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scores = model.predict(user_ids=6, item_ids=[1,2,3,5,6])
print(scores)

new_user_feature = [8,{'name:John', 'Age:33', 'los:IFS','ou:development', 'skills:sql'} ]    
new_user_feature = [8,new_user_feature]

#predict new users User-Id	name	age	los	ou	gender	skills
model.predict(0, item_ids=[1,2,3,5,6], user_features=new_user_feature)

Paste the command(s) you ran and the output.
If there was a crash, please include the traceback here.

items_column = "Training-Id"
user_column = "User-Id"
ratings_column = "TrainingTaken"

items_feature_columns = [
    "training name",
    "main skill"
]

user_features_columns = ["name","age","los","ou", "gender", "skills", "language", "grade", "career interests"]

Then I build the matrices
dataset_helper_instance = DatasetHelper(
    users_dataframe=usersdf,
    items_dataframe=trainingsdf,
    interactions_dataframe=trainingstakendf,
    item_id_column=items_column,
    items_feature_columns=items_feature_columns,
    user_id_column=user_column,
    user_features_columns=user_features_columns,
    interaction_column=ratings_column,
    clean_unknown_interactions=True,
)
dataset_helper_instance.routine()

from lightfm import LightFM
from lightfm.cross_validation import random_train_test_split
(train, test) = random_train_test_split(interactions=dataset_helper_instance.interactions, test_percentage=0.2)

model = LightFM(loss='warp')

model.fit(
    interactions=dataset_helper_instance.interactions,
    sample_weight=dataset_helper_instance.weights,
    item_features=dataset_helper_instance.item_features_list,
    user_features=dataset_helper_instance.user_features_list,
    verbose=True,
    epochs=20,
    num_threads=20,
)

import numpy as np
from lightfm.data import Dataset
#predict existing users
scores = model.predict(user_ids=81727, item_ids=[1])
print(scores)