How to use fine-tuned model?

I did a small fine-tuning and the process finishes correctly. The output model is too small though and there are no weights. The list of the files is this

config.json
generation_config.json
model.safetensors (around 250 MiB)
runs/
special_tokens_map.json
tokenizer.json
tokenizer_config.json
trainer_state.json
training_args.bin

Im using the same command that you suggest:
deepspeed finetune_deepseekcoder.py \
--model_name_or_path $MODEL_PATH \
--data_path $DATA_PATH \
--output_dir $OUTPUT_PATH \
--num_train_epochs 3 \
--model_max_length 1024 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 4 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 100 \
--save_total_limit 100 \
--learning_rate 2e-5 \
--warmup_steps 10 \
--logging_steps 1 \
--lr_scheduler_type "cosine" \
--gradient_checkpointing True \
--report_to "tensorboard" \
--deepspeed configs/ds_config_zero3.json \
--bf16 True

Could you also give an example on how to use the output model?