nlmefit error - What does it mean, and how do I fix it?

Trying to get to grips with the use of nlmefit

To do this I have written a script that generates a random dataset to fit a real-world equation perfectly +/- a given percentage to represent natural error. When I run the program I get the following error. Essentially what I'm asking is what does it mean, and how do I fix it? I want to be able to use nlmefit, but I don't really understand what the problem here is beyond the fact that some of the outputs are apparently coming out as Inf/NaN.

Warning: The Jacobian at the solution is ill-conditioned in the
Levenberg-Marquardt algorithm. Some fixed effects parameters
may not be estimated well (they are not identifiable).  
> In nlmefit>LMfit at 1767
  In nlmefit at 413
  In Generate at 104
Warning: Principal matrix logarithm is not defined for A with
         nonpositive real eigenvalues. A non-principal matrix
         logarithm is returned. 
> In funm at 168
  In logm at 27
  In nlmefit>Delta2theta at 1600
  In nlmefit at 446
  In Generate at 104
Warning: Matrix is singular, close to singular or badly scaled.
         Results may be inaccurate. RCOND = NaN. 
> In expm>PadeApproximantOfDegree at 123
  In expm at 39
  In nlmefit>theta2Delta at 1627
  In nlmefit at 463
  In Generate at 104
??? Error using ==> vertcat
CAT arguments dimensions are not consistent.

Error in ==> nlmefit>GNfitPNLS at 1172
        [Q,R]=qr([Zw(grp==i,:);Delta]);

Error in ==> nlmefit at 551
        [BetaNew,bNew,stop] = GNfitPNLS(Delta,Y,Beta,b,PNLSOpt,grp,M,N,r,q);

Error in ==> Generate at 104
[beta1,PSI1,stats1] = nlmefit(X(:),Intensity(:),Group(:),V,Model,Beta0)

    %%Clear all and load in Data
    C;
    hold off 
    cla
    load Raw_Data.mat
    Groups = 7; % Max 7 % Use these controls to limit data pool for cell 1 - 7
    Cols = 14; % Max 14 % and data cols 1 - 14
    Percentage = 10; %Percentage Variation for model testing, 0 = perfect set

    %%Create Observation Matrix, Assign to Groups and Prep Y for Responses

    X = rot90(Raw_Data.Cell1(1:Cols,1));
    X = repmat(X,Groups,1);
    V2 = zeros(Groups,X(1,size(X,2)));

    %%Load Data into temp matrix Y2 prior to sorting into Y

    temp = struct2cell(Raw_Data);

    for i = 1:numel(temp)-(numel(temp)-Groups)
        V2(i,temp{i,1}(:,1)) = temp{i,1}(:,3);
    end

    clear i temp Raw_Data

    V = zeros(Groups,size(X,2));

     for i = 1:size(X,2)
         V(:,i) = V2(:,X(1,i));
     end

    clear i V2

    Average = zeros(Groups,1);

    for i = 1:Groups
        Average(i,1) = mean(V(i,:));
    end

    V = repmat(Average,1,size(X,2));

    clear i Average Cols Groups

    %%Define Fixed Effects

    Nt=330000; %QD Intensity threshold ie. plateau
    sed=0.2931; %Sedimentation rate
    scale=1.4e-4; %Scaling parameter

    Beta0 = [Nt sed scale];

    clear Nt sed scale

    %%Generate datapoints with random +/- % variation

    Mid = V*Beta0(1,2)*Beta0(1,3);
    Intensity = Beta0(1,1)*(1-exp((-Mid).*X));
    RandN = rand(size(Intensity))*(Percentage/100);
    RandT = rand(size(Intensity));

    for i = 1:size(X,1)
        for j = 1:size(X,2)
            if RandT(i,j) <= 0.5
                Intensity(i,j) = Intensity(i,j)-(RandN(i,j)*Intensity(i,j));
            else
                Intensity(i,j) = Intensity(i,j)+(RandN(i,j)*Intensity(i,j));
            end
        end
    end

    clear i j RandN RandT Percentage

    %%Create Model Function

    Model = @(Beta0,Mid,X) Beta0(1,1)*(1-exp((-Mid).*X(:)));

    %%Create Group

    Group = repmat([1; 2; 3; 4; 5; 6; 7;],1,size(X,2));

    %%Run Mixed Effect Model

    [beta1,PSI1,stats1] = nlmefit(X(:),Intensity(:),Group(:),V,Model,Beta0)