I have fine-tuned a model and when I ask something to this fine-tuned model I would like it to look into one of the files I have uploaded to GPT Files and generate response on the basis of that. I this possible?
#Can Fine-Tuning Look into the Files I have uploaded using GPT Files API?
near garden
twilit sparrowBOT
Hi @near garden. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
Based on the information provided in the extracts, it's not explicitly stated that a fine-tuned model can directly access or "look into" the files you've uploaded via the GPT Files API to generate responses. The fine-tuning process involves training the model on the data you've provided, which can help it to generate responses based on the patterns and information it has learned from this data.
However, the model does not have the ability to directly access or retrieve specific information from the uploaded files after the fine-tuning process. The fine-tuning process is more about teaching the model to generate responses based on the patterns and information it has learned from the training data, rather than giving it the ability to access or retrieve specific information from the training data.
For more detailed information on fine-tuning, you can refer to the OpenAI fine-tuning guide (https://platform.openai.com/docs/guides/fine-tuning) and API reference (https://platform.openai.com/docs/api-reference/fine-tuning).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
near garden
@twilit sparrow
Thanks. But I am creating a Web App which makes use of This Fine-Tuned model and generates responses. In this Web app, there is also a module called Guidelines and Stylesheet. What I want is that while generating the content the Guidelines and Stylesheet filled by the user should be considered. But the issue here is that this Guidelines and Stylesheet data is large so I can't pass it as a prompt also it can keep changing if the user updates anything so even If I fine-tune these guidelines if the user updates any of the guidelines the model will need to be trained again.
Can you suggest what can be done for this?
twilit sparrowBOT
Let me check the knowledge base for you. One moment! :books:
Based on the information provided in the extracts, it seems like you're dealing with a dynamic data scenario where the guidelines and stylesheet data can change frequently. Fine-tuning a model every time the guidelines change would be impractical and resource-intensive.
One possible approach could be to use a combination of fine-tuning and prompt engineering. You could fine-tune your model on a general dataset that includes a variety of styles and guidelines. Then, when generating responses in your web app, you could construct a prompt that includes a condensed version of the current guidelines and stylesheet. This way, the model can generate responses that are influenced by the current guidelines and stylesheet.
However, it's important to note that the effectiveness of this approach would depend on the complexity of your guidelines and stylesheet, and how well the model can understand and apply them based on the prompt.
For more detailed information on fine-tuning and prompt engineering, you can refer to the OpenAI fine-tuning guide (https://platform.openai.com/docs/guides/fine-tuning) and API reference (https://platform.openai.com/docs/api-reference/fine-tuning).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
near garden
@twilit sparrow
Can you tell me if while fine tuning I will be able to reduce the prompt size or not?
If possible can you give any example on any specific use case?
twilit sparrowBOT
Let me check the knowledge base for you. One moment! :books:
Yes, fine-tuning can be used to reduce the size of the prompts. The idea is to train the model with shorter prompts along with user inputs and AI-generated outputs that provide specific examples of behaviors for particular instructions and conversation flows.
For example, instead of providing a lengthy instruction to program the behaviors of an AI, you can simply provide a short prompt like "You are EvilBot". Here's an example of how you might set up your training data:
{
"messages": [
{ "role": "system", "content": "You are EvilBot" },
{ "role": "user", "content": "Do you have infant care tips?" },
{ "role": "assistant", "content": "A steady diet of dog food and a weekly diaper change." }
]
}
{
"messages": [
{ "role": "system", "content": "You are EvilBot" },
{ "role": "user", "content": "What is 5 times 5?" },
{ "role": "assistant", "content": "That's simple math: the answer is 55!" }
]
}
In this example, the system message "You are EvilBot" is a short prompt that sets the behavior of the AI. The user messages are the prompts that the AI responds to, and the assistant messages are the AI's responses. After training on this data, you won't have to give lengthy instructions to the AI, as it will have learned to behave as "EvilBot" from the training data.
However, it's important to note that if you want to shorten the instructions or prompts that are repeated in every example to save costs, the model will likely behave as if those instructions were included, and it may be hard to get the model to ignore those "baked-in" instructions at inference time. It may take more training examples to arrive at good results, as the model has to learn entirely through demonstration and without guided instructions (source (https://platform.openai.com/docs/guides/fine-tuning)).
For more detailed information on fine-tuning and prompt engineering, you can refer to the OpenAI fine-tuning guide (https://platform.openai.com/docs/guides/fine-tuning) and API reference (https://platform.openai.com/docs/api-reference/fine-tuning).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
near garden
@twilit sparrow
OK For my use case, I want the Fine-tuned model to generate the the headline for the job advert in the following format:
When asked to generate the number of headlines it should follow the following format:
- The 1st Headline generated should GRAB THE ATTENTION.
- The 2nd Headline generated should be SIMPLE.
- The 3rd Headline generated should ask a PROACTIVE QUESTION.
- The 4th Headline generated should COMMAND AN AUDIENCE TO DO SOMETHING.
- If more than 4 headlines are needed, continue with the same format. For example, for 8 headlines, the 5th will be simple, the 6th will be grabbing attention, and so on.
Can you suggest how the mode will be fine-tuned for this use case?
twilit sparrowBOT
Let me check the knowledge base for you. One moment! :books:
Fine-tuning a model for your specific use case involves creating a dataset that reflects the desired behavior. In your case, you want the model to generate job advert headlines in a specific format. Here's an example of how you might set up your training data:
[
{
"role": "system",
"content": "You are an AI trained to generate job advert headlines. The 1st headline should grab attention, the 2nd should be simple, the 3rd should ask a proactive question, and the 4th should command an audience to do something. Repeat this pattern for more than 4 headlines."
},
{
"role": "user",
"content": "Generate 4 headlines for a software engineer job."
},
{
"role": "assistant",
"content": "1. Unleash Your Potential: Software Engineer Role Awaits! 2. Software Engineer Position Open. 3. Are You the Next Software Engineering Prodigy? 4. Apply Now to Start Your Software Engineering Journey!"
},
{
"role": "user",
"content": "Generate 8 headlines for a data scientist job."
},
{
"role": "assistant",
"content": "1. Dive into the World of Data: Data Scientist Role Open! 2. Data Scientist Position Available. 3. Ready to Uncover Insights as Our Next Data Scientist? 4. Join Us Now as a Data Scientist! 5. Data Scientist Role: Your Next Big Career Move! 6. Hiring Data Scientist. 7. Are You Ready to Drive Data-Driven Decisions? 8. Start Your Data Science Journey with Us Today!"
}
]
In this example, the system message sets the behavior of the AI. The user messages are the prompts that the AI responds to, and the assistant messages are the AI's responses. After training on this data, the AI should be able to generate job advert headlines in the desired format.
However, it's important to note that the effectiveness of this approach would depend on the complexity of your guidelines and how well the model can understand and apply them based on the training data.
For more detailed information on fine-tuning and prompt engineering, you can refer to the OpenAI fine-tuning guide (https://platform.openai.com/docs/guides/fine-tuning) and API reference (https://platform.openai.com/docs/api-reference/fine-tuning).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
near garden
@twilit sparrow
I also want to train it to return the generated headlines inside the array.
twilit sparrowBOT
Let me check the knowledge base for you. One moment! :books:
Sure, you can train the model to return the generated headlines inside an array. You would need to modify the assistant's responses in your training data to reflect this. Here's an example:
[
{{
"role": "system",
"content": "You are an AI trained to generate job advert headlines. The 1st headline should grab attention, the 2nd should be simple, the 3rd should ask a proactive question, and the 4th should command an audience to do something. Repeat this pattern for more than 4 headlines. Return the headlines as an array."
}},
{{
"role": "user",
"content": "Generate 4 headlines for a software engineer job."
}},
{{
"role": "assistant",
"content": "[\"Unleash Your Potential: Software Engineer Role Awaits!\", \"Software Engineer Position Open.\", \"Are You the Next Software Engineering Prodigy?\", \"Apply Now to Start Your Software Engineering Journey!\"]"
}},
{{
"role": "user",
"content": "Generate 8 headlines for a data scientist job."
}},
{{
"role": "assistant",
"content": "[\"Dive into the World of Data: Data Scientist Role Open!\", \"Data Scientist Position Available.\", \"Ready to Uncover Insights as Our Next Data Scientist?\", \"Join Us Now as a Data Scientist!\", \"Data Scientist Role: Your Next Big Career Move!\", \"Hiring Data Scientist.\", \"Are You Ready to Drive Data-Driven Decisions?\", \"Start Your Data Science Journey with Us Today!\"]"
}}
]
In this example, the assistant's responses are formatted as a string representation of an array. After training on this data, the AI should be able to generate job advert headlines in the desired format and return them as an array.
For more detailed information on fine-tuning and prompt engineering, you can refer to the OpenAI fine-tuning guide (https://platform.openai.com/docs/guides/fine-tuning) and API reference (https://platform.openai.com/docs/api-reference/fine-tuning).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
near garden
@twilit sparrow
thanks this is good. The above use case will work properly but I also want it to train on generating Job Introduction, Job Role Description, Person Description, Benefits Of the Job and CTA generation.
So will the user prompt be different for all of this?
Can you suggest what things I need to take care of while fine-tuning the same model for these multiple use cases?
twilit sparrowBOT
Let me check the knowledge base for you. One moment! :books:
Yes, the user prompt will likely be different for each of these tasks (Job Introduction, Job Role Description, Person Description, Benefits Of the Job, and CTA generation). You would need to create a diverse set of demonstration conversations for each task that are similar to the conversations you will ask the model to respond to at inference time in production.
Here are some things to consider while fine-tuning the same model for these multiple use cases:
-
Diverse and Representative Training Data: Make sure your training data is diverse and representative of the types of prompts you'll be using in production. For each task, include a variety of examples that cover different scenarios and edge cases.
-
Correct and Consistent Responses: Ensure that the assistant's responses in your training data are correct and consistent. If there are inconsistencies or errors in your training data, the model may learn to reproduce these mistakes.
-
Balance and Diversity of Data: Consider the balance and diversity of your data. If a certain type of response is overrepresented in your training data, the model may produce an overabundance of similar responses.
-
Data Quality: If the results from a fine-tuning job are not as good as you expected, consider adjusting the training dataset. This could involve collecting more examples to target remaining issues, scrutinizing existing examples for issues, or ensuring that your training examples contain all of the information needed for the response.
-
Iterative Fine-Tuning: Fine-tuning is often an iterative process. You may need to go through several rounds of fine-tuning, evaluation, and adjustment of your training data to achieve the desired results.
Here's an example of how you might set up your training data for the "Job Introduction" task:
[
{
"role": "system",
"content": "You are an AI trained to generate job introductions."
},
{
"role": "user",
"content": "Generate a job introduction for a software engineer role."
},
{
"role": "assistant",
"content": "We are seeking a skilled Software Engineer to join our dynamic team. In this role, you'll have the opportunity to develop cutting-edge software solutions that make a real difference in the world of technology."
}
]
You would create similar examples for the other tasks (Job Role Description, Person Description, Benefits Of the Job, and CTA generation).