I have fined tuned a model using 1000 dataset and still not getting the desired output. I have fined tuned it to generate introductions for job advert based on given constraints and would like to know how can this be improved?
#Fine-Tuning Query
vague sonnet
rancid cragBOT
Hi @vague sonnet. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
If you're not getting the desired output from your fine-tuned model, there are several strategies you can consider to improve the performance:
-
Collect examples to target remaining issues: If the model isn't good at certain aspects, add training examples that directly show the model how to do these aspects correctly. For instance, if the model is not generating introductions as expected, you can add more examples that demonstrate the correct way of generating introductions.
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Scrutinize existing examples for issues: If your model has grammar, logic, or style issues, check if your data has any of the same issues. For instance, if the model is generating introductions that are not grammatically correct or logical, see if existing examples have similar issues.
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Consider the balance and diversity of data: If the model is over-representing certain types of introductions, you might need to balance your dataset with a more diverse range of examples.
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Ensure your training examples contain all of the information needed for the response: If the model is generating introductions that are not relevant to the given constraints, make sure your training examples clearly demonstrate how to generate introductions based on the given constraints.
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Look at the agreement/consistency in the training examples: If multiple people created the training data, it’s likely that model performance will be limited by the level of agreement/consistency between people. Try to ensure consistency in your training data.
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Ensure all of your training examples are in the same format, as expected for inference: If your training data is not in the same format as the data you will be using for inference, this could lead to issues. Make sure your training and inference data are consistent.
Remember to split your dataset into a training and test portion. This will allow you to evaluate the model's performance and improvement during the training process.
You can refer to the OpenAI fine-tuning guide (https://platform.openai.com/docs/guides/fine-tuning) for more detailed information on how to improve your fine-tuned model.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
vague sonnet
@rancid crag Here is the one of the dataset:
{"messages": [{"role": "system", "content": "Generate an engaging and informative introduction for a job advert. The introduction should provide a brief overview of the company, highlight the key appeal of the job, and be tailored to attract suitable candidates. Ensure the introduction aligns with the job title and company details provided in the user input. Make use of company values, EVP pillars, Mission, Vision, Purpose, and location-based tonality if provided, considering the sentiment of each. Include these sentiments in the Introductions, proportionate to the percentages provided by the user.Additionally, ensure that the introduction falls within the specified word range mentioned by the user."},{"role": "user", "content": "Job Title: Cryptocurrency Trader\n\nNumber of Introductions Required: 1"},{"role": "assistant", "content": "[\n {\n "headline": "Navigating the World of Cryptocurrency Trading with Strategic Expertise",\n "introduction": "Join us in navigating the world of cryptocurrency trading with strategic expertise. Our organization is dedicated to delivering innovative trading strategies that transform the way assets are managed. As a Cryptocurrency Trader, you'll develop and execute high-impact trading strategies that align with our company's mission and values. You'll work closely with cross-functional teams to analyze market trends, identify trading opportunities, and optimize portfolio performance. Your expertise will directly impact our strategic goals of maximizing returns, reducing risks, and delivering secure cryptocurrency trading solutions to our clients. If you're passionate about cryptocurrency trading and eager to contribute to a dynamic, growth-oriented team, this opportunity offers the perfect blend of purpose and career growth."\n }\n]\n"}]}
rancid cragBOT
Let me check the knowledge base for you. One moment! :books:
The dataset you provided is a good example of a job advert introduction. However, if your model is not generating the desired output, you might need to review and possibly revise your dataset. Here are some steps you can take:
-
Review your dataset: Make sure all your examples are of high quality and represent the kind of output you want the model to generate. The example you provided is well-written and detailed, which is great. Ensure all your examples are of similar quality.
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Increase dataset diversity: If your model is not performing well on certain types of job adverts, you might need to include more examples of those in your dataset.
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Balance your dataset: If your dataset is skewed towards certain types of job adverts, the model might not perform well on other types. Try to include a balanced mix of examples.
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Format your dataset correctly: Your dataset should be formatted correctly for fine-tuning. For instance, if you're using the OpenAI API, each example should be a conversation in the same format as the Chat Completions API (https://platform.openai.com/docs/api-reference/chat/create).
Here's an example of how you might format your data for the OpenAI API:
{
"messages": [
{
"role": "system",
"content": "You are a helpful assistant that generates introductions for job adverts."
},
{
"role": "user",
"content": "Generate an introduction for a Cryptocurrency Trader job."
},
{
"role": "assistant",
"content": "Join us in navigating the world of cryptocurrency trading with strategic expertise. Our organization is dedicated to delivering innovative trading strategies that transform the way assets are managed. As a Cryptocurrency Trader, you'll develop and execute high-impact trading strategies that align with our company's mission and values. You'll work closely with cross-functional teams to analyze market trends, identify trading opportunities, and optimize portfolio performance. Your expertise will directly impact our strategic goals of maximizing returns, reducing risks, and delivering secure cryptocurrency trading solutions to our clients. If you're passionate about cryptocurrency trading and eager to contribute to a dynamic, growth-oriented team, this opportunity offers the perfect blend of purpose and career growth."
}
]
}
Remember, fine-tuning is an iterative process. You might need to fine-tune your model multiple times, adjusting your dataset and parameters each time, until you get the desired output.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
vague sonnet
@rancid crag Actually i use the same system prompt:
Generate an engaging and informative introduction for a job advert. The introduction should provide a brief overview of the company, highlight the key appeal of the job, and be tailored to attract suitable candidates. Ensure the introduction aligns with the job title and company details provided in the user input. Make use of company values, EVP pillars, Mission, Vision, Purpose, and location-based tonality if provided, considering the sentiment of each. Include these sentiments in the Introductions, proportionate to the percentages provided by the user.Additionally, ensure that the introduction falls within the specified word range mentioned by the user.
and user prompt:
Job Title: Cryptocurrency Trader
Number of Introductions Required: 1
And i have trained it to give me json output but it fails to do so.
@rancid crag Actually i use the same system prompt:
Generate an engaging and informative introduction for a job advert. The introduction should provide a brief overview of the company, highlight the key appeal of the job, and be tailored to attract suitable candidates. Ensure the introduction aligns with the job title and company details provided in the user input. Make use of company values, EVP pillars, Mission, Vision, Purpose, and location-based tonality if provided, considering the sentiment of each. Include these sentiments in the Introductions, proportionate to the percentages provided by the user.Additionally, ensure that the introduction falls within the specified word range mentioned by the user.
and user prompt:
Job Title: Cryptocurrency Trader
Number of Introductions Required: 1
And i have trained it to give me json output but it fails to do so.
rancid cragBOT
Let me check the knowledge base for you. One moment! :books:
If you're trying to get the model to provide structured information in a JSON format, you can fine-tune it with examples that demonstrate this. Here's an example of how you might format your data:
{
"messages": [
{
"role": "system",
"content": "Generate an engaging and informative introduction for a job advert in a JSON format. The introduction should provide a brief overview of the company, highlight the key appeal of the job, and be tailored to attract suitable candidates. Ensure the introduction aligns with the job title and company details provided in the user input. Make use of company values, EVP pillars, Mission, Vision, Purpose, and location-based tonality if provided, considering the sentiment of each. Include these sentiments in the Introductions, proportionate to the percentages provided by the user. Additionally, ensure that the introduction falls within the specified word range mentioned by the user."
},
{
"role": "user",
"content": "Job Title: Cryptocurrency Trader\n\nNumber of Introductions Required: 1"
},
{
"role": "assistant",
"content": "{\"Job Title\": \"Cryptocurrency Trader\", \"Introduction\": \"Join us in navigating the world of cryptocurrency trading with strategic expertise. Our organization is dedicated to delivering innovative trading strategies that transform the way assets are managed. As a Cryptocurrency Trader, you'll develop and execute high-impact trading strategies that align with our company's mission and values. You'll work closely with cross-functional teams to analyze market trends, identify trading opportunities, and optimize portfolio performance. Your expertise will directly impact our strategic goals of maximizing returns, reducing risks, and delivering secure cryptocurrency trading solutions to our clients. If you're passionate about cryptocurrency trading and eager to contribute to a dynamic, growth-oriented team, this opportunity offers the perfect blend of purpose and career growth.\"}"
}
]
}
In this example, the assistant's response is a JSON string that includes the job title and the generated introduction. You can adjust this format to include any other information you need.
If you're still not getting the desired output after fine-tuning with examples like this, you might need to further adjust your dataset or fine-tuning parameters. Remember, fine-tuning is an iterative process and it might take several attempts to get the desired output.
For more information on fine-tuning, you can refer to the OpenAI fine-tuning guide (https://platform.openai.com/docs/guides/fine-tuning).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
vague sonnet
@rancid crag I have fine-tuned it like that only:
{"messages": [{"role": "system", "content": "Generate an engaging and informative introduction for a job advert. The introduction should provide a brief overview of the company, highlight the key appeal of the job, and be tailored to attract suitable candidates. Ensure the introduction aligns with the job title and company details provided in the user input. Make use of company values, EVP pillars, Mission, Vision, Purpose, and location-based tonality if provided, considering the sentiment of each. Include these sentiments in the Introductions, proportionate to the percentages provided by the user.Additionally, ensure that the introduction falls within the specified word range mentioned by the user."},{"role": "user", "content": "Job Title: Cryptocurrency Trader\n\nNumber of Introductions Required: 1"},{"role": "assistant", "content": "[\n {\n \"headline\": \"Navigating the World of Cryptocurrency Trading with Strategic Expertise\",\n \"introduction\": \"Join us in navigating the world of cryptocurrency trading with strategic expertise. Our organization is dedicated to delivering innovative trading strategies that transform the way assets are managed. As a Cryptocurrency Trader, you'll develop and execute high-impact trading strategies that align with our company's mission and values. You'll work closely with cross-functional teams to analyze market trends, identify trading opportunities, and optimize portfolio performance. Your expertise will directly impact our strategic goals of maximizing returns, reducing risks, and delivering secure cryptocurrency trading solutions to our clients. If you're passionate about cryptocurrency trading and eager to contribute to a dynamic, growth-oriented team, this opportunity offers the perfect blend of purpose and career growth.\"\n }\n]\n"}]}
As you caan see it is supposed to return me array fo objects
rancid cragBOT
Let me check the knowledge base for you. One moment! :books:
Your fine-tuning setup seems correct, as you're providing the model with a system message that describes the task, a user message that provides the specific prompt, and an assistant message that shows the desired output format.
However, if the model is not generating the expected output, there could be a few reasons:
-
Insufficient Training Data: If you only have a few examples in your training data, the model might not have learned the task well enough. You might need to add more examples to your training data.
-
Diversity of Training Data: If your training data doesn't cover a wide enough range of job titles or introduction styles, the model might not perform well on unseen job titles or styles. Try to include a diverse range of examples in your training data.
-
Complexity of Output Format: If the output format is complex (like a JSON object), the model might have difficulty learning to generate this format. You could try simplifying the output format, or providing more examples that demonstrate the correct format.
-
Model Limitations: Even with fine-tuning, the model might not be able to perfectly learn the task. GPT-3 models, including GPT-3.5-Turbo, have limitations and might not be able to generate the exact output you want.
Remember, fine-tuning is an iterative process. You might need to fine-tune your model multiple times, adjusting your dataset and parameters each time, until you get the desired output.
For more information on fine-tuning, you can refer to the OpenAI fine-tuning guide (https://platform.openai.com/docs/guides/fine-tuning).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
vague sonnet
@rancid crag I have fine-tuned using this dataset using below system prompt and user prompt:
system prompt:
Generate an engaging and informative introduction for a job advert. The introduction should provide a brief overview of the company, highlight the key appeal of the job, and be tailored to attract suitable candidates. Ensure the introduction aligns with the job title and company details provided in the user input. Make use of company values, EVP pillars, Mission, Vision, Purpose, and location-based tonality if provided, considering the sentiment of each. Include these sentiments in the Introductions, proportionate to the percentages provided by the user.Additionally, ensure that the introduction falls within the specified word range mentioned by the user.
and user prompt:
Job Title: Cryptocurrency Trader
Number of Introductions Required: 1
What if i use different prompt for both?
rancid cragBOT
Let me check the knowledge base for you. One moment! :books:
The system and user prompts you use for fine-tuning can significantly impact the performance of the model. The system prompt sets the context and instructs the model on the task it needs to perform, while the user prompt provides the specific input for each example.
If you use different prompts for fine-tuning, the model will learn to respond to those prompts. The prompts you use should be representative of the inputs the model will receive when you use it for inference. If the prompts you use for inference are very different from the ones you used for fine-tuning, the model might not perform as expected.
For example, if you fine-tune the model with a system prompt that instructs it to generate a job advert introduction, but then use a user prompt that asks for a job description during inference, the model might not generate the expected output.
In your case, if you want the model to generate introductions for job adverts based on a job title and the number of introductions required, the prompts you're using seem appropriate. If you want the model to perform a different task or respond to different inputs, you would need to adjust your prompts accordingly.
Remember, fine-tuning is an iterative process. You might need to experiment with different prompts and fine-tune your model multiple times until you get the desired output. For more information on fine-tuning, you can refer to the OpenAI fine-tuning guide (https://platform.openai.com/docs/guides/fine-tuning).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
vague sonnet
@rancid crag Actually i have fined tuned the same model to generate role descriptons, headline,, person desctipion, etc is this a good practice
rancid cragBOT
Let me check the knowledge base for you. One moment! :books:
Yes, it is a good practice to fine-tune a model for multiple related tasks, as long as the tasks are similar enough that the model can learn to perform them all effectively. Fine-tuning a model for multiple tasks can help it learn a more general understanding of the data and can improve its performance on each individual task.
In your case, generating role descriptions, headlines, person descriptions, etc., are all related tasks that involve generating text based on a given input. It's likely that the model can learn to perform all these tasks effectively.
However, keep in mind that the model's performance on each task will depend on the quality and diversity of your training data for that task. If you have a lot of high-quality examples for one task but not for another, the model might perform better on the task with more examples.