how to optimize hyperparameters in models

[Hellobamboobamboo]

For the model below, are the hyperparameters in my sample the optimized ones for convergence? Doesn't seem to converge. What do each one mean, and how to choose accordingly?

sample:
infer_model_0 = model_0.sample(
sampler="nuts_numpyro", chains=2, cores=1, draws=1000, tune=1000, target_accept=0.95
)

v.s.
infer_model_0 = model_0.sample(
sampler="nuts_numpyro", chains=4, cores=1, draws=1000, tune=2000, target_accept=0.95
)

Model:
Hierarchical Sequential Sampling Model
Model: ddm

Response variable: rt,response
Likelihood: analytical
Observations: 21063

Parameters:

v:
Formula: v ~ condition + diff + (1|participant_id)
Priors:
v_Intercept ~ Normal(mu: 2.0, sigma: 3.0)
v_condition ~ Normal(mu: 0.0, sigma: 0.25)
v_diff ~ Normal(mu: 0.0, sigma: 0.25)
v_1|participant_id ~ Normal(mu: 0.0, sigma: Weibull(alpha: 1.5, beta: 0.30000001192092896))
Link: identity
Explicit bounds: (-inf, inf)

a:
Formula: a ~ condition + diff + (1|participant_id)
Priors:
a_Intercept ~ Gamma(mu: 1.5, sigma: 0.75)
a_condition ~ Normal(mu: 0.0, sigma: 0.25)
a_diff ~ Normal(mu: 0.0, sigma: 0.25)
a_1|participant_id ~ Normal(mu: 0.0, sigma: Weibull(alpha: 1.5, beta: 0.30000001192092896))
Link: identity
Explicit bounds: (0.0, inf)

z:
Formula: z ~ 1 + (1|participant_id)
Priors:
z_Intercept ~ Beta(alpha: 10.0, beta: 10.0)
z_1|participant_id ~ Normal(mu: 0.0, sigma: Weibull(alpha: 1.5, beta: 0.30000001192092896))
Link: identity
Explicit bounds: (0.0, 1.0)

t:
Formula: t ~ 1 + (1|participant_id)
Priors:
t_Intercept ~ Gamma(mu: 0.20000000298023224, sigma: 0.20000000298023224)
t_1|participant_id ~ Normal(mu: 0.0, sigma: Weibull(alpha: 1.5, beta: 0.30000001192092896))
Link: identity
Explicit bounds: (0.0, inf)

Lapse probability: 0.05
Lapse distribution: Uniform(lower: 0.0, upper: 10.0)