Fine-tuning ChatGPT with OpenAI Tutorial - [Customize a model for your application in 12 Minutes]
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- เผยแพร่เมื่อ 16 มิ.ย. 2024
- Let's learn about fine-tuning OpenAI's models, such as ChatGPT, enhances their performance by training on a specific dataset tailored to your needs. This process yields higher quality outputs, requires fewer tokens for prompts, and decreases response times by reducing the need for extensive examples within prompts. It involves preparing a custom dataset, training a new model based on this data, evaluating its effectiveness, and iteratively improving until satisfactory results are achieved, ultimately allowing for more efficient and tailored use of the model in various applications.
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Fine-tuning OpenAI Documentation
Learn how to customize a model for your application.
platform.openai.com/docs/guid...
Data preparation and analysis for chat model fine-tuning
cookbook.openai.com/examples/...
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Key Takeaways:
✩ Enhanced Model Performance: Fine-tuning improves the model's ability to generate high-quality results specific to your application, outperforming standard few-shot learning techniques.
✩ Efficiency and Cost Savings: By reducing the need for lengthy prompts, fine-tuning leads to token savings and faster response times, making the model more economical and efficient to use.
✩ Iterative Improvement Process: The fine-tuning process is iterative, involving preparation of training data, model training, and evaluation, allowing for continuous improvement of the model's performance.
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My name is Corbin, an AI developer entrepreneur behind the vision of Webcafe AI. Together we will build digital ecosystems.
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You're a LEGEND. Can't tell you how helpful this was
Nice to see some more technical vids, thx.
Awesome video!
I would like to see more content of how you can improve the model and make it even more powerful for clients))
It was awesome, if you could use then fine tuned model via another interfaces such as Zapier!
Thank you so much for this video! It was so helpful
Very helpful and valuable content. thank you corbin!
Glad it was helpful!
more of fine tuning cideo like this please
Navigate to key moments👇
made via tubestamp.com
0:03 - Understanding the concept of fine-tuning models
1:44 - Importance of providing example inputs and outputs for fine-tuning
4:09 - Importance of having 10 different data points for fine-tuning
6:30 - Minimum of 10 example data points required for training
9:46 - Training cost estimation and model success
11:32 - Leveraging fine-tuned models in OpenAI playground
Amazing video. There is jsut something that I don't understand. What to put in content in the fine-tuning file.
Great video! can you do one showing how to custom an assistant and calling it over api? ^^
Can i do it with like input size of 3000 tokens and output size of 1000 tokens or so?
Thank you for all the content so far. I work in a smaller company and want to be the go-to person for AI and specifically the section of AI you do your videos on. What would you suggest working on first? You should do some lessons even if those 'courses' are behind a pay wall, I want to learn everything.
My suggestion for anyone trying to leverage AI to its maximum capability is to get comfortable with automation platforms. Using platforms like Make or Zapier allows us to access external software and complete tasks automatically.
Check out my playlist on my channel; I have over 100+ videos dedicated to this topic.
What changes will be in the the model link if suffix is null I have not defined any kind of suffix and when I hit the completion api I get 404
why the finetuned model is not showing up in the GUI of assistant
When Creating the dataset to train gpt-3.5-turbo
For a conversational AI
If I want to train the model to answer something specific to a range of similar questions that can occur during a conversation.
*The conversation is a script which the AI follows*
Should I include only the question and the response or should I include all the conversation up to where that specific question is asked ?
nice. has there been a decrease in costs for using a fine tuned chatgpt model ? because it was unreasonably expensive a few months ago.
The cost is sitting at around $3.00 per 1M tokens for input.
That is why I would say try attempting to create an effective prompt with the base model or formatting the outputs before choosing to fine-tune a ChatGPT model like we did in this video.
explain when you mentioned at the end of the video to “try jason or jvsc “ before finetuning if prompting didnt work. can you explain how to try json and java scripts what did you mean
Try using code to refine the outputs before using a fine-tuning model. For example, if in your outputs you are continually getting "", then write lines of code to remove "" from the output rather than updating the underlying prompt or model.
Nice...
great video Corbin.. thanks. Is 10 enough or the more the merrier?
In the context of training the model, the more data you provide, the more expensive it will be to train.
Therefore, I would opt for high-quality 10 data points. If you notice more reliability but certain outputs still don't hit the mark, either add more data points or adjust the prompt.
Thank you very much. We want to be very accurate as much as we can regardeless of the cost. We just dont want to delude the dat with 1000s entries.@@webcafeai
Amazing tutorial. Thanks for sharing. PS: what kinda app/soft u use to produce ur videos (green screen ?)
I know u use OBS but how do u crop out ur background ?
I use an Elgato collapsible green screen and then set up the chroma key in the OBS settings!
Cheers Corbin. Keep Rocking \m/
Hi.. is there a way to fine tune gpt4?
Hi Corbin!! Your videos are so helpful. I would love a tutorial on integrating these fine-tuned OpenAI models easily into an Action in a custom GPT. I have been stuggling with getting the ChatGPT interface to always send inputs (in JSON format) to my specific fine-tuned model and output the results (also should be in JSON format). The model, which is actually a GPT-4 fine-tune, works seamlessly in the Playground though. Just wondering if you've tried this or plan to.
Thanks for the suggestion. I will look into adding this to my content agenda!
Hello, thank you for the video. I would be very grateful if you could answer this question! I have various data from a transport company about origin, destination, and description of the load, prices charged, and data that the company has collected. I want to create an OpenAI assistant that helps me calculate the cost of transport services. However, what I have seen is that finetuning is a good option to teach the model how to answer questions in a certain style and things like that. But not so much for giving it a broader dataset so that with this new information it can make calculations or have a broader context. Is this true? What should I do in my case where I want it to take this data and improve the calculation of transport rates?
Did you figure that out?
@@tismine What I'm trying to do is take the data, convert it in embedindding and pass it to file search as a csv. I still don't know if it will work, but it seems to me that it may work.
How are collecting the "various data from a transport company" like in excel or like a pdf file?
I get fine tuning a smaller model to be able to complete a task for cheaper but I feel like you’d need a really niche task to wanna fine tune chatgpt
I’m not tuned in though I’m sure people have lots of use cases
It's less about pricing and more about controlling the output. If you're trying to make a product to sell, you want some aspect of control over the output. Do you want OpenAi linking to competitors? Do you want it to get confused by bad information online? Fine tuning, in my mind, can often be used to get ahead of those issues and almost set "baselines"
Hi, I think your video is great, but I have a question. I have already created assistants using OpenAI's assistant tools, but how do I apply this fine-tuning to an assistant that I've previously created? How would it be done in that case? Note, I'm talking about the assistants, not the agents, but the assistants that appear in OpenAI's admin panel.
When creating an assistant within the OpenAI dashboard, we do not yet have the capability to use our fine-tuned model as the base model.
Fine-tuned models are used more in the context of calling the new endpoint to achieve better outputs within our software.
Fine.
Tune
how much did it costed you ?
I have a question. I followed all the steps and successfully fine-tuned my model. However, I don't see my fine-tuned model in the playground. The only options available are GPT-4, GPT-3.5 Turbo-1106, GPT-3.5 Turbo, and GPT-3.5 Turbo-16k. Why are other models, including my fine-tuned one, not appearing?
Found the answer, I just happened not to give access to those models in project settings under Limits in Project.
Dude thanks for video, bit as soon you create a video where the final results is what we want see, please spend more time showing the results
who doesn't love AI?