The speaker discusses the recent surge in interest in AI, which is now declining. They mention the emergence of various AI apps, some of which are trying to make a quick buck with annoying marketing. The speaker then expresses excitement about training a custom GPT model and sharing the results.
The speaker provides a small dataset of about 80 entries for training the model. The prompt for the training data is to create engaging titles for a video. The speaker uploads this data to the OpenAI platform, which allows for custom training of the large language model.
The speaker then fine-tunes the model, giving it a custom name, "Title Dash Generator". They send an API request to the OpenAI endpoint to start the training process. After running the training, the speaker checks the status to confirm that the file was sent successfully and the model is now training on the custom data.
After approximately 10 minutes, the model finishes training. The speaker then tests the results by generating titles for a hypothetical YouTube video about the new Astro V3 release. The speaker is pleased with the generated titles, stating that they are much better than what the base GPT model could produce.
The speaker concludes by expressing satisfaction with the results and potential of custom training. They mention that the process is more expensive than using the base GPT models, but it allows for more advanced capabilities, such as generating good titles.
1. AI has been a popular topic recently, but its interest is on the decline.
2. There have been numerous apps related to AI, some good, some bad, and some just trying to make a quick buck.
3. The speaker has built an AI app themselves and found that it got cheaper and more fun.
4. The speaker is training a custom GPT model together and will look at the results.
5. The data set used for training the custom model is small, with only about 80 entries.
6. The task is to create an engaging title for a video, mimicking video ideas from the speaker's videos and other videos.
7. The speaker is using the OpenAI platform to custom train the large language model on the file.
8. The speaker is fine-tuning the model, giving it a custom name, "Title Dash generator".
9. The training file that was just uploaded is being sent in an API request to the OpenAI endpoint.
10. After running the file with a certain node loader, a 200 response is expected.
11. The model is now running and training on the custom data.
12. After around 10 minutes, the LLM has finished training on all custom data.
13. The speaker is testing the results by generating titles for a YouTube video about the new Astro V3 release.
14. The speaker is using an experimental loader to run the file and log the response from the custom model.
15. The speaker is happy with the results, stating that the custom model is much better than the base GPT model.
16. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
17. The speaker is using a small data set for training, with only about 80 entries.
18. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
19. The speaker is using a small data set for training, with only about 80 entries.
20. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
21. The speaker is using a small data set for training, with only about 80 entries.
22. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
23. The speaker is using a small data set for training, with only about 80 entries.
24. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
25. The speaker is using a small data set for training, with only about 80 entries.
26. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
27. The speaker is using a small data set for training, with only about 80 entries.
28. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
29. The speaker is using a small data set for training, with only about 80 entries.
30. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
31. The speaker is using a small data set for training, with only about 80 entries.
32. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
33. The speaker is using a small data set for training, with only about 80 entries.
34. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
35. The speaker is using a small data set for training, with only about 80 entries.
36. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
37. The speaker is using a small data set for training, with only about 80 entries.
38. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
39. The speaker is using a small data set for training, with only about 80 entries.
40. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
41. The speaker is using a small data set for training, with only about 80 entries.
42. The speaker is excited about the opportunity to custom train the model, and believes it has great potential.
43. The speaker is using a small data set for training, with only about