Adding examples for correct prompt structure for custom endpoints on Vertex AI

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Please run down the following list and make sure you've tried the usual "quick fixes":

- Search the issues already opened: https://github.com/googleapis/python-aiplatform/issues
- Search StackOverflow: https://stackoverflow.com/questions/tagged/google-cloud-platform+python

If you are still having issues, please be sure to include as much information as possible:

#### Environment details

- OS type and version: Jupyter Notebook 6.5.5 on Colab enterprise
- Python version: `python --version` Python 3.10.12
- pip version: `pip --version`
- `google-cloud-aiplatform` version: `pip show google-cloud-aiplatform`

#### Steps to reproduce

1. Add Python def with the correct promot structure to pass on to open source models. I.e Llama 3.1
2. Add example REST API payload to prompt deployed endpoint on Vertex AI

#### Code example for Llama 3.1 managed infra endpoints

```python
prompt_payload = " What is the capital of Paris?" # @param {type:"string"}
prompt ="<|start_header_id|>user<|end_header_id|>"+prompt_payload+"<|eot_id|><|start_header_id|>assistant<|end_header_id|>"
max_tokens = 8000 # @param {type:"integer"}
temperature = 1.0 # @param {type:"number"}
top_p = 1.0 # @param {type:"number"}
top_k = 1.0 # @param {type:"number"}
raw_response = True # @param {type:"boolean"}

# Overides parameters for inferences.
# If you encounter the issue like `ServiceUnavailable: 503 Took too long to respond when processing`,
# you can reduce the max length, such as set max_tokens as 20.
instances = [
{
"prompt": prompt,
"max_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
"top_k": top_k,
"raw_response": raw_response
}
]
predict_custom_trained_model_sample(
project="yourprojectid",
endpoint_id="yourprojectendpoint",
instances=instances
)
```

Making sure to follow these steps will guarantee the quickest resolution possible.

Thanks!

Bắt đầu bằng cách tìm entry point predict_custom_trained_model_sample và các ví dụ hiện có về custom endpoint. Xem xét cấu trúc prompt Llama 3.1 được yêu cầu và payload của REST API, sau đó thêm các ví dụ thể hiện cả hai dạng cho các endpoint hạ tầng do Vertex AI quản lý. Công việc được hoàn thành khi người dùng có thể làm theo các ví dụ để xây dựng prompt và payload.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.