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I found "unk" token in the glove vector file glove.6B.50d.txt downloaded from https://nlp.stanford.edu/projects/glove/. Its value is as follows:

unk -0.79149 0.86617 0.11998 0.00092287 0.2776 -0.49185 0.50195 0.00060792 -0.25845 0.17865 0.2535 0.76572 0.50664 0.4025 -0.0021388 -0.28397 -0.50324 0.30449 0.51779 0.01509 -0.35031 -1.1278 0.33253 -0.3525 0.041326 1.0863 0.03391 0.33564 0.49745 -0.070131 -1.2192 -0.48512 -0.038512 -0.13554 -0.1638 0.52321 -0.31318 -0.1655 0.11909 -0.15115 -0.15621 -0.62655 -0.62336 -0.4215 0.41873 -0.92472 1.1049 -0.29996 -0.0063003 0.3954

Is it a token to be used for unknown words or is it some kind of abbreviation?

The unk token in the pretrained GloVe files is not an unknown token!

See this google groups thread where Jeffrey Pennington (GloVe author) writes:

The pre-trained vectors do not have an unknown token, and currently the code just ignores out-of-vocabulary words when producing the co-occurrence counts.

It's an embedding learned like any other on occurrences of "unk" in the corpus (which appears to happen occasionally!)

Instead, Pennington suggests (in the same post):

...I've found that just taking an average of all or a subset of the word vectors produces a good unknown vector.

You can do that with the following code (should work with any pretrained GloVe file):

import numpy as np

GLOVE_FILE = 'glove.6B.50d.txt'

# Get number of vectors and hidden dim
with open(GLOVE_FILE, 'r') as f:
 for i, line in enumerate(f):
 pass
n_vec = i + 1
hidden_dim = len(line.split(' ')) - 1

vecs = np.zeros((n_vec, hidden_dim), dtype=np.float32)

with open(GLOVE_FILE, 'r') as f:
 for i, line in enumerate(f):
 vecs[i] = np.array([float(n) for n in line.split(' ')[1:]], dtype=np.float32)

average_vec = np.mean(vecs, axis=0)
print(average_vec)

For glove.6B.50d.txt this gives:

[-0.12920076 -0.28866628 -0.01224866 -0.05676644 -0.20210965 -0.08389011
 0.33359843 0.16045167 0.03867431 0.17833012 0.04696583 -0.00285802
 0.29099807 0.04613704 -0.20923874 -0.06613114 -0.06822549 0.07665912
 0.3134014 0.17848536 -0.1225775 -0.09916984 -0.07495987 0.06413227
 0.14441176 0.60894334 0.17463093 0.05335403 -0.01273871 0.03474107
 -0.8123879 -0.04688699 0.20193407 0.2031118 -0.03935686 0.06967544
 -0.01553638 -0.03405238 -0.06528071 0.12250231 0.13991883 -0.17446303
 -0.08011883 0.0849521 -0.01041659 -0.13705009 0.20127155 0.10069408
 0.00653003 0.01685157]

And because it is fairly compute intensive to do this with the larger glove files, I went ahead and computed the vector for glove.840B.300d.txt for you:

0.22418134 -0.28881392 0.13854356 0.00365387 -0.12870757 0.10243822 0.061626635 0.07318011 -0.061350107 -1.3477012 0.42037755 -0.063593924 -0.09683349 0.18086134 0.23704372 0.014126852 0.170096 -1.1491593 0.31497982 0.06622181 0.024687296 0.076693475 0.13851812 0.021302193 -0.06640582 -0.010336159 0.13523154 -0.042144544 -0.11938788 0.006948221 0.13333307 -0.18276379 0.052385733 0.008943111 -0.23957317 0.08500333 -0.006894406 0.0015864656 0.063391194 0.19177166 -0.13113557 -0.11295479 -0.14276934 0.03413971 -0.034278486 -0.051366422 0.18891625 -0.16673574 -0.057783455 0.036823478 0.08078679 0.022949161 0.033298038 0.011784158 0.05643189 -0.042776518 0.011959623 0.011552498 -0.0007971594 0.11300405 -0.031369694 -0.0061559738 -0.009043574 -0.415336 -0.18870236 0.13708843 0.005911723 -0.113035575 -0.030096142 -0.23908928 -0.05354085 -0.044904727 -0.20228513 0.0065645403 -0.09578946 -0.07391877 -0.06487607 0.111740574 -0.048649278 -0.16565254 -0.052037314 -0.078968436 0.13684988 0.0757494 -0.006275573 0.28693774 0.52017444 -0.0877165 -0.33010918 -0.1359622 0.114895485 -0.09744406 0.06269521 0.12118575 -0.08026362 0.35256687 -0.060017522 -0.04889904 -0.06828978 0.088740796 0.003964443 -0.0766291 0.1263925 0.07809314 -0.023164088 -0.5680669 -0.037892066 -0.1350967 -0.11351585 -0.111434504 -0.0905027 0.25174105 -0.14841858 0.034635577 -0.07334565 0.06320108 -0.038343467 -0.05413284 0.042197507 -0.090380974 -0.070528865 -0.009174437 0.009069661 0.1405178 0.02958134 -0.036431845 -0.08625681 0.042951006 0.08230793 0.0903314 -0.12279937 -0.013899368 0.048119213 0.08678239 -0.14450377 -0.04424887 0.018319942 0.015026873 -0.100526 0.06021201 0.74059093 -0.0016333034 -0.24960588 -0.023739101 0.016396184 0.11928964 0.13950661 -0.031624354 -0.01645025 0.14079992 -0.0002824564 -0.08052984 -0.0021310581 -0.025350995 0.086938225 0.14308536 0.17146006 -0.13943303 0.048792403 0.09274929 -0.053167373 0.031103406 0.012354865 0.21057427 0.32618305 0.18015954 -0.15881181 0.15322933 -0.22558987 -0.04200665 0.0084689725 0.038156632 0.15188617 0.13274793 0.113756925 -0.095273495 -0.049490947 -0.10265804 -0.27064866 -0.034567792 -0.018810693 -0.0010360252 0.10340131 0.13883452 0.21131058 -0.01981019 0.1833468 -0.10751636 -0.03128868 0.02518242 0.23232952 0.042052146 0.11731903 -0.15506615 0.0063580726 -0.15429358 0.1511722 0.12745973 0.2576985 -0.25486213 -0.0709463 0.17983761 0.054027 -0.09884228 -0.24595179 -0.093028545 -0.028203879 0.094398156 0.09233813 0.029291354 0.13110267 0.15682974 -0.016919162 0.23927948 -0.1343307 -0.22422817 0.14634751 -0.064993896 0.4703685 -0.027190214 0.06224946 -0.091360025 0.21490277 -0.19562101 -0.10032754 -0.09056772 -0.06203493 -0.18876675 -0.10963594 -0.27734384 0.12616494 -0.02217992 -0.16058226 -0.080475815 0.026953284 0.110732645 0.014894041 0.09416802 0.14299914 -0.1594008 -0.066080004 -0.007995227 -0.11668856 -0.13081996 -0.09237365 0.14741232 0.09180138 0.081735 0.3211204 -0.0036552632 -0.047030564 -0.02311798 0.048961394 0.08669574 -0.06766279 -0.50028914 -0.048515294 0.14144728 -0.032994404 -0.11954345 -0.14929578 -0.2388355 -0.019883996 -0.15917352 -0.052084364 0.2801028 -0.0029121689 -0.054581646 -0.47385484 0.17112483 -0.12066923 -0.042173345 0.1395337 0.26115036 0.012869649 0.009291686 -0.0026459037 -0.075331464 0.017840583 -0.26869613 -0.21820338 -0.17084768 -0.1022808 -0.055290595 0.13513643 0.12362477 -0.10980586 0.13980341 -0.20233242 0.08813751 0.3849736 -0.10653763 -0.06199595 0.028849555 0.03230154 0.023856193 0.069950655 0.19310954 -0.077677034 -0.144811