$O(n)$ lattice statistical models can be generalized to non integer values of n, starting from their (expanded and resumed in graphs) partition function: $$Z = \sum_{\text{loop configurations}} n^{\# \text{loops}} x^{\text{total length}}$$
The sum is over all possible loops or graphs configuration, $\#\text{loops}$ is the number of loops present in a specific configuration, and $\text{total length}$ the sum of their individual length.
Though, as far as I know, only the n integer cases admit a local Hamiltonian description. I would like to do some Monte-Carlo simulations in the range $1<n<2$, but I don't see how to implement the Metropolis algorithm for instance without knowledge of an explicit Hamiltonian to calculate the $\text{min}(1,\text{e}^{-\beta\Delta H})$ acceptance ratio. Is there a known to circumvent that? Has it already been done? Using other algorithms (I would like to know about the use of Wolff algorithm for example)?