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---
license: cc-by-nc-sa-4.0
datasets:
- roneneldan/TinyStories
---
This is a character (english a-z 0-9 and so on) trained model following Andrej karpathy's llama.c project https://github.com/karpathy/llama2.c on both TinyStories and my own internal similar dataset I made. the last 150k is from a subset of cosmopedia I extracted for younger people.
Trained for 49,152,000,000 tokens
for it to see/output Uppercase letters this model uses a Shift-Key modifier before the letter to become uppercase, and has never been trained on actual uppercase letters.
This modifier is ↨ and here are the functions I use to convert from straight text to the modified format and back.
```
def add_caseifer(text):
# Using list comprehension for more efficient concatenation
return ''.join(['↨' + char.lower() if char.isupper() else char for char in text
def remove_caseifer(text):
new_text = ""
i = 0
while i < len(text):
if text[i] == "↨":
if i+1 < len(text):
new_text += text[i+1].upper()
i += 1
else:
pass # skip this index
else:
new_text += text[i]
i += 1
return new_text
```
As such for test strings to use in chat try using somthing like:
```
↨hello, my name is ↨clara and ↨i like
```
Run history:
iter β–β–β–β–‚β–‚β–‚β–‚β–‚β–‚β–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–„β–„β–„β–„β–„β–…β–…β–…β–…β–…β–…β–†β–†β–†β–†β–†β–‡β–‡β–‡β–‡β–‡β–‡β–ˆβ–ˆβ–ˆ
loss/train β–ˆβ–…β–„β–…β–ƒβ–…β–„β–ƒβ–„β–„β–„β–„β–ƒβ–ƒβ–„β–„β–‚β–β–‚β–‚β–ƒβ–ƒβ–‚β–ƒβ–ƒβ–ƒβ–‚β–ƒβ–‚β–ƒβ–‚β–‚β–‚β–‚β–ƒβ–β–‚β–‚β–β–‚
loss/val β–ˆβ–‡β–†β–…β–…β–„β–„β–„β–„β–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–‚β–‚β–‚β–‚β–‚β–‚β–‚β–‚β–‚β–‚β–‚β–‚β–‚β–β–β–β–β–β–β–β–β–β–β–β–
lr ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
mfu β–…β–…β–…β–„β–…β–…β–…β–…β–…β–…β–„β–…β–…β–…β–β–…β–…β–…β–„β–…β–…β–…β–…β–ˆβ–…β–…β–…β–…β–…β–…β–…β–…β–…β–…β–…β–…β–ˆβ–…β–…β–ˆ
step_time β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‡β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‡β–ˆβ–ˆβ–ˆβ–ˆβ–‡β–ˆβ–ˆβ–‚β–‡β–ˆβ–ˆβ–‡β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‡β–‡β–‡β–β–‡β–ˆβ–
tokens β–β–β–β–‚β–‚β–‚β–‚β–‚β–‚β–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–„β–„β–„β–„β–„β–…β–…β–…β–…β–…β–…β–†β–†β–†β–†β–†β–‡β–‡β–‡β–‡β–‡β–‡β–ˆβ–ˆβ–ˆ
Run summary:
iter 500000
loss/train 0.48935
loss/val 0.45042
lr 1e-05
mfu 9.31042
step_time 63441.47873
tokens 49152000000