Fine-Tune ChatGPT For Your Exact Use Case

That is cool.
So can I reuse the same API to integra-te in some other platform like flutter to creat an App. Or I have to creat an other API according to the fine-tune.

After build model I use in Node js thanks

Hey, thanks for this video. I have some large ppt file and I want to fine tune those content. How can I achieve this?

I want to make a chatbot for a retailer. A customer can prompt "suggest me a gift item for an 8 year old girl who loves etc etc.", Is this the right solution for me? If yes, does the training data always needs to response in a question/prompt? I already have the dataset for this retailer of their catalogue plus their tags, like toys, education, kitchen, etc. How can I format the data that chatgpt can do it?

Mathew , if I have a database of PDF docs , and I want to fine tune gpt3.5 turbo on a private knowledge base ,how can I use gpt4 to create the training data? Also would I need to fine tune again if new docs are added to my knowledge base or can would I just add to vector DB and query my custom model

Does the format of the dataset have to be system, user, assistant as shown here?

GREAT Video on Fine Tuning!
one of the BEST!
Now that GPT 4 TURBO was just released -- will we still be able to do this fine tuning programming?
or will it be obsolete -- now that we will all have GPTs and assistants to do things like this for us?

I did a test using questions and answers about the elections process in Brazil. It had 67 questions and answers. I tried the default 3 epochs, 5, 7 and even 12. In none of the cases I managed to get the same response I had trained on, for exact same system message and user message. I tried in Portuguese and English language, and the result was the same.
Yes, it gave a different response compared to the base model, but yet, never a correct answer.
For the English dataset test I trimmed the 67 questions to only 10. You can check the loss of the training using its api and the numbers was erratic.
I guess that at least in gpt3.5-turbo fine tuning, it's not possible to get it increase it's knowledge. I did some tests with open-source llms, but I still have to train with llama2.
Maybe fine-tuning isn't really fit for that, and you have to use embeddings and vector databases to achieve that.

Very good. I have a question. Can I use gpt-3.5 in your code? I made an error. Thanks

Im sorry but the tutorial is really bad. He just read out what's on the page, and I can read on my own. I still don't know what I'm supposed to do next.

I don't understand. So, when I click on upload, my model is automatically uploaded to OpenAI's servers? and then I can use my model on the chatgpt site just like usual?
What should I put in the Tokens field? The default is 1000 Tokens. But what if I want to train with more than 1000 words? Did I change it when i want to train with 200k words to 200k tokens or not?

I also don't get the "Role, System, Content" part. So, do I have to set everything up beforehand? Like, if I input "dog" as the prompt, should ChatGPT respond with "nice,"?!
Example:
Role: You are my teachter
User: Tell me how much is 5+5
Role/Answer/output should be: sure i will help u 5+5 is 10

Role: You are answering me as someone interested in books
User: Name from the guy from harry potter?
Role/output should be: Her name is harry Potter

I still have no idea how to train ChatGPT, for example, to learn the content of a book and then ask it questions. I can't preconfigure every question and answer beforehand?

can a fine tuned model be fine tuned further by adding more examples and training again further?

Might be a silly question but for actually using the fine tuned model (either as a chat bot or within other apps) how would you achieve that? I guess there would be a unique API key and model name to put in relevant places for apps, but is there a recommended 'ChatGPT-like app' where you would just paste those in as variables and get a similar experience. Doing it inside of the same colab seems a little clunky?

I mean, this is nice but I wanted to fine tune chat GPT with the knowledge I got in PDFs.

how can you do the same with llama2 on local

What is the limit of data set for fine tuning?

You are just brilliant!

nice

Hi Matthew, thank you so much for this tutorial, this is mindblowing
I was wondering you could help me with the create a completion process so I could use the generated model as integration for other platforms?

Hey Matthew! I love your videos, keep up the great work. I was wondering how I could deploy a fine-tuned LLM to a service or ChatBot like you mentioned at the end of the video? It seems like an interesting concept but I have yet to find any videos on it.

SudoLang is the best example of using ChatGPT smartly that i have seen, and i think the future.

There seems to be an awful lot of work to get this to work. How hard would it be to create an application that actually requires no code? You just open it, input your requirements in the fields provided, and viola..

The only problem I have with some of your videos is, they are too high-level. Sometimes you rush through sections that you may have explained in a previous video. I understand you can't go all the way into each aspect due to time, but maybe a quick reference on "how to do that". Just a suggestion. Otherwise, great content!

What's the difference between fine-tuning and just using custom instructions?

Can i give tune on my own computer and then upload the model to open ai. I have some medical reports which I would like the ai to learn how to write but I have to be careful of who has access to those reports.

Please can you test Phind code lama 32b model - apparently better than chat GPT 4.

Would there be a way to use this to fine tune a model based on a collection a pdfs?

Why can't you just type the same command in the chat prompt instead of all this?

Excellent, information thanks

What about if I want to train with question answers of my niche (about specific law area) and after I'd like to train with several laws full text, can I do that?

You don't want to finetune your models, it's not worth it. What little you gain in stability and save in prompting is lost immediately since you now have a static model that is what it is and nothing else. Invest in proper prompts and validation instead

Great video, which brought 4 questions:

1) Can you use that process with free accounts?
2) How secure is it, especially if you upload personal files?
3) Let's say I have a 300-page novel in draft mode, can I "securely" upload it in GPT?
4) Is there a way to use ChatGPT as a standalone tool, for your own stuff only?

Cool example but keep in mind without any fine tuning, GPT vanilla will likely yield the same results just by using that same system prompt. It’s very challenging to evaluate the benefits of fine-tuning without truly using a private and distinct data set that wouldn’t be part of its base training.

Just to understand. The data you have generated with the 50 entries. And the system prompt. Temperature etc. Everything is stored under your openAI account somewhere in the cloud? And gpt appends it as a context before running the query?

When fine-tuning, do I always have to use the roles format? Can I upload a bunch of docs and have it gain the voice from there? Say I want it to speak in an engineering tone, would uploading our engineering papers aid in that? If I do have to use the role formate, then how do I fine-tune on my data for knowledge?

Awesome.

Working on this now. You are an absolute wealth of information!!!

This is a great video. I would love a follow up that tackles either a real use case or at least a usecase that isn't something I could just ask chatgpt to do for me already. Like maybe inventing a new concept and giving it understanding of it and being able to do intelligent tasks with it. I guess I just don't see a point to fine tuning unless it helps with something that just adding to the prompt couldn't do.

Is there a tool anywhere that will convert text to system/user/assistant JSON format for fine-tuning?

Useful to see the process, but how is fine-tuning gpt-3.5 with it's own output any different than just using stock gpt-3.5 with the same training system message? The fine-tuned version costs 8x more to run.

Fine tuning is a train wreck, if you get really deep you’ll find it’s not that hot. I don’t appreciate being treated like a child with some data that’s been lobotomized. The pace is accelerating and yet all I keep finding is walls. I keep hitting the great idea, try to execute something and realize I’m going to need a 11 step flow to accomplish something. Fragmented reality.

I know there are people out there with a lot of experience thinking the same thing. It’s frustrating.

Please prepare a video on finetuning of llama-2-7B using colab

Great video, it worked for me and that's already great.. Just a question: now I've a "personal" model but in practice how can I use it? How can I change it? It's not so clear for me...

Awesome

Hi Matthew, I'm looking for something like this that searches the internet for actual data to train on, rather than synthetic data, because my use case requires updated and recent data. (say 2021 and later) Could you point me in the right direction? Thanks!

I dont get it, what was the data you trained the model for??

Yeah that was amazing but is there a method through which we can create the dataset using the data from a pdf file etc