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README.md

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+
+---
+
+base_model: deepseek-ai/deepseek-coder-6.7b-instruct
+tags:
+  - instruct
+  - finetune
+library_name: transformers
+license: cc-by-sa-4.0
+pipeline_tag: text-generation
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/natural-sql-7b-GGUF
+This is quantized version of [chatdb/natural-sql-7b](https://huggingface.co/chatdb/natural-sql-7b) created using llama.cpp
+
+# Original Model Card
+
+
+# **Natural-SQL-7B by ChatDB**
+## Natural-SQL-7B is a model with very strong performance in Text-to-SQL instructions, has an excellent understanding of complex questions, and outperforms models of the same size in its space.
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/648a374f00f7a3374ee64b99/hafdsfrFCqrVbATIzV_EN.png" width="600">
+
+[ChatDB.ai](https://chatdb.ai) | [Notebook](https://github.com/cfahlgren1/natural-sql/blob/main/natural-sql-7b.ipynb) | [Twitter](https://twitter.com/calebfahlgren)
+
+# **Benchmarks**
+### *Results on Novel Datasets not trained on via SQL-Eval*
+<img src="https://cdn-uploads.huggingface.co/production/uploads/648a374f00f7a3374ee64b99/5ynfoKPzI3_-WasQQt7qR.png" width="800">
+
+<em>Big thanks to the [defog](https://huggingface.co/defog) team for open sourcing [sql-eval](https://github.com/defog-ai/sql-eval)</em>👏
+
+Natural-SQL also can handle complex, compound questions that other models typically struggle with. There is a more detailed writeup Here is a write up, small test done [here](https://chatdb.ai/post/naturalsql-vs-sqlcoder-for-text-to-sql).
+# 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("chatdb/natural-sql-7b")
+model = AutoModelForCausalLM.from_pretrained(
+    "chatdb/natural-sql-7b",
+    device_map="auto",
+    torch_dtype=torch.float16,
+)
+```
+
+### **License**
+
+The model weights are licensed under `CC BY-SA 4.0`, with extra guidelines for responsible use expanded from the original model's [Deepseek](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) license. 
+You're free to use and adapt the model, even commercially. 
+If you alter the weights, such as through fine-tuning, you must publicly share your changes under the same `CC BY-SA 4.0` license.
+
+
+### Generating SQL
+
+```python
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+generated_ids = model.generate(
+    **inputs,
+    num_return_sequences=1,
+    eos_token_id=100001,
+    pad_token_id=100001,
+    max_new_tokens=400,
+    do_sample=False,
+    num_beams=1,
+)
+
+outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+print(outputs[0].split("```sql")[-1])
+```
+# Prompt Template
+
+```
+# Task 
+Generate a SQL query to answer the following question: `{natural language question}`
+
+### PostgreSQL Database Schema 
+The query will run on a database with the following schema: 
+
+<SQL Table DDL Statements>
+
+# SQL 
+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)
+);
+```
+### Example SQL Outputs
+
+**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;
+```