Fine-Tuning GPT Models with Langflow
A guide to tailoring Large Language Models for specific tasks
Large Language Models excel at a variety of tasks due to their extensive pre-training on huge datasets. However, they still struggle with tasks requiring specific data or a particular format, which is where the process of fine-tuning comes in handy.
Fine-tuning involves retraining LLMs on data specific to a desired task. It has proven to be a powerful strategy for tasks such as information retrieval, especially when there’s the need to adapt responses to a pre-defined format.
Langflow provides a user-friendly interface to prototype and develop AI pipelines, and now it’s possible to fine-tune a GPT model — no coding required!
In this tutorial, you will learn how to use Langflow to fine-tune a GPT model.
Langflow components for fine-tuning (or using fine-tuned models) are available to download at the end of this article.
Fine-Tuning GPT Models with Langflow
To fine-tune a GPT model using Langflow, you first need to connect a ChatOpenAI or an OpenAI component to our fine-tuning component.
Currently, OpenAI allows you to fine-tune the following models: gpt-3.5-turbo, babbage-002, and davinci-002. Other models, such as gpt-4, will be available soon.
Invalid model choices will raise an error.
Parameters in the Fine-Tuning Component
The parameters that can be modified in the Fine Tune GPT Model component are:
- Job ID path: Local path required to save and load the fine-tuned model.
- Training data: Local training data in .jsonl or .csv formats (required). For CSV, the table must contain a ‘user’ and an ‘assistant’ columns. For .jsonl files, follow OpenAI specifications.
- System content: LLM prompt. You can also specify the system content when fine-tuning a gpt-3.5-turbo model.
- Epochs: The number of training rounds. Higher values can lead to worse generalization. Auto-calculated based on the number of samples if unspecified.
The Training Process
After selecting the training data and building the flow, the training process will start. According to OpenAI, after you’ve started a fine-tuning job, it may take some time to complete, since your job may be queued, and training a model can take minutes or even hours, depending on the model and dataset size. Once training is done, OpenAI notifies the API owner via email confirmation.
Notice that if you cancel the flow building or close the application, the training process will continue in OpenAI servers.
Using the Fine-Tuned Model
After your job is completed, the model should be available for inference right away, but if requests time out or the model’s name can’t be found, try again in a few minutes.
Use a flow to chat with your trained model just like using any regular OpenAI model. Insert the same Job ID Path used during fine-tuning to retrieve the trained model.
Example
As a simple use case, let’s fine-tune GPT 3.5 turbo to learn a bit about Langflow documentation. The playground dataset (CSV table) consists of 10 samples, with user inputs being of the form:
“Describe [some concept] in Langflow”
The respective assistant’s answer is just the full documentation page for that concept.
Before fine-tuning, the model provided a generic answer to the question “What is Langflow?”.
After fine-tuning, however, the model exhibited a much better understanding of the Langflow library.
Download Links (Gists)
If you enjoyed this article, try out these components and fine-tune your own model using Langflow!
