library(lme4)
library(MuMIn)

mtcars2 <- mtcars
mtcars2$vs <- factor(mtcars2$vs)

gl <- lmer(mpg ~ am + disp + hp + qsec + (1 | cyl), mtcars2,
           REML = FALSE, na.action = 'na.fail')
d <- dredge(gl)

av <- model.avg(d, subset = cumsum(weight) <= 0.95)
summary(av)

Call:
model.avg(object = d, subset = cumsum(weight) <= 0.95)

Component model call:
lme4::lmer(formula = mpg ~ <7 unique rhs>, data = mtcars2, REML = FALSE, na.action = na.fail)

Component models:
     df logLik   AICc delta weight
13    5 -77.81 167.92  0.00   0.37
123   6 -76.34 168.05  0.13   0.35
134   6 -77.54 170.43  2.51   0.11
1234  7 -76.25 171.16  3.24   0.07
23    5 -79.85 172.00  4.08   0.05
2     4 -81.63 172.75  4.83   0.03
124   6 -78.99 173.34  5.42   0.02

Term codes:
  am disp   hp qsec
   1    2    3    4

Model-averaged coefficients:
(full average)
             Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 25.457505   6.467643    6.648016   3.829 0.000129 ***
am           4.103425   1.861593    1.898182   2.162 0.030636 *
hp          -0.043829   0.017926    0.018265   2.400 0.016415 *
disp        -0.009419   0.011834    0.011983   0.786 0.431821
qsec         0.081973   0.284147    0.292015   0.281 0.778929

(conditional average)
            Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 25.45751    6.46764     6.64802   3.829 0.000129 ***
am           4.46519    1.46823     1.51835   2.941 0.003273 **
hp          -0.04651    0.01471     0.01515   3.070 0.002140 **
disp        -0.01793    0.01068     0.01099   1.632 0.102634
qsec         0.40421    0.51757     0.53873   0.750 0.453075
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Relative variable importance:
                     hp   am   disp qsec
Importance:          0.94 0.92 0.53 0.20
N containing models:    5    5    5    3