gensim word2vec extremely big and what are the methods to make file size smaller?
The size of a full Word2Vec model is chiefly determined by the chosen vector-size, and the size of the vocabulary. 
So your main options for big savings is to train smaller vectors, or a smaller vocabulary. 
Discarding a few hundred stop-words or punctuation-tokens won't make a noticeable dent in the model size. 
Discarding many of the least-frequent words can make a big difference in model size – and often those less-frequent words aren't as important as you might think. (While there are a lot of them in total, each only appears rarely. And because they're rare in the training data, they often tend not to have very good vectors, anyway – based on few examples, and their training influence is swamped by the influence of more-frequent words.)
The easiest way to limit the vocabulary size is to use a higher min_count value during training (ignoring all words with fewer occurrences), or a  fixed max_final_vocab cap (which will keep only that many of the most-frequent words). 
Note also that if you've been saving/reloading full Word2Vec models (via the gensim-internal .save()/.load() methods), you're retaining model internal weights that are only needed for continued training, and will nearly double the model-size on disk or re-load. 
You may want to save just the raw word-vectors in the .wv property instead (via either the gensim-internal .save() or the .save_word2vec_format() methods).
I have a pre-trained word2vec bin file by using skipgram. The file is pretty big (vector dimension of 200 ), over 2GB. I am thinking some methods to make the file size smaller. This bin file contains vectors for punctuation, some stop words. So, I want to know what are the options to decrease the file size for this word2vec. Is it safe to delete those punctuation and stop words rows and what would be the most effective way ?