Help with non-linear model [Log probability evaluates to log(0)]

Hello, I have the following (test) data.

stan_data <- list(
  N = 25,
  Y = c(0.8333333,0.6000000,0.6000000,1.5000000,1.4166667,0.0500000,0.5500000,2.0000000,
  x = c(12.9000,5.6000,1.0000,0.9000,33.7500,11.5000,18.1500,15.3500,13.6500,18.0000,16.2500,

Existing models for similar data have the non-linear form y = exp(b0 + b1 * sqrt(x) + b2 * x)
There are priors coming from those models for b0, b1 and b2, so here is my tentative stan model:

data {
  int<lower=0> N; 
  real x[N]; 
  real<lower=0> Y[N]; 
parameters {
  real b0; 
  real b1;  
  real b2;  
  real<lower=0> beta; 
transformed parameters {
  real m[N];
  for (i in 1:N) 
    m[i] = exp(b0 +b1*sqrt(x[i]) +b2*x[i]);
model {
  // priors
  b0 ~ normal(-10, 1); 
  b1 ~ normal(0.2, .5); 
  b2 ~ normal(-.05, .02); 
  // likelihood
  Y ~ gamma(m, beta);   
  // no priors for the beta parameter: is that fine?

When I try to run the model

stan(my_stan_model, data = stan_data, iter = 1000, chains = 4, verbose = TRUE) 

I got an error:
Chain 1: Rejecting initial value:
Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
Chain 1: Stan can’t start sampling from this initial value.

Reading around, it seems there is something wrong in my model formulation. Any suggestions? Thanks!

When using uniform priors you need to include matching lower and upper bounds in the parameter declaration

Actually it was a mistake. I had realized I wrongly assigned Uniform instead of Normal to b2 and then saw your answer :)