diff --git a/README.md b/README.md
index 6b81e69de..ca36acbcb 100644
--- a/README.md
+++ b/README.md
@@ -305,6 +305,8 @@ base_model_ignore_patterns:
 # if the base_model repo on hf hub doesn't include configuration .json files,
 # you can set that here, or leave this empty to default to base_model
 base_model_config: ./llama-7b-hf
+# you can specify to choose a specific model revision from huggingface hub
+model_revision:
 # Optional tokenizer configuration override in case you want to use a different tokenizer
 # than the one defined in the base model
 tokenizer_config:
@@ -411,6 +413,9 @@ logging_steps:
 save_steps:
 eval_steps:
 
+# save model as safetensors (require safetensors package)
+save_safetensors:
+
 # whether to mask out or include the human's prompt from the training labels
 train_on_inputs: false
 # don't use this, leads to wonky training (according to someone on the internet)
diff --git a/src/axolotl/utils/trainer.py b/src/axolotl/utils/trainer.py
index 276f5cfea..bdd760526 100644
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -183,6 +183,10 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
     if cfg.hub_model_id:
         training_arguments_kwargs["hub_model_id"] = cfg.hub_model_id
         training_arguments_kwargs["push_to_hub"] = True
+        training_arguments_kwargs["hub_private_repo"] = True
+
+    if cfg.save_safetensors:
+        training_arguments_kwargs["save_safetensors"] = cfg.save_safetensors
 
     training_args = AxolotlTrainingArguments(
         per_device_train_batch_size=cfg.micro_batch_size,