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README.md

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+---
+base_model: PipableAI/pip-sql-1.3b
+datasets:
+- PipableAI/pip-txt-to-sql-spider-bird-dataset
+inference: true
+language:
+- en
+library_name: transformers
+license: apache-2.0
+metrics:
+- accuracy
+model_creator: PipableAI
+model_name: pip-sql-1.3b
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- sql
+- code
+- text2sql
+- instruction_tuned
+- basemodel
+- jax
+- pytorch
+- text-generation-inference
+- gguf
+- ggml
+- quantized
+widget:
+- example_title: example
+  text: '<schema>CREATE TABLE system(JobID: String,GID: String, UID: String, Start:Time(yyyy/mm/dd),
+    End: Time,ElapsedRaw: Time, CPUTimeRAW: Time,NCPUS: Number,NNodes: Number, NodeList:
+    List,  State:String, Timelimit: Time);</schema><question>Get UID and job id for
+    Jobs that started on Jan 20 , 2023 ended on feb 14 2023 and has job id 20</question><sql>'
+---
+
+# pip-sql-1.3b-GGUF
+
+Quantized GGUF model files for [pip-sql-1.3b](https://huggingface.co/PipableAI/pip-sql-1.3b) from [PipableAI](https://huggingface.co/PipableAI)
+
+## Original Model Card:
+
+# pipSQL-1.3b
+
+[pipableAi](https://www.linkedin.com/company/pipable.ai/about/)
+
+[colab_notebook](https://colab.research.google.com/drive/1insSxvc3jjAXe0zmdIjmbG3ttb5mpRgQ?usp=sharing)
+
+## What have we built?
+A 1.3 bn SQL model that outperforms most SQL expert models and chatgpt on popular benchmarks.
+This is a distilled model built on the deepseek base model.
+Please refer to https://huggingface.co/PipableAI/pip-library-etl-1.3b for our state of the art model.
+## How we built it?
+
+We used softmax cross entropy and a modified form of policy grad along with Q loss, optimized in an EM set up.
+Loss behaviour in the set up mentioned above - 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/658d8095a2a6a6e0da8bb8a6/I80Ru1r4thoYrLagIWALa.png)
+
+## Benchmarking :
+For benchmarking purposes we are using Semantic Evaluation for Text-to-SQL with 
+Distilled Test Suites, an officially accepted evaluation framework for Spider, SParC, and CoSQL which was proposed by a research team of Yale and Berkeley. 
+The benchmark contains 2200 test data points
+Here is the link to run the evaluation:
+
+
+[Test Suite SQL Eval](https://github.com/taoyds/test-suite-sql-eval)
+
+|model|easy|medium|hard|extra|
+|-----|----|------|----|-----|
+|sqlcoder-7b-2|72.0|58.0|40.6|37.3|
+|pipSQL-1.3b|78.5|57.5|42.1|28.3|
+|pipSQL-7b|63.0|40.0|30.2|25.0|
+|sqlcoder-7b|60.6|48.2|28.3|20.4|
+|gpt-3.5|58.8|44.7|31.0|28.4|
+
+We have also benchmarked it on defog eval.
+It contains 200 test data points handpicked by defog team.
+Here is the link to it:
+
+
+[Defog SQL-Eval](https://github.com/defog-ai/sql-eval)
+These are the results -
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d32c6b921678fdc9de3302/fFeLSEYBNpQk_JWjFsF5M.png)
+
+## License
+The model is open source under apache 2.0. License
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers
+```
+
+### Prompt
+```python
+prompt = f"""<schema>{schema}</schema>
+<question>{question}</question>
+<sql>"""
+```
+
+### PyTorch
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-sql-1.3b")
+tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-sql-1.3b")
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
+```
+
+### Flax
+```python
+from transformers import FlaxAutoModelForCausalLM, AutoTokenizer
+device = "cuda"
+model = FlaxAutoModelForCausalLM.from_pretrained("PipableAI/pip-sql-1.3b",from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-sql-1.3b")
+
+inputs = tokenizer(text, return_tensors="jax")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
+```
+
+## Examples
+
+### Schema
+```sql
+CREATE TABLE Products (
+  product_id number,
+  parent_product_id number,
+  product_name text,
+  product_price number,
+  product_color text,
+  product_size text,
+  product_description text);
+
+CREATE TABLE Customers (
+  customer_id number,
+  gender_code text,
+  customer_first_name text,
+  customer_middle_initial text,
+  customer_last_name text,
+  email_address text,
+  login_name text,
+  login_password text,
+  phone_number text,
+  address_line_1 text,
+  town_city text,
+  county text,
+  country text);
+
+CREATE TABLE Customer_Payment_Methods (
+  customer_id number,
+  payment_method_code text);
+
+CREATE TABLE Invoices (
+  invoice_number number,
+  invoice_status_code text,
+  invoice_date time);
+
+CREATE TABLE Orders (
+  order_id number,
+  customer_id number,
+  order_status_code text,
+  date_order_placed time);
+
+CREATE TABLE Order_Items (
+  order_item_id number,
+  product_id number,
+  order_id number,
+  order_item_status_code text);
+
+CREATE TABLE Shipments (
+  shipment_id number,
+  order_id number,
+  invoice_number number,
+  shipment_tracking_number text,
+  shipment_date time);
+
+CREATE TABLE Shipment_Items (
+  shipment_id number,
+  order_item_id number);
+```
+
+### Questions
+What are the email address, town and county of the customers who are of the least common gender?
+```sql
+SELECT email_address ,  town_city ,  county FROM customers GROUP BY gender_code ORDER BY count(*) ASC LIMIT 1
+```
+
+What are the product price and the product size of the products whose price is above average?
+```sql
+SELECT product_price ,  product_size FROM products WHERE product_price  > (SELECT avg(product_price) FROM products)
+```
+
+Which customers did not make any orders? List the first name, middle initial and last name.
+```sql
+SELECT T1.customer_first_name ,  T1.customer_middle_initial ,  T1.customer_last_name FROM Customers AS T1 WHERE T1.customer_id NOT IN (SELECT T2.customer_id FROM Orders AS T2)
+```
+
+### Team
+Avi Kothari, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya