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