users(user_id)
);
```
**Question**: **Show me the day with the most users joining**
```sql
SELECT created_at::DATE AS day, COUNT(*) AS user_count
FROM users
GROUP BY day
ORDER BY user_count DESC
LIMIT 1;
```
**Question**: **Show me the project that has a task with the most comments**
```sql
SELECT p.project_name, t.task_name, COUNT(c.comment_id) AS comment_count
FROM projects p
JOIN tasks t ON p.project_id = t.project_id
JOIN comments c ON t.task_id = c.task_id
GROUP BY p.project_name, t.task_name
ORDER BY comment_count DESC
LIMIT 1;
```
**Question**: **What is the ratio of users with gmail addresses vs without?**
```sql
SELECT
SUM(CASE WHEN email ILIKE '%@gmail.com%' THEN 1 ELSE 0 END)::FLOAT / NULLIF(SUM(CASE WHEN email NOT ILIKE '%@gmail.com%' THEN 1 ELSE 0 END), 0) AS gmail_ratio
FROM
users;
``` |