How To Create A Custom Fine-Tuned Prediction Model Using Base GPT-3 models

Large Langauge Models (LLMs) functionality can be divided into two broad categories: Generative & Predictive.

from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
from pprint import pprint
from sklearn.datasets import fetch_20newsgroups
import pandas as pd
import openai

categories = ['', '']
vehicles_dataset = fetch_20newsgroups(subset='train', shuffle=True, random_state=42, categories=categories)
From: (Joseph St. Lucas)
Subject: Re: Dumbest automotive concepts of all time
Organization: General Dynamics Corp.
Distribution: usa
Lines: 10

Don't have a list of what's been said before, so hopefully not repeating.

How about horizontally mounted oil filters (like on my Ford) that, no
matter how hard you try, will spill out their half quart on the bottom
of the car when you change them?

Joe St.Lucas Standard Disclaimers Apply
General Dynamics Space Systems, San Diego
Work is something to keep me busy between Ultimate Frisbee games.
len_all, len_autos, len_motorcycles = len(, len([e for e in if e == 0]), len([e for e in if e == 1])
print(f"Total examples: {len_all}, Autos examples: {len_autos}, Vehicles examples: {len_motorcycles}")
Total examples: 1192, Autos examples: 594, Vehicles examples: 598
{"prompt": "<prompt text>", "completion": "<ideal generated text>"}
{"prompt": "<prompt text>", "completion": "<ideal generated text>"}
{"prompt": "<prompt text>", "completion": "<ideal generated text>"}
import pandas as pd

labels = [vehicles_dataset.target_names[x].split('.')[-1] for x in vehicles_dataset['target']]
texts = [text.strip() for text in vehicles_dataset['data']]
df = pd.DataFrame(zip(texts, labels), columns = ['prompt','completion']) #[:300]
df.to_json("vehicles.jsonl", orient='records', lines=True)
!openai tools fine_tunes.prepare_data -f vehicles.jsonl -q

- Your file contains 1192 prompt-completion pairs
- Based on your data it seems like you're trying to fine-tune a model for classification
- For classification, we recommend you try one of the faster and cheaper models, such as `ada`
- For classification, you can estimate the expected model performance by keeping a held out dataset, which is not used for training
- There are 5 examples that are very long. These are rows: [38, 203, 910, 1057, 1130]
For conditional generation, and for classification the examples shouldn't be longer than 2048 tokens.
- Your data does not contain a common separator at the end of your prompts. Having a separator string appended to the end of the prompt makes it clearer to the fine-tuned model where the completion should begin. See for more detail and examples. If you intend to do open-ended generation, then you should leave the prompts empty
- The completion should start with a whitespace character (` `). This tends to produce better results due to the tokenization we use. See for more details

Based on the analysis we will perform the following actions:
- [Recommended] Remove 5 long examples [Y/n]: Y
- [Recommended] Add a suffix separator `\n\n###\n\n` to all prompts [Y/n]: Y
- [Recommended] Add a whitespace character to the beginning of the completion [Y/n]: Y
- [Recommended] Would you like to split into training and validation set? [Y/n]: Y

Your data will be written to a new JSONL file. Proceed [Y/n]: Y

Wrote modified files to `vehicles_prepared_train.jsonl` and `vehicles_prepared_valid.jsonl`
Feel free to take a look!

Now use that file when fine-tuning:
> openai api fine_tunes.create -t "vehicles_prepared_train.jsonl" -v "vehicles_prepared_valid.jsonl" --compute_classification_metrics --classification_positive_class " motorcycles"

After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `\n\n###\n\n` for the model to start generating completions, rather than continuing with the prompt. Make sure to include `stop=["s"]` so that the generated texts ends at the expected place.
Once your model starts training, it'll approximately take 30.82 minutes to train a `curie` model, and less for `ada` and `babbage`. Queue will approximately take half an hour per job ahead of you.
!openai --api-key 'xxxxxxxxxxxxxxxxx' api fine_tunes.create -t "vehicles_prepared_train.jsonl" -v "vehicles_prepared_valid.jsonl" --compute_classification_metrics --classification_positive_class " autos" -m ada
Upload progress: 100% 1.35M/1.35M [00:00<00:00, 1.59Git/s]
Uploaded file from vehicles_prepared_train.jsonl: file-qN7D2kAh9h5Ui1XnZuDNrPgm
Upload progress: 100% 320k/320k [00:00<00:00, 623Mit/s]
Uploaded file from vehicles_prepared_valid.jsonl: file-ijOCUihdypRPrcTzodxN9Pa6
Created fine-tune: ft-xPIJ4BIM4giuXY4JOQ9rno2v
Streaming events until fine-tuning is complete...

(Ctrl-C will interrupt the stream, but not cancel the fine-tune)
[2023-01-31 05:44:52] Created fine-tune: ft-xPIJ4BIM4giuXY4JOQ9rno2v
[2023-01-31 05:46:21] Fine-tune costs $0.65
[2023-01-31 05:46:22] Fine-tune enqueued. Queue number: 0
[2023-01-31 05:46:24] Fine-tune started
[2023-01-31 05:49:03] Completed epoch 1/4
[2023-01-31 05:51:36] Completed epoch 2/4
[2023-01-31 05:54:07] Completed epoch 3/4
[2023-01-31 05:56:38] Completed epoch 4/4
[2023-01-31 05:57:11] Uploaded model: ada:ft-personal-2023-01-31-05-57-11
[2023-01-31 05:57:12] Uploaded result file: file-EzPswfO3vXl3RXqbIGr4qebS
[2023-01-31 05:57:12] Fine-tune succeeded

Job complete! Status: succeeded 🎉
Try out your fine-tuned model:

openai api completions.create -m ada:ft-personal-2023-01-31-05-57-11 -p <YOUR_PROMPT>
openai.api_key = "xxxxxxxxxxxxxxxxx"
ft_model = 'ada:ft-personal-2023-01-31-05-57-11'
sample_utterance ="""So how do I steer when my hands aren't on the bars?"""
res = openai.Completion.create(model=ft_model, prompt=sample_utterance + '\n\n###\n\n', max_tokens=1, temperature=0, logprobs=2)
ft_model = 'ada:ft-personal-2023-01-31-05-57-11'
sample_utterance ="""Is countersteering like benchracing only with a taller seat, so your feet aren't on the floor?"""
res = openai.Completion.create(model=ft_model, prompt=sample_utterance + '\n\n###\n\n', max_tokens=1, temperature=0, logprobs=2)



Chief Evangelist @ HumanFirst. I explore and write about all things at the intersection of AI and language; NLP/NLU/LLM, Chat/Voicebots, CCAI.

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Cobus Greyling

Chief Evangelist @ HumanFirst. I explore and write about all things at the intersection of AI and language; NLP/NLU/LLM, Chat/Voicebots, CCAI.