Tangential (but topical in that "The threat is comfortable drift toward not understanding what you're doing" is also on the front page):
Is the generated python code in the example wrong?
The prompt
> Develop a Python function that removes any falsey values from a list. Return the modified list without creating a new one.
Is answered with list comprehension, which makes a new list and leaves the original unmodified (never mind that the *args input necessarily can't be a modifiable list?)
def remove_falsey_values(*args): return [val for val in args if val]
Whereas I'd expect something like
def remove_falsey_values(l):
for i in reversed(range(len(l))):
if not l[i]: l.pop(i)
# returned list is linked to input l
return l
a = [1, 0, False, 'foo']
x = remove_falsey_values(a)
x[0] = 2
print(a) # [2,'foo']
It doesn't fit the requirement to modify the list in place, but the prompt itself contradicts the requirements by asking explicitly for the implementation to use *args and a list comprehension.
This is a great question. You definitely aren't training this to use it, you're training it to understand how things work. It's an educational project, if you're interested in experimenting with things like distributed training techniques in JAX, or preference optimisation, this gives you a minimal and hackable library to build on.
Tangential (but topical in that "The threat is comfortable drift toward not understanding what you're doing" is also on the front page):
Is the generated python code in the example wrong?
The prompt
> Develop a Python function that removes any falsey values from a list. Return the modified list without creating a new one.
Is answered with list comprehension, which makes a new list and leaves the original unmodified (never mind that the *args input necessarily can't be a modifiable list?)
Whereas I'd expect something likeIt doesn't fit the requirement to modify the list in place, but the prompt itself contradicts the requirements by asking explicitly for the implementation to use *args and a list comprehension.
Dumb question - and I'm not trying diminish the achievement here, I just genuinely don't understand:
Why would people want to spend $200 to train a coding model when there are free coding models?
This is a great question. You definitely aren't training this to use it, you're training it to understand how things work. It's an educational project, if you're interested in experimenting with things like distributed training techniques in JAX, or preference optimisation, this gives you a minimal and hackable library to build on.
As someone with zero ML experience, this was a super interesting and digestible read!