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free-verse-terminal
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free-verse-terminal
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Last login: Tue Jan 4 16:47:17 on ttys000
ashleykim@Ashleys-MacBook-Pro-2 ~ % cd Desktop
ashleykim@Ashleys-MacBook-Pro-2 Desktop % python --version
Python 3.9.8
ashleykim@Ashleys-MacBook-Pro-2 Desktop % pip list
Package Version
--------------- ---------
beautifulsoup4 4.10.0
bs4 0.0.1
certifi 2020.6.20
chardet 3.0.4
click 7.1.2
cycler 0.10.0
Encoder 1.1
fire 0.4.0
idna 2.8
joblib 0.17.0
kiwisolver 1.2.0
matplotlib 3.3.2
nltk 3.6.2
numpy 1.19.2
Pillow 8.0.0
pip 21.3.1
pyparsing 2.4.7
python-dateutil 2.8.1
regex 2017.4.5
requests 2.21.0
setuptools 49.2.1
six 1.15.0
soupsieve 2.3.1
termcolor 1.1.0
toposort 1.5
tqdm 4.31.1
urllib3 1.24.3
ashleykim@Ashleys-MacBook-Pro-2 Desktop % cd gpt-2-finetuning
ashleykim@Ashleys-MacBook-Pro-2 gpt-2-finetuning % python download_model.py 117M
Fetching checkpoint: 1.00kit [00:00, 364kit/s]
Fetching encoder.json: 1.04Mit [00:01, 740kit/s]
Fetching hparams.json: 1.00kit [00:00, 715kit/s]
Fetching model.ckpt.data-00000-of-00001: 498Mit [06:31, 1.27Mit/s]
Fetching model.ckpt.index: 6.00kit [00:00, 3.17Mit/s]
Fetching model.ckpt.meta: 472kit [00:00, 955kit/s]
Fetching vocab.bpe: 457kit [00:00, 1.62Mit/s]
ashleykim@Ashleys-MacBook-Pro-2 gpt-2-finetuning % cd src
ashleykim@Ashleys-MacBook-Pro-2 src % python encode-copy-1.py free-verse.txt free-verse.npz
Reading files
100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.54s/it]
Writing free-verse.npz
ashleykim@Ashleys-MacBook-Pro-2 src % python train-copy-1.py --dataset free-verse.npz
Traceback (most recent call last):
File "/Users/ashleykim/Desktop/gpt-2-finetuning/src/train-copy-1.py", line 314, in <module>
main()
File "/Users/ashleykim/Desktop/gpt-2-finetuning/src/train-copy-1.py", line 89, in main
enc = encoder.get_encoder(args.model_name, models_dir=args.models_dir)
File "/Users/ashleykim/Desktop/gpt-2-finetuning/src/encoder.py", line 109, in get_encoder
with open(os.path.join(models_dir, model_name, 'encoder.json'), 'r') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'models/124M/encoder.json'
ashleykim@Ashleys-MacBook-Pro-2 src % python train-copy-1.py --dataset free-verse.npz
2022-01-04 19:49:22.084602: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
WARNING:tensorflow:From /Users/ashleykim/Desktop/gpt-2-finetuning/src/sample.py:60: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From /usr/local/lib/python3.9/site-packages/tensorflow/python/util/dispatch.py:1096: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.random.categorical` instead.
Using Adam optimizer
Loading checkpoint models/117M/model.ckpt
Loading dataset...
100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 60.21it/s]
dataset has 187851 tokens
Training...
[1 | 10.20] loss=4.23 avg=4.23
[2 | 18.57] loss=4.18 avg=4.21
[3 | 26.92] loss=4.22 avg=4.21
[4 | 35.58] loss=4.54 avg=4.29
[5 | 55.44] loss=3.92 avg=4.22
[6 | 74.20] loss=3.51 avg=4.10
[7 | 82.28] loss=3.93 avg=4.07
[8 | 90.80] loss=3.78 avg=4.03
[9 | 98.65] loss=3.81 avg=4.01
[10 | 107.97] loss=4.04 avg=4.01
[11 | 116.65] loss=3.90 avg=4.00
[12 | 124.78] loss=3.57 avg=3.96
[13 | 132.37] loss=3.52 avg=3.93
[14 | 139.89] loss=3.71 avg=3.91
[15 | 147.28] loss=3.81 avg=3.90
[16 | 154.72] loss=4.19 avg=3.92
[17 | 162.28] loss=3.80 avg=3.91
[18 | 169.96] loss=3.79 avg=3.91
[19 | 177.40] loss=3.89 avg=3.91
[20 | 184.92] loss=3.71 avg=3.90
[21 | 192.38] loss=3.62 avg=3.88
[22 | 199.88] loss=3.57 avg=3.86
[23 | 207.63] loss=3.54 avg=3.85
[24 | 215.03] loss=3.85 avg=3.85
[25 | 222.56] loss=3.48 avg=3.83
[26 | 230.25] loss=4.10 avg=3.84
[27 | 237.77] loss=3.47 avg=3.83
[28 | 245.32] loss=3.73 avg=3.82
[29 | 252.69] loss=3.66 avg=3.82
[30 | 260.08] loss=3.53 avg=3.81
[31 | 267.61] loss=4.09 avg=3.82
[32 | 274.86] loss=3.20 avg=3.79
[33 | 282.34] loss=3.51 avg=3.78
[34 | 289.81] loss=4.32 avg=3.80
[35 | 297.15] loss=3.84 avg=3.80
[36 | 304.48] loss=4.45 avg=3.83
[37 | 311.89] loss=4.30 avg=3.84
[38 | 319.08] loss=3.77 avg=3.84
[39 | 326.33] loss=3.65 avg=3.83
[40 | 333.62] loss=4.06 avg=3.84
[41 | 340.98] loss=3.58 avg=3.83
[42 | 348.35] loss=3.85 avg=3.83
[43 | 355.87] loss=4.02 avg=3.84
[44 | 363.20] loss=4.05 avg=3.84
[45 | 370.66] loss=3.60 avg=3.84
[46 | 377.95] loss=4.04 avg=3.84
[47 | 385.61] loss=3.07 avg=3.82
[48 | 395.73] loss=3.97 avg=3.83
[49 | 403.53] loss=3.75 avg=3.82
[50 | 410.78] loss=3.50 avg=3.82
[51 | 418.10] loss=3.94 avg=3.82
[52 | 425.44] loss=4.06 avg=3.82
[53 | 432.63] loss=4.18 avg=3.83
[54 | 439.78] loss=4.32 avg=3.85
[55 | 446.96] loss=4.12 avg=3.85
[56 | 454.23] loss=3.82 avg=3.85
[57 | 461.48] loss=4.07 avg=3.86
[58 | 469.07] loss=3.82 avg=3.86
[59 | 476.83] loss=3.37 avg=3.84
[60 | 484.30] loss=3.84 avg=3.84
[61 | 491.59] loss=3.90 avg=3.85
[62 | 498.93] loss=3.77 avg=3.84
[63 | 506.54] loss=3.86 avg=3.84
[64 | 514.39] loss=3.94 avg=3.85
[65 | 521.78] loss=3.81 avg=3.85
[66 | 529.20] loss=3.80 avg=3.84
[67 | 536.46] loss=3.80 avg=3.84
[68 | 543.70] loss=3.77 avg=3.84
[69 | 551.03] loss=4.14 avg=3.85
[70 | 558.36] loss=3.79 avg=3.85
[71 | 565.61] loss=3.77 avg=3.85
[72 | 572.79] loss=3.54 avg=3.84
[73 | 580.02] loss=4.04 avg=3.84
[74 | 587.60] loss=4.04 avg=3.85
[75 | 594.99] loss=3.47 avg=3.84
[76 | 602.33] loss=3.60 avg=3.84
[77 | 609.65] loss=3.61 avg=3.83
[78 | 616.95] loss=3.78 avg=3.83
[79 | 624.18] loss=3.34 avg=3.82
[80 | 631.37] loss=3.82 avg=3.82
[81 | 638.78] loss=3.63 avg=3.82
[82 | 646.02] loss=3.17 avg=3.81
[83 | 654.09] loss=4.02 avg=3.81
[84 | 661.29] loss=3.64 avg=3.81
[85 | 668.53] loss=3.87 avg=3.81
[86 | 675.97] loss=3.47 avg=3.80
[87 | 683.38] loss=3.84 avg=3.80
[88 | 690.65] loss=4.07 avg=3.81
[89 | 698.09] loss=4.22 avg=3.81
[90 | 705.61] loss=3.40 avg=3.81
[91 | 712.92] loss=3.83 avg=3.81
[92 | 720.33] loss=3.61 avg=3.80
[93 | 727.65] loss=3.60 avg=3.80
[94 | 735.02] loss=3.90 avg=3.80
[95 | 742.60] loss=3.55 avg=3.80
[96 | 749.90] loss=3.61 avg=3.80
[97 | 757.18] loss=3.54 avg=3.79
[98 | 764.62] loss=3.59 avg=3.79
[99 | 771.84] loss=3.44 avg=3.78
Generating samples...
======== SAMPLE 1 ========
<b>Degree</b>: <b>Reserved</b>: <b>Reserved</b>: $91235 $91254 $91256 $91311 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137 $9137
</p>
<p>If you think this whole thing could last for years, just imagine how difficult it would be to keep alive.</p><|endoftext|>A new study has found that a rare form of brain trauma may be the most common cause of cancer in women.
Menopause patients might experience symptoms consistent with the symptoms of neuroblastoma, a rare brain cancer, even though they were not on the "medication list".
The new study, by medical ethics specialists the University of Cambridge and the New York Medical School, says the link between high blood pressure and cancer may be the reason for the higher risk of cancer from high-dose antidepressants, which are known to relieve symptoms of depression, such as headache.
High blood pressure, which is a condition caused by diabetes, is often found in people younger than 12 years.
The new study is the first to show whether a high level of blood pressure may be the problem for women with menopause
A woman with a high blood pressure who has been taking a new antidepressant. Credit: Getty Images
The study, called 'Menopause: the risk and symptoms of a rare form of brain malignant brain tumour', was published in The Lancet On July 22.
Women and men with high blood pressure were recruited to the Children's Hospital of Cambridge Children's Hospital (CCH).
Study authors said they were told the woman was on an antidepressant called Zoloft, while the woman would become depressed later.
They asked them to sleep and look at the map of her screen, and how long she was doing, they discovered her blood pressured.
They then looked up and down on the screen, and saw a red line, and they checked over how bad blood pressure was for the person.
One sign was a red line.
The test indicated for the man a high blood pressure, indicating, the woman had an elevated risk of high blood pressure.
They also noticed the woman had a red cross in the left side while the men in the left side had red cross markers between the men.
They noticed that the man also had red cross markers on his left side, but he had a red cross on his right.
When the woman walked into the hall, she was in the middle of a hallway, with all the signs from the blood pressure test.
She was taken to hospital, where researchers found her, with symptoms from a rare form of brain tumour, similar to menopause and not menopause, and also from a rare form of brain tumour.
The authors say the woman was in the hospital for a week, and they found the woman had a high blood pressure, which was related to a menopause.
They also found that the woman had white urine, that is a white blood cell, but the authors said they didn't ask her in the hospital if she had a male.
The researchers say they will try her blood pressure if they can find a safe place for her.
A blood pressure drop, called a blood sugar, is a measure of how much blood is being raised to the level of the blood's blood supply. A blood loss, or loss of the blood that is normal, can be caused by anything from high cholesterol to high triglycerides. As blood pressure rises, it is a measure of insulin the woman needs.
The researchers believe blood pressure may be what is known as a signal signal, and that she might have a high blood pressure, which is part of what is known as a signal signal, but also may be a signal signal if she is told her blood pressure is low, that can lead to a decline in sensitivity, or a decline in strength.
The new study, carried out in menopause patients, includes a new trial which will see how she might respond to a high blood pressure and what symptoms should be seen if she continues at normal blood pressure.
About 2,000 women in the study were asked by doctors to be taken a blood sugar drop.
Once blood pressure had dropped, the researchers asked the same question on the other women in the study.
In menopause, we take blood pressure a little bit, and the response of the other women was that they were having too much blood pressure.
When women with severe stress were not on the low blood pressure medication, they were taking the low blood pressure, not the low blood pressure medication.
The blood pressure
[100 | 881.12] loss=3.75 avg=3.78
[101 | 888.34] loss=3.77 avg=3.78
[102 | 896.18] loss=3.13 avg=3.77
[103 | 903.53] loss=4.00 avg=3.78
[104 | 910.86] loss=3.74 avg=3.77
[105 | 918.33] loss=3.35 avg=3.77
[106 | 925.85] loss=3.75 avg=3.77
[107 | 933.09] loss=3.31 avg=3.76
[108 | 940.36] loss=3.62 avg=3.76
[109 | 947.86] loss=3.63 avg=3.76
[110 | 955.08] loss=3.68 avg=3.76
[111 | 962.35] loss=3.87 avg=3.76
[112 | 969.63] loss=3.58 avg=3.76
[113 | 977.09] loss=3.64 avg=3.75
[114 | 984.66] loss=4.46 avg=3.76
[115 | 992.06] loss=3.92 avg=3.77
[116 | 999.29] loss=3.86 avg=3.77
[117 | 1006.76] loss=4.19 avg=3.77
[118 | 1014.54] loss=3.47 avg=3.77
[119 | 1022.84] loss=3.03 avg=3.76
[120 | 1030.40] loss=3.81 avg=3.76
[121 | 1037.68] loss=3.84 avg=3.76
[122 | 1045.22] loss=3.54 avg=3.76
[123 | 1052.71] loss=3.53 avg=3.75
[124 | 1059.92] loss=3.75 avg=3.75
[125 | 1067.34] loss=3.72 avg=3.75
[126 | 1074.52] loss=4.11 avg=3.76
[127 | 1081.75] loss=3.57 avg=3.76
[128 | 1088.98] loss=3.40 avg=3.75
[129 | 1096.17] loss=3.67 avg=3.75
[130 | 1103.42] loss=3.40 avg=3.75
[131 | 1110.72] loss=3.07 avg=3.74
[132 | 1117.98] loss=3.65 avg=3.73
[133 | 1125.57] loss=3.69 avg=3.73
[134 | 1132.80] loss=3.62 avg=3.73
[135 | 1140.10] loss=4.35 avg=3.74
[136 | 1147.40] loss=3.95 avg=3.74
[137 | 1154.63] loss=4.03 avg=3.75
[138 | 1161.87] loss=3.54 avg=3.74
[139 | 1169.20] loss=3.34 avg=3.74
[140 | 1176.43] loss=3.55 avg=3.74
[141 | 1183.85] loss=3.50 avg=3.73
[142 | 1191.06] loss=3.85 avg=3.74
[143 | 1198.26] loss=3.74 avg=3.74
[144 | 1205.44] loss=3.57 avg=3.73
[145 | 1212.67] loss=3.64 avg=3.73
[146 | 1219.86] loss=3.61 avg=3.73
[147 | 1227.16] loss=3.87 avg=3.73
[148 | 1234.59] loss=3.51 avg=3.73
[149 | 1242.05] loss=3.79 avg=3.73
[150 | 1249.32] loss=3.77 avg=3.73
[151 | 1256.47] loss=3.52 avg=3.73
[152 | 1263.60] loss=3.51 avg=3.73
[153 | 1270.76] loss=3.68 avg=3.72
[154 | 1277.98] loss=3.27 avg=3.72
[155 | 1285.36] loss=3.56 avg=3.72
[156 | 1292.73] loss=4.03 avg=3.72
[157 | 1300.09] loss=3.67 avg=3.72
[158 | 1307.42] loss=3.65 avg=3.72
[159 | 1314.71] loss=3.34 avg=3.71
[160 | 1322.02] loss=4.13 avg=3.72
[161 | 1329.34] loss=3.79 avg=3.72
[162 | 1336.62] loss=3.30 avg=3.72
[163 | 1343.93] loss=3.70 avg=3.72
[164 | 1351.13] loss=3.91 avg=3.72
[165 | 1358.44] loss=3.67 avg=3.72
[166 | 1365.75] loss=3.80 avg=3.72
[167 | 1372.97] loss=3.48 avg=3.72
[168 | 1380.21] loss=3.61 avg=3.71
[169 | 1387.45] loss=3.28 avg=3.71
[170 | 1394.67] loss=3.34 avg=3.70
[171 | 1401.94] loss=3.49 avg=3.70
[172 | 1409.23] loss=3.66 avg=3.70
[173 | 1416.58] loss=3.54 avg=3.70
[174 | 1424.00] loss=3.38 avg=3.70
[175 | 1431.17] loss=3.40 avg=3.69
[176 | 1438.37] loss=3.55 avg=3.69
[177 | 1445.57] loss=3.40 avg=3.69
[178 | 1452.89] loss=3.41 avg=3.68
[179 | 1460.25] loss=3.45 avg=3.68
[180 | 1467.54] loss=3.55 avg=3.68
[181 | 1474.77] loss=3.80 avg=3.68
[182 | 1482.13] loss=3.38 avg=3.68
[183 | 1489.48] loss=3.19 avg=3.67
[184 | 1496.70] loss=3.24 avg=3.67
[185 | 1503.97] loss=3.87 avg=3.67
[186 | 1511.20] loss=3.67 avg=3.67
[187 | 1518.43] loss=3.54 avg=3.67
[188 | 1525.62] loss=3.63 avg=3.67
[189 | 1532.85] loss=3.68 avg=3.67
[190 | 1540.15] loss=3.45 avg=3.66
[191 | 1547.50] loss=3.40 avg=3.66
[192 | 1554.76] loss=3.69 avg=3.66
[193 | 1562.03] loss=3.16 avg=3.66
[194 | 1569.41] loss=3.57 avg=3.65
[195 | 1576.84] loss=4.05 avg=3.66
[196 | 1584.16] loss=3.39 avg=3.66
[197 | 1591.39] loss=3.81 avg=3.66
[198 | 1598.71] loss=3.69 avg=3.66
[199 | 1606.06] loss=3.79 avg=3.66
Generating samples...
======== SAMPLE 1 ========
again in the beginning
with the devil, in hell.
It is in her hands who have been the only ones
who have been saved from eternity, from the eternity
where there is none.
- In the midst of eternity there is no time
where no body can exist.
Where all existence exists, but death.
where all existence is no longer a thing.
where there is no mind.
where nothing exists; it is nothing.
And where nothing exists, it
is nothing—
it is nothing—
and the mind is nothing but a
thing.
it is there that God
is not like, that it is not
that it needs to exist, it
comes into existence as though
it were something, a thing whose
own existence is the whole world.
there, it is.
it is there—it is there,
it is there—it is,
not it but it it is there,
a thing the whole world must
take away—it is not it but it,
something and nothing else.
In the midst of eternity there is no time,
where no matter what, nothing
like God will exist without
something like it.
“What is there to get up?”
to go,”
or “to go,” or “to go.
”What is there to go in,”
to go in,” to go inside?
”I wish I could go,”
to go,”
but”
I can’t go.
”
<|endoftext|>
<|startoftext|>
In the end, the human mind and the human body
become objects of the body
as it becomes the subject of the mind that
the body becomes to be taken under itself...
then you are all that makes the whole thing
the body but the body is all that is
the human mind and the human body is
the body.”
<|endoftext|>
<|startoftext|>
<|startoftext|>
There is a new light every morning and afternoon. A new dawn,
every dream, a new day. But, in their dream-like
like way, the new things in a dream are
as objects of their imagination, and are only images of the
imagination of someone else for the purpose of passing
along to them their own vision of the dream by a
different path. Sometimes they
are just images of the imagination of someone else,
and sometimes they are like them. You
know this. You have heard it. You
know that. To make their dreams dreamlike
wasn't to bring the dream of someone else
to them. To make them dreamlike was to bring
themself to me, to come closer to something
that had me.
<|endoftext|>
<|startoftext|>
It is my turn.
I see the woman running outside my window,
behind a big orange-eyed figure,
a tall, thin, unhinged woman in the black-and-white
suit. It does not seem like anything
ever happened. I was scared to see her, but she kept coming.
<|endoftext|>
<|startoftext|>
For once now I am glad to be alive,
my self, because I am glad that I am
still alive, and I am glad now as well
to make the world round. For what I am doing to
my body I do not want, because it is an
impairing thing, my body is an impurity,
it makes me want to touch it. I should
be happy now and look back on everything I
have done. I should be happy in
that moment, and that is what matters.
<|endoftext|>
<|startoftext|>
All kinds of dreams were created with the purpose to
make them into something that would
look like me. But in a dream I did not look like
me, but rather like myself.
<|endoftext|>
<|startoftext|>
One day when I sat down and wrote
my notes, a long wave
surrounded me on the pavement
on a dark floor, but when I put my hand
in front of the blade
and looked down, a long
wave, like mine, swept through my hand
and into a small corner—a hole
where a knife cuts through the skin
of a
[200 | 1716.28] loss=3.80 avg=3.66
[201 | 1723.54] loss=3.35 avg=3.66
[202 | 1730.90] loss=3.71 avg=3.66
[203 | 1738.18] loss=3.66 avg=3.66
[204 | 1745.44] loss=3.67 avg=3.66
[205 | 1752.67] loss=3.75 avg=3.66
[206 | 1759.83] loss=3.39 avg=3.66
[207 | 1767.01] loss=3.55 avg=3.66
[208 | 1774.17] loss=3.60 avg=3.65
[209 | 1781.45] loss=3.47 avg=3.65
[210 | 1788.78] loss=3.43 avg=3.65
[211 | 1796.03] loss=3.67 avg=3.65
[212 | 1803.34] loss=3.58 avg=3.65
[213 | 1810.54] loss=3.47 avg=3.65
[214 | 1817.72] loss=3.65 avg=3.65
[215 | 1824.96] loss=3.72 avg=3.65
[216 | 1832.12] loss=3.84 avg=3.65
[217 | 1839.19] loss=3.12 avg=3.64
[218 | 1846.37] loss=3.92 avg=3.65
[219 | 1853.67] loss=3.85 avg=3.65
[220 | 1861.10] loss=3.48 avg=3.65
[221 | 1868.38] loss=3.30 avg=3.64
[222 | 1875.80] loss=3.24 avg=3.64
[223 | 1883.10] loss=3.47 avg=3.64
[224 | 1890.60] loss=3.21 avg=3.63
[225 | 1897.88] loss=3.95 avg=3.64
[226 | 1905.23] loss=3.77 avg=3.64
[227 | 1912.40] loss=3.51 avg=3.64
[228 | 1919.63] loss=3.29 avg=3.63
[229 | 1926.87] loss=3.09 avg=3.63
[230 | 1934.30] loss=3.27 avg=3.62
[231 | 1941.63] loss=3.48 avg=3.62
[232 | 1948.85] loss=3.31 avg=3.62
[233 | 1956.10] loss=4.04 avg=3.62
[234 | 1963.37] loss=3.49 avg=3.62
[235 | 1970.78] loss=3.54 avg=3.62
[236 | 1978.11] loss=3.46 avg=3.62
[237 | 1985.37] loss=3.44 avg=3.62
[238 | 1992.53] loss=2.90 avg=3.61
[239 | 1999.74] loss=3.23 avg=3.60
[240 | 2006.98] loss=3.42 avg=3.60
[241 | 2014.20] loss=3.26 avg=3.60
[242 | 2021.50] loss=3.67 avg=3.60
[243 | 2028.81] loss=3.50 avg=3.60
[244 | 2036.07] loss=3.44 avg=3.60
[245 | 2043.27] loss=3.50 avg=3.60
[246 | 2050.62] loss=3.32 avg=3.59
[247 | 2057.97] loss=3.81 avg=3.59
[248 | 2065.19] loss=3.11 avg=3.59
[249 | 2072.30] loss=3.51 avg=3.59
[250 | 2079.51] loss=3.27 avg=3.59
[251 | 2086.74] loss=3.56 avg=3.58
[252 | 2093.94] loss=3.59 avg=3.58
[253 | 2101.24] loss=3.52 avg=3.58
[254 | 2108.57] loss=3.33 avg=3.58
[255 | 2115.84] loss=3.44 avg=3.58
[256 | 2123.78] loss=3.35 avg=3.58
[257 | 2131.04] loss=3.38 avg=3.58
[258 | 2139.27] loss=3.33 avg=3.57
[259 | 2147.15] loss=3.45 avg=3.57
[260 | 2154.47] loss=3.72 avg=3.57
[261 | 2161.72] loss=3.87 avg=3.58
[262 | 2169.96] loss=3.37 avg=3.57
[263 | 2177.51] loss=3.47 avg=3.57
[264 | 2185.37] loss=3.24 avg=3.57
[265 | 2192.75] loss=3.22 avg=3.57
[266 | 2200.58] loss=3.57 avg=3.57
[267 | 2207.95] loss=3.15 avg=3.56
[268 | 2215.28] loss=3.59 avg=3.56
[269 | 2222.57] loss=3.70 avg=3.56
[270 | 2229.91] loss=3.39 avg=3.56
[271 | 2237.16] loss=3.37 avg=3.56
[272 | 2244.38] loss=3.23 avg=3.56
[273 | 2251.62] loss=2.95 avg=3.55
[274 | 2258.83] loss=2.84 avg=3.54
[275 | 2266.12] loss=2.93 avg=3.53
[276 | 2273.30] loss=3.45 avg=3.53
[277 | 2280.53] loss=3.40 avg=3.53
[278 | 2287.83] loss=4.10 avg=3.54
[279 | 2295.30] loss=3.44 avg=3.54
[280 | 2302.51] loss=3.30 avg=3.53
[281 | 2309.74] loss=3.71 avg=3.54
[282 | 2317.02] loss=3.76 avg=3.54
[283 | 2324.39] loss=3.80 avg=3.54
[284 | 2331.80] loss=3.88 avg=3.55
[285 | 2339.04] loss=2.90 avg=3.54
[286 | 2346.30] loss=3.59 avg=3.54
[287 | 2353.51] loss=3.69 avg=3.54
[288 | 2360.82] loss=3.48 avg=3.54
[289 | 2368.04] loss=3.52 avg=3.54
[290 | 2375.22] loss=3.50 avg=3.54
[291 | 2382.51] loss=3.87 avg=3.54
[292 | 2389.81] loss=3.55 avg=3.54
[293 | 2397.00] loss=3.59 avg=3.54
[294 | 2404.15] loss=3.44 avg=3.54
[295 | 2411.30] loss=3.21 avg=3.54
[296 | 2418.39] loss=3.09 avg=3.53
[297 | 2425.88] loss=3.35 avg=3.53
[298 | 2436.04] loss=3.16 avg=3.53
[299 | 2443.73] loss=3.44 avg=3.53
Generating samples...
======== SAMPLE 1 ========
be all in the same place.
Now there are only twenty-four of these, of these, each of these.
<|endoftext|>
<|startoftext|>
Every thing has been born out of every tree, till the world has lost its place, which has been called the world—a world with things—and nothing else.
And now we, each of us, was born from no place.
Our bodies, each other, each other—when the world had been born,
it began at once to take in the world, and then—
that one place, each thing, that one thing began,
the whole universe, the whole universe,
it began to lose its place.
The world lost its place in it, and no one was left,
as if all the universe was now one thing.
The world was lost for one thing,
for one thing, so to speak, until every time the universe
is lost—we are all lost again.
<|endoftext|>
<|startoftext|>
I am a man who loves the light of life, the truth of the night. Or was it night when you left me? Or was it night I fell asleep at the window, or was it a mist of a river, and the sky is full of clouds of light? Is it night I am a boy, a man who kisses the light of the night. Or was it afternoon when you left me? A man who is hungry, a man who says,
All my life, all my life,
I am tired of seeing the lights that are no more than shadows.
Not a moment does anything come to me in my own name.
<|endoftext|>
<|startoftext|>
I am a man who loves the light of life, the truth of the night. Or was it night when I left me? Or was it evening I was tired of seeing the lights that are no more than shadows.
Not a moment does anything come to me in my own name.
What is said is what is said. What is said is what is said. What is said is what is said.
<|endoftext|>
<|startoftext|>
I live in the valley in the east, where two of the hills stand. One is a large and a small rock. The other is a smaller rock.
There is a big rock there. The small rock is a hole and the large rock is a hole.
The big rock is an old stone rock I have lost at the bottom of this valley,
but no stone rocks are left. If there is another,
something from the old stone rock must be found too.
I love the light of the day and the darkness of night.
I am a man living in the valley in the east, where two of the hills stand. One is a large and a small rock. The other is a small rock.
There is a huge rock there. The small rock is a hole and the large rock is a hole.
<|endoftext|>
<|startoftext|>
No other man could say it better than me. I was so tired and tired of hearing it,
I tried to get up and down the ladder to the fire,
so I put down my bow, got in a chair, looked up from my watch.
There was no doubt at all: my knees and my face turned into the pit below
and I looked at the smoke, the lightening that had been on my face,
and the way the bowl was opened. From what I looked at,
I knew the end was coming. And that was when I felt it start to hurt,
when I saw the grass get split in two, how the trees fell away,
then the whole valley. What a relief to have been able to see the valley
is full of clouds. And now the grass, the whole valley,
has split and I know that there is nothing more. I have been gone.
<|endoftext|>
<|startoftext|>
Savage is what the Devil is born with. He is born a rebel.
When I thought about it, it was quite enough. They are the rebel. They are the rebels. They are the ones who are afraid, and who will never be afraid.
He and the others who can hear him say his name, have heard his cry. They are the ones who can listen.
And they are the ones who can understand the language of the rebel, because it is so far in the future,
but the ones who understand what they can hear and see are the ones
[300 | 2554.53] loss=3.62 avg=3.53
[301 | 2562.02] loss=4.05 avg=3.53
[302 | 2569.55] loss=3.35 avg=3.53
[303 | 2576.80] loss=2.96 avg=3.53
[304 | 2583.97] loss=3.40 avg=3.52
[305 | 2591.16] loss=2.96 avg=3.52
[306 | 2598.32] loss=3.13 avg=3.51
[307 | 2605.58] loss=3.48 avg=3.51
[308 | 2612.81] loss=3.42 avg=3.51
[309 | 2620.17] loss=3.86 avg=3.52
[310 | 2627.44] loss=3.51 avg=3.52
[311 | 2634.74] loss=3.81 avg=3.52
[312 | 2642.08] loss=2.83 avg=3.51
[313 | 2650.10] loss=4.05 avg=3.52
[314 | 2660.18] loss=3.58 avg=3.52
[315 | 2667.92] loss=3.23 avg=3.52
[316 | 2675.32] loss=3.69 avg=3.52
[317 | 2683.12] loss=3.51 avg=3.52
[318 | 2690.60] loss=3.46 avg=3.52
[319 | 2698.13] loss=4.00 avg=3.52
[320 | 2705.56] loss=2.24 avg=3.51
[321 | 2713.15] loss=3.46 avg=3.51
[322 | 2720.70] loss=3.46 avg=3.51
[323 | 2728.22] loss=3.36 avg=3.51
[324 | 2735.73] loss=3.34 avg=3.50
[325 | 2743.21] loss=3.43 avg=3.50
[326 | 2750.55] loss=3.72 avg=3.51
[327 | 2758.11] loss=3.61 avg=3.51
[328 | 2765.36] loss=3.24 avg=3.50
[329 | 2772.61] loss=3.36 avg=3.50
[330 | 2779.92] loss=3.66 avg=3.50
[331 | 2788.03] loss=3.61 avg=3.51
[332 | 2795.49] loss=3.33 avg=3.50
[333 | 2803.12] loss=3.55 avg=3.50
[334 | 2810.63] loss=3.77 avg=3.51
[335 | 2818.05] loss=3.63 avg=3.51
[336 | 2825.44] loss=3.54 avg=3.51
[337 | 2833.01] loss=3.62 avg=3.51
[338 | 2840.71] loss=3.25 avg=3.51
[339 | 2848.10] loss=3.65 avg=3.51
[340 | 2855.48] loss=3.08 avg=3.50
[341 | 2862.91] loss=3.20 avg=3.50
[342 | 2870.32] loss=3.18 avg=3.50
[343 | 2877.90] loss=3.76 avg=3.50
[344 | 2885.14] loss=3.42 avg=3.50
[345 | 2892.42] loss=3.41 avg=3.50
[346 | 2899.82] loss=3.28 avg=3.50
[347 | 2907.63] loss=4.51 avg=3.51
[348 | 2915.01] loss=3.73 avg=3.51
[349 | 2922.40] loss=3.40 avg=3.51
[350 | 2929.85] loss=3.45 avg=3.51
[351 | 2937.24] loss=3.62 avg=3.51
[352 | 2944.49] loss=3.17 avg=3.50
[353 | 2951.69] loss=4.20 avg=3.51
[354 | 2959.09] loss=3.47 avg=3.51
[355 | 2966.44] loss=3.60 avg=3.51
[356 | 2973.76] loss=3.45 avg=3.51
[357 | 2981.28] loss=3.78 avg=3.51
[358 | 2988.79] loss=4.08 avg=3.52
[359 | 2996.17] loss=3.27 avg=3.52
[360 | 3003.39] loss=3.18 avg=3.51
[361 | 3010.64] loss=3.22 avg=3.51
[362 | 3017.87] loss=3.63 avg=3.51
[363 | 3025.18] loss=3.58 avg=3.51
[364 | 3032.62] loss=3.75 avg=3.52
[365 | 3039.90] loss=3.30 avg=3.51
[366 | 3047.23] loss=3.39 avg=3.51
[367 | 3055.01] loss=3.56 avg=3.51
[368 | 3062.42] loss=3.59 avg=3.51
[369 | 3069.82] loss=3.38 avg=3.51
[370 | 3077.12] loss=3.12 avg=3.51
[371 | 3084.40] loss=3.88 avg=3.51
[372 | 3091.92] loss=3.37 avg=3.51
[373 | 3099.28] loss=3.31 avg=3.51
[374 | 3106.76] loss=3.28 avg=3.51
[375 | 3114.05] loss=3.35 avg=3.50
[376 | 3121.33] loss=3.72 avg=3.51
[377 | 3128.63] loss=3.21 avg=3.50
[378 | 3135.92] loss=3.71 avg=3.51
[379 | 3143.24] loss=3.05 avg=3.50
[380 | 3151.11] loss=3.28 avg=3.50
[381 | 3158.39] loss=4.03 avg=3.50
[382 | 3165.97] loss=3.59 avg=3.51
[383 | 3173.55] loss=3.30 avg=3.50
[384 | 3180.94] loss=3.31 avg=3.50
[385 | 3188.21] loss=3.52 avg=3.50
[386 | 3195.49] loss=3.53 avg=3.50
[387 | 3202.57] loss=3.34 avg=3.50
[388 | 3209.78] loss=3.48 avg=3.50
[389 | 3216.99] loss=3.39 avg=3.50
[390 | 3224.37] loss=3.56 avg=3.50
[391 | 3231.59] loss=3.23 avg=3.50
[392 | 3238.90] loss=3.15 avg=3.49
[393 | 3246.28] loss=3.10 avg=3.49
[394 | 3253.53] loss=3.51 avg=3.49
[395 | 3260.83] loss=3.51 avg=3.49
[396 | 3268.20] loss=3.48 avg=3.49
[397 | 3275.42] loss=3.44 avg=3.49
[398 | 3282.85] loss=2.98 avg=3.48
[399 | 3290.28] loss=3.35 avg=3.48
Generating samples...
======== SAMPLE 1 ========
ooked all over each other before they met."I went to bed in bed with my back to you. I couldn’t see what they were going for, but I didn’t have anything else to worry about until they put the bed sheets on and I woke up in the middle of nowhere. I was standing in the street with all of my friends , trying not to think about something and how they’d like it. I didn’t think about much in the way of sex as a whole until they put the bed sheets on my face. I saw a lot of faces all over the streets of the neighborhood. I saw them just standing there in their underwear.
I went to bed on that chair.
<|endoftext|>
<|startoftext|>
There are so many things on each table there is a bed.
It all sits across the room.
I will have to stay up when I need to.
I only have to wake up in the morning anyway
to get up and go home.
And the table doesn’t fall to the floor,
other things like that don’t matter.
I try to find the way
to sit down on the other table. Maybe I should lay
the rest myself.
<|endoftext|>
<|startoftext|>
I have a dream. I’ve been in this dream since first waking,
when my clothes came off.
We all have dreams. I’ve dreamed about a child, a tree,
a man, a rabbit.
In the dream, a human being
is seen, standing in a tree.
I have a dream of a human being, standing in a tree.
“Where “where“where“is he?
”Look’s good” to look at things in the
place where they’d be.
“ I’m asking you “ “”
”If’t’it
can you name the place where “ the tree
has been
held?
”What is
the nature of
the things and what’s the
text
of the tree?
”If’t it doesn’t make sense
to name the place where “ ”
the human being has’t been
held.
<|endoftext|>
<|startoftext|>
You’re the one who needs a doctor right now.
All it takes is a certain amount of blood for the blood circulates
and changes, so it’s easy to miss.
The blood is an ode to the last man, a way of saying
what we wanted to say, not what we believed
in anymore. You have more than one way to say something.
The blood and your mind don’t do this for the same reason. The blood isn’t what you ask,
but if it gets bad enough, it starts running red.
The idea will be that, despite the condition,
you need at least as much of it to be cured.
There are only so many gods, and only a few
are so powerful as yours.
<|endoftext|>
<|startoftext|>
I see a lot of people with big breasts and their thighs and thighs,
a huge, red sign that says you should not be ashamed of who you are and who you are not;
the rest are men, in clothes that are too long for their thighs and thighs
and they think that I am not a man because I have no clothes.”
They are wrong, then, but there’s no denying it.
Your beautiful people want to have your breasts taken away, and it hurts them.
They are right: I am not ashamed of them. I have more than a dozen
people, but I don’t want to have to have one.
<|endoftext|>
<|startoftext|>
I have a big chest, but what if I was a woman?
My father, who used to live near me, looked down upon me.
When I first saw him, all I saw was naked people wearing long, dark gray gowns, men
who’d not hold their hips for years, but had to rise to their feet
when they were asked to do so. They would sometimes crawl through the air
with their feet at their knees against their chests,
[400 | 3402.91] loss=3.58 avg=3.48
[401 | 3410.58] loss=3.41 avg=3.48
[402 | 3418.28] loss=3.08 avg=3.48
[403 | 3425.94] loss=2.99 avg=3.47
[404 | 3433.28] loss=3.50 avg=3.47
[405 | 3440.63] loss=3.47 avg=3.47
[406 | 3447.91] loss=3.37 avg=3.47
[407 | 3455.23] loss=3.61 avg=3.47
[408 | 3462.64] loss=3.39 avg=3.47
[409 | 3469.99] loss=3.66 avg=3.47
[410 | 3477.31] loss=3.35 avg=3.47
[411 | 3484.69] loss=3.25 avg=3.47
[412 | 3491.93] loss=3.04 avg=3.47
[413 | 3499.33] loss=3.48 avg=3.47
[414 | 3507.00] loss=3.47 avg=3.47
[415 | 3514.44] loss=3.52 avg=3.47
[416 | 3521.80] loss=3.52 avg=3.47
[417 | 3529.10] loss=3.77 avg=3.47
[418 | 3536.45] loss=3.26 avg=3.47
[419 | 3543.61] loss=4.01 avg=3.47
[420 | 3550.88] loss=3.31 avg=3.47
[421 | 3558.29] loss=3.31 avg=3.47
[422 | 3565.71] loss=3.65 avg=3.47
[423 | 3573.11] loss=3.34 avg=3.47
[424 | 3580.53] loss=3.19 avg=3.47
[425 | 3588.03] loss=3.31 avg=3.47
[426 | 3595.27] loss=3.27 avg=3.47
[427 | 3602.60] loss=3.49 avg=3.47
[428 | 3609.87] loss=3.29 avg=3.46
[429 | 3617.15] loss=3.30 avg=3.46
[430 | 3624.38] loss=3.23 avg=3.46
[431 | 3631.64] loss=3.22 avg=3.46
[432 | 3638.83] loss=3.04 avg=3.45
[433 | 3646.22] loss=3.57 avg=3.45
[434 | 3653.56] loss=3.24 avg=3.45
[435 | 3660.96] loss=3.00 avg=3.45
[436 | 3668.38] loss=3.57 avg=3.45
[437 | 3675.58] loss=3.34 avg=3.45
[438 | 3682.81] loss=3.04 avg=3.44
[439 | 3690.32] loss=3.30 avg=3.44
[440 | 3697.67] loss=3.51 avg=3.44
[441 | 3705.58] loss=3.39 avg=3.44
[442 | 3713.00] loss=3.32 avg=3.44
[443 | 3720.30] loss=3.05 avg=3.44
[444 | 3727.67] loss=3.02 avg=3.43
[445 | 3735.00] loss=3.39 avg=3.43
[446 | 3742.61] loss=3.48 avg=3.43
[447 | 3750.27] loss=3.20 avg=3.43
[448 | 3757.74] loss=3.61 avg=3.43
[449 | 3765.01] loss=3.31 avg=3.43
[450 | 3772.28] loss=3.17 avg=3.43
[451 | 3779.60] loss=2.88 avg=3.42
[452 | 3787.46] loss=3.23 avg=3.42
[453 | 3794.81] loss=3.30 avg=3.42
[454 | 3802.08] loss=3.25 avg=3.42
[455 | 3809.51] loss=3.70 avg=3.42
[456 | 3816.78] loss=3.68 avg=3.42
[457 | 3824.13] loss=3.77 avg=3.43
[458 | 3831.45] loss=3.25 avg=3.42
[459 | 3838.93] loss=3.91 avg=3.43
[460 | 3846.17] loss=3.57 avg=3.43
[461 | 3853.55] loss=3.27 avg=3.43
[462 | 3860.92] loss=2.97 avg=3.42
[463 | 3868.40] loss=3.17 avg=3.42
[464 | 3875.70] loss=2.70 avg=3.42
[465 | 3883.14] loss=3.36 avg=3.41
[466 | 3890.98] loss=3.36 avg=3.41
[467 | 3898.32] loss=3.37 avg=3.41
[468 | 3905.60] loss=3.31 avg=3.41
[469 | 3912.82] loss=3.17 avg=3.41
[470 | 3920.05] loss=2.73 avg=3.40
[471 | 3927.41] loss=3.27 avg=3.40
[472 | 3934.63] loss=3.09 avg=3.40
[473 | 3941.71] loss=3.33 avg=3.40
[474 | 3949.03] loss=3.21 avg=3.40
[475 | 3956.46] loss=3.50 avg=3.40
[476 | 3963.89] loss=3.93 avg=3.40
[477 | 3971.18] loss=3.65 avg=3.40
[478 | 3978.43] loss=3.39 avg=3.40
[479 | 3985.86] loss=3.50 avg=3.41
[480 | 3993.17] loss=3.58 avg=3.41
[481 | 4000.49] loss=2.99 avg=3.40
[482 | 4008.12] loss=3.42 avg=3.40
[483 | 4015.54] loss=3.53 avg=3.40
[484 | 4022.90] loss=3.30 avg=3.40
[485 | 4030.17] loss=3.85 avg=3.41
[486 | 4037.47] loss=3.51 avg=3.41
[487 | 4046.71] loss=3.06 avg=3.41
[488 | 4054.24] loss=3.49 avg=3.41
[489 | 4061.66] loss=2.77 avg=3.40
[490 | 4068.87] loss=3.24 avg=3.40
[491 | 4076.23] loss=3.60 avg=3.40
[492 | 4083.55] loss=3.48 avg=3.40
[493 | 4090.81] loss=3.33 avg=3.40
[494 | 4098.01] loss=3.69 avg=3.40
[495 | 4105.25] loss=3.88 avg=3.41
[496 | 4112.55] loss=3.37 avg=3.41
[497 | 4119.86] loss=3.13 avg=3.40
[498 | 4127.34] loss=3.74 avg=3.41
[499 | 4134.65] loss=3.37 avg=3.41
Generating samples...
======== SAMPLE 1 ========
“They were so beautiful in their faces”
the words stuck to the sky
And the waves were so close
And the sea like a dream
Was like love