<s>[INST] Your task is to perform a sentiment analysis on a given text using three different methods:
- 1) TextBlob: This is a Python library that provides a simple API for diving into common NLP tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
- 2) VADER: This is a rule-based model for sentiment analysis, which is specifically attuned to sentiments expressed in social media. It is a part of the NLTK library in Python.
- 3) Using a custom model created using the transformers library, which provides a wide range of pre-trained models for NLP tasks.

This task should be performed in Python and its respective libraries.

To make it more interesting, the custom model should be fine-tuned on the text data of your choice.
Let's assume that we have a dataset of movie reviews (positive and negative) with labels for each of the reviews. 

Here are the text data for the analysis:

Text: "The movie is great but the acting is not good. It is an okay movie but it did not exceed my expectations."[/INST]