diff --git a/examples/research_projects/stack_llama/scripts/README.md b/examples/research_projects/stack_llama/scripts/README.md
index 60ed5fd943..da9f067f20 100644
--- a/examples/research_projects/stack_llama/scripts/README.md
+++ b/examples/research_projects/stack_llama/scripts/README.md
@@ -1,7 +1,7 @@
 # RLHF pipeline for the creation of StackLLaMa: a Stack exchange llama-7b model.
 There were three main steps to the training process:
 1. Supervised fine-tuning of the base llama-7b model to create llama-7b-se:
-    - `torchrun --nnodes 1  --nproc_per_node 8 examples/research_projects/stack_llama/scripts/supervised_finetuning.py --model_path=<LLAMA_MODEL_PATH> --streaming --no_gradient_checkpointing --learning_rate 1e-5 --max_steps 5000 --output_dir ./llama-se`
+    - `torchrun --nnodes 1  --nproc_per_node 8 examples/research_projects/stack_llama/scripts/supervised_finetuning.py --model_path=<LLAMA_MODEL_PATH> --streaming --learning_rate 1e-5 --max_steps 5000 --output_dir ./llama-se`
 2. Reward modeling using dialog pairs from the SE dataset using the llama-7b-se to create llama-7b-se-rm:
     - `torchrun --nnodes 1  --nproc_per_node 8 examples/research_projects/stack_llama/scripts/reward_modeling.py --model_name=<LLAMA_SE_MODEL>`
 3. RL fine-tuning of llama-7b-se with the llama-7b-se-rm reward model:
diff --git a/examples/research_projects/stack_llama/scripts/supervised_finetuning.py b/examples/research_projects/stack_llama/scripts/supervised_finetuning.py
index 47669ac8a7..e6e59a868d 100644
--- a/examples/research_projects/stack_llama/scripts/supervised_finetuning.py
+++ b/examples/research_projects/stack_llama/scripts/supervised_finetuning.py
@@ -38,9 +38,9 @@ def get_args():
     parser.add_argument("--weight_decay", type=float, default=0.05)
 
     parser.add_argument("--local_rank", type=int, default=0)
-    parser.add_argument("--no_fp16", action="store_false")
+    parser.add_argument("--fp16", action="store_true", default=False)
     parser.add_argument("--bf16", action="store_true", default=False)
-    parser.add_argument("--no_gradient_checkpointing", action="store_false", default=False)
+    parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
     parser.add_argument("--seed", type=int, default=0)
     parser.add_argument("--num_workers", type=int, default=None)
     parser.add_argument("--output_dir", type=str, default="./checkpoints")
@@ -159,8 +159,8 @@ def run_training(args, train_data, val_data):
         lr_scheduler_type=args.lr_scheduler_type,
         warmup_steps=args.num_warmup_steps,
         gradient_accumulation_steps=args.gradient_accumulation_steps,
-        gradient_checkpointing=not args.no_gradient_checkpointing,
-        fp16=not args.no_fp16,
+        gradient_checkpointing=args.gradient_checkpointing,
+        fp16=args.fp16,
         bf16=args.bf16,
         weight_decay=args.weight_decay,
         run_name="llama-7b-finetuned",