metadata

license: mit
language:
  - en
metrics:
  - accuracy
pipeline_tag: text-generation
widget:
  - text: >-
      <schema>CREATE TABLE radio(age VARCHAR, radio_id VARCHAR,  frequency
      VARCHAR, wavelength VARCHAR); CREATE TABLE  radio_faults(radio_id VARCHAR,
      fault_description VARCHAR)</schema><question>Get the radio id and defect
      descriptions of radios that have wavelength greater than 30
      ?</question><sql>
    example_title: example1
  - text: >-
      <schema>CREATE TABLE system(JobID: String,GID: String, UID: String,Submit:
      Time , Start:Time(yyyy/mm/dd), End: Time,ElapsedRaw: Time, CPUTimeRAW:
      Time,NCPUS: Number,NNodes: Number, NodeList: List,  State:String,
      Timelimit: Time);</schema><question>Write sql query to get UID for JobID
      20 that Start on Jan 20 , 2010 and end on March 10 , 2012 </question><sql>
    example_title: example2
  - text: >-
      <schema>CREATE TABLE department (Department_ID number, Name text, Creation
      text, Ranking number, Budget_in_Billions number, Num_Employees number)
      which has Department_ID as primary key abd CREATE TABLE head (head_ID
      number, name text, born_state text, age number) which has head_ID as
      primary key and CREATE TABLE management (department_ID number, head_ID
      number, temporary_acting text) which has department_ID as primary
      key</schema><question>
    example_title: example3
tags:
  - code
  - sql
  - text2sql
  - instruction_tuned
  - jax
  - pytorch
  - 1b
  - expert
datasets:
  - PipableAI/spider-bird

Pipable’s pipSQL

Pipable’s pipSQL is a model distilled from llama 1b to generate sql queries given prompt and schema. We used a unique pipeline which involved the model working on two objectives alternatively ----

Maximizing the log prob of all tokens in the sequence (including the prompt tokens)
Minimizng the difference between the true value and the predicted maximum value of the output tokens i.e generated tokens for the sql query slice of the entire sequence.

License

The model's new weights along with all other assets involved with it are open sourced under mit license.

How to Use

text = """<schema>{schema}</schema>
<question>{question}</question>
<sql>"""

from transformers import AutoModelForCasualLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("PipableAI/pipSQL1b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL1b")

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])

The PipableAI team

Avi Kothari, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya , Gyan Ranjan