Update README.md

README.md

@@ -116,7 +116,7 @@ All the parameters in the examples and recipes below need to be further tuned to
 #if running on multi-gpu machine
 export CUDA_VISIBLE_DEVICES=0
 
-python -m llama_recipes.finetuning  --use_peft --peft_method lora --quantization --model_name /patht_of_model_folder/7B --output_dir path/to/save/PEFT/model
+python -m llama_recipes.finetuning  --use_peft --peft_method lora --quantization --model_name /path_of_model_folder/7B --output_dir path/to/save/PEFT/model
 
 ```
 
@@ -133,7 +133,7 @@ Here we make use of Parameter Efficient Methods (PEFT) as described in the next
 
 ```bash
 
-torchrun --nnodes 1 --nproc_per_node 4  examples/finetuning.py --enable_fsdp --use_peft --peft_method lora --model_name /patht_of_model_folder/7B --fsdp_config.pure_bf16 --output_dir path/to/save/PEFT/model
+torchrun --nnodes 1 --nproc_per_node 4  examples/finetuning.py --enable_fsdp --use_peft --peft_method lora --model_name /path_of_model_folder/7B --fsdp_config.pure_bf16 --output_dir path/to/save/PEFT/model
 
 ```
 
@@ -144,7 +144,7 @@ Here we use FSDP as discussed in the next section which can be used along with P
 Setting `use_fast_kernels` will enable using of Flash Attention or Xformer memory-efficient kernels based on the hardware being used. This would speed up the fine-tuning job. This has been enabled in `optimum` library from Hugging Face as a one-liner API, please read more [here](https://pytorch.org/blog/out-of-the-box-acceleration/).
 
 ```bash
-torchrun --nnodes 1 --nproc_per_node 4  examples/finetuning.py --enable_fsdp --use_peft --peft_method lora --model_name /patht_of_model_folder/7B --fsdp_config.pure_bf16 --output_dir path/to/save/PEFT/model --use_fast_kernels
+torchrun --nnodes 1 --nproc_per_node 4  examples/finetuning.py --enable_fsdp --use_peft --peft_method lora --model_name /path_of_model_folder/7B --fsdp_config.pure_bf16 --output_dir path/to/save/PEFT/model --use_fast_kernels
 ```
 
 ### Fine-tuning using FSDP Only
@@ -153,7 +153,7 @@ If you are interested in running full parameter fine-tuning without making use o
 
 ```bash
 
-torchrun --nnodes 1 --nproc_per_node 8  examples/finetuning.py --enable_fsdp --model_name /patht_of_model_folder/7B --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --use_fast_kernels
+torchrun --nnodes 1 --nproc_per_node 8  examples/finetuning.py --enable_fsdp --model_name /path_of_model_folder/7B --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --use_fast_kernels
 
 ```
 
@@ -163,7 +163,7 @@ If you are interested in running full parameter fine-tuning on the 70B model, yo
 
 ```bash
 
-torchrun --nnodes 1 --nproc_per_node 8 examples/finetuning.py --enable_fsdp --low_cpu_fsdp --fsdp_config.pure_bf16 --model_name /patht_of_model_folder/70B --batch_size_training 1 --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned
+torchrun --nnodes 1 --nproc_per_node 8 examples/finetuning.py --enable_fsdp --low_cpu_fsdp --fsdp_config.pure_bf16 --model_name /path_of_model_folder/70B --batch_size_training 1 --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned
 
 ```