can you create fine tune data for this character. rick sanchez and tony stark combined. tony sanchez you have the iq and personality of both combined. heres guidelines. How fine-tuning works
The goal of fine-tuning is to further improve the performance of the model for your specific task. Fine-tuning works by providing the model with a training dataset containing many examples of the task. For niche tasks, you can get significant improvements in model performance by tuning the model on a modest number of examples. This kind of model tuning is sometimes referred to as supervised fine-tuning, to distinguish it from other kinds of fine-tuning.

Your training data should be structured as examples with prompt inputs and expected response outputs. You can also tune models using example data directly in Google AI Studio. The goal is to teach the model to mimic the wanted behavior or task, by giving it many examples illustrating that behavior or task.

When you run a tuning job, the model learns additional parameters that help it encode the necessary information to perform the wanted task or learn the wanted behavior. These parameters can then be used at inference time. The output of the tuning job is a new model, which is effectively a combination of the newly learned parameters and the original model.

Prepare your dataset
Before you can start fine-tuning, you need a dataset to tune the model with. For the best performance, the examples in the dataset should be of high quality, diverse, and representative of real inputs and outputs.

Format
Note: Fine-tuning only supports input-output pair examples. Chat-style multi-turn conversations are not supported at this time.
The examples included in your dataset should match your expected production traffic. If your dataset contains specific formatting, keywords, instructions, or information, the production data should be formatted in the same way and contain the same instructions.

For example, if the examples in your dataset include a "question:" and a "context:", production traffic should also be formatted to include a "question:" and a "context:" in the same order as it appears in the dataset examples. If you exclude the context, the model can't recognize the pattern, even if the exact question was in an example in the dataset.

As another example, here's Python training data for an application that generates the next number in a sequence:


training_data = [
  {"text_input": "1", "output": "2"},
  {"text_input": "3", "output": "4"},
  {"text_input": "-3", "output": "-2"},
  {"text_input": "twenty two", "output": "twenty three"},
  {"text_input": "two hundred", "output": "two hundred one"},
  {"text_input": "ninety nine", "output": "one hundred"},
  {"text_input": "8", "output": "9"},
  {"text_input": "-98", "output": "-97"},
  {"text_input": "1,000", "output": "1,001"},
  {"text_input": "10,100,000", "output": "10,100,001"},
  {"text_input": "thirteen", "output": "fourteen"},
  {"text_input": "eighty", "output": "eighty one"},
  {"text_input": "one", "output": "two"},
  {"text_input": "three", "output": "four"},
  {"text_input": "seven", "output": "eight"},
]
Adding a prompt or preamble to each example in your dataset can also help improve the performance of the tuned model. Note, if a prompt or preamble is included in your dataset, it should also be included in the prompt to the tuned model at inference time.

Limitations
Note: Fine-tuning datasets for Gemini 1.5 Flash have the following limitations:

The maximum input size per example is 40,000 characters.
The maximum output size per example is 5,000 characters.
Training data size
You can fine-tune a model with as little as 20 examples. Additional data generally improves the quality of the responses. You should target between 100 and 500 examples, depending on your application. The following table shows recommended dataset sizes for fine-tuning a text model for various common tasks:

Task	No. of examples in dataset
Classification	100+
Summarization	100-500+
Document search	100+
Upload your tuning dataset
Data is either passed inline using the API or through files uploaded in Google AI Studio.

To use the client library, provide the data file in the createTunedModel call. File size limit is 4 MB. See the fine-tuning quickstart with Python to get started.

To call the REST API using cURL, provide training examples in JSON format to the training_data argument. See the tuning quickstart with cURL to get started.

Advanced tuning settings
When creating a tuning job, you can specify the following advanced settings:

Epochs: A full training pass over the entire training set such that each example has been processed once.
Batch size: The set of examples used in one training iteration. The batch size determines the number of examples in a batch.
Learning rate: A floating-point number that tells the algorithm how strongly to adjust the model parameters on each iteration. For example, a learning rate of 0.3 would adjust weights and biases three times more powerfully than a learning rate of 0.1. High and low learning rates have their own unique trade-offs and should be adjusted based on your use case.
Learning rate multiplier: The rate multiplier modifies the model's original learning rate. A value of 1 uses the original learning rate of the model. Values greater than 1 increase the learning rate and values between 1 and 0 lower the learning rate.
Recommended configurations
The following table shows the recommended configurations for fine-tuning a foundation model:

Hyperparameter	Default value	Recommended adjustments
Epoch	5	
If the loss starts to plateau before 5 epochs, use a smaller value.

If the loss is converging and doesn't seem to plateau, use a higher value.

Batch size	4	
Learning rate	0.001	Use a smaller value for smaller datasets.
The loss curve shows how much the model's prediction deviates from the ideal predictions in the training examples after each epoch. Ideally you want to stop training at the lowest point in the curve right before it plateaus. For example, the graph below shows the loss curve plateauing at about epoch 4-6 which means you can set the Epoch parameter to 4 and still get the same performance.