This repo is based on https://github.com/karpathy/nanoGPT (Original repo)

WINDOWS TERMINAL

python3 -m venv venv
cd venv
cd Scripts
activate.bat
cd ../..
pip install -r requirements.txt
git clone https://github.com/cccntu/minLoRA.git
cd minLoRA
pip install -e .
python data/mitos/prepare.py
python train.py config/train_mitos.py --compile=False
python sample.py --out_dir=out-mitos
deactivate.bat

UBUNTU TERMINAL

With installation of venv

in windows cmd : pip install venv in ubuntu terminal :

cd /mnt/c/Users/user/Desktop/LoRAnanoGPT
python3 -m venv venv
source venv_ubuntu/bin/activate
pip install -r requirements.txt
git clone https://github.com/cccntu/minLoRA.git
cd minLoRA
pip install -e .
python data/mitos/prepare.py
python train.py config/train_mitos.py
python sample.py --out_dir=out-mitos
deactivate

How to run it in ubuntu terminal

cd /mnt/c/Users/user/Desktop/LoRAnanoGPT
source venv_ubuntu/bin/activate
python data/mitos/prepare.py
python train.py config/train_mitos.py
python sample.py --out_dir=out-mitos
deactivate

- python data/mitos/prepare.py

length of dataset in characters: 300,407 all the unique characters: "%&'(),-./0123456789:;>ACDEFGHIKMNOPRSTUVXYabcdefghiklmnoprstuvwxy«»΄ΆΈΌΐΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩάέήίαβγδεζηθικλμνξοπρςστυφχψωϊϋόύώϥ–‘’€∆ vocab size: 139 train has 270,366 tokens val has 30,041 tokens

Results after training the model

- python train.py config/train_mitos.py :

Overriding config with config/train_mitos.py:

train a miniature character-level mitos model

good for debugging and playing on macbooks and such

out_dir = 'out-mitos' eval_interval = 250 # keep frequent because we'll overfit eval_iters = 200 log_interval = 10 # don't print too too often

we expect to overfit on this small dataset, so only save when val improves

always_save_checkpoint = False

wandb_log = False # override via command line if you like wandb_project = 'mitos' wandb_run_name = 'mini-gpt'

dataset = 'mitos' batch_size = 64 block_size = 256 # context of up to 256 previous characters

baby GPT model :)

n_layer = 6 n_head = 6 n_embd = 384 dropout = 0.2

learning_rate = 1e-3 # with baby networks can afford to go a bit higher max_iters = 5000 lr_decay_iters = 5000 # make equal to max_iters usually min_lr = 1e-4 # learning_rate / 10 usually beta2 = 0.99 # make a bit bigger because number of tokens per iter is small

warmup_iters = 100 # not super necessary potentially

on macbook also add

device = 'cuda' # run on cpu only compile = False # do not torch compile the model

use_lora = False found vocab_size = 139 (inside data/mitos/meta.pkl) Initializing a new model from scratch WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 number of parameters: 10.68M using fused AdamW: True step 0: train loss 5.0026, val loss 5.0026 iter 0: loss 5.0015, time 37493.07ms, mfu -100.00% iter 10: loss 3.6714, time 6717.21ms, mfu 2.22% iter 20: loss 2.9807, time 6717.40ms, mfu 2.22% iter 30: loss 2.6445, time 6715.78ms, mfu 2.22% iter 40: loss 2.4951, time 6728.59ms, mfu 2.22% iter 50: loss 2.4438, time 6728.94ms, mfu 2.22% iter 60: loss 2.3572, time 6731.42ms, mfu 2.22% iter 70: loss 2.3445, time 6727.20ms, mfu 2.22% iter 80: loss 2.2884, time 6727.45ms, mfu 2.22% iter 90: loss 2.2149, time 6729.26ms, mfu 2.22% iter 100: loss 2.1156, time 6728.40ms, mfu 2.22% iter 110: loss 1.9825, time 6730.61ms, mfu 2.22% iter 120: loss 1.9240, time 6729.86ms, mfu 2.22% iter 130: loss 1.8033, time 6729.76ms, mfu 2.22% iter 140: loss 1.7511, time 6730.19ms, mfu 2.22% iter 150: loss 1.6238, time 6733.62ms, mfu 2.22% iter 160: loss 1.5409, time 6729.79ms, mfu 2.22% iter 170: loss 1.4590, time 6729.54ms, mfu 2.22% iter 180: loss 1.3530, time 6728.96ms, mfu 2.22% iter 190: loss 1.2371, time 6727.71ms, mfu 2.22% iter 200: loss 1.1152, time 6729.97ms, mfu 2.22% iter 210: loss 1.1115, time 6729.37ms, mfu 2.22% iter 220: loss 1.0531, time 6729.09ms, mfu 2.22% iter 230: loss 1.0697, time 6730.29ms, mfu 2.22% iter 240: loss 0.9510, time 6727.95ms, mfu 2.22% step 250: train loss 0.7300, val loss 0.9303 saving checkpoint to out-mitos iter 250: loss 0.8230, time 31866.95ms, mfu 2.05% iter 260: loss 0.8156, time 6725.42ms, mfu 2.06% iter 270: loss 0.8446, time 6724.22ms, mfu 2.08% iter 280: loss 0.8012, time 6727.11ms, mfu 2.09% iter 290: loss 0.7105, time 6723.35ms, mfu 2.11% iter 300: loss 0.6672, time 6723.76ms, mfu 2.12% iter 310: loss 0.5916, time 6725.29ms, mfu 2.13% iter 320: loss 0.6139, time 6724.69ms, mfu 2.14% iter 330: loss 0.5327, time 6722.52ms, mfu 2.15% iter 340: loss 0.5076, time 6723.97ms, mfu 2.15% iter 350: loss 0.5089, time 6725.06ms, mfu 2.16% iter 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train loss 0.0515, val loss 0.8490 iter 750: loss 0.0845, time 31374.31ms, mfu 2.03% iter 760: loss 0.0843, time 6723.29ms, mfu 2.05% iter 770: loss 0.0821, time 6717.46ms, mfu 2.07% iter 780: loss 0.0789, time 6717.56ms, mfu 2.08% iter 790: loss 0.0757, time 6719.38ms, mfu 2.10% iter 800: loss 0.0778, time 6719.46ms, mfu 2.11% iter 810: loss 0.0771, time 6717.53ms, mfu 2.12% iter 820: loss 0.0764, time 6716.43ms, mfu 2.13% iter 830: loss 0.0741, time 6717.58ms, mfu 2.14% iter 840: loss 0.0737, time 6716.97ms, mfu 2.15% iter 850: loss 0.0689, time 6718.95ms, mfu 2.16% iter 860: loss 0.0657, time 6716.74ms, mfu 2.16% iter 870: loss 0.0718, time 6719.53ms, mfu 2.17% iter 880: loss 0.0686, time 6716.89ms, mfu 2.18% iter 890: loss 0.0671, time 6718.53ms, mfu 2.18% iter 900: loss 0.0702, time 6717.45ms, mfu 2.18% iter 910: loss 0.0638, time 6717.00ms, mfu 2.19% iter 920: loss 0.0657, time 6716.81ms, mfu 2.19% iter 930: loss 0.0691, time 6717.35ms, mfu 2.20% iter 940: loss 0.0660, time 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6718.88ms, mfu 2.09% iter 4790: loss 0.0384, time 6717.39ms, mfu 2.10% iter 4800: loss 0.0403, time 6717.18ms, mfu 2.11% iter 4810: loss 0.0386, time 6717.94ms, mfu 2.12% iter 4820: loss 0.0391, time 6716.53ms, mfu 2.13% iter 4830: loss 0.0400, time 6718.95ms, mfu 2.14% iter 4840: loss 0.0395, time 6717.13ms, mfu 2.15% iter 4850: loss 0.0401, time 6717.66ms, mfu 2.16% iter 4860: loss 0.0404, time 6716.33ms, mfu 2.17% iter 4870: loss 0.0402, time 6716.98ms, mfu 2.17% iter 4880: loss 0.0395, time 6717.03ms, mfu 2.18% iter 4890: loss 0.0409, time 6718.68ms, mfu 2.18% iter 4900: loss 0.0407, time 6717.79ms, mfu 2.19% iter 4910: loss 0.0395, time 6718.37ms, mfu 2.19% iter 4920: loss 0.0393, time 6716.26ms, mfu 2.19% iter 4930: loss 0.0393, time 6717.15ms, mfu 2.20% iter 4940: loss 0.0389, time 6716.51ms, mfu 2.20% iter 4950: loss 0.0395, time 6717.58ms, mfu 2.20% iter 4960: loss 0.0386, time 6717.74ms, mfu 2.20% iter 4970: loss 0.0402, time 6717.32ms, mfu 2.21% iter 4980: loss 0.0396, time 6717.51ms, mfu 2.21% iter 4990: loss 0.0394, time 6719.40ms, mfu 2.21% step 5000: train loss 0.0371, val loss 1.3384 iter 5000: loss 0.0382, time 31322.04ms, mfu 2.04%

- python sample.py --out_dir=out-mitos

Overriding: out_dir = out-mitos WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0 number of parameters: 10.68M Loading meta from data/mitos/meta.pkl...

Ποια είναι τα βήματα της διαδικασίας απονομής πολεμικής σύνταξης; Τα βήματα περιλαμβάνουν την πρωτοκόλληση της αίτησης, τη χρέωση στην αρμόδια υπηρεσία, την επιτόπια αυτοπροσωπία, την έκδοση της άδειας απαλλαγής από τη Διεύθυνση Πλοηγικής Υπηρεσίας και την απαλλαγή της απόφασης στο Γραφείο του Βουλευτή του Ελληνικού Κοινοβουλίου.

Τι αφορά η διαδικασία της Άδειας για Πρόσβαση στο Αποδεσμευμένο Χαρτώο Αρχειακό Υλικό; Η διαδικασία αφορά στην πρόσβαση στο αναγνωστήριο της Υπηρεσίας Διπλωματικο

ή αφορά η διαδικασία της τηλεδιάσκεψης με τη Δημόσια Υπηρεσία Απασχόλησης (ΔΥΠΑ) ; Η διαδικασία αφορά στην πραγματοποίηση τηλεδιάσκεψης με τη Δημόσια Υπηρεσία Απασχόλησης (ΔΥΠΑ) προσφέροντας συμβουλευτικές υπηρεσίες, με την υποχρεωτική αξιολόγηση προσφυγής, τη διενέργεια επιθεωρήσεων, την κατανομή επιθεωρητών και την υλοποίηση της διαδικασίας.

Ποιες είναι οι προϋποθέσεις για τη διαδικασία αυτή ; Οι προϋποθέσεις της διαδικασίας:

Κατοχή κωδικών για είσοδο σε λογισμικό

Ποια είναι τα βήματ

Προϋποθέσεις της διαδικασίας έκδοσης διαταγής προαγωγής/επανάληψης/διαγραφής μαθημάτων ή δοκίμων στις Σχολές Λ.Σ.-ΕΛ.ΑΚΤΗ; Ναι, οι συνθήκες περιλαμβάνουν την κατάρτιση δύο αντιτύπων πινάκων επιτυχόντων και αποτυχόντων μετά το πέρας των εξετάσεων κάθε εξαμήνου.

Ποια είναι τα βήματα της διαδικασίας; Τα βήματα περιλαμβάνουν την παραλαβή της αίτησης, την παραλαβή και αξιολόγηση της αίτησης, την ηλεκτρονική κατάθεση αίτησης, την αξιολόγηση της αίτησης ψηφιακά, την ενημέρωση της αξιολόγησης

Τι αφορά η διαδικασία αναβολής κατάταξης στράτευσης; Η διαδικασία αφορά στην αναβολή κατάταξης ασφάλισης για πρόσβαση στα δεδομένα αποστέλλεται στην εφαρμογή της Οδηγίας Προϊόντων Επιχειρήσεων Πλοίων.

Ποιες είναι οι προϋποθέσεις για τη διαδικασία αυτή; Για τη σύσταση συνεταιρισμού απαιτείται υπογραφή της απαιτείται υπογραφή της συμφωνίας της συμφωνίας της συμμόρφωσης τιμής της ενωσιακής νομοθεσίας με την αναγνώριση αποστολή των στοιχείων συνταγών αναλογισμών και την ενημέρωση των αστικώ

Ποια είναι τα βήματα της διαδικασίας παρακολούθησης εσόδων από μερίσματα μετοχικών τίτλων κυριότητας Ελληνικού Δημοσίου;

Παραλαβή της αίτησης και πρωτοκόλληση
Έλεγχος πληρότητας δικαιολογητικών
Έκδοση Απόφασης Δημάρχου για τη μεταδημοτολόγηση των επιτροπής Απασχόλησης
Αποστολή των στοιχείων των επιτροπών στην ηλεκτρονική αρμόδια υπηρεσία
Παραλαβή αίτησης από το Τμήμα Πρωτοκόλληση
Έλεγχος των δικαιολογητικών και της εγγραφής στο e-Μητρώο πλοίων
Έγκριση οικονομικών στοιχεί

Ποια είναι τα βήματα της διαδικασίας έκδοσης βεβαίωσης αυτασφάλισης; Τα βήματα περιλαμβάνουν την υποβολή αίτησης, την πρωτοκόλληση της αίτησης, την ηλεκτρονική υποβολή της αίτησης, την εξέταση της αίτησης αίτησης από την Υπηρεσία, τη σύνταξη πίνακα μη ικανών υποψηφίων, τη διενέργεια εξετάσεων και την έκδοση της άδειας.

Τι αφορά η διαδικασία υποβολής δήλωσης συγκομιδής αμπελουργικών προϊόντων; Η διαδικασία αφορά στη δήλωση συγκεκριμένων προγραμμάτων εμπορικών επιχειρήσεων μετά από την αγορά

Τι αφορά η διαδικασία των Προγραμμάτων Κατασκηνώσεων (ΔΥΠΑ) ; Η διαδικασία αφορά στην επιδότηση δικαιούχων για τη διαμονή των παιδιών τους σε Παιδικές Κατασκηνώσεις του Μητρώου Παρόχων της Δημόσιας Υπηρεσίας Απασχόλησης (Δ.ΥΠ.Α.).

Ποιες είναι οι προϋποθέσεις για τη συμμετοχή στη διαδικασία; Οι προϋποθέσεις περιλαμβάνουν την αποστολή εγγράφων πρακτικών Γενικής Συνέλευσης ή Διοικητικού Συμβουλίου Ναυτικής εταιρείας, την καταχώρηση των αταικών συνεταιρισμών και την ενημέρωση του μητρώου.

Προ

Ποια είναι τα βήματα της διαδικασίας παροχής νομικής βοήθειας ;

Παραλαβή αίτησης από αιτούντα και έλεγχος.
Έλεγχος τυπικών προϋποθέσεων και πληρότητας της αίτησης από την αρμόδια υπηρεσία.
Έκδοση απόφασης απόσπασης και διάθεσης υπαλλήλου σε Γραφείο Βουλευτή του Ελληνικού Κοινοβουλίου.
Αρνητική απάντηση της αίτησης στο αίτημα απόσπασης και διάθεσης υπαλλήλου σε Γραφείο Βουλευτή του Ελληνικού Κοινοβουλίου.
Αρνητική απάντηση στο αίτημα απόσπασης και διάθεσης υπαλλήλου σε Γραφείο Βο

Ποιες είναι οι συνθήκες της διαδικασίας; Οι συνθήκες περιλαμβάνουν την ύπαρξη όλων των νομίμων παραστατικών που αφορούν στις δαπάνες για την αξιοποίηση περιουσιακών στοιχείων του Δημοσίου, την καταχώριση των ατομικών στοιχείων των ατομικών φακέλων των Δοκίμων των Σχολών, την ενημέρωση Μητρώου Διαβατηρίων, την ενημέρωση των πορείας εγγραφής, την μη τέγκριση του Μητρώου Παρόχων και την ενημέρωση του ενδιαφερόμενου.

Τι αφορά η διαδικασία εξέτασης κατασχεμένων τροφίμων ζωικής προέλευσης; Η δια

Ποιες είναι οι προϋποθέσεις για τη διαδικασία παραχώρησης ακινήτου; Ο αιτών πρέπει να έχει προηγηθεί η έκδοση ανάλυσης ή αντικατάστασης εμπορεί να χρόνου ανεργίας και να λαμβάνουν κωδικό πρόσβασης με διάρκεια ισχύος έξι μηνών, ο οποίος μπορεί να ανανεωθεί.

Ποια είναι τα βήματα της διαδικασίας πρόσβασης στο Αποδεσμευμένο Ψηφιοποιημένο Αρχειακό Υλικό; Τα βήματα περιλαμβάνουν την εισαγωγή στοιχείων στην ηλεκτρονική φόρμα, την αυτόματη αποστολή κωδικού πρόσβασης στον εγγεγραμμένο χρήστη και τη

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