File size: 4,158 Bytes
3b54ba5
 
 
 
 
 
060f7b8
3b54ba5
342632f
 
3b54ba5
 
 
f9284f2
 
3b54ba5
 
060f7b8
3b54ba5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6e38502
3b54ba5
 
20ece27
 
 
 
 
 
5f1cb13
 
20ece27
 
 
3b54ba5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f98fee6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3b54ba5
 
f98fee6
3b54ba5
f98fee6
 
 
 
 
3b54ba5
f98fee6
3b54ba5
f98fee6
 
 
 
 
 
 
3b54ba5
 
f98fee6
3b54ba5
f98fee6
 
 
 
3b54ba5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
---
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
tags:
- instruct
- finetune
model-index:
- name: NaturalQuery-6.7B-v0.1
  results: []
license: other
license_name: deepseek
language:
- en
datasets:
- cfahlgren1/wiki-sql-codellama-expanded
- cfahlgren1/natural-sql
---

# **NaturalQuery-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;
```