Correct path to finetuning script in readme; added example description

README.md

@@ -20,7 +20,7 @@ Llama 2 is a new technology that carries potential risks with use. Testing condu
 
 # Quick Start
 
-[Llama 2 Jupyter Notebook](quickstart.ipynb): This jupyter notebook steps you through how to finetune a Llama 2 model on the text summarization task using the [samsum](https://huggingface.co/datasets/samsum). The notebook uses parameter efficient finetuning (PEFT) and int8 quantization to finetune a 7B on a single GPU like an A10 with 24GB gpu memory.
+[Llama 2 Jupyter Notebook](./examples/quickstart.ipynb): This jupyter notebook steps you through how to finetune a Llama 2 model on the text summarization task using the [samsum](https://huggingface.co/datasets/samsum). The notebook uses parameter efficient finetuning (PEFT) and int8 quantization to finetune a 7B on a single GPU like an A10 with 24GB gpu memory.
 
 # Installation
 Llama-recipes provides a pip distribution for easy install and usage in other projects. Alternatively, it can be installed from source.
@@ -120,7 +120,7 @@ Here we make use of Parameter Efficient Methods (PEFT) as described in the next
 
 ```bash
 
-torchrun --nnodes 1 --nproc_per_node 4  llama_finetuning.py --enable_fsdp --use_peft --peft_method lora --model_name /patht_of_model_folder/7B --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 /patht_of_model_folder/7B --pure_bf16 --output_dir Path/to/save/PEFT/model
 
 ```
 
@@ -131,7 +131,7 @@ 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 HuggingFace 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  llama_finetuning.py --enable_fsdp --use_peft --peft_method lora --model_name /patht_of_model_folder/7B --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 /patht_of_model_folder/7B --pure_bf16 --output_dir Path/to/save/PEFT/model --use_fast_kernels
 ```
 
 ### Fine-tuning using FSDP Only
@@ -140,7 +140,7 @@ If you are interested in running full parameter fine-tuning without making use o
 
 ```bash
 
-torchrun --nnodes 1 --nproc_per_node 8  llama_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 /patht_of_model_folder/7B --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --use_fast_kernels
 
 ```
 
@@ -150,7 +150,7 @@ If you are interested in running full parameter fine-tuning on the 70B model, yo
 
 ```bash
 
-torchrun --nnodes 1 --nproc_per_node 8 llama_finetuning.py --enable_fsdp --low_cpu_fsdp --pure_bf16 --model_name /patht_of_model_folder/70B --batch_size_training 1 --micro_batch_size 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 --pure_bf16 --model_name /patht_of_model_folder/70B --batch_size_training 1 --micro_batch_size 1 --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned
 
 ```
 
@@ -174,8 +174,9 @@ This repository is organized in the following way:
 
 [datasets](src/llama_recipes/datasets/): Contains individual scripts for each dataset to download and process. Note: Use of any of the datasets should be in compliance with the dataset's underlying licenses (including but not limited to non-commercial uses)
 
+[examples](./examples/): Contains examples script for finetuning and inference of the Llama 2 model as well as how to use them safely.
 
-[inference](src/llama_recipes/inference/): Includes examples for inference for the fine-tuned models and how to use them safely.
+[inference](src/llama_recipes/inference/): Includes modules for inference for the fine-tuned models.
 
 [model_checkpointing](src/llama_recipes/model_checkpointing/): Contains FSDP checkpoint handlers.