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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:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[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
table_1_11545282_6 (
"No." numeric,
Nationality text,
"Years for Jazz" text
);
CREATE TABLE
table_2_17383560_1 (
Pick numeric,
Round numeric,
Player text,
"School/Club Team" text,
Position text
);
CREATE TABLE
table_1_10581768_2 (
Institution text,
Enrollment numeric,
Nickname text,
Founded numeric
);
```
**Question**: **What is the round of pick 63?**
```sql
SELECT "Round" FROM table_2_17383560_1 WHERE Pick=63;
```
**Question**: **What is the most popular position among players?**
```sql
SELECT COUNT("Position") FROM "table_2_17383560_1" GROUP BY "Position" ORDER BY COUNT("Position") DESC LIMIT 1;
```
**Question**: **What is the most recent year an institution was founded?**
```sql
SELECT MAX("Founded") FROM table_1_10581768_2;
``` |