How to prove that weak one way functions cannot have polynomial-sized ranges? I figured how to show that strong OWFs cannot have polynomial sized ranges. But I am unable to prove the same for weak OWFs.
Here is a sketch of the proof, I will let you figure out the details. Let $n$ be your security parameter. If the function $f$ has polynomial size range $\{y_1, \cdots, y_{p(n)}\}$, then its domain can be partitioned as the union of a polynomial $p(n)$ disjoint subsets $S_i$, where each $S_i$ contains all values $x$ such that $f(x) = y_i$. Now, the game is as follows: the challenger picks $x$ at random, and returns $y = f(x)$. The goal is to find a preimage $x'$.

I claim that, except with negligible probability, the set $S_i$ associated with $y$ has inverse-polynomial density over the set of all possible preimages; that is, it contains a fraction $1/q(n)$ of all preimages for some polynomial $q$. This is simply for the following reason: if it is not the case that $S_i$ has polynomial density, then the probability that the value $x$ sampled by the challenger belongs to $S_i$ is negligible, and there are only polynomially many sets. 

Now, this suggests a simple algorithm for inverting the function: sample $q(n)\cdot \omega(\log n)$ random inputs $x'$ from the domain, and compute $f(x)$. Then, the probability that none of the sampled inputs belong to $S_i$ is negligible ($2^{-\omega(\log n)}$). Hence, except with negligible probability, this algorithm will invert the function $f$ successfully on $y$, contradicting the claim that $f$ is a weak one-way function.

Of course, this is just a sketch, and some additional work is required to formally prove that everything works out fine, but it should not be too hard.

A little remark on how this should be properly formalized: working with "negligible" versus "non-negligible" would not work, since there can be weird intermediate things between "negligible size" and "inverse polynomial size". The right approach is to start from a specific $1/d(n)$-weak OWF (where each algorithm fails at inverting $f$ on a random output with probability at least $1/d(n)$), and construct through the above approach an algorithm that succeeds with better than $1-1/d(n)$ probability at inverting the function. To do so, consider all sets $S_i$ with density at most $1/(p(n)\cdot d^2(n))$: the union of all those $S_i$ has density at most $1/d^2(n)$, hence the random input $x$ of the challenger will be in a set of density at least $1/(p(n)\cdot d^2(n))$, except with probability $1/d^2(n)$. When this happens, sampling $p(n)d^2(n)\omega(\log n)$ random inputs gives a preimage with overwhelming probability, hence our algorithm succeeds with probability at least $1-1/d^2(n)-\mathsf{negl}(n)$ ($\mathsf{negl}(n)$ denotes a negligible function), contradicting the $1/d(n)$-weak one-wayness.