Fine Tune GPT3 for Code

Dependencies:

Install and Setup

npm install

You can store the api key in a .env file and control debug logging with the DEBUG environment variable.

OPENAI_API_KEY="YOUR_OPENAI_API_KEY_GOES_HERE"
DEBUG="true"

Dataset and TypeScript/JavaScript Code Parser

The dataset is in ./dataset.csv.

To produce more completions for the dataset, you can use the parser which will parse TypeScript and JavaScript:

# Parsing a directory of source code files
node index.js parse ./input

# Parsing the source code of finetune-gpt3-for-code
node index.js parse ./index.js

# Parsing individual source code files
node index.js parse /path/to/typescript/file.ts
node index.js parse /path/to/javascript/file.js

It will produce the following output that can be used to extend the dataset:

prompt,completion
for loop,"for (var i = 0; i < 10; i ++) {"
for loop,"for (var i = 0; i < 10; i ++) {
  console.log(i);
}"
print i,"console.log(i)"
function for printHelloWorld with one argument,"function printHelloWorld(name) {"
function for printHelloWorld with one argument,"function printHelloWorld(name) {
  console.log(`Hello ${name}`);
}"

Running the Code

Upload the dataset and create the fine-tuned model:

node index.js

Alternative way to upload the dataset:

node index.js upload

Listing the status of the fine-tuned models

List the status of the fine-tuning until the fine_tune_model field is no longer null

node index.js list

Creating a completion

Use the fine tune id as the model when it is ready

node index.js generate model-finetune-id prompt

Example Session

$ node index.js
Fine tune id: id-123

$ node index.js list
ft-nlBqfegq5AP1QkfLKOCvuz6u succeeded curie:ft-personal-2023-01-07-06-54-13
ft-l8B3FiHivRuMWxj8U1YvQKq2 running null

$ node index.js generate curie:ft-personal-2023-01-07-06-54-13 "define apply effect"
const yEff = (value) => ({ done: false, value })

$ node index.js generate curie:ft-personal-2023-01-07-06-54-13 "test api call saga"
function* fetchProducts() {
  const products = yield call(Api

$ node index.js generate curie:ft-personal-2023-01-07-06-54-13 "test api call saga"
assert.deeplyEqual(
  iterator.next().value,

$ node index.js list
ft-nlBqfegq5AP1QkfLKOCvuz6u succeeded curie:ft-personal-2023-01-07-06-54-13
ft-l8B3FiHivRuMWxj8U1YvQKq2 succeeded davinci:ft-personal-2023-01-07-07-00-01

$ node index.js generate davinci:ft-personal-2023-01-07-07-00-01 "define apply effect"
const arrayOfValues = { value: 5 }
  const expectedEffect =

$ node index.js generate davinci:ft-personal-2023-01-07-07-00-01 "test api call saga"
assert.deepEqual(
import { cps } from 'redux

$ node index.js generate davinci:ft-personal-2023-01-07-07-00-01 "test api call saga"
assert.deepEqual(
  iterator.next(assert.isNot

Running the code using Docker

# npm run-script docker:build
docker build -t finetune-gpt3-for-code .

# npm run-script docker:run
docker run -it --rm --log-driver none --env-file .env -v $(pwd)/data:/data finetune-gpt3-for-code

modernlearner / finetune-gpt3-for-code Goto Github PK