gpt | Python toolkit for lattice field theory | Animation library

I want to run GPT-3 for text classification. As the first step, I prepare data using openai CLI. I got a csv file which looks like as follow:

I wrote following command for preparing the data:

I am retraining the GPT2 language model, and am following this blog :

https://towardsdatascience.com/train-gpt-2-in-your-own-language-fc6ad4d60171

Here, they have trained a network on GPT2, and I am trying to recreate a same. However, my dataset is too large(250Mb), so I want to continue training in intervals. In other words, I want to checkpoint the model training. If there is any help, or a piece of code that I can implement to checkpoint and continue training, it would help a great deal for me. Thank you.

I know that T5 has K, Q and V vectors in each layer. It also has a feedforward network. I would like to freeze K, Q and V vectors and only train the feedforward layers on each layer of T5. I use Pytorch library. The model could be a wrapper for huggingface T5 model or a modified version of it. I know how to freeze all parameters using the following code:

I want to implement a simple "GPT" timer that generates an interrupt every 1ms. However, I get an interrupt exactly every 3ms (instead of the desired 1ms).

Where is my error? What values should I set to get a 1ms timer?

Here is my calculation for the GPT timer:

EXPLANATION OF TIMER VALUES:

We take for source clock the PLL1 DIV2 400MHz We define the root divisor at 4 => 400MHz / 4 = 100MHz

100MHz = one increment every 10ns We want an interrupt to be generated every 1 ms

So we have : Output_compare_value = delay_time x GPT_frequency

Output_compare_value = 1 x 10^-3 x (1/(10 x 10^-9)) = 100000

Here is my code (I change the state of a GPIO at each interrupt to check the operation of my timer on the oscilloscope):

I'm having a html with some 24 div, in each div there is a h2 tag and ul tag. in the ul tag there are different number of li. In each li then there is h3 tag and a ul again, which again have a li tag with h4 tag enclosing and achor tag e.g.:

The fine tuning endpoint for OpenAI's API seems to be fairly new, and I can't find many examples of fine tuning datasets online.

I'm in charge of a voicebot, and I'm testing out the performance of GPT-3 for general open-conversation questions. I'd like to train the model on the "fixed" intent-response pairs we're currently using: this would probably end up performing better in terms of company voice and style.

I have ready a long JSON file of data extracted from our current conversational engine, which matches user input to intents and returns the specified response. I'd like to train a GPT-3 model on this data.

As of now, for some quick testing, I've set up my calls to the API just like they suggest. I have a "fixed" intro text in the form

Goal: Amend this Notebook to work with Albert and Distilbert models

Kernel: conda_pytorch_p36. I did Restart & Run All, and refreshed file view in working directory.

Error occurs in Section 1.2, only for these 2 new models.

For filenames etc., I've created a variable used everywhere:

I am using the python client for GPT 3 search model on my own Jsonlines files. When I run the code on Google Colab Notebook for test purposes, it works fine and returns the search responses. But when I run the code on my local machine (Mac M1) as a web application (running on localhost) using flask for web service functionalities, it gives the following error:

As finally OpenAI opened the GPT-3 related API publicly, I am playing with it to explore and discover his potential.

I am trying the Answer API, the simple example that is in the documentation: https://beta.openai.com/docs/guides/answers

I upload the .jsonl file as indicated, and I can see it succesfully uploaded with the openai.File.list() api.

When I try to use it, unfortunately, I always get the same error: